# data[].attributes.body
However, construction of effective predictive models, both for ICI and for personalized medicine in general, require an enormous amount of genomic and other sensitive data from the population. The handling of this data is difficult because it is both personal and very extensive, requiring significant investments in cyber security to avoid data theft or leakage.

Additionally, not everyone might be willing to allow their genome to be sequenced and shared with the appropriate research entities, resulting in less data and perhaps worse and less encompassing predictive models that will be less effective at saving lives. Should then the people be forced to share their genomes or be paid for them? Or living in a world with good predictive models and personalized medicine is reward enough?

The problem becomes more complicated when we consider that individuals share large amounts of their genome with their families, so even if someone doesn’t want to share their genome, if a few members of their family do share, then most of this individual’s genome will be shared against their will.

# data[].attributes.body
If the genomic data is acquired by a government or legal entity, then it might be used for forensic purposes. At this point it becomes very difficult to hide one’s identity as any DNA containing sample will point to an individual or at least one family, this can be a good thing, to identify criminals, but also brings privacy problems.
Should then the genomes of the people be sequenced and used, but without sharing the identity of the people? There would be less privacy issues, but the utility of the data might be limited.

# data[].attributes.body
With such a large pool of genes, and especially if tumour genomics is involved it is a matter of time to find a useful gene, in this case should the gene be in the public domain? What if it is a very rare gene only found in one person? Should the person be allowed to patent it? Or be allowed to deny the gene’s use to rest of the world? What if the gene was found on a tumour? Who owns it then? What if a synthetic sequence is patented but later it is found an identical natural occurring sequence in someone’s genome? Who owns what then?

# data[].attributes.body
It is therefore very important to define the status of genes and DNA legally and universally in the light of our Human Rights to allow for effective progress without screwing anyone over.

# data[].attributes.body
Ana Rita Filipe - nº 84611
António Coelho - nº 94434
Bernardo Tavares - nº 94322
Hugo Fernandes - nº 94172
João Borges - nº 94259

# data[].attributes.attachment_caption
Schematic comparison of a cancer cell with low TMB with one with high TMB.

# data[].attributes.body
Tumor mutation burden, or TMB, is the number of non-synonymous mutations per number of bases in a genomic sequence.

High TMB is strongly associated with increased production of neoantigen, non-self molecules like novel peptides bound to tumour's MHCs that can trigger the immune response. It is easy to see how mutation can produce mutated proteins that induce the activation of T-cells and lead to destruction of the tumor cells.

# data[].attributes.body
This correlation has been observed in several cancers such as melanoma, non-small-cell lung carcinoma, urothelial carcinoma, small cell lung cancer and human papilloma virus.

Furthermore, a meta-analysis across 27 tumour types obtained a positive correlation between the average response rate and log(TMB) by linear regression analysis, but with some major outliers, also seen in other studies.

TMB seems to be pretty robust as a predictive factor for ICI response but these discrepancies highlight how TMB alone can not form an adequate predictive model.

# data[].attributes.body
All websites were last consulted as of 10/04/2022

Many of the figures were designed with Biorender.

# data[].attributes.body
The use of ICI can be used by oncology patients as a treatment option, however only a fraction experience significant positive clinical effects. The development of a predictive model is crucial in order to raise the percentage of successfully treated patients and so that benefits may overcome the major adverse effects produced on their bodies. So how can we develop one?

# data[].attributes.attachment_link.title
Decoding cancer immunology: Hunting hidden tumours

# data[].attributes.body
This is the article that inspired this Padlet

# data[].attributes.attachment_link.readable_text
This is a preview of subscription content

Subscribe to Nature+

Get immediate online access to the entire Nature family of 50+ journals

Subscribe to Journal

Get full journal access for 1 year

All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Additional access options:

References

1. Rech, A. J. et al. Tumor immunity and survival as a function of alternative neopeptides in human cancer. Cancer Immunol. Res. https://doi.org/10.1158/2326-6066.CIR-17-0559 (2018).

2. U.S. Food and Drug Administration. FDA approves first cancer treatment for any solid tumor with a specific genetic feature. FDA https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm560167.htm (2017).

3. Marsh, S. G., Parham, P. & Barber, L. D. The HLA FactsBook (Academic Press, 1999).

4. Tang, H. et al. PD-L1 on host cells is essential for PD-L1 blockade-mediated tumor regression. J. Clin. Invest. 128, 580-588 (2018).

5. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568-571 (2014). This study demonstrates an association between PDL1+CD8+ T cells at the invasive tumour margin and anti-PD1 response, highlighting the importance of intratumoural immune cell phenotypes and localization.

6. Thommen, D. S. et al. A transcriptionally and functionally distinct PD⁻1⁺ CD8⁺ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994-1004 (2018).

7. Sade-Feldman, M. et al. Clinical significance of circulating CD33⁺CD11b⁺HLA-DR − myeloid cells in patients with stage IV melanoma treated with ipilimumab. Clin. Cancer Res. 22, 5661-5672 (2016).

8. Beatty, G. L. et al. Exclusion of T cells from pancreatic carcinomas in mice is regulated by Ly6C low F4/80⁺ extratumoral macrophages. Gastroenterology 149, 201-210 (2015).

9. Sato, E. et al. Intraepithelial CD8⁺ tumor-infiltrating lymphocytes and a high CD8⁺/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc. Natl Acad. Sci. USA 102, 18538-18543 (2005).

Download references

Acknowledgements

The authors thank the Chan laboratory, members of the Immunogenomics and Precision Oncology Platform and members of the Immunology Program at Memorial Sloan Kettering Cancer Center for helpful discussions. The work was supported in part by US National Institutes of Health (NIH) National Cancer Institute (NCI) Cancer Center Support Grant P30 CA008748.

Reviewer information

Nature Reviews Cancer thanks I. Melero, M. Smyth and other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

These authors contributed equally: Jonathan J. Havel, Diego Chowell

Affiliations

1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Jonathan J. Havel, Diego Chowell & Timothy A. Chan

2. Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Jonathan J. Havel, Diego Chowell & Timothy A. Chan

3. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Timothy A. Chan

Contributions

All authors researched data for the article, substantially contributed to the discussion of content and wrote, reviewed and edited the manuscript.

Corresponding author

Correspondence to Timothy A. Chan.

Ethics declarations

Competing interests

T.A.C. is a co-founder of Gritstone Oncology and holds equity. T.A.C. holds equity in An2H. T.A.C. acknowledges grant funding from An2H, AstraZeneca, Bristol-Myers Squibb, Eisai, Illumina and Pfizer. T.A.C. has served as an adviser for An2H, Bristol-Myers Squibb, Eisai, Illumina and MedImmune. T.A.C. holds ownership of intellectual property on using tumour mutation burden to predict immunotherapy response, with a pending patent, which has been licensed to Personal Genome Diagnostics. J.J.H.'s spouse is a full-time employee of Regeneron Pharmaceuticals.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

FoundationOne CDx panel: https://www.fda.gov/medicaldevices/productsandmedicalprocedures/deviceapprovalsandclearances/recently-approveddevices/ucm590331.htm

Friends of Cancer Research Consortium: https://www.focr.org/tmb

MSK-IMPACT panel: https://www.mskcc.org/msk-impact

Glossary

Predictive biomarkers

Measurable biological entities or phenotypes that are associated with response to a specific therapy.

MHC class I

(MHC I). A group of cell surface molecules (major histocompatibility complex (MHC) molecules) present on essentially all nucleated cells that bind and present peptides of approximately 8-10 amino acids in length to CD8⁺ T cells.

MHC class II

(MHC II). A group of cell surface molecules (major histocompatibility complex (MHC) molecules) expressed primarily on professional antigen-presenting cells such as dendritic cells and macrophages that bind and present peptides of approximately 13-25 amino acids in length to CD4⁺ T cells.

Central tolerance

The elimination of developing self-reactive T cell clones in order to prevent autoimmunity.

Static biomarkers

Biomarkers determined at a single point in time.

Dynamic biomarkers

Biomarkers determined by the relative change in a measurement over time.

Sensitivity

A measure of the proportion of patients who respond to therapy who were predicted to respond according to a biomarker, for example, biomarker-positive patients who actually respond divided by all patients who actually respond.

Specificity

A measure of the proportion of patients who do not respond to therapy who were predicted not to respond according to a biomarker, for example, biomarker-negative patients who do not respond divided by all patients who do not respond.

Positive predictive value

The probability that a biomarker-positive patient will benefit from a therapy.

Clonal mutations

Mutations acquired early in tumorigenesis that are present in all or most clones. These are also sometimes referred to as truncal mutations.

Subclonal mutations

Mutations acquired later in tumorigenesis that are present in a much smaller percentage of clones relative to clonal mutations.

Somatic copy number alterations

(SCNAs). Focal or chromosome-level deletions and amplifications that arise in tumours.

Epistatic interactions

Interactions of multiple genes that influence a phenotype.

HLA-I supertypes

Individual human leukocyte antigen class I (HLA-I) alleles can be classified into discrete HLA-I supertypes on the basis of similar peptide anchor binding specificities.

About this article

Cite this article

Havel, J.J., Chowell, D. & Chan, T.A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 19, 133-150 (2019). https://doi.org/10.1038/s41568-019-0116-x
Download citation

Get shareable link

Further reading

Mary Philip

Andrea Schietinger

Nature Reviews Immunology (2022)

# data[].attributes.attachment_link.title
The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy - Nature Reviews Cancer

# data[].attributes.attachment_link.description
This Review discusses recent work demonstrating that immune checkpoint inhibitor efficacy is affected by a combination of factors involving tumour genomics, host germline genetics, programmed cell death 1 ligand 1 (PDL1) levels and other features of the tumour microenvironment, as well as the gut microbiome.

# data[].attributes.attachment_link.readable_text
A Collection of Related Diseases

Cancer is the name given to a collection of related diseases. In all types of cancer, some of the body's cells begin to divide without stopping and spread into surrounding tissues.

Cancer can start almost anywhere in the human body, which is made up of trillions of cells. Normally, human cells grow and divide to form new cells as the body needs them. When cells grow old or become damaged, they die, and new cells take their place.

When cancer develops, however, this orderly process breaks down. As cells become more and more abnormal, old or damaged cells survive when they should die, and new cells form when they are not needed. These extra cells can divide without stopping and may form growths called tumors.

Many cancers form solid tumors, which are masses of tissue. Cancers of the blood, such as leukemias, generally do not form solid tumors.

Cancerous tumors are malignant, which means they can spread into, or invade, nearby tissues. In addition, as these tumors grow, some cancer cells can break off and travel to distant places in the body through the blood or the lymph system and form new tumors far from the original tumor.

Unlike malignant tumors, benign tumors do not spread into, or invade, nearby tissues. Benign tumors can sometimes be quite large, however. When removed, they usually don't grow back, whereas malignant tumors sometimes do. Unlike most benign tumors elsewhere in the body, benign brain tumors can be life threatening.

Differences between Cancer Cells and Normal Cells

Cancer cells differ from normal cells in many ways that allow them to grow out of control and become invasive. One important difference is that cancer cells are less specialized than normal cells. That is, whereas normal cells mature into very distinct cell types with specific functions, cancer cells do not. This is one reason that, unlike normal cells, cancer cells continue to divide without stopping.

In addition, cancer cells are able to ignore signals that normally tell cells to stop dividing or that begin a process known as programmed cell death, or apoptosis, which the body uses to get rid of unneeded cells.

Cancer cells may be able to influence the normal cells, molecules, and blood vessels that surround and feed a tumor-an area known as the microenvironment. For instance, cancer cells can induce nearby normal cells to form blood vessels that supply tumors with oxygen and nutrients, which they need to grow. These blood vessels also remove waste products from tumors.

Cancer cells are also often able to evade the immune system, a network of organs, tissues, and specialized cells that protects the body from infections and other conditions. Although the immune system normally removes damaged or abnormal cells from the body, some cancer cells are able to "hide" from the immune system.

Tumors can also use the immune system to stay alive and grow. For example, with the help of certain immune system cells that normally prevent a runaway immune response, cancer cells can actually keep the immune system from killing cancer cells.

How Cancer Arises

Cancer is a genetic disease-that is, it is caused by changes to genes that control the way our cells function, especially how they grow and divide.

Genetic changes that cause cancer can be inherited from our parents. They can also arise during a person's lifetime as a result of errors that occur as cells divide or because of damage to DNA caused by certain environmental exposures. Cancer-causing environmental exposures include substances, such as the chemicals in tobacco smoke, and radiation, such as ultraviolet rays from the sun. (Our Cancer Causes and Prevention section has more information.)

Each person's cancer has a unique combination of genetic changes. As the cancer continues to grow, additional changes will occur. Even within the same tumor, different cells may have different genetic changes.

In general, cancer cells have more genetic changes, such as mutations in DNA, than normal cells. Some of these changes may have nothing to do with the cancer; they may be the result of the cancer, rather than its cause.

"Drivers" of Cancer

The genetic changes that contribute to cancer tend to affect three main types of genes- proto-oncogenes, tumor suppressor genes, and DNA repair genes. These changes are sometimes called "drivers" of cancer.

Proto-oncogenes are involved in normal cell growth and division. However, when these genes are altered in certain ways or are more active than normal, they may become cancer-causing genes (or oncogenes), allowing cells to grow and survive when they should not.

Tumor suppressor genes are also involved in controlling cell growth and division. Cells with certain alterations in tumor suppressor genes may divide in an uncontrolled manner.

DNA repair genes are involved in fixing damaged DNA. Cells with mutations in these genes tend to develop additional mutations in other genes. Together, these mutations may cause the cells to become cancerous.

As scientists have learned more about the molecular changes that lead to cancer, they have found that certain mutations commonly occur in many types of cancer. Because of this, cancers are sometimes characterized by the types of genetic alterations that are believed to be driving them, not just by where they develop in the body and how the cancer cells look under the microscope.

When Cancer Spreads

A cancer that has spread from the place where it first started to another place in the body is called metastatic cancer. The process by which cancer cells spread to other parts of the body is called metastasis.

Metastatic cancer has the same name and the same type of cancer cells as the original, or primary, cancer. For example, breast cancer that spreads to and forms a metastatic tumor in the lung is metastatic breast cancer, not lung cancer.

Under a microscope, metastatic cancer cells generally look the same as cells of the original cancer. Moreover, metastatic cancer cells and cells of the original cancer usually have some molecular features in common, such as the presence of specific chromosome changes.

Treatment may help prolong the lives of some people with metastatic cancer. In general, though, the primary goal of treatments for metastatic cancer is to control the growth of the cancer or to relieve symptoms caused by it. Metastatic tumors can cause severe damage to how the body functions, and most people who die of cancer die of metastatic disease.

Tissue Changes that Are Not Cancer

Not every change in the body's tissues is cancer. Some tissue changes may develop into cancer if they are not treated, however. Here are some examples of tissue changes that are not cancer but, in some cases, are monitored:

Hyperplasia occurs when cells within a tissue divide faster than normal and extra cells build up, or proliferate. However, the cells and the way the tissue is organized look normal under a microscope. Hyperplasia can be caused by several factors or conditions, including chronic irritation.

Dysplasia is a more serious condition than hyperplasia. In dysplasia, there is also a buildup of extra cells. But the cells look abnormal and there are changes in how the tissue is organized. In general, the more abnormal the cells and tissue look, the greater the chance that cancer will form.

Some types of dysplasia may need to be monitored or treated. An example of dysplasia is an abnormal mole (called a dysplastic nevus) that forms on the skin. A dysplastic nevus can turn into melanoma, although most do not.

An even more serious condition is carcinoma in situ. Although it is sometimes called cancer, carcinoma in situ is not cancer because the abnormal cells do not spread beyond the original tissue. That is, they do not invade nearby tissue the way that cancer cells do. But, because some carcinomas in situ may become cancer, they are usually treated.

Types of Cancer

There are more than 100 types of cancer. Types of cancer are usually named for the organs or tissues where the cancers form. For example, lung cancer starts in cells of the lung, and brain cancer starts in cells of the brain. Cancers also may be described by the type of cell that formed them, such as an epithelial cell or a squamous cell.

You can search NCI's website for information on specific types of cancer based on the cancer's location in the body or by using our A to Z List of Cancers. We also have collections of information on childhood cancers and cancers in adolescents and young adults.

Here are some categories of cancers that begin in specific types of cells:

Carcinoma

Carcinomas are the most common type of cancer. They are formed by epithelial cells, which are the cells that cover the inside and outside surfaces of the body. There are many types of epithelial cells, which often have a column-like shape when viewed under a microscope.

Carcinomas that begin in different epithelial cell types have specific names:

Adenocarcinoma is a cancer that forms in epithelial cells that produce fluids or mucus. Tissues with this type of epithelial cell are sometimes called glandular tissues. Most cancers of the breast, colon, and prostate are adenocarcinomas.

Basal cell carcinoma is a cancer that begins in the lower or basal (base) layer of the epidermis, which is a person's outer layer of skin.

Squamous cell carcinoma is a cancer that forms in squamous cells, which are epithelial cells that lie just beneath the outer surface of the skin. Squamous cells also line many other organs, including the stomach, intestines, lungs, bladder, and kidneys. Squamous cells look flat, like fish scales, when viewed under a microscope. Squamous cell carcinomas are sometimes called epidermoid carcinomas.

Transitional cell carcinoma is a cancer that forms in a type of epithelial tissue called transitional epithelium, or urothelium. This tissue, which is made up of many layers of epithelial cells that can get bigger and smaller, is found in the linings of the bladder, ureters, and part of the kidneys (renal pelvis), and a few other organs. Some cancers of the bladder, ureters, and kidneys are transitional cell carcinomas.

Sarcoma

Sarcomas are cancers that form in bone and soft tissues, including muscle, fat, blood vessels, lymph vessels, and fibrous tissue (such as tendons and ligaments).

Osteosarcoma is the most common cancer of bone. The most common types of soft tissue sarcoma are leiomyosarcoma, Kaposi sarcoma, malignant fibrous histiocytoma, liposarcoma, and dermatofibrosarcoma protuberans.

Our page on soft tissue sarcoma has more information.

Leukemia

Cancers that begin in the blood-forming tissue of the bone marrow are called leukemias. These cancers do not form solid tumors. Instead, large numbers of abnormal white blood cells (leukemia cells and leukemic blast cells) build up in the blood and bone marrow, crowding out normal blood cells. The low level of normal blood cells can make it harder for the body to get oxygen to its tissues, control bleeding, or fight infections.

There are four common types of leukemia, which are grouped based on how quickly the disease gets worse (acute or chronic) and on the type of blood cell the cancer starts in (lymphoblastic or myeloid).

Our page on leukemia has more information.

Lymphoma

Lymphoma is cancer that begins in lymphocytes (T cells or B cells). These are disease-fighting white blood cells that are part of the immune system. In lymphoma, abnormal lymphocytes build up in lymph nodes and lymph vessels, as well as in other organs of the body.

There are two main types of lymphoma:

Hodgkin lymphoma - People with this disease have abnormal lymphocytes that are called Reed-Sternberg cells. These cells usually form from B cells.

Non-Hodgkin lymphoma - This is a large group of cancers that start in lymphocytes. The cancers can grow quickly or slowly and can form from B cells or T cells.

Our page on lymphoma has more information.

Multiple Myeloma

Multiple myeloma is cancer that begins in plasma cells, another type of immune cell. The abnormal plasma cells, called myeloma cells, build up in the bone marrow and form tumors in bones all through the body. Multiple myeloma is also called plasma cell myeloma and Kahler disease.

Our page on multiple myeloma and other plasma cell neoplasms has more information.

Melanoma

Melanoma is cancer that begins in cells that become melanocytes, which are specialized cells that make melanin (the pigment that gives skin its color). Most melanomas form on the skin, but melanomas can also form in other pigmented tissues, such as the eye.

Our pages on skin cancer and intraocular melanoma have more information.

Brain and Spinal Cord Tumors

There are different types of brain and spinal cord tumors. These tumors are named based on the type of cell in which they formed and where the tumor first formed in the central nervous system. For example, an astrocytic tumor begins in star-shaped brain cells called astrocytes, which help keep nerve cells healthy. Brain tumors can be benign (not cancer) or malignant (cancer).

Our page on brain and spinal cord tumors in adults has more information, as does our overview of brain and spinal cord tumors in children.

Other Types of Tumors

Germ Cell Tumors

Germ cell tumors are a type of tumor that begins in the cells that give rise to sperm or eggs. These tumors can occur almost anywhere in the body and can be either benign or malignant.

Our page of cancers by body location/system includes a list of germ cell tumors with links to more information.

Neuroendocrine Tumors

Neuroendocrine tumors form from cells that release hormones into the blood in response to a signal from the nervous system. These tumors, which may make higher-than-normal amounts of hormones, can cause many different symptoms. Neuroendocrine tumors may be benign or malignant.

Our definition of neuroendocrine tumors has more information.

Carcinoid Tumors

Carcinoid tumors are a type of neuroendocrine tumor. They are slow-growing tumors that are usually found in the gastrointestinal system (most often in the rectum and small intestine). Carcinoid tumors may spread to the liver or other sites in the body, and they may secrete substances such as serotonin or prostaglandins, causing carcinoid syndrome.

Our page on gastrointestinal carcinoid tumors has more information.

# data[].attributes.attachment_link.description
Cancer is the name given to a collection of related diseases. In all types of cancer, some of the body's cells begin to divide without stopping and spread into surrounding tissues. Cancer can start almost anywhere in the human body, which is made up of trillions of cells.

# data[].attributes.attachment_link.readable_text
The fundamental abnormality resulting in the development of cancer is the continual unregulated proliferation of cancer cells. Rather than responding appropriately to the signals that control normal cell behavior, cancer cells grow and divide in an uncontrolled manner, invading normal tissues and organs and eventually spreading throughout the body. The generalized loss of growth control exhibited by cancer cells is the net result of accumulated abnormalities in multiple cell regulatory systems and is reflected in several aspects of cell behavior that distinguish cancer cells from their normal counterparts.

Types of Cancer

Both benign and malignant tumors are classified according to the type of cell from which they arise. Most cancers fall into one of three main groups: carcinomas, sarcomas, and leukemias or lymphomas. Carcinomas, which include approximately 90% of human cancers, are malignancies of epithelial cells. Sarcomas, which are rare in humans, are solid tumors of connective tissues, such as muscle, bone, cartilage, and fibrous tissue. Leukemias and lymphomas, which account for approximately 8% of human malignancies, arise from the blood-forming cells and from cells of the immune system, respectively. Tumors are further classified according to tissue of origin (e.g., lung or breast carcinomas) and the type of cell involved. For example, fibrosarcomas arise from fibroblasts, and erythroid leukemias from precursors of erythrocytes (red blood cells).

Although there are many kinds of cancer, only a few occur frequently ( Table 15.1). More than a million cases of cancer are diagnosed annually in the United States, and more than 500,000 Americans die of cancer each year. Cancers of 10 different body sites account for more than 75% of this total cancer incidence. The four most common cancers, accounting for more than half of all cancer cases, are those of the breast, prostate, lung, and colon/rectum. Lung cancer, by far the most lethal, is responsible for nearly 30% of all cancer deaths.

The Development of Cancer

The clonal origin of tumors does not, however, imply that the original progenitor cell that gives rise to a tumor has initially acquired all of the characteristics of a cancer cell. On the contrary, the development of cancer is a multistep process in which cells gradually become malignant through a progressive series of alterations. One indication of the multistep development of cancer is that most cancers develop late in life. The incidence of colon cancer, for example, increases more than tenfold between the ages of 30 and 50, and another tenfold between 50 and 70 ( Figure 15.3). Such a dramatic increase of cancer incidence with age suggests that most cancers develop as a consequence of multiple abnormalities, which accumulate over periods of many years.

At the cellular level, the development of cancer is viewed as a multistep process involving mutation and selection for cells with progressively increasing capacity for proliferation, survival, invasion, and metastasis ( Figure 15.4). The first step in the process, tumor initiation, is thought to be the result of a genetic alteration leading to abnormal proliferation of a single cell. Cell proliferation then leads to the outgrowth of a population of clonally derived tumor cells. Tumor progression continues as additional mutations occur within cells of the tumor population. Some of these mutations confer a selective advantage to the cell, such as more rapid growth, and the descendants of a cell bearing such a mutation will consequently become dominant within the tumor population. The process is called clonal selection, since a new clone of tumor cells has evolved on the basis of its increased growth rate or other properties (such as survival, invasion, or metastasis) that confer a selective advantage. Clonal selection continues throughout tumor development, so tumors continuously become more rapid-growing and increasingly malignant.

Causes of Cancer

Substances that cause cancer, called carcinogens, have been identified both by studies in experimental animals and by epidemiological analysis of cancer frequencies in human populations (e.g., the high incidence of lung cancer among cigarette smokers). Since the development of malignancy is a complex multistep process, many factors may affect the likelihood that cancer will develop, and it is overly simplistic to speak of single causes of most cancers. Nonetheless, many agents, including radiation, chemicals, and viruses, have been found to induce cancer in both experimental animals and humans.

Radiation and many chemical carcinogens ( Figure 15.6) act by damaging DNA and inducing mutations. These carcinogens are generally referred to as initiating agents, since the induction of mutations in key target genes is thought to be the initial event leading to cancer development. Some of the initiating agents that contribute to human cancers include solar ultraviolet radiation (the major cause of skin cancer), carcinogenic chemicals in tobacco smoke, and aflatoxin (a potent liver carcinogen produced by some molds that contaminate improperly stored supplies of peanuts and other grains). The carcinogens in tobacco smoke (including benzo( a)pyrene, dimethylnitrosamine, and nickel compounds) are the major identified causes of human cancer. Smoking is the undisputed cause of 80 to 90% of lung cancers, as well as being implicated in cancers of the oral cavity, pharynx, larynx, esophagus, and other sites. In total, it is estimated that smoking is responsible for nearly one-third of all cancer deaths-an impressive toll for a single carcinogenic agent.

Other carcinogens contribute to cancer development by stimulating cell proliferation, rather than by inducing mutations. Such compounds are referred to as tumor promoters, since the increased cell division they induce is required for the outgrowth of a proliferative cell population during early stages of tumor development. The phorbol esters that stimulate cell proliferation by activating protein kinase C (see Figure 13.26) are classic examples. Their activity was defined by studies of chemical induction of skin tumors in mice ( Figure 15.7). Tumorigenesis in this system can be initiated by a single treatment with a mutagenic carcinogen. Tumors do not develop, however, unless the mice are subsequently treated with a tumor promoter (usually a phorbol ester) to stimulate proliferation of the mutated cells.

Hormones, particularly estrogens, are important as tumor promoters in the development of some human cancers. The proliferation of cells of the uterine endometrium, for example, is stimulated by estrogen, and exposure to excess estrogen significantly increases the likelihood that a woman will develop endometrial cancer. The risk of endometrial cancer is therefore substantially increased by long-term postmenopausal estrogen replacement therapy with high doses of estrogen alone. Fortunately, this risk is minimized by administration of progesterone to counteract the stimulatory effect of estrogen on endometrial cell proliferation. However, long-term therapy with combinations of estrogen and progesterone may lead to an increased risk of breast cancer.

In addition to chemicals and radiation, some viruses induce cancer both in experimental animals and in humans. The common human cancers caused by viruses include liver cancer and cervical carcinoma, which together account for 10 to 20% of worldwide cancer incidence. These viruses are important not only as causes of human cancer; as discussed later in this chapter, studies of tumor viruses have played a key role in elucidating the molecular events responsible for the development of cancers induced by both viral and nonviral carcinogens.

Properties of Cancer Cells

The uncontrolled growth of cancer cells results from accumulated abnormalities affecting many of the cell regulatory mechanisms that have been discussed in preceding chapters. This relationship is reflected in several aspects of cell behavior that distinguish cancer cells from their normal counterparts. Cancer cells typically display abnormalities in the mechanisms that regulate normal cell proliferation, differentiation, and survival. Taken together, these characteristic properties of cancer cells provide a description of malignancy at the cellular level.

The uncontrolled proliferation of cancer cells in vivo is mimicked by their behavior in cell culture. A primary distinction between cancer cells and normal cells in culture is that normal cells display density-dependent inhibition of cell proliferation ( Figure 15.8). Normal cells proliferate until they reach a finite cell density, which is determined in part by the availability of growth factors added to the culture medium (usually in the form of serum). They then cease proliferating and become quiescent, arrested in the stage of the cell cycle (see Figure 14.6). The proliferation of most cancer cells, however, is not sensitive to density-dependent inhibition. Rather than responding to the signals that cause normal cells to cease proliferation and enter G ₀, tumor cells generally continue growing to high cell densities in culture, mimicking their uncontrolled proliferation in vivo.

A related difference between normal cells and cancer cells is that many cancer cells have reduced requirements for extracellular growth factors. As discussed in Chapter 13, the proliferation of most cells is controlled, at least in part, by polypeptide growth factors. For some cell types, particularly fibroblasts, the availability of serum growth factors is the principal determinant of their proliferative capacity in culture. The growth factor requirements of these cells are closely related to the phenomenon of density-dependent inhibition, since the density at which normal fibroblasts become quiescent is proportional to the concentration of serum growth factors in the culture medium.

The growth factor requirements of many tumor cells are reduced compared to their normal counterparts, contributing to the unregulated proliferation of tumor cells both in vitro and in vivo. In some cases, cancer cells produce growth factors that stimulate their own proliferation ( Figure 15.9). Such abnormal production of a growth factor by a responsive cell leads to continuous autostimulation of cell division ( autocrine growth stimulation), and the cancer cells are therefore less dependent on growth factors from other, physiologically normal sources. In other cases, the reduced growth factor dependence of cancer cells results from abnormalities in intracellular signaling systems, such as unregulated activity of growth factor receptors or other proteins (e.g., Ras proteins or protein kinases) that were discussed in Chapter 13 as elements of signal transduction pathways leading to cell proliferation.

Cancer cells are also less stringently regulated than normal cells by cell-cell and cell- matrix interactions. Most cancer cells are less adhesive than normal cells, often as a result of reduced expression of cell surface adhesion molecules. For example, loss of E-cadherin, the principal adhesion molecule of epithelial cells, is important in the development of carcinomas (epithelial cancers). As a result of reduced expression of cell adhesion molecules, cancer cells are comparatively unrestrained by interactions with other cells and tissue components, contributing to the ability of malignant cells to invade and metastasize. The reduced adhesiveness of cancer cells also results in morphological and cytoskeletal alterations: Many tumor cells are rounder than normal, in part because they are less firmly attached to either the extracellular matrix or neighboring cells.

A striking difference in the cell-cell interactions displayed by normal cells and those of cancer cells is illustrated by the phenomenon of contact inhibition ( Figure 15.10). Normal fibroblasts migrate across the surface of a culture dish until they make contact with a neighboring cell. Further cell migration is then inhibited, and normal cells adhere to each other, forming an orderly array of cells on the culture dish surface. Tumor cells, in contrast, continue moving after contact with their neighbors, migrating over adjacent cells, and growing in disordered, multilayered patterns. Not only the movement but also the proliferation of many normal cells is inhibited by cell-cell contact, and cancer cells are characteristically insensitive to such contact inhibition of growth.

Another general characteristic of most cancer cells is that they fail to differentiate normally. Such defective differentiation is closely coupled to abnormal proliferation, since, as discussed in Chapter 14, most fully differentiated cells either cease cell division or divide only rarely. Rather than carrying out their normal differentiation program, cancer cells are usually blocked at an early stage of differentiation, consistent with their continued active proliferation.

The leukemias provide a particularly good example of the relationship between defective differentiation and malignancy. All of the different types of blood cells are derived from a common stem cell in the bone marrow (see Figure 14.44). Descendants of these cells then become committed to specific differentiation pathways. Some cells, for example, differentiate to form erythrocytes whereas others differentiate to form lymphocytes, granulocytes, or macrophages. Cells of each of these types undergo several rounds of division as they differentiate, but once they become fully differentiated, cell division ceases. Leukemic cells, in contrast, fail to undergo terminal differentiation ( Figure 15.11). Instead, they become arrested at early stages of maturation at which they retain their capacity for proliferation and continue to reproduce.

As discussed in Chapter 13, programmed cell death, or apoptosis, is an integral part of the differentiation program of many cell types, including blood cells. Many cancer cells fail to undergo apoptosis, and therefore exhibit increased life spans compared to their normal counterparts. This failure of cancer cells to undergo programmed cell death contributes substantially to tumor development. For example, the survival of many normal cells is dependent on signals from growth factors or from the extracellular matrix that prevent apoptosis. In contrast, tumor cells are often able to survive in the absence of growth factors required by their normal counterparts. Such a failure of tumor cells to undergo apoptosis when deprived of normal environmental signals may be important not only in primary tumor development but also in the survival and growth of metastatic cells in abnormal tissue sites. Normal cells also undergo apoptosis following DNA damage, while many cancer cells fail to do so. In this case, the failure to undergo apoptosis contributes to the resistance of cancer cells to irradiation and many chemotherapeutic drugs, which act by damaging DNA. Abnormal cell survival, as well as cell proliferation, thus plays a major role in the unrelenting growth of cancer cells in an animal.

Transformation of Cells in Culture

The study of tumor induction by radiation, chemicals, or viruses requires experimental systems in which the effects of a carcinogenic agent can be reproducibly observed and quantitated. Although the activity of carcinogens can be assayed in intact animals, such experiments are difficult to quantitate and control. The development of in vitro assays to detect the conversion of normal cells to tumor cells in culture, a process called cell transformation, therefore represented a major advance in cancer research. Such assays are designed to detect transformed cells, which display the in vitro growth properties of tumor cells, following exposure of a culture of normal cells to a carcinogenic agent. Their application has allowed experimental analysis of cell transformation to reach a level of sophistication that could not have been attained by studies in whole animals alone.

The first and most widely used assay of cell transformation is the focus assay, which was developed by Howard Temin and Harry Rubin in 1958. The focus assay is based on the ability to recognize a group of transformed cells as a morphologically distinct "focus" against a background of normal cells on the surface of a culture dish ( Figure 15.12). The focus assay takes advantage of three properties of transformed cells: altered morphology, loss of contact inhibition, and loss of density-dependent inhibition of growth. The result is the formation of a colony of morphologically altered transformed cells that overgrow the background of normal cells in the culture. Such foci of transformed cells can usually be detected and quantified within a week or two after exposure to a carcinogenic agent. In general, cells transformed in vitro are able to form tumors following inoculation into susceptible animals, supporting in vitro transformation as a valid indicator of the formation of cancer cells.

# data[].attributes.attachment_link.description
The fundamental abnormality resulting in the development of cancer is the continual unregulated proliferation of cancer cells. Rather than responding appropriately to the signals that control normal cell behavior, cancer cells grow and divide in an uncontrolled manner, invading normal tissues and organs and eventually spreading throughout the body.

# data[].attributes.attachment_link.readable_text
Abstract

Cellular senescence is a key component of human aging that can be induced by a range of stimuli, including DNA damage, cellular stress, telomere shortening, and the activation of oncogenes. Senescence is generally regarded as a tumour suppressive process, both by preventing cancer cell proliferation and suppressing malignant progression from pre-malignant to malignant disease. It may also be a key effector mechanism of many types of anticancer therapies, such as chemotherapy, radiotherapy, and endocrine therapies, both directly and via bioactive molecules released by senescent cells that may stimulate an immune response. However, senescence may contribute to reduced patient resilience to cancer therapies and may provide a pathway for disease recurrence after cancer therapy. A new group of drugs, senotherapies, (drugs which interact with senescent cells to interfere with their pro-aging impacts by either selectively destroying senescent cells (senolytic drugs) or inhibiting their function (senostatic drugs)) are under active investigation to determine whether they can enhance the efficacy of cancer therapies and improve resilience to cancer treatments. Senolytic drugs include quercetin, navitoclax, and fisetin and preclinical and early phase clinical data are emerging of their potential role in cancer treatments, although none are yet in routine use clinically. This article provides a review of these issues.

Keywords: senescence, cancer, aging, frailty, senolytics, senotherapies, geriatric oncology

1. An Aging Population and Cancer

In the western world, life-expectancy has doubled in the past 200 years and, whilst most of the early gains were in childhood longevity, in the past 70 years the majority of gains have been in older people [ 1, 2]. Consequently, the global proportion of people over 65 is predicted to increase from 18% now to 26% in 2041 [ 3]. The oldest old group of over 85 year olds is also predicted to double over the same period, from 2 to 4% [ 3]. This increase in lifespan has been accompanied by an increase in rates of chronic disease. Consequently, life-span has outpaced health-span [ 4], as the burden of chronic health conditions has increased in this older population. As a result almost 50% of over 75s have 2 or more chronic health conditions [ 3].

One such age-related condition is cancer. Cancer is more common in older age groups with predictions of substantial increases in the next 15 years of approximately 50% in developed countries, (largely due to the increase in the older population), and even higher in developing and middle income countries [ 5]. Aging interacts with cancer in a range of ways: some of the molecular pathways and causes of aging [ 6] and the pathways and causes of cancer [ 7] overlap significantly. Aging is integral to the causation of many cancers and also impacts on treatment response, prognosis, treatment allocation, and treatment tolerance. One of the key processes of aging-senescence-links aging and cancer together, and this is the focus of this article.

2. Biological Changes of Aging and Senescence

Aging is a complex process involving multiple factors, including genetic and epigenetic alterations to DNA, cellular damage from reactive oxygen species (ROS) produced by incomplete aerobic metabolism, age-dependent decline in mitochondrial function, stem cell exhaustion, and telomere shortening [ 6, 8]. The rate at which aging occurs is highly variable and depends on both polygenic hereditary factors [ 9] and a wide range of environmental factors (such as obesity, diet, exercise, and exposure to drugs and chemicals), which is why biological and chronological age are often mis-matched. A key factor in the aging process is cellular senescence. Senescent cells are rare in the tissues of young organisms but become more common as tissues age, especially in adipose tissue, muscle, and skin [ 10].

At a cellular level senescence refers specifically to a progression of complex changes culminating in the loss of proliferative potential (which may be reversed in some circumstances), the inhibition of cellular apoptosis, chromatin alterations, and metabolic and synthetic changes. Senescent cells release a complex, cell type-specific, mixture of bioactive molecules (senescence-associated secretory phenotype or SASP), which impacts on the adjacent cells and extracellular matrix and contributes to age-related tissue degeneration.

Senescence is increasingly recognized as a heterogeneous group of cell states, depending on the type, degree, and duration of stimuli that triggered it [ 11]. Senescence may also be categorized as acute or chronic, depending on the size and duration of the stimulus. Acute senescence is a short-term process, often triggered to limit an inflammatory response or physiological process, such as wound healing or embryological development [ 12]. Chronic senescence is triggered by feedback loops when the senescent stimulus is prolonged, and may cause diseases such as atherosclerosis, arthritis, neurodegenerative disease, and diabetes [ 13] as well as effecting classical age-related decline in tissue function [ 14].

There are several regulatory pathways through which senescence may be stimulated under appropriate conditions. These include the pathways for cellular proliferation (the cell cycle and its regulatory machinery), the regulation of apoptosis, the DNA damage response (DDR) pathway, cellular energy metabolism, and the unfolded protein response (UPR) [ 15]. In turn, senescence may impact on these same pathways via positive or negative feedback loops as well as other intracellular pathways and extracellular processes, such as the immune modulation, inflammation [ 16], regulation, and maintenance of the extracellular matrix, and angiogenesis [ 17]. Consequently, senescence can have wide ranging impacts on tissue structure and function.

Many of these pathways are also involved in the process of cancer development, suppression, progression, recurrence, and response to therapies ( Figure 1 ). The interactions of senescence and cancer are therefore complex and poorly understood at present.

It has been proposed that, whilst senescence itself is largely tumour suppressive [ 18], the SASP of chronically senescent cells may promote tumour recurrence or progression [ 19]. There are also potential interactions with cancer therapies, in particular, radiotherapy and chemotherapy, which may trigger either cell death at high doses or senescence at non-lethal doses. This induced senescence may be viewed as a cytostatic clinical response, but some of these therapy induced senescent cells may reactivate and lead to recurrence [ 20, 21, 22]. They may also be an opportunity for tumour targeting to enhance cell killing.

In addition, senescent cells, by contributing to the pathogenesis of the aging phenotype (frailty, sarcopenia, lack of resilience, and aging-related disease) [ 14], impact on the ability of a patient with cancer to tolerate a range of cancer therapies. Consequently, there is great academic interest in senescence, and researchers have developed reliable methods to identify these cells in tissue sections [ 23]. No one marker alone is sufficient to identify senescence and most researchers use a panel of biomarkers, such as senescence-associated β-galactosidase, p21, and p16 INK4A, as well as a number of other biomarkers, such as heterochromatin and proliferation markers ( Table 1 ) combined [ 24] with a marker of cellular proliferation (Ki-67, for instance).

Table 1

Characteristic

Marker

Proliferative arrest

Low expression of Ki-67 or bromodeoxyuridine BrdU

Persistent activation of the DNA damage response

Activation of tumour suppressors, such as p53, p16 INK4A, cyclins, and cyclin-dependent kinases

Heterochromatic foci (DNA becomes denser than normal)

Senescence-associated heterochromatic foci on DNA staining with DAPI

Cells become flattened and enlarged

Light microscopy changes

Altered metabolism including increased β-galactosidase activity, which is part of carbohydrate metabolism

Measurement of β-galactosidase levels

Senescence-associated secretory phenotype

Interleukins-1, -6, and -8, matrix metalloproteinases, plasminogen activator inhibitor-1

Interest is also growing in a new group of drugs called senotherapies. These are drugs that either suppress the process of senescence or destroy senescent cells, and which may have potential in cancer therapy.

3. Regulatory Pathway of Senescence

The regulation of senescence is complex and incompletely understood, and a detailed review is beyond the scope of this article, but excellent reviews are available [ 13, 25]. A range of factors may trigger senescence, including cell stressors, such as reactive oxygen species, DNA damage due to aging, radiation, and genotoxic substances (including many chemotherapy agents), age-related telomere shortening, adverse growth conditions, tumour suppressor gene activation, and oncogene activity. (These are summarized in Figure 1 ). Several key pathways into senescence have been described: replicative senescence, oncogene-induced senescence, stress-induced senescence, and therapy-induced senescence.

3.1. Replicative Senescence

The process of senescence activation is closely linked to cell cycle regulatory machinery. The Gap One (G1) phase may progress to the synthetic (S) phase or be side tracked into a reversible state of dormancy (G0), terminal differentiation, or senescence (usually, but not always, causing irreversible cell cycle arrest) depending on the activation of a range of cell cycle regulatory proteins. Key triggers for the activation of this G1-S phase checkpoint are telomere shortening and DNA damage.

Cells have a finite ability to replicate (the Hayflick Limit [ 26]) and once this limit is reached, they lose the ability to divide but remain alive and metabolically active as senescent cells. One of the key effectors of this transition is telomere shortening. Telomeres are tandem repeats of a TTAGGG nucleotide found at the end of all chromosomes that are progressively lost with each cell division. Once the remaining telomere is critically shortened, it triggers a DNA damage response (DDR), which induces proliferative arrests via the p53/cyclin E/CDK2/RB pathway that controls the cell cycle G1 to S phase checkpoint. As telomere shortening is irreversible in normal cells and cannot be repaired, it triggers senescence rather than temporary proliferative arrest.

The same pathway is triggered by non-lethal DNA damage, which again activates the DDR. DNA damage accumulates progressively with age but also occurs in the majority of cancers, where a key initiation event is often the loss of function of critical DNA repair pathways via loss of function of tumour suppressor genes. For example, the p53 and BRCA genes are key tumour suppressor genes that are mutated and lose function at an early stage in the process of cancer development. Again, if this DNA damage cannot be repaired, the cell may die (usually via apoptosis) or enter senescence via the p53/cyclin E/CDK2/RB signaling pathway. Whether a cell undergoes apoptosis or senescence depends on the degree of damage, and whether it is repairable. In this context, senescence is tumour suppressive in that it prevents the damaged cell from proliferating. This pathway to senescence is a key effector of chemotherapy and radiotherapy damage.

3.2. Oncogene-Induced Senescence

Another regulatory pathway for senescence activation is via the p16 inhibitor of kinase 4(INK4), a regulatory protein that inhibits CDK4/6 and cyclin 2 and so blocks cell cycle progression via the RB protein. This pathway may be stimulated by the loss of tumour suppressor function, oncogene activation, epigenetic changes (DNA methylation, for example, which may alter gene expression), or damage to the microtubules forming the mitotic spindles (a mechanism of action of some chemotherapy agents, such as vinca alkaloids and taxanes). Once again, mild stimulation induces senescence whereas severe stimulation induces cell death.

3.3. Stress-Induced Senescence

Some oncogenes, such as the BRAF oncogene (which is commonly mutated in melanoma), may trigger senescence acting via mitochondrial pyruvate kinase metabolism to regulate cell cycle progression. Metabolic stress may also induce senescence and trigger the unfolded protein response (UPR). This pathway, which is designed to protect the cell from exporting misfolded proteins due to adverse environmental conditions, is activated by a range of cell stressors, such as hypoxia, acidosis, or other metabolic stresses. It triggers cell cycle arrest and senescence if the stressor is prolonged. The UPR is often activated in many cancers [ 27] due to their abnormal microcirculation (which leads to cellular hypoxia and acidosis) and the high glucose needs of tumours (Warberg effect), resulting in metabolic stress.

4. Senescence-Associated Secretory Phenotype, SASP

Whereas acute senescence is largely beneficial in the regulation of transient injury or stress, if a cell experiences prolonged non-lethal stress, a chronic senescent state is entered where the continued production of SASP perpetuates cellular and extracellular matrix damage and results in the acquisition of an aging or disease linked phenotype.

Senescent cells release SASP factors that damage nearby non-senescent cells and the extracellular matrix, triggering inflammation, fibrosis, and apoptosis of adjacent healthy cells, but the senescent cells themselves are resistant to these effects and accumulate, thus generating more SASP factors. The precise content of the SASP released by a senescent cell may vary according to the host tissue and the degree and type of senescent stimulus. In some cases, the SASP may have a beneficial effect-when triggered acutely in wound healing, for example, the SASP is involved in triggering inflammation and an immune response and, once wound closure has occurred, the myofibroblasts become senescent to prevent excessive fibrosis. Similarly, during embryogenesis senescence is a mechanism whereby certain processes may be physiologically stopped [ 12].

The SASP is a melange of over 100 molecules, including inflammatory cytokines, growth factors, and proteases. These include interleukins 1 and 6 (pro-inflammatory cytokines that may trigger or promote inflammation), interleukin 8 (which attracts neutrophils towards a tissue and stimulates their activity in the inflammatory process), and matrix metalloproteinases (which break down the various components of the extracellular matrix, such as collagen). The SASP of a particular senescent cell type may have a tumour suppressive function and trigger immune activation or promote tumour growth and inflammation depending on the host tissue. This variation in function makes study complex, as senescent cells in different tissues secrete slightly varying mixtures of SASP factors and so the inhibition of senescent cells may have positive or negative effects on cancer growth.

5. Role of Senescence in the Development of Cancer

Senescence is a key mechanism of tumour suppression. This may be mediated by the DNA damage response or via key oncogenes. Several key oncogenes, including ras, cyclin E, raf, and E2F3 expression, are linked to senescence induction and may have a tumour suppressive role [ 28, 29, 30]. This may be via the inhibition of proliferation of malignant cells or by stimulating immune surveillance. Studies have demonstrated high levels of senescence in premalignant lesions and lower levels in invasive disease, suggesting a role for senescence in blocking malignant progression [ 2, 31, 32]. Mutations in key oncogenes often trigger senescence to eliminate premalignant cells before they acquire further mutations and become invasive [ 33]. There is evidence of senescence being triggered by the ras oncogene during lung and pancreatic tumorigenesis [ 2]. Some mechanisms to evade senescence are therefore a key feature of malignant progression from pre-malignant to invasive disease. It has been proposed that a loss of one of the key senescence effectors, such as the tumour suppressors p16 INKA4 or p53, may be mechanisms whereby the failure of senescence occurs [ 2]. This allows the oncogene to stimulate progression without check. Senescent cells may evoke an anti-tumour immune response-so called "senescence surveillance"-mediated by the cytokines within SASP. This has been shown to suppress the progression of malignancy in hepatocytes in a mouse model [ 34]. Other mechanisms of tumour suppression have been proposed [ 35].

In contrast there is also evidence that stromal cell senescence may have a tumour promoting effect in some models-this may be because of the proangiogenic influence of some components of SASP, such as vascular endothelial growth factor (VEGF) or the impact of senescent fibroblasts on adjacent tumour cells [ 17, 36]. There is also evidence that "immune senescence", associated with the aging of the immune system in older age may contribute to the failure of immune surveillance and contribute to cancer development in older people. This was shown in a study where telomere shortening in peripheral blood T cells, was linked to the development of cancer [ 37].

6. Role of Senescence as a Prognostic Marker for Cancer

Senescent cells are more common in the normal tissues of aged individuals, especially in certain tissue types, such as skin and adipose tissue [ 38, 39, 40]. They are also present in cancer and in premalignant lesions and have been evaluated as prognostic markers [ 41]. The detection of senescence is complicated, as many of the molecules involved in senescence signalling are also oncogenes or tumour suppressors that may be up or down regulated as part of the carcinogenic process. The assessment of combinations of markers is therefore required in addition to a marker of proliferation, such as Ki67. Common markers of senescence include β-galactosidase activity, p16 INK4A, p21, and heterochromatin levels combined with a marker of proliferative arrest [ 23].

Markers of senescence have been assessed in a number of human cancers. In breast cancer, p16 levels correlate with breast cancer subtype, proliferative status, and prognosis [ 42]. Another study of breast cancer demonstrated that the increased expressions of senescence markers p14 ARF and p16 INK4a were associated with increased risk of disease recurrence and poor survival outcomes [ 43]. Another study of a range of cancer types, including breast, lymphoma, colon, sarcoma and lung, found that senescence markers were linked to better prognosis [ 29]. Colon and endometrial cancers have also been studied, as well as senescence markers linked to improved prognosis [ 44, 45]. Table 2 shows a summary of a range of studies linking senescence with tumour prognosis.

Table 2

Model

Cancer Type

Prognostic Significance of Senescence

Reference

7. Role of Senescence in Cancer Treatment Response

Chemotherapy

Chemotherapy may cause cell death, often by apoptosis, resulting clinically in tumour regression. It may also cause cellular senescence, leading clinically to tumour stasis (growth arrest) ( Figure 2 ). Many types of chemotherapy cause DNA damage (DNA strand breaks or cross linking), which can, if severe, cause cell death via the DNA damage response, or they may trigger a non-lethal DDR, leading to acute or chronic senescence, depending on the extent and duration of the stimulus [ 52]. Entry into senescence or cell death may also depend on whether the cell has functional tumour suppressor genes, such as p53 or p16 INK4A to regulate cell behaviour [ 53]. Moderate chemotherapy doses are more likely to cause senescence and higher doses more likely to cause cell death [ 54]. Different types of chemotherapy damage DNA in a number of different ways. For example, doxorubicin prevents the resealing of the DNA double helix by inhibiting topoisomerase 2, which triggers a DDR and thereby may cause senescence [ 54]. Others, such as vinca alkaloids and taxanes, work by causing damage to the mitotic spindle during mitosis, resulting in cell death. Cyclophosphamide causes DNA cross linking, which again may trigger a DDR.

The role of senescence in response to chemotherapy is more complicated, however, in that the SASP of senescent cells induced by treatment varies between tissues and cell types, according to the precise senescent stimulus [ 55]. In particular, some senescent cells secrete exosomes [ 56] (small "packages" containing a variety of proteins and mRNA) and these may have a tumour promoter function [ 55]. Consequently, senescence induced by some cancer therapies may be harmful and promote tumour growth.

In a mouse model, lymphomas induced by the myc oncogene responded to cyclophosphamide by undergoing senescence mediated by p16 INK4A and p53 [ 57]. It has been hypothesised that the induction of permanent senescent cytostasis may be an effective strategy for cancer treatment, rather than killing the cancer cell [ 58], and may have reduced toxicity. However, there are concerns that these senescent cells may serve as a potential reservoir for resistance if their senescent state is reversed.

There are some human data about the role of senescence in chemotherapy response in lung, breast, and prostate cancer and lymphoma. In breast cancer cases treated with neoadjuvant chemotherapy, 41% of tumours were stained by senescence markers compared to only 10% of untreated cases, showing that senescence is induced by CAF (cyclophosphamide, Adriamycin, and fluorouracil) chemotherapy [ 53]. Senescence is also an important mechanism of efficacy in the treatment of breast cancer with PARP inhibitors [ 59] and many typical chemotherapy agents used to treat breast cancer, such as epirubicin and cyclophosphamide [ 60]. The treatment of breast cancer cells with Adriamycin for 5 days induces 60% of cells to become senescent [ 61]. A very small study of neoadjuvant chemotherapy in patients with non-small cell lung cancer showed increased senescence biomarkers in patients treated with a taxane and carboplatin [ 62]. Similarly, in prostate cancers treated with neoadjuvant mitozantrone, elevated levels of senescence biomarkers were found [ 31, 63]. Similarly, senescence has been seen in chemotherapy-treated lymphoma [ 64].

Whilst cells that undergo apoptosis are permanently removed from a cancer, senescent cells remain and secrete various inflammatory cytokines, which may have both positive and negative impacts [ 65]. There have been concerns that these senescent cells may resist further chemotherapy damage and be a potential reservoir for recurrence. There is evidence that senescent cells may also be re-programmed to re-enter the cell cycle after certain types of chemotherapy [ 20, 21, 62] and may acquire a more "stem cell"-like phenotype, which may in turn contribute to tumour regrowth and evolution [ 66, 67]. Recently, another form of tumour dormancy has been described whereby multinucleate senescent giant cells may re-enter the cell cycle and produce viable diploid daughter cells which may repopulate a tumour [ 68, 69].

Conversely, there is evidence that the secretion of SASP factors may enhance the immune surveillance of tumours and induce bystander cells to become senescent thereby inhibiting tumour progression [ 66]. It can be seen from the above that the role of senescence in the response of cancer to chemotherapy is highly complex and more research is needed to clarify these interactions.

8. Role of Senescence in Radiotherapy

Radiotherapy, which is one of the mainstays of cancer therapy, acts by causing direct DNA damage and has wide ranging impacts on cancer cells mediated by reactive oxygen species. The DNA damage response is triggered and if repair is not possible, cells either die if the damage is severe or enter senescence if less severe. In irradiated cancer cells, the percentage of senescent cells therefore increases in the remaining "radio-resistant" clones [ 70]. The concern is that these senescent cells may be released from senescence and assume a more "stem cell"-like phenotype, which may result in aggressive recurrence [ 71]. Cerebral glioblastoma, in which radiotherapy induces senescent multinucleate giant cells to form and reactivate these cells, is now recognized as an important mechanism of relapse [ 72].

In contrast, radiotherapy also triggers an immune response, making the treated cells more immunogenic in a variety of ways [ 73]. Part of this immunogenicity may be due to the release of SASP factors from senescent cells.

Another way in which senescence may be a clinically important part of radiotherapy response is in causing radiation-induced fibrosis. This can be a potentially severe complication of radiotherapy, especially in the lung where pulmonary fibrosis may occur [ 74]. Senescent cells also appear to be linked to skin fibrosis and ulceration following radiotherapy [ 75]. Senolytic agents may have a role in abrogating this fibrotic response, although to date there have been no human trials to evaluate this.

9. Role of Senescence in Response to Hormonal Therapies (Anti-Oestrogens, Anti-Androgens)

The mechanism of action of antioestrogen therapies in breast cancer and the mechanisms of resistance are very complex, but senescence may be one of the pathways activated when disease responds to antioestrogens [ 76]. Similarly androgen deprivation therapy induces senescence in prostate cancer cells and the SASP has been implicated in disease progression [ 77].

10. Role of Senescence in Surgery

Surgery is the single most important curative modality in the treatment of most human solid tumours, and cure is unlikely without it for most cancer types. Successful surgery, however, requires wound healing for recovery. Wound healing is a complex multistep process and senescence may be involved in both positive and negative ways.

After an initial haemostatic phase, an inflammatory response is triggered, and a range of cells migrate into the area or differentiate locally to effect healing. Collagen synthesis and remodelling occur, and the skin re-epithelializes. A wide range of cells, biologically active molecules, and signalling systems that both start and stop the process are involved. At an early stage in the process, senescent epithelial cells and fibroblasts appear and induce myofibroblast differentiation by secretion of platelet derived growth factor AA (PDGF-AA), which is a component of the SASP [ 78]. These myofibroblasts assist healing by the secretion of collagen and, by their contractile properties, help to draw the wound together (contraction). In addition, senescence is involved in "switching off" the healing process to prevent excess fibrosis. Matrix metalloproteinases within the SASP help to degrade collagen as part of remodelling [ 79]. Consequently, the failure of senescence may have a detrimental effect on wound healing, as seen in a mouse model where senescent cells were eliminated, resulting in disorganised collagen remodelling [ 78].

Conversely, chronic senescence has been causally implicated in non-healing ulcers [ 75] and delayed wound healing in diabetes, for example [ 80]. Levels of senescent fibroblasts are correlated with the length of time taken by venous ulcers to heal [ 81]. Wound healing is impaired in older organisms [ 82] for a variety of reasons and it is known that the skin is an area where senescent cells accumulate with age.

Few studies have directly assessed the relationship with senescence and surgical outcomes; however, p16 expression is negatively correlated with liver regeneration after hepatectomy in older patients [ 83] and the molecular aging marker CDKN2A has been shown to predict renal allograft function after 1 year in renal transplant patients [ 84].

It is therefore not clear what impact senolytic therapies might have on wound healing in the acute surgical setting and this requires urgent further study if these agents are to be adopted into clinical practice. This is especially important in this era of multimodal therapy regimes. If senotherapies are used as adjuncts to chemotherapy, it would be critical to ensure any negative impacts on wound healing are resolved before surgery. Similarly, if senotherapies are used to enhance resilience to surgery, the acute effect on wound healing must be properly evaluated. There are currently no studies published of senotherapies on acute wound healing.

11. Frailty and Senescence Reducing Resilience to Cancer Therapies

Older patients are less resilient to many cancer therapies. This is due, in part, to comorbidity rates being higher in older people, but also due to age-related organ dysfunction and frailty. Frailty is present in approximately 25% of the older surgical population and is linked to longer hospital stays and higher post-surgical mortality rates [ 85, 86]. Adverse events are also more likely following chemotherapy [ 87]. Biomarkers of frailty are generally linked to the presence of chronic low grade inflammation [ 88] and a range of inflammatory markers have been studied and correlate with poor treatment outcomes across a range of diseases [ 89]. It is thought that SASP factors may contribute to this age and frailty-linked inflammation, and its abrogation may alleviate it [ 14].

This principle was elegantly demonstrated in a mouse model, where the implantation of senescent cells into young mice induced physical dysfunction, which spread beyond the injected senescent cells and reduced lifespan. In contrast, treating naturally aged mice or senescent cell injected mice with the senolytic drug combination of dasatinib and quercetin both reduced the number of senescent cells and caused a reduction in physical dysfunction and extended lifespan [ 14].

In the context of chemotherapy tolerance, there is evidence that some of the adverse effects of chemotherapy are mediated by the therapy-induced senescent cells which have a pro-inflammatory effects (due to SASP) in a doxorubicin or paclitaxel treated mouse model [ 67]. Removal of these therapy-induced senescent cells abrogated many of the adverse effects of chemotherapy (reduced fatigue, increased activity levels, reduced cardiac functional impairment) [ 67]. In a separate study, again in a mouse model, the elimination of senescent cells by the use of dasatinib and quercetin, reduced the impact of radiotherapy, improved cardiac function and exercise tolerance and increased life expectancy [ 90]. Data in humans are also available that show that higher levels of senescence biomarkers are linked with higher rates of treatment-induced adverse events following doxorubicin chemotherapy [ 67].

12. Senotherapies

This term refers to a group of pharmacological agents that interact with senescent cells to interfere with their pro-aging impacts. There are two main categories: senolytic drugs, which selectively destroy senescent cells and senostatic drugs, which inhibit their function by suppression of their release of SASP factors. Of the two drug groups, senolytics have been more extensively studied and show promise of therapeutic value. These are of particular interest as an adjunct to chemotherapy, where the senolytic drug may be able to target cells induced to become senescent by the cancer. They may also improve treatment resilience. There are several agents under investigation.

12.1. Navitoclax

Navitoclax interacts with the BCL-2 pathway and prevents it from inhibiting apoptosis, causing the senescent cells to undergo apoptosis [ 91]. The rationale is that the chemotherapy drug causes cells to undergo either apoptosis or senescence and the Navitoclax then induces the senescent cells to undergo apoptosis by inhibiting BCL-2 [ 92]. This should therefore enhance cell killing.

There is widespread evidence that Navitoclax potentiates the effect of a range of anticancer therapies in vitro and in vivo ( Table 3 ), but efficacy in clinical studies has been limited by toxicity, in particular thrombocytopenia, which significantly limits its dose in human studies. Response rates for both single agent and combination studies have been disappointing ( Table 3 ). There have been recent attempts to increase the specificity of Navitoclax to senescent cells by combining the drug with galactose. As senescent cells have higher levels of the galactosidase enzyme (SA β-galactosidase), the release of active Navitoclax is targeted to the senescent cells, potentially reducing platelet toxicity [ 93]. There are currently a few early phase clinical trials in progress, but unless the toxicity issue can be addressed it may not have a role in routine oncology practice.

Table 3

Reference

Model

Drug

Cancer Type

Effect

Tolcher et al. 2015 [ 99]

Phase 1 clinical trial

Irinotecan+Navitoclax

Advanced solid tumours, n = 31

6% rate of partial response

Rudin et al. 2012 [ 101]

Phase 2 clinical trial

Single agent navitoclax

Relapsed small cell lung cancer, n = 39

23% static disease, 2.6% partial response

12.2. Dasatinib Plus Quercetin

The combination of dasatinib (a tyrosine kinase inhibitor) plus quercetin (a flavonoid) (D + Q) may act in part via senescence induction, although this combination has quite wide-ranging cellular impacts. It reduces levels of senescent cells in a range of in vitro models and in mouse in vivo models [ 14, 90]. That D + Q acts principally by senolytic effects for particular conditions, as opposed to effects on these other pathways, has been indicated in studies of, for example, osteoporosis. In mice with age-related osteoporosis, D + Q is as effective in restoring bone if administered once every few weeks as opposed to continuously despite D + Q having an elimination half-life of less than 11 h. Senescent cells take weeks to reappear. Thus, it seems very unlikely that in osteoporosis the beneficial effects of D + Q are due to mechanisms requiring continuous engagement of a classical biochemical target, such as occupancy of a receptor or inhibiting an enzyme, and senolytic "hit and run" effects are more plausible [ 102]. Consistent with this, in the case of frailty, which can be induced in younger healthy mice by transplanting senescent cells, a brief course of D + Q eliminates senescent cells and causes long term resolution of frailty [ 14]. Thus, the development of successful senolytics has more in common with developing antibiotics than the old fashioned one target-one drug-one disease drug development paradigm [ 103, 104].

While D + Q has been shown to reduce levels of senescent cells in a range of in vitro models and in mouse in vivo models [ 14, 90], to date there are few clinical trial data of its efficacy in the cancer setting, although some evidence in non-cancer settings. For example in pulmonary fibrosis, where it is thought that the secretome of senescent fibroblasts mediates fibrosis, Quercetin and Dasatinib reduce this effect in vivo [ 105]. In a cancer setting, it has been used to reduce radiation-induced skin ulceration in a mouse model by reducing senescent cells in the skin [ 75]. In human subjects, senescent cells may be implicated in the development of radiation-induced skin ulceration as shown recently by a study where p16 expression was associated with radiation-induced ulcers in human subjects [ 75]. This is potentially of great interest as radiation induced fibrosis and ulceration are a major cause of morbidity following radiotherapy in clinical practice.

12.3. Fisetin

Flavonoids are a group of naturally occurring chemicals found widely in fruit and vegetables and which are well known for their beneficial antioxidant properties. Studies looking at the senolytic properties of a range of flavonoids have found that fisetin is potent [ 106, 107] and roughly twice as potent as quercetin, with an excellent toxicity profile in animal studies [ 108]. High levels are found in strawberries, apples, persimmons and lower levels in grapes and cucumbers [ 109]. It has been extensively investigated in vitro and in vivo, where it has wide ranging effects on a number of key pathways involved in cell cycle regulation, apoptosis, the suppression of inflammation, angiogenesis, and metastasis (reviewed in Kashjap at al. [ 109]). It has been investigated in studies of combined treatment with other anticancer agents to determine whether it may potentiate their effect. These studies are summarised in Table 4 .

Table 4

Reference

Model

Drug

Cancer Type

Effect

It is already available for human consumption in low doses as a nutritional supplement as 100 mg capsules where it is marketed to enhance brain health. To put this into context, the average daily consumption of Fisetin is thought to be 0.4 mg per day [ 109]. In terms of clinical trials, this 100 mg dose has been used in a colorectal cancer chemotherapy study and has been shown to reduce inflammatory markers, although numbers were small and no clinical cancer end points were assessed [ 121]. There were no safety issues at this dose and the research team felt there was potential for further studies to investigate its adjuvant value.

There is an on-going phase 2 trial (ClinicalTrials.gov Identifier: NCT03675724, AFFIRM_LITE) looking at much higher doses, (20 mg/kg orally for 2 days, so roughly 10- to 15-fold higher than the previous study, with an average 70 kg person getting 1400 mg per day). The study is evaluating the impact of fisetin on a range of biomarkers in older women with frailty syndrome [ 122]. The initial results are awaited. It is hoped that the AFFIRM LITE study will demonstrate the safety of higher dose Fisetin, to permit clinical trials to assess the potential impact of Fisetin on resilience, age-related dysfunction, and chronic diseases, and as an adjunctive treatment for cancer.

12.4. Metformin

As a result of these interactions, there has been recent interest in the use of adjuvant senolytics and senostatics to selectively remove or inhibit senescent cells. For example, the diabetic drug, metformin has senostatic properties and reduces the stimulatory effect of SASP intermediates on prostate cancer cells [ 123] and is effective in reducing the incidence (chemoprevention) of a range of different cancers [ 124]. Whether this is due to its senostatic activities or to its impact on metabolism is not yet clear.

12.5. Other Agents

A number of other agents with senotherapeutic potential are under investigation. These include HSP90 chaperone inhibitors [ 125] (such as geldanamycin) and FOXO4 p53 interfering peptide. Whilst trials are in progress with some of these agents, none are yet approved for clinical use. A note of caution is needed in that, whilst some cancers and cancer treatments may react positively to senotherapies, the variation in SASP composition [ 55] may mean that these findings cannot be extrapolated to other cancers and treatment types.

13. Impact of Frailty on Cancer Treatment and Outcomes

There is another way in which senolytics may impact on cancer outcomes-by enhancing resilience and reducing frailty. It is already recognised that long-term survivors of cancer have increased rates of frailty and reduced longevity, some of which are thought to be due to the direct and indirect induction of senescent cells by cancer therapies (chemotherapy and radiotherapy). A trial is currently running to assess the impact of senolytic therapy on stem cell transplant survivors using dasatinib and quercetin in a small number of patients and assessing the impact on frailty [ 126].

Another important patient group is the elderly with cancer. It is well recognized that treatments such as surgery and chemotherapy have a significant negative impact on physical function, with studies showing an increase in measures of frailty after treatment, which may never recover back to baseline levels. This loss of function is one of the reasons that older patients require longer hospital admission after surgery and sometimes require social care support in the longer term after surgery. This physical dysfunction is therefore a major burden on both the NHS and social care resources and is a research priority for the UK government. If use of senolytic therapies could reduce the frailty phenotype and enhance resilience, this would be a major advance in cancer therapies.

14. Conclusions

As can be seen from the above, senescence is a complex process closely related to cell cycle regulatory processes and is integral to human aging and the genesis of many human diseases. It is thought to be a tumour suppressive process, inhibiting both the formation of cancer by blocking transition from premalignant to malignant disease and preventing damaged cells from proliferating after suffering DNA damage or cellular stress, which is one of the key events promoting cancer development. Senescence is also a key effector mechanism of many chemotherapy agents, both directly induced but also via the immune stimulator effects of the SASP. Conversely, it may be a route to tumour resistance and recurrence if the senescent cells reacquire the ability to proliferate when they may become more stem-cell like or via the proinflammatory and apoptosis inhibitory effects of the SASP.

Senotherapies may become a valuable adjunct to cancer therapies both by direct destruction of the chemotherapy-induced senescent cell population within a tumour and by the inhibition of the proinflammatory SASP, which may enhance treatment tolerance by the patient.

Research into the potential of a range of senotherapies is rapidly growing and hopefully new agents will enter clinical practice in the near future.

Author Contributions

Conceptualization, L.W., I.B.; writing-original draft preparation, L.W., I.B., J.L.K., F.F., J.M., J.G., T.T., O.T., and S.D.; writing-review and editing, L.W., I.B., J.L.K., F.F., J.M., J.G., T.T., O.T., and S.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. J.L.K. and T.T. acknowledge support from US NIH grants R37 AG013925, P01 AG062413, and R33 AG061456 (Translational Geroscience Network), the Connor Fund, Robert J. and Theresa W. Ryan, and the Noaber Foundation.

Conflicts of Interest

Authors J.L.K. and T.T. have a financial interest related to this research. Patents on senolytic drugs are held by Mayo Clinic. This research has been reviewed by the Mayo Clinic Conflict of Interest Review Board and was conducted in compliance with Mayo Clinic Conflict of Interest policies. The other authors declare no conflict of interest.

References

3. Storey A. Living Longer: How Our Population Is Changing and Why It Matters. Office for National Statistics; London, UK: 2018. [ Google Scholar]

53. Te Poele R.H., Okorokov A.L., Jardine L., Cummings J., Joel S.P. DNA damage is able to induce senescence in tumor cells in vitro and in vivo. Cancer Res. 2002;62:1876-1883. [[ PubMed] Google Scholar]

54. Roninson I.B. Tumor cell senescence in cancer treatment. Cancer Res. 2003;63:2705-2715. [[ PubMed] Google Scholar]

119. Zhuo W., Zhang L., Zhu Y., Zhu B., Chen Z. Fisetin, a dietary bioflavonoid, reverses acquired Cisplatin-resistance of lung adenocarcinoma cells through MAPK/Survivin/Caspase pathway. Am. J. Transl. Res. 2015;7:2045-2052. [[ PMC free article] [ PubMed] Google Scholar]

122. Kirkland J.L. Alleviation by Fisetin of Frailty, Inflammation, and Related Measures in Older Adults (AFFIRM-LITE.; NCT03675724) [(accessed on 29 July 2020)]; Available online: https://clinicaltrials.gov/ct2/show/ NCT03675724.

126. Patnaik M. Hematopoietic Stem Cell Transplant Survivors Study (HTSS Study; NCT02652052) [(accessed on 29 July 2020)];2020 Clinical TrialsGov. Available online: https://clinicaltrials.gov/ct2/show/ NCT02652052.

Articles from Cancers are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

# data[].attributes.attachment_link.title
Senescence and Cancer: A Review of Clinical Implications of Senescence and Senotherapies

# data[].attributes.attachment_link.description
Cellular senescence is a key component of human aging that can be induced by a range of stimuli, including DNA damage, cellular stress, telomere shortening, and the activation of oncogenes. Senescence is generally regarded as a tumour suppressive process, ...

# data[].attributes.body
Mismatch repair-deficient (MMRd) genomes are associated to high Microsatallite instability (MSI) which consequently lead to thousands of somatic mutations potentially encoding neoantigens, as such MMRd tumours have an unique genetic signature. MSI-positive tumours tend to express high levels of PD1, PDL1, CTLA4 among other immune checkpoint molecules, that generally can inactivate T cells.

Work is currently in progress to determine if these mutation signatures allow for the prediction of ICI effectiveness.

# data[].attributes.attachment_link.readable_text
Abstract

Telomere overhangs are essential for telomere end protection and telomerase extension, but how telomere overhangs are generated is unknown. Leading daughter strands synthesized by conventional semiconservation DNA replication are initially blunt, while lagging daughter strands are shorter by at least the size of the final RNA primer, which is thought to be located at extreme chromosome ends. We developed a variety of new approaches to define the steps in the processing of these overhangs. We show that the final lagging RNA primer is not terminal but is randomly positioned ∼70-100 nucleotides from the ends and is not removed for more than an hour. This identifies an important intrinsic step in replicative aging. Telomeric termini are processed in two distinct phases. During the early phase, which occupies 1-2 h following replication of the duplex telomeric DNA, several steps occur on both leading and lagging daughters. Leading telomere processing remains incomplete until late S/G2, when the C-terminal nucleotide is specified-referred to as the late phase. These observations suggest the presence of previously unsuspected complexes and signaling events required for the replication of the ends of human chromosomes.

Keywords: telomere overhangs, DNA replication, Okazaki fragment RNA primer, aging, cancer

Telomeres are nucleoprotein structures at the ends of linear chromosomes that are essential for preventing chromosomal end-to-end fusions and degradation. Adult human telomeres contain ∼5-15 kb of hexameric DNA repeats (5′-TTAGGG-3′) ending with 3′ single-stranded G-rich overhangs ranging in size from ∼12 to 300 nucleotides (nt) ( Makarov et al. 1997; Wright et al. 1997; Stewart et al. 2003; Zhao et al. 2008; Dai et al. 2010). In cancer and stem cells, this overhang provides the single-stranded substrate required for telomerase extension. This G overhang can be inserted into homologous double-stranded regions, resulting in a lasso-like telomere loop (t-loop) structure thought to prevent chromosome ends from being recognized as double-stranded breaks ( Griffith et al. 1999).

The mechanism generating these overhangs has been sought for many years. Many DNA damage signaling factors such as ATM, ATR, the MRN complex, and their homologs in other species ( Chai et al. 2006b; Goudsouzian et al. 2006; Denchi and de Lange 2007; Deng et al. 2009; Dimitrova and de Lange 2009); helicases ( Tomita et al. 2004; Bonetti et al. 2009; Li et al. 2009); telomere-associated proteins ( Garvik et al. 1995; Nugent et al. 1996; Grandin et al. 1997, 2001; van Steensel et al. 1998; Churikov et al. 2006; Wu et al. 2006; Song et al. 2008; Ballal et al. 2009; Price et al. 2010); and nucleases ( Tomita et al. 2003; Tom and Greider 2010; Wu et al. 2010) have been found to affect overhang length. However, it is very difficult to interpret the mechanism of action of these factors without knowing the specific steps involved in overhang generation. Some of these factors may affect overhang length by influencing the action of telomerase ( Chai et al. 2006b; Zhao et al. 2009), while others may affect additional overhang processing steps. Chromatin immunoprecipitation (ChIP) analysis has shown that the association of a variety of proteins at telomeres change at S/G2 in normal diploid cells ( Verdun et al. 2005; Verdun and Karlseder 2006), suggesting that some processing events might occur at S/G2. However, the number, nature, and timing of the steps involved in producing an appropriately sized G-rich 3′ overhang following replication of the duplex telomeric DNA in the absence of telomerase remain unknown. How overhang maturation is coupled with duplex telomeric DNA replication also remains undefined. In particular, the differences in the biogenesis of leading versus lagging overhangs have not been characterized. It is generally assumed that the nuclease activity of C-strand resection is tightly associated with the replication machinery and is dependent on the alteration of telomere chromatin during replication fork progression.

The original hypothesis of the end replication problem by Watson (1972) and Olovnikov (1973) ∼40 years ago has traditionally been formulated as the inability to copy the 10-14 nt at the end of the chromosome after the final lagging Okazaki fragment RNA primer has been removed. This has led to the model presented in most modern textbooks: The final primer of Okazaki fragment synthesis is positioned at the very end of the chromosome. Short G overhangs have been observed in several organisms; e.g., ∼14 nt in ciliates ( Klobutcher et al. 1981; Jacob et al. 2001) and ∼12-14 nt in Saccharomyces cerevisiae ( Larrivee et al. 2004). This is approximately the size of the RNA primer laid down by polymerase α during lagging strand synthesis ( Kunkel and Burgers 2008; Nick McElhinny et al. 2008). However, it is also possible that this final overhang size of 12-14 nt in S. cerevisiae ( Larrivee et al. 2004) may not actually represent the site of the final Okazaki priming event because both leading and lagging strands have a transient >30-nt overhang that is later filled in by an unknown mechanism to generate the final overhang ( Wellinger et al. 1992, 1993, 1996). Although in some organisms it may be possible for the conventional replication machinery to position the final lagging RNA primer at the very terminus, the presence and location of the final RNA primer has not yet been directly demonstrated in any organism.

In the absence of telomerase, the size of the overhangs represents a reduction in the length of C strands. In the absence of damage to telomeres, this determines the rate of telomere shortening and the amount that telomerase would have to extend to compensate and maintain telomere length ( Zhao et al. 2009). In yeast, the rate of telomere shortening in the absence of telomerase (estimated at 3-5 base pairs [bp] per division) ( Marcand et al. 1999) is close to what would be expected based on the observed 12- to 14-nt overhangs ( Larrivee et al. 2004). Telomerase only adds ∼44 nt to ∼7% of the ends ( Teixeira et al. 2004), for an average addition that matches the shortening rate. The rate of telomere shortening in cultured telomerase-negative human cells is 50-100 bp per division, which is also close to what would be expected when the average overhang in telomerase-negative cells is ∼60-70 nt (∼30 nt for the daughters of leading strand semiconservative replication ["leading daughters"] and ∼100 nt for the daughters of lagging strand semiconservative replication ["lagging daughters"]) ( Zhao et al. 2008). In human cancer cells, telomerase adds ∼60 nt to every chromosome ( Zhao et al. 2009), again closely matching the shortening rate. There are two alternate mechanisms thought to explain why the length of telomere loss per cell division in human cells is considerably longer than a simple result of the end replication problem upon removal of a terminal RNA primer. The first assumes that the RNA primer is positioned at the very terminal end and that both leading and lagging daughters are processed by nuclease resection on the C strands. An alternative model would be that the initial lagging overhang reflects the position of the final Okazaki RNA priming event as the replication complex runs off its linear template. The removal of the RNA primer could then generate an initial overhang that is almost the size of the fully processed overhang. Meanwhile, the initially blunt leading daughter strands resulting from semiconservative DNA replication would be processed by nucleases or helicases to generate mature overhangs ( Sfeir et al. 2005). Uncovering the steps that regulate the processing of human telomeric overhangs is thus fundamental in understanding the roles of candidate proteins involved and potentially manipulating rates of replicative aging and telomerase action.

In the present study, combinations of modified and newly developed biochemical assays were designed to monitor the dynamics of overhang sizes, RNA primers on lagging daughters, and the C-terminal nucleotides during S phase. We found that lagging daughters had an almost mature overhang size very soon after replication of the duplex telomeric DNA and were capped by a terminal RNA primer for ∼1 h following replication prior to its removal, leaving a specified C-terminal nucleotide. In contrast, leading daughters only developed a mature size 1-2 h after duplex telomere replication and did not acquire a specific C-terminal sequence until S/G2. We present a model characterizing overhang processing in two distinct phases to summarize the complex series of steps by which human linear chromosomal ends are replicated.

Results

Mature overhang sizes are present by 2 h after replication

In order to establish the window within which mature overhang sizes were established, we examined the end structure in telomeres labeled for increasing times with 5-bromodeoxyuridine (BrdU). Normal human foreskin fibroblast BJ cells were synchronized at the G1/S interface with an efficiency of 65%-75% ( Fig. 1B ) and with an S phase of ∼6-8 h. Telomeres in BJ cells replicate throughout S phase, but there is a delay of ∼2 h before significant numbers of telomeres begin to replicate ( Wright et al. 1999). Cells were first released into S phase in fresh medium for 2 h before treating with pulses of 100 μM BrdU ( Fig. 1A ). This procedure maximized the amount of BrdU incorporated into the newly synthesized telomeres during short periods. BrdU was differentially incorporated into daughter telomeres such that two BrdU molecules were incorporated per leading repeat (TTAGGG) versus only one BrdU per complementary lagging repeat (CCCTAA) ( Fig. 1A ). Leading and lagging daughter strands synthesized by conventional semiconservation DNA replication were then separated on cesium chloride (CsCl) gradients, where leading daughters banded at a heavier density than lagging daughters, with unreplicated telomeres having the lightest density. Aliquots of the gradient fractions were hybridized with a telomere-specific probe on a slot blot to differentiate leading versus lagging daughters from unreplicated telomeres. Cesium centrifugation of samples labeled for 2, 4, 6, and 8 h showed distinct leading versus lagging peaks, as expected ( Fig. 1C ). Cells labeled for one population doubling ([PD], ∼48 h) served as a positive control for fully processed overhang sizes. The additional fourth peak of the highest density observed at 48 h represented cells that had entered the second cell cycle and had incorporated BrdU on both leading and lagging daughter strands ( Fig. 1C ). The fractions corresponding to leading versus lagging telomeres were then pooled, purified, and analyzed.

Two independent approaches were used to quantify overhang lengths. Signals from single-stranded overhangs hybridized to a telomere probe under native conditions were normalized to the total telomere signal obtained after the dsDNA was denatured. This native in-gel hybridization approach measures relative changes in overhang abundance. Electrophoresis was stopped after telomeres had penetrated ∼1 cm into the gel, so that telomeres of various lengths did not separate significantly according to size but remained as a compact band that concentrated the very low overhang signal into a small area. Alternatively, duplex-specific nuclease (DSN), which digests double-stranded telomere DNA to <10 bp in length with no effect on single-stranded telomere G overhangs, was used for the quantitative determination of overhang sizes as short as 12 nt ( Zhao et al. 2008). Due to the heterogeneity of overhangs, a wide range of sizes were distributed on a denaturing gel.

In control cells labeled with BrdU for one PD, in-gel hybridization showed that lagging overhangs were approximately threefold more intense than leading overhangs after normalizing the overhang signals (obtained from native hybridization) to the total genomic DNA input (obtained from hybridization upon denaturing the gel) ( Fig. 1D,F ). Consistently, average leading overhangs were ∼30 nt and lagging overhangs were ∼100 nt (thus, lagging overhangs were approximately threefold longer than that of leading) using DSN assays on a denaturing polyacrylamide gel ( Fig. 1E,F ). In both assays, a 3′ → 5′ exonuclease (ExoI) was used as a negative control to digest 3′ overhangs. These controls were consistent with our previous observations ( Chai et al. 2006a; Zhao et al. 2008). Using the same analyses, we showed that leading and lagging overhangs were processed to nearly mature sizes within 2 h (0-2 h released, 2-4 h BrdU incorporation) ( Fig. 1D-F ). The DSN at 2 h was performed on an alkaline agarose gel, in which the decreased resolution makes it visually easier to estimate overhang sizes without formal quantitation. In summary, 2-h-labeled telomeres had almost mature overhang lengths, similar to that of the one-PD control.

Lagging but not leading overhangs have almost mature overhang size immediately following replication

We then determined whether overhangs were processed to mature sizes immediately following semiconservative replication at the earliest time point we could analyze. Only fully replicated telomeres can be resolved into distinct peaks on a CsCl gradient, since partially replicated telomeres have densities ranging from that of unreplicated DNA to midway between the leading and lagging peaks (Supplemental Fig. S1). Five-hundred micrograms of genomic DNA from a 1-h BrdU label (0-2 h released, 2-3 h BrdU incorporation) generated only minimal leading and lagging peaks on a CsCl gradient centrifuguation. Leading and lagging peaks from two independent CsCl gradient centrifugations (∼1 mg of total genomic DNA) were thus combined and rebanded on a second CsCl gradient to better resolve the replicated leading and lagging daughter strands. In addition, a hypersensitive telomere probe was developed to maximize the sensitivity of overhang signal detection (see the Materials and Methods). In some cases, iododeoxyuridine (IdU), which has a higher density than BrdU, was used to increase resolution (data not shown). Distinct leading and lagging peaks were present within 1 h of BrdU labeling, indicating that some BJ telomeres had been completely replicated within 1 h ( Fig. 2A ). The 1-h-labeled leading and lagging daughters were purified, and overhang lengths were examined.

By in-gel hybridization, 1-h- and 2-h-labeled lagging daughter telomeres had similar overhang signals (0.8 vs. 1.0, normalized signals), indicating that lagging telomeres had almost mature overhang sizes at the earliest time point we could analyze following replication ( Fig. 2B ). Using 1-h lagging daughter overhang sizes as a reference point, we examined whether mature leading daughter overhang sizes were observed by 1 h of BrdU labeling. However, the overhang signal from 1-h leading daughters was barely detectable-only an average of ∼15-fold less intense than the almost mature 1-h lagging overhangs ( Fig. 2C,D ), far less than the roughly threefold difference found in the mature overhangs control ( Fig. 1D-F ). Leading overhangs were thus not processed to almost mature lengths until an additional hour had passed. Thus, while lagging daughter overhangs had close to mature overhang lengths after a 1-h label, it took an additional hour before leading daughters exhibited approximately mature sizes.

The terminal lagging Okazaki RNA primers remain for ∼1 h after replication on lagging daughters

We developed two independent assays to determine whether the final primer of Okazaki fragment synthesis was still present immediately following replication of the duplex telomeric DNA or whether nuclease processing had removed the final primer and potentially generated the long overhang on lagging daughters.

λ Exonuclease (λ exo) assay to monitor lagging terminal RNA primers

λ Exo is a highly processive 5′ → 3′ exonuclease that removes 5′ mononucleotides from dsDNA. Using model oligonucleotides, we first verified that λ exo was able to digest dsDNA but unable to digest RNA in RNA/DNA heteroduplexes. Eighteen-nucleotide DNA or RNA oligos were first 5′-end-labeled with ³²P-γ ATP and annealed to an excess of complementary 88-nt oligos to generate either dsDNA or heteroduplex substrates with a 3′ single-stranded overhang ( Fig. 3A ). After λ exo digestion, the products were analyzed on a denaturing polyacrylamide gel to detect whether the 18-nt radiolabeled DNA or RNA had been digested. The 18-nt DNA oligonucleotide was entirely digested, confirming the sensitivity of dsDNA to λ exo treatment. However, the 18-nt RNA oligonucleotide in the RNA/DNA heteroduplex was resistant to λ exo digestion. λ Exo was unable to remove even the single 5′-labeled ribonucleotide in the DNA/RNA heteroduplex ( Fig. 3A ). This assay was then applied to telomeres, where purified lagging daughter strands should be resistant to λ exo digestion if the final RNA primer had not been removed.

Semiconservative replication intermediates will have densities on CsCl gradients ranging from slightly more dense than unreplicated DNA at the earliest times to half-way between the peaks of lagging and leading daughters just before replication is complete and the daughter strands separate (Supplemental Fig. S1). Although HeLa cells and BJ fibroblasts progress through S phase at approximately the same rate (Supplemental Fig. S2), the rate of telomere replication is much more rapid in HeLa cells, and fully replicated telomeres are present within 15 min of adding a density label (TT Chow, PG Smiraldo, Y Zhao, S Mak, JW Shay, and WE Wright, in prep.). The presence of semiconservative replication intermediates could potentially complicate the interpretation of λ exo assays due to the presence of the RNA primer on Okazaki fragments near the replication fork. HeLa cells rather than BJ fibroblasts were used for these experiments because after a 1- or 2-h label, the fraction of replication intermediates is too small to affect the interpretation of the results (Supplemental Fig. S3).

The C strand of leading daughters terminates in DNA (with no RNA primer immediately following duplex DNA replication) and was used as a positive control for the action of λ exo on telomeres. HeLa cell leading daughters labeled for 2, 3, and 4 h (0-2, 0-3, and 0-4 h BrdU incorporation upon release into S phase) showed the expected decrease in size and the expected 50% decrease in intensity of the C strand following λ exo digestion ( Fig. 3B,D ). However, the C strand of lagging daughters from HeLa cells labeled for 2 or 3 h immediately upon release into S (0-2 h or 0-3 h of BrdU incorporation upon release into S phase) were resistant to digestion. The lagging daughters became sensitive to λ exo after 4-h and 10-h labeling periods (0-4 h and 0-10 h of BrdU incorporation upon release into S phase), suggesting that the RNA primer initially protecting the C strands from digestion was removed by 4 h ( Fig. 3C,D ). Quantification demonstrated that the 2- and 3-h lagging daughters retained most of the C-strand signal, while the 4-h lagging daughters had an ∼50% C-strand signal reduction. Unpaired two-tailed t-test with 99% confidence indicated a significant difference between the combined 2-, 3-, and 4-h leading versus 2- and 3-h lagging signals, with a P-value of <0.0001 (SEM error bars in Fig. 3D ). Meanwhile, 4-h lagging daughters became similar to the control leading daughters with an ∼50% C-strand signal (with no significant difference upon t-test analysis) ( Fig. 3D ). T7 exonuclease (T7 exo) digestion was used as an additional positive control.

T7 exo is a 5′ → 3′ exonuclease with low processivity that can degrade both DNA duplexes and RNA/DNA heteroduplexes. The 5′ end of both lagging daughters (with intact RNA primers) and leading daughters should be sensitive to T7 exo. 5′ End resection of the C strand should produce an increased G-overhang signal (C-probe hybridization). Meanwhile, G-probe hybridization upon denaturation should show the amount of C strands that had survived digestion ( Fig. 3E ). HeLa 2-h-labeled lagging and leading daughters both showed increased G-overhang and decreased C-strand signal upon T7 exo treatment ( Fig. 3F ), as expected for this positive control. These results indicate that something, presumably an RNA primer, was inhibiting λ exo but not T7 exo for ∼3 h following the completion of lagging strand replication.

Pull-down assay to monitor lagging terminal RNA primers

The identification of RNA as the block to λ exo digestion was determined by the ability of RNase HII (an endonuclease that nicks heteroduplexes at multiple sites within the RNA) to release purified lagging C strands following ligation of biotinylated oligonucleotides to their 5′ ends ( Fig. 4A ). A mixture of six biotinylated telorette oligos (as used for single-telomere length analysis [STELA] in Figs. 5 , ​,6) 6 ) was ligated to either purified leading (control) or lagging 2-h-labeled HeLa DNA and retrieved using streptavidin-coated magnetic beads. RNase HII should release telomeres anchored to the beads via ligation to RNA but not those in which the telorette oligo is ligated directly to DNA ( Fig. 4A ). We first verified that the ligation was telomere-specific and ligase-dependent by ligating purified HeLa leading 4-h-labeled telomeres to either a mixture of the six telorette oligos or biotinylated random oligos ( Fig. 4B ). Telomeres retrieved from the streptavidin pull-down were then released using formamide. Only ligation with the telorette oligos yielded a C-strand signal upon release, and the signal was dependent on the inclusion of ligase ( Fig. 4B ). The experiment was then repeated using 2-h-labeled HeLa DNA (0-2 h of BrdU label upon release into S phase) and released with RNase HII rather than formamide ( Fig. 4C ). Leading control telomeres showed no detectable released telomeres, while lagging telomeres were liberated ( Fig. 4C ). This confirmed that RNA was still present on telomeric lagging daughters for a significant time following replication of the duplex telomeric DNA.

Leading terminal nucleotide is specified at S/G2

Fully processed overhangs exhibit a specific C-terminal nucleotide ( Sfeir et al. 2005). We then asked when the processing of leading daughter C strands to produce this CCAATC-5′-terminal sequence occurred. This timing was monitored using a modified STELA ( Fig. 5A ; Baird et al. 2003; Sfeir et al. 2005), which involved a ligation step followed by PCR with oligonucleotides (Telorettes 1-6) representing the six possible permutations of the hexameric telomere repeat. Six independent samples (each containing a specific telorette) were first ligated to the C strand of DNA. PCR was then performed using a forward primer located at the Xp/Yp subtelomeric region and a reverse "teltail" primer complementary to a unique sequence at the 5′ end of the telorettes. Amplified PCR products were hybridized with subtelomeric probe such that each band represented the terminal nucleotide specificity of a single telomere. Quantification of the number of amplified PCR bands by individual telorette (T1-T6) defined the C-terminal nucleotide distribution across S phase. T3 corresponds to the mature specification of the 5′ end as 3′-CCAATC-5′. Two independent experimental designs were applied such that BJ cells were either BrdU-labeled for increasing periods of time starting at 2 h after release into S phase (i.e., 0-2 h of release in fresh medium followed by x h of BrdU incorporation) ( Fig. 5B ) or BrdU-labeled for 3 h at the time of release into S phase and chased with thymidine for increasing periods of time (i.e., 0-3 h of BrdU incorporation followed by x h of thymidine chase) ( Fig. 5E ). Replicated leading daughters were then isolated on CsCl gradients and analyzed. Both protocols indicated that C-strand 5′ ends of leading daughters were initially random (PCR products were observed across T1-T6)-although mature overhang sizes have already been generated-and were only processed so that most ended in CCAATC-5′ late in S phase or S/G2 (corresponding to the majority of the PCR products resulting from T3 reactions) ( Fig. 5C,D,F,G ). These results suggested that there was a delay in C-strand-terminal nucleotide specification between when leading overhang length approached mature sizes and the end of S/G2.

Lagging terminal nucleotide is specified rapidly

In contrast to leading ends, the terminal nucleotides of lagging daughters were specified within 2 h after replication (0-2-h released and 2-4 h of BrdU incorporation). BJ cells were labeled for increasing periods of time starting at 2 h after release into S phase ( Fig. 6A ). The sequence CCAATC-5′ was predominant even at the earliest times examined, at which the majority of the PCR products were detected with the T3 telorette ( Fig. 6B,C ).

Discussion

Telomeres require 3′ overhangs for chromosome end protection. The details of how these overhangs are generated in normal human cells are unknown. The results presented here provide important insights implicating several distinguishable steps in telomere end processing that occur in two distinct phases that are summarized in Figure 7 . The early phase lasts several hours following the completion of basic semiconservative replication (the ability to physically separate the daughters of leading and lagging strand synthesis). The late phase occurs at S/G2. Since human telomeres replicate throughout S phase ( Ten Hagen et al. 1990; Wright et al. 1999), there can be many hours intervening between the first and second phases of end processing.

We showed previously that telomerase extended leading strands at the shortest BrdU incorporation time (30-min pulse) that we could measure ( Zhao et al. 2009). Telomerase requires at least 6 nt of single-stranded G-rich overhangs to function ( Lai et al. 2001); thus, we infer that there is a very rapid processing event that generates an initial overhang. However, we were unable to detect meaningful overhang signals from leading strands in normal diploid cells after a 1-h label. Unless the timing is different in normal versus tumor cells or there is an additional rapid processing event that is specifically present in telomerase-positive tumor cells, this initial overhang must be very short. The nearly mature size of the leading overhang was only detected later, after an additional hour.

Eukaryotic Okazaki fragments are ∼100-200 nt in length ( Sakabe and Okazaki 1966; Okazaki et al. 1968; Blumenthal and Clark 1977; Bielinsky and Gerbi 1999; Burgers 2009; Balakrishnan and Bambara 2011). If the replication apparatus simply ran off the end of the telomere while the position of the final RNA primer is random, then lagging daughters should have the final RNA priming event occur at approximately half of the average Okazaki fragment size (or 50-100 nt from the end). We observed that the final lagging RNA primer remained intact immediately following replication, with an overhang size of ∼80 nt at that time, consistent with the above prediction. This resolves a long-standing question about the mechanism of overhang generation at lagging daughter telomeres that is fundamental to our understanding of telomere shortening. Since the gap produced by removal of the RNA primer only contributes ∼10% to the size of the human overhang, the end replication problem in humans should now be revised to state that the conventional replication complex cannot copy the gap between the final RNA priming event and the extreme end of the chromosome. Therefore, the position of the final priming event of replication (rather than processing that degrades the C strand) is an important intrinsic step in the rate of telomere shortening in human cells. Other mechanisms, such as oxidative stress, may also contribute to the rate of shortening.

The first nucleotide incorporated during RNA primer synthesis contains a 5′ triphosphate. Neither T7 exonuclease nor T4 ligase should be able to function on an RNA containing a 5′ triphosphate ( Kerr and Sadowski 1972; Engler and Richardson 1982), yet both of these enzymes are able to function on the RNA-capped DNA in our assays. We infer that there is a rapid initial processing of the RNA primer after replication that removes at least the triphosphates, followed by a delay of several hours before the rest of the RNA is removed in the early phase.

In telomerase-positive tumor cells, C-strand fill-in of the telomerase-extended G strands is delayed until S/G2 ( Zhao et al. 2009). Normal diploid cells do not have extended G strands needing fill-in, but they also exhibit processing events during this second phase of replication of the duplex telomeric DNA. The C-rich strand-terminal nucleotides of leading daughter telomeres remain random until S/G2, when CCAATC-5′ specification is acquired. In contrast, lagging C strands exhibit specific ends once the RNA primer is removed during the early phase following replication. As leading daughters have DNA while lagging daughters have RNA at their C terminus, it is possible that entirely different mechanisms are used to generate C-strand end specificity on leading versus lagging daughters, reflecting the activation of separate enzymatic complexes. However, if the phenomenon of the presence of a transient overhang independent of telomerase in yeast ( Wellinger et al. 1992, 1993, 1996; Dionne and Wellinger 1996) also exists in humans, a transient short resection of the leading daughter C strand could occur at S/G2 and be filled in by the same mechanism that fills in the extended overhangs in telomerase-positive cells. This mechanism presumably requires an RNA primer, possibly produced by polymerase α recruited by the Cst1-Stn1-Ten1 (CST) complex ( Price et al. 2010). In this scenario, leading strands would acquire an RNA primer at their C-strand termini. Since leading daughters acquire an almost mature overhang size within ∼1 h of replication, any transient resection and fill-in at S/G2 would thus need to produce minimum net change in overhang size. It is possible that both leading and lagging daughters undergo a transient resection so that the final end specification of both C strands could occur at S/G2 by the same mechanism.

The telomeric G strand is always generated by leading strand synthesis and should end in GGTTAG, since 80%-85% of fully processed telomeres have C strands ending in the complementary sequence CCAATC-5′ ( Sfeir et al. 2005). However, approximately equal amounts of G ends of the sequences GGTTAG, GGTTA, and GGTT were found in normal diploid cells ( Sfeir et al. 2005). This is consistent with the concept that the replication apparatus often simply runs off and dissociates from the extreme chromosome termini prior to incorporating the final 1 or 2 nt ( Sfeir et al. 2005). The initial result of leading strand synthesis thus probably has a 0-, 1-, or 2-nt 5′ overhang due to the difficulty of making fully blunt-ended products ( Fig. 7 ).

Telomeres present many challenges to replication. It is unknown whether t-loops refold following replication or remain unfolded following replication until the end of S phase. The long delay in processing of both leading and lagging telomeric daughters suggests that telomeres remain in an unfolded state for at least 1-2 h following replication. The mechanism preventing the single-stranded overhang at telomeres from initiating an ATR-mediated DNA damage response during this time may be Pot1-mediated, but that remains to be determined. The repetitive nature of the telomeric repeat and the ability to form G quartets make telomeres sensitive to fork stalling ( Hao and Tan 2002; Lipps and Rhodes 2009; Paeschke et al. 2011), and a variety of proteins are required to allow replication of duplex telomeric DNA to proceed ( Crabbe et al. 2004; Sfeir et al. 2009). The series of separate steps that we presented here introduce another layer of choreography required to successfully reproduce the structures present prior to replication. This will now allow an analysis of the specific steps affected by different factors that influence overhang structure, and understanding the regulation of these steps may provide the opportunities for manipulation that can be beneficial to both cancer therapy and regenerative medicine.

Materials and methods

Cell culture and cell cycle synchronization

BJ foreskin fibroblasts were cultured at 37°C in 5% CO ₂ in high-glucose DMEM medium (Hyclone) containing 15% Cosmic calf serum (Hyclone). For synchronization, logarithmically growing cells were washed twice with 1× PBS, presynchronized with low serum (high-glucose DMEM with 0.1% fetal bovine serum, 20 mM HEPES) for 48 h, and then fed fresh medium containing 15% fetal bovine serum and 2 μg/mL aphidicolin (A.G. Scientific) for 24 h. Cells were then washed three times with 1× PBS, released into fresh medium for 2 h, and then labeled with 100 μM BrdU. For pulse-chase experiments, cells were washed twice with 1× PBS and chased with 100 μM thymidine.

HeLa cervical carcinoma cells were used for some experiments requiring short labeling times and a large number of cells. HeLa cells were cultured at 37°C in 5% CO ₂ in high-glucose DMEM medium with 10% Cosmic calf serum (Hyclone). Exponentially growing HeLa cells were synchronized with 2 mM thymidine (Sigma) for 19 h, washed three times with 1× PBS, and incubated with fresh medium for 9 h. Two millimolar thymidine was added again for 16 h. Cells were washed three times with 1× PBS and released into fresh medium with 100 μM BrdU for 0-10 h.

Genomic DNA isolation

Genomic DNA was purified by Qiagen Blood and Cell Culture Midi kit. Precipitated DNA was washed twice with 70% ethanol and suspended in 10 mM Tris-HCl (pH 8). DNA was dissolved overnight at 37°C.

Generation of telomere probes

Twenty-four-nucleotide probes containing six ³²P-dC or six ³²P-dG were synthesized as described ( Herbert et al. 2003). A hypersensitive C probe was designed to incorporate 13 total radioactive nucleotides (nine ³²P-dC + four ³²P-dA). T3C3+3 (5′-TTTCCCTAA) was used instead of T3C3+9 (5′-TTTCCCTAACCCTAA) and annealed to GTU4 (5′-GGGUUAGGGUUAGGGUUAGGGAAA) for 1 min at 90°C, for 15 min at 20°C, and for 15 min at 17°C. The synthesis reaction ( Herbert et al. 2003) was modified as follows: 3.1 μL of 8× Roche buffer M, 1 μL of annealed template oligo (1.7 pmol/μL), 1 μL of dTTP (1.25 mM stock, 50 μM final), 7 μL of ³²P-dCTP (3000 Ci/mmol), 4 μL of ³²P-dATP (3000 Ci/mmol), 7.9 μL of Millipore H ₂ O, and 1 μL of Klenow (5 U/μL) were combined in a final volume of 25 μL. After extension for 30 min at 20°C and for 5 min at 95°C (to inactivate Klenow to prevent probe degradation upon uracil deglycosylase [UDG] treatment), the reaction was cooled to room temperature. UDG (0.5 μL; 1 U/μL) was added to degrade the GTU template, incubated for 15 min at 37°C, and then inactivated for 10 min at 95°C. Free isotopes were then removed using an Illustra Microspin G-25 column (GE Healthcare).

CsCl separation of leading and lagging telomeric daughters

CsCl gradient separation was performed as described ( Zhao et al. 2011b) with modifications. In brief, 500 μg of purified genomic DNA was digested with HinfI and RsaI overnight in 250 μL and terminated by the addition of EDTA to 10 mM. DNA was mixed with CsCl solution (density of 1.79 g/mL with 5 mM Tris-HCl at pH 8, 2 mM EDTA) to obtain a final density of ∼1.760-1.770 g/mL and added to a polyallomer quick-seal centrifuge tube (Beckman). Samples were centrifuged at 55,000 rpm for 20 h at 25°C using a VTi-80 vertical rotor (Beckman). Fractions of the sample were collected, and aliquots were denatured and hybridized with a telomere-specific probe on a slot blot to identify the fractions that contained telomere DNA. The corresponding densities were obtained by measuring the refractive index. Leading DNA was located at a density of 1.790-1.800 g/mL, lagging DNA was located at a density of 1.760-1.770 g/mL, and unreplicated DNA was located at a density of 1.740-1.750 g/mL. Four to five fractions of each peak were pooled and desalted by agarose dialysis (rocking on a plug of 2% agarose made with 10 mM Tris-HCl at pH 8 in a 50-mL tube for 1 h at room temperature) followed by ethanol precipitation. DNA was then dissolved in 10 mM Tris-HCl (pH 8).

Telomere overhang analysis by in-gel hybridization

Five micrograms of purified leading or lagging DNA was briefly run into an 0.8% agarose gel in 1× TAE so that telomeres were not significantly separated by size and remained as a relatively tight band. DNA that had been digested with 10 U of exonuclease I (20 U/μL) (Epicentre Biotechnologies) in a 20-μL reaction for 1 h at 37°C and terminated by adding 0.5 μL of EDTA (0.5 M stock) served as a negative control. The gel was dried, prehybridized (6× SSC, 5× Denhardt's solution, 0.5% [w/v] SDS), and hybridized with C probe for G overhangs under native conditions. Upon obtaining the signal from the native hybridization, the gel was denatured (0.5 M NaOH, 1.5 M NaCl for 1 h), rinsed three times with distilled water, neutralized (0.5 M Tris-HCl at pH 8, 1.5 M NaCl for 30 min), and then hybridized again with the C probe to obtain the denatured signal for total telomere input.

Overhang analysis by DSN assay

DSN assay was performed as described ( Zhao et al. 2008, 2011a). In brief, 2-5 μg of purified leading versus lagging was digested with 2 U of DSN (Evrogen) for 2 h at 37°C. For negative controls, 10 U of ExoI (Epicentre Biotechnologies) was added to the genomic DNA for 1 h at 37°C prior to DSN digestion. DSN digestion was stopped by adding EDTA to a final concentration of 25 mM. For denaturing polyacrylamide gels, a 1:1 ratio of deionized formamide was added. DNA was then heated for 5 min at 65°C and loaded on a 6% polyacrylamide gel with 8 M urea. The gel was run at 15 V/cm in 0.5× TBE until the bromophenyl blue dye front-migrated two thirds into the length of the gel. DNA was electro-transferred onto a Amersham Hybond-N⁺ membrane (GE Healthcare) with 0.5× TBE. The membrane was air-dried, UV-cross-linked, hybridized to a telomere-specific probe as described ( Herbert et al. 2003), and exposed on a PhosphorImager (GE Healthcare). Alkaline agarose gels (1% agarose made with 50 mM NaOH, 1 mM EDTA) were run in 50 mM NaOH and 1 mM EDTA at 4°C with a low voltage (1-2 V/cm) until the dye migrated ∼6-8 cm. DNA was capillary-transferred to Amersham Hybond-XL (GE Healthcare) and processed as above. Average overhang sizes were calculated using the formula mean average length = ∑(Inti)/∑(Inti/MWi), where Inti is the signal intensity, and MWi is the molecular weight of the DNA at position i ( Chai et al. 2006a; Zhao et al. 2008).

Modified STELA

STELA was performed as described ( Sfeir et al. 2005) with modifications. Ligations were performed using each of the six C-telorette oligos (T1-T6) corresponding to six permutations of the hexameric telomere repeat. Five-hundred nanogram of purified leading or lagging daughters were incubated in a 10-μL reaction (1× T4 ligase buffer, 0.001 μM individual C telorettes, 50 U New England Biolabs T4 ligase) for 20 h at 35°C. Multiple PCR amplifications were performed with 0.5 ng of ligated DNA and 0.5 μM primers (XpYpE2 forward and C-teltail reverse primers) using Abgene Hi-Fidelity PCR Master 2× mix (AB-0792, Thermo Scientific) in a final volume of 25 μL for 28 cycles of 15 sec at 94°C, 30 sec at 65°C, and 15 min at 68°C. Amplified products were resolved on a 0.7% agarose with 1× TAE and capillary-transferred onto Amersham Hybond-N⁺ membrane overnight. Membranes were fixed for 2 h at 80°C, hybridized with a subtelomeric probe generated by PCR amplification with Xp/YpE2 and Xp/YpB2 primers, and ³² P-labeled by random priming. The membrane was then exposed to a PhosphorImager screen.

λ Exo assay

Two micrograms of purified leading or lagging DNA was digested with 1 μL of λ exo (10 U/μL) (Epicenter Biotechnologies) for 1.5 h at 37°C. DNA was run on a 0.8% agarose gel in 1× TAE at 10 V/cm for 40 min, denatured with an alkaline solution containing 0.5 M NaOH and 1.5 M NaCl for 30 min, rinsed three times with distilled water, and dried at room temperature. The gel was then neutralized with 0.5 M Tris-HCl (pH 8) and 1.5 M NaCl for 30 min and hybridized overnight at 42°C with G-telomere probes to detect the C strands.

RNA primer pull-down assay

Prior to CsCl gradient centrifugation, genomic DNA was digested with RsaI and HaeIII, generating only blunt ends to minimize background. Two micrograms of purified leading or lagging DNA was ligated overnight at 16°C to a 0.5 μM (final concentration) mixture of the six telorette oligos ( Sfeir et al. 2005) that were biotinylated. DNA was pulled down using Dynabeads kilobaseBINDER kit (Invitrogen) overnight at room temperature. DNA was released by 7.5 U of RNase HII (New England Biolabs) for 2 h. Control DNA was released using 95% formamide and 10 mM EDTA (pH 8.2) for 10 min at 90°C.

Acknowledgments

We thank Jinyong Kim, PhD, and Guido Stadler, PhD, for their insightful scientific discussions. This research was supported by BC083375 from the Department of Defense Predoctoral Cancer Research Program (T.T.C) and AG01228 from the National Institute of Aging (W.E.W).

References

Engler MJ, Richardson CC 1982. DNA ligases. In [The enzymes (ed. PD Boyer), pp. 3-29. Academic Press, San Diego Google Scholar]

Herbert BS, Shay JW, Wright WE 2003. Analysis of telomeres and telomerase. Current Protoc Cell Biol 18.6.1-18.6.20 doi: 10.1002/0471143030.cb1806s20 [Google Scholar]

Articles from Genes & Development are provided here courtesy of Cold Spring Harbor Laboratory Press

# data[].attributes.attachment_link.title
Early and late steps in telomere overhang processing in normal human cells: the position of the final RNA primer drives telomere shortening

# data[].attributes.attachment_link.description
Telomere overhangs are essential for telomere end protection and telomerase extension, but how telomere overhangs are generated is unknown. Leading daughter strands synthesized by conventional semiconservation DNA replication are initially blunt, while lagging daughter strands are shorter by at least the size of the final RNA primer, which is thought to be located at extreme chromosome ends.

# data[].attributes.attachment_caption
Blood or serum derived predictive biomarkers

# data[].attributes.attachment_link.readable_text
Abstract

The presence of inflammatory immune cells in human tumors raises a fundamental question in oncology: How do cancer cells avoid the destruction by immune attack? In principle, tumor development can be controlled by cytotoxic innate and adaptive immune cells; however, as the tumor develops from neoplastic tissue to clinically detectable tumors, cancer cells evolve different mechanisms that mimic peripheral immune tolerance in order to avoid tumoricidal attack. Here, we provide an update of recent accomplishments, unifying concepts, and future challenges to study tumor-associated immune cells, with an emphasis on metastatic carcinomas.

Keywords: disseminated tumor cells, tumor-associated macrophages, metastasis-associated immune cells, patient-derived xenograft, immune cross-talk, cancer heterogeneity

Cancer remains a major cause of death worldwide, and, with an aging population, its annual toll of 8.2 million is only expected to increase ( Ferlay et al. 2015). In this respect, carcinomas can be broadly divided into two groups: metastatic (the principal cause of cancer-related deaths) and nonmetastatic ( Siegel et al. 2016). Traditionally, metastasis has been considered to occur in later stages of cancer progression; however, accumulating evidence has also described metastatic dissemination during early tumor formation ( Hosseini et al. 2016). During metastasis, disseminating cancer cells escape from primary tumors and acquire cellular traits that allow them to travel and colonize distant organs ( Chambers and Werb 2015; Lambert et al. 2017; Gonzalez et al. 2018).

Primary and metastatic tumors are complex ecosystems composed of neoplastic cells, extracellular matrix (ECM), and "accessory" nonneoplastic cells, which include resident mesenchymal support cells, endothelial cells, and infiltrated inflammatory immune cells. Cross-talk between cancer cells and accessory cells fuels and shapes tumor development. During tumor formation, the tissue architecture evolves into a highly specialized microenvironment characterized by a corrupted ECM and chronic inflammation ( Coussens and Werb 2002).

Cancer-associated inflammation, which is present at different stages of tumorigenesis, contributes to genomic instability, epigenetic modification, induction of cancer cell proliferation, enhancement of cancer anti-apoptotic pathways, stimulation of angiogenesis, and, eventually, cancer dissemination ( Hanahan and Weinberg 2011). Studies during the last two decades have demonstrated that inflammatory immune cells are essential players of cancer-related inflammation. Efforts have focused on understanding how immune cells impact tumor fate in different stages of disease: early neoplastic transformation, clinically detected tumors, metastatic dissemination, and therapeutic intervention. In this review, we focus on recent results, unifying concepts, limits, and futures challenges in studying cancer-associated inflammatory cells, with an emphasis on metastatic carcinomas.

Cancer-related inflammatory conditions

Since 1863, when Virchow first hypothesized that cancer develops as the product of unresolved inflammation ( Balkwill and Mantovani 2001), tumor-associated inflammation has been key to shaping our modern understanding of cancer progression ( Fig. 1 ). Today, it is accepted that chronic inflammation is a critical hallmark of cancer, with at least 25% of cancers associated with it ( Hussain et al. 2000; Coussens and Werb 2002; Beaugerie et al. 2013), and possible underlying causes include microbial infections, autoimmunity, and immune deregulation. For example, human papilloma viruses (HPVs) induce inflammation and are responsible for 90%-100% of all cervical cancers ( Bosch et al. 2002). Similarly, chronic infection with Helicobacter pylori elevates the risk for gastric cancer ( Hussain et al. 2000). In addition, the immune deregulation seen in inflammatory bowel disease increases colorectal cancer incidence ( Lakatos and Lakatos 2008). The nonhuman form of sialic acid-N-glycolylneuroaminic acid (Neu5Gc)-in red meat can be incorporated into human tissue and recruit inflammatory cells ( Samraj et al. 2015). In this sense, diet may play a causal role in induction of cancer-associated inflammation ( Gupta et al. 2010). Importantly, tobacco and obesity, both of which induce low-grade inflammation, give rise to elevated risks of cancer ( Howe et al. 2013); this evidence suggests that the majority of cancers is associated with unresolved inflammation.

While chronic inflammation has an important role in cancer, less is known about the impact of acute inflammation on tumor progression. For example, inducing acute inflammation locally in the bladder with a vaccine containing an attenuated Mycobacterium bovis strain successfully treats squamous cancer of the bladder ( Askeland et al. 2012). Hence, with the infiltration of leukocytes and subsequent inflammation, the impact from inflammatory mediators can both initiate and, in certain cases, eliminate tumor cells and prevent tumor development ( Shalapour and Karin 2015). This dual role of inflammation also becomes evident in the clinic, where immunodeficient patients are more often diagnosed with cancer ( Frisch et al. 2001). Interestingly, long-term use of nonsteroidal anti-inflammatory drugs (NSAIDs), which suppresses the immune system, has been linked to a lower risk of cancer ( Thun et al. 2002).

Whether or not inflammation is a cause or a consequence, the tumor microenvironment (TME) is compromised, triggering an immune inflammatory response, and histopathological analyses provide evidence for the presence of innate and adaptive immune cells in most human tumors, which are characterized as features of cancer progression ( Fridman et al. 2012).

Role of inflammatory cells during cancer progression

The presence of tumor-associated inflammatory cells in tumors raises an important question, which is one of the most important challenges in oncology: How do cancer cells avoid destruction by the immune system? Since inflammatory cells were first observed in human tumors, much effort has been invested in better understanding the complex role of inflammatory cells in carcinomas. It is currently accepted that an aberrant innate and adaptive immune response contributes to tumorigenesis by selecting aggressive clones, inducing immunosuppression, and stimulating cancer cell proliferation and metastasis ( Fig. 2 ; Palucka and Coussens 2016). During the early stages of tumor development, cytotoxic immune cells such as natural killer (NK) and CD8⁺ T cells recognize and eliminate the more immunogenic cancer cells ( Teng et al. 2015). This first phase of elimination selects the proliferation of cancer cell variants that are less immunogenic and therefore invisible to immune detection. As the neoplastic tissue evolves to a clinically detectable tumor, different subsets of inflammatory cells impact tumor fate. For example, high levels of tumor-infiltrated T cells correlate with good prognosis in many solid cancers ( Clemente et al. 1996; Oldford et al. 2006; Dieu-Nosjean et al. 2008); on the other hand, high levels of macrophage infiltration correlate with a worse prognosis ( Zhang et al. 2012; Mantovani et al. 2017; Gonzalez et al. 2018). Here, we review the important aspects and different facets of cancer-associated immune cells, focusing on progression from tumor initiation to metastatic colonization

Macrophages

Macrophages are innate immune cells that differentiate from circulating classical monocytes after extravasation into tissues. Upon differentiation, macrophages are equipped to sense and respond to infections and tissue injuries, playing a key role in tissue homeostasis and repair ( Lavin et al. 2015). As crucial drivers of chronic cancer-associated inflammation, their involvement has been described in every step of cancer progression, from early neoplastic transformation throughout metastatic progression to therapy resistance ( Fig. 3 ; Noy and Pollard 2014; Kitamura et al. 2015; Gonzalez et al. 2018). In oncological patients and preclinical experimental models, high-grade tumor-associated macrophages (TAMs) correlate with poor prognosis and reduced overall survival ( Zhang et al. 2012; Noy and Pollard 2014).

Activated macrophages are referred to as either proinflammatory ("M1 type," driven by LPS and IFNγ) or anti-inflammatory ("M2-type," driven by IL-4 or IL-13) ( Mantovani et al. 2002). During carcinogenesis, anti-tumor macrophages display an M1-like polarization that plays a relevant role in the elimination of more immunogenic cancer cells. As the tumor progresses, the TME elicits an M2-like polarization of TAMs that is protumorigenic ( Mantovani et al. 2017). TAMs promote tumor progression in different ways, such as stimulating angiogenesis and lymphangiogenesis, stimulating both cancer cell proliferation and epithelial-mesenchymal transition, limiting the efficacy of therapies, remodeling the ECM, promoting metastasis, and inducing immunosuppression of anti-tumor effector immune cells ( DeNardo et al. 2011; Qian et al. 2015; Mantovani et al. 2017). Accordingly, TAMs secrete cytokines such as IL-10 ( Ng et al. 2013) and TGF-β ( McIntire et al. 2004) that induce immunosuppression, impairing the activity of effector T cells and inhibition of dendritic cell (DC) maturation ( Rubtsov et al. 2008). TAMs also directly stimulate cancer cell proliferation through the secretion of epidermal growth factor (EGF) ( O'Sullivan et al. 1993), promote tumor angiogenesis by vascular EGF (VEGF) secretion ( Shojaei et al. 2008), and remodel the ECM by secreting metalloproteinases (MMPs) ( Kessenbrock et al. 2010). For example, macrophage-derived MMP-9 promotes tumorigenesis and angiogenesis ( Huang et al. 2002).

Although TAMs mostly play protumorigenic roles, they can also sometimes exert anti-tumoral roles. For example, nonclassical NR4A1⁺ patrolling monocytes that, in steady state conditions, are located in the microvasculature of different organs inhibit lung metastasis in MMTV-PyMT mice by direct induction of NK cell recruitment to the metastatic site ( Hanna et al. 2015). Additionally, TAMs mediate the efficacy of the anti-tumor and anti-metastatic effects of the histone deacetylase inhibitor TMP195, which reprograms TAMs to a highly phagocytic phenotype ( Guerriero et al. 2017).

Similar to other tumor-associated immune cells, TAMs have been described mostly in primary tumors. However, understanding the roles of macrophages as promoters or inhibitors in the metastatic cascade and their role in metastasis is a growing field. In xenograft models of breast cancer, TAMs regulate invasion of stroma and intravasation of cancer cells via cell contact-mediated signaling ( Roh-Johnson et al. 2014). Circulating CCR2⁺ monocytes are attracted to metastatic sites by CCL2 secreted by cancer cells and endothelial cells, resulting in promotion of metastatic seeding ( Srivastava et al. 2014). In MMTV-PyMT mice, Tie2⁺ metastasis-associated macrophages (MAMs) enhance angiogenesis and tumor proliferation, while the inhibition of angiogenesis by blocking of angiopoetin-2 inhibits metastatic burden and induces regression of established metastases ( Mazzieri et al. 2011). In a Kras-driven mouse model of pancreatic cancer, exosomes derived from malignant lesions induce activation of tissue-resident macrophages called Kupffer cells, establishing a premetastatic inflammatory milieu in the liver. This is followed by subsequent arrest of bone marrow-derived macrophages and neutrophils in the liver, resulting in a prometastatic effect, reversible by macrophage ablation ( Costa-Silva et al. 2015).

TAMs are recruited to the tumors by a range of chemokines, including CCL2 ( Nakatsumi et al. 2017), VEGF ( Qian et al. 2011), CCL5 ( Halama et al. 2016), and CSF1 ( Abraham et al. 2010). The role of VEGF as a proangiogenic and protumor factor is well established and accepted; for example, VEGF ablation in monocytes decreases metastatic burden in experimental breast cancer metastasis ( Qian et al. 2011). The role of CCL2 in cancer is more controversial, as previous studies have shown contradictory findings ( Rollins and Sunday 1991; Manome et al. 1995; Lu and Kang 2009; Takahashi et al. 2009; Granot et al. 2011; Qian et al. 2011; Bonapace et al. 2014; Espinosa et al. 2014; Headley et al. 2016; Long et al. 2016). The inhibition of CCL2 in a syngeneic xenograft model of breast cancer results in increased metastasis and accelerated death ( Bonapace et al. 2014), while tumor-derived CCL2 results in the induction of anti-metastatic neutrophils in an MMTV-PyMT model, resulting in inhibition of metastatic seeding in premetastatic lungs ( Granot et al. 2011). Furthermore, the increase of CCL2 in a 4T1 breast cancer model negatively regulates metastatic spread to the bone marrow and lungs ( Takahashi et al. 2009). Using similar approaches, such as MMTV-PyMT and xenograft models of breast cancer, high CCL2 expression correlates with increased infiltration of CCR2 macrophages in primary tumors and metastatic sites, including the lungs and bone, and is associated with poor prognosis ( Lu and Kang 2009; Qian et al. 2011). These apparent contradictions cannot be explained merely by genetic differences between preclinical models used and highlight the complex role of CCR2 in the TME and likely heterogeneity in phenotypes within CCR2⁺ TAMs/MAMs. This dichotomy highlights the necessity of validating these findings in more reliable models that better represent human tumors of cancer, such as humanized patient-derived xenograft (PDX) models, and interrogating, at the single-cell level, the diversity of TAMs/MAMs in human tumors. Likewise, although the M1-like/M2-like paradigm has proved to be useful, transcriptomic analysis suggests that it is likely that a spectrum of differentiated TAMs/MAMs exists and that the current model is oversimplified ( Xue et al. 2014).

Neutrophils

Neutrophils are recognized as key players during inflammation. They are among the first immune cells to be recruited to damaged tissue, where they can eliminate pathogens and modulate inflammation by mechanisms such as phagocytosis, secretion of antibacterial proteins, deposit of neutrophil extracellular traps (NETs), and exocytosis of protease-containing granules ( Kolaczkowska and Kubes 2013). In cancer patients, high levels of tumor-associated neutrophils (TANs), high levels of neutrophilia, and/or high neutrophil/lymphocyte ratios have been associated with an adverse prognosis in different malignances ( Keizman et al. 2012; Donskov 2013). Similar to the M1/M2 phenotype of macrophages, it has been proposed that TANs exist in two polarization states, called "N1" and "N2," to describe protumor and anti-tumor populations, respectively ( Fridlender et al. 2009). This paradigm is still a matter of debate due to the lack of specific markers to identify these two populations. However, it is clear that TANs display functional heterogeneity. The recruitment of TANs to the TME is thought be mediated mainly by CXCR2 ligands such as CXCL1, CXCL2 and CXCL5 ( Jamieson et al. 2012; Katoh et al. 2013), secreted by cancer and stromal cells; TGF-β has also been associated with recruitment and reprogramming to protumor TANs ( Fridlender et al. 2009).

In xenograft models of melanoma and lung cancer, TANs expressing hepatocyte growth factor receptor (c-MET) play important anti-tumor and anti-metastatic roles. Interestingly, c-MET expression is induced by tumor-derived tumor necrosis factor-α (TNFα) ( Finisguerra et al. 2015), and it is likely that NK and effector T cells are a source of TNF-α within the TME. Similarly, in human colorectal cancer, high levels of CD66b⁺ TANs have been associated with better prognosis by enhancing the tumoricidal capacity of CD8⁺ T cells ( Governa et al. 2017). Neutrophils also exert a tumoricidal function during radiotherapy. As they are rapidly and transiently recruited to tumor sites in syngeneic xenograft breast cancer models, the concurrent administration of granulocyte colony-stimulating factor (G-CSF) enhances radiotherapy effectiveness ( Takeshima et al. 2016).

In contrast, TANs are thought to contribute to nascent inflammation during cancer initiation and progression. In a Kras-driven lung adenocarcinoma mouse model, IL-17-responsive TANs promote tumor growth ( Chang et al. 2014). Also, neutrophil elastase acts as a potent elastolytic enzyme that, when secreted in a site of inflammation, promotes tumor cell invasion, angiogenesis, and cancer cell proliferation ( Houghton et al. 2010; Gong et al. 2013). Moreover, TANs contribute to the tumor angiogenesis by the secretion of MMP9 and VEGF in genetic mouse models of pancreatic and colon cancer ( Bergers et al. 2000; Shojaei et al. 2007, 2008). In gastric cancer, TANs induce direct immunosuppression in T cells by PD-L1 expression induced by tumor-derived granulocyte macrophage-CSF (GM-CSF) ( Wang et al. 2017). A population of cells phenotypically and morphologically similar to neutrophils, called polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), has been identified in cancer patients and preclinical models (for review, see Gabrilovich 2017). The presence of PMN-MDSCs in tumors is associated with induction of chronic inflammation and antigen-specific tolerance by T cells ( Marigo et al. 2010). Using the MMTV-PyMT transgenic breast cancer model, we described that the production of tumor-derived G-CSF during tumor progression induces the systematic differentiation and activation of PMN-MDSCs, characterized by CD11b⁺ Rb1lo Ly6G⁺ ( Casbon et al. 2015).

Neutrophils have been proposed to be pioneer cells in the lung premetastatic niche, supporting the arrival of disseminated cells in the MMTV-PyMT model ( Wculek and Malanchi 2015). Moreover, in response to the secretion of IL-7 by γδ T cells, neutrophils are recruited to the lungs, where they support the survival and proliferation of disseminated cancer cells by suppressing effector CD8⁺ T cells ( Coffelt et al. 2015). This prometastatic motif is also seen during liver metastasis in the KPC model of pancreatic cancer ( Steele et al. 2016). These results demonstrate that the protumor and anti-tumor functions of TANs are highly context-dependent and likely depend on immune cross-talk with other tumor-associated immune cells.

In recent years, the presence of NETs in the TME has been linked to cancer progression in animal models and cancer patients ( Cools-Lartigue et al. 2013, 2014; Tohme et al. 2016), NETs are extracellular networks released by neutrophils-composed mostly of chromatin, proteases (such as elastase, cathepsin G, and MMP9), and intracellular proteins-that immobilize pathogens to facilitate their subsequent elimination ( Papayannopoulos 2018). An increase in NET formation has been correlated with progression to metastatic disease in colorectal cancer patients after surgery ( Tohme et al. 2016). Additionally, NETs trap circulating cancer cells, increasing the adhesion within hepatic sinusoids, which favors extravasation and parenchyma colonization ( Cools-Lartigue et al. 2013). In breast cancer, NETs accumulate around metastatic cells that have reached the lungs of mice. Notably, targeting NETs in vivo with DNase I-coated particles reduces metastatic burden ( Park et al. 2016). NETs seem to play a protumor role by the direct activity of NET-derived proteases and also by holding cancer cells in place, likely facilitating the concentration and localization of cancer effectors that result in increased degradation of the ECM, migration, and invasion ( Cools-Lartigue et al. 2014).

There is a growing recognition of neutrophils as relevant players at different steps of cancer progression; however, a better characterization of the phenotypic heterogeneity and plasticity of cancer-associated neutrophils is needed, considering that they are the most abundant immune cells in circulation. The understanding of where and how the neutrophils are programmed or reprogrammed to be protumor and/or anti-tumor will lead to rational designing of targeted therapies.

NK cells

NK cells are innate immune cells that display rapid and potent cytolytic activity in response to infected or transformed cells ( Cerwenka and Lanier 2016). NK cells have a wide array of inhibitory and stimulatory receptors on their cell surface that are used for immune surveillance. The inhibitory receptors target cancer cells lacking major histocompatibility class I (MHC-I), marking them for programmed cell death ( Marcus et al. 2014). In contrast, in healthy cells, the binding of MHC-I molecules to their receptors on NK cells has a profound inhibitory effect on NK cell function ( Bix et al. 1991; Liao et al. 1991; Colonna et al. 1992; Karlhofer et al. 1992; Wagtmann et al. 1995; Lanier 2005). NK cells have a well-documented anti-tumor effect ( Marcus et al. 2014; Iannello et al. 2016). In this regard, the presence of NK cell infiltration in colorectal ( Coca et al. 1997) and gastric ( Ishigami et al. 2000) tumors correlates with a favorable outcome. Hence, there appears to be an intricate link between incipient tumor transformation and the ability of innate immune cells to recognize it. Indeed, in mice, aberrant cell proliferation induces production of the ligand retinoic acid early transcript 1 (RAE1), which is recognized by the stimulatory receptor NKG2D, expressed on NK cells ( Raulet et al. 2013). Besides aberrant cell proliferation, DNA damage ( Gasser et al. 2005) and RAS pathway activation ( Liu et al. 2012b) induce production of ligands in tumor cells, which are recognized by NKG2D on NK cells. In line with the important role of NKG2D for immune surveillance, mice deficient in NKG2D receptor are more susceptible to tumor development ( Guerra et al. 2008). Besides NKG2D, NK cells have a repertoire of different stimulatory cell surface receptors, which, upon binding to their tumor-derived ligands, activate NK cells ( Cerwenka et al. 2000; Diefenbach et al. 2000, 2001; Raulet 2003). In a mouse model of hepatic carcinoma, the restoration of the tumor suppressor p53 in cancer cells promotes the elimination of senescent cells ( Iannello et al. 2013). Upon activation, NK cells mediate the tumor killing mainly by releasing cytotoxic perforin ( Voskoboinik et al. 2006) and granzyme, eliminating tumor cells and also triggering apoptotic pathways in tumor cells through the production of TNFα or via direct cell-cell contact through activation of the TRAIL and FASL pathways. Densely granulated NK cells are recruited into large solid tumors by tumor-produced IL-15, where they successfully eliminate established tumors ( Liu et al. 2012a). The natural cytotoxicity receptor NKp46 and inhibitory receptor Ly49 on NK cells prevent metastatic outgrowth in melanoma, lung, and fibrosarcoma models ( Andrews et al. 2012; Glasner et al. 2012). These data highlight the importance of NK cells' function in controlling cancer progression. Future efforts should be addressed to define the regulatory circuits of the innate and adaptive immunity that results in tumor killing by NK cells.

DCs

These specialized antigen-presenting cells (APCs) that represent the interface between innate and adaptive immunity are able to present endogenous and exogenous antigens to T cells in the context of MHC molecules. With the exception of the brain parenchyma, DCs are located in every tissue across the body ( Mildner and Jung 2014). During tumor development, DCs prime naïve and memory T cells, and, depending on the inflammatory context and the costimulatory signals, the antigen presentation can result in antigen tolerance or priming and triggering of an effector T-cell response. Tumor-infiltrating DCs have been described in many cancer types ( Tran Janco et al. 2015), and, although their activity is necessary to explain the role of T cells during cancer progression, DC involvement in cancer progression remains understudied.

Insights into DC mechanisms are limited. A mouse model of fibrosarcoma lacking CD8a⁺ DCs shows impaired tumor rejection mediated by CD4⁺ and CD8⁺ T cells ( Hildner et al. 2008). This anti-tumor effect of CD8a⁺ DCs priming effector T cells is selectively dependent on type I interferon production ( Diamond et al. 2011). CD103⁺ DCs have critical roles in tumor antigen presentation in transgenic and xenograft mouse models of melanoma and breast and cervical cancer ( Broz et al. 2014; Moynihan et al. 2016; Roberts et al. 2016). During chemotherapy-induced anti-tumor immune responses, ATP and damage signals released by necrotic cells induce the recruitment of myeloid cells; this is followed by local differentiation to CD11c⁺ CD11b⁺ Ly6C hi DCs that efficiently engulf tumor antigens in situ and prime the anti-tumor effector T-cell response ( Ma et al. 2013). Mechanistically, the expression of formyl-peptide receptor 1 on DCs favors the recognition and stable interaction with dying cancer cells followed by maturation, engulfing, and antigen presentation in breast and colorectal cancer ( Vacchelli et al. 2015). Intravital imaging analysis identified lung-resident CD103⁺ DCs as direct suppressors of metastatic melanoma cells ( Headley et al. 2016). These findings highlight the essential role of DCs.

Between 1995 and 2004, several clinical trials in phases I, II, and II have tested the use of autologous DCs pulsed with tumor antigens (DC vaccine) to initiate an anti-tumor T-cell response, with promising but limited success, especially in melanoma and prostate cancer ( Mukherji et al. 1995; Nestle et al. 1998; Beer et al. 2011). The limitations in the use of DC vaccines include ex vivo manipulation such as antigen loading, which impacts DC function in vivo, and also the lack of deep insights into DC subsets and their functional specialization in cancer (for review, see Santos and Butterfield 2018). More recently, a phase II study in 39 melanoma patients showed that the combination of an intradermal DC vaccine combined with CTLA-4 blockade resulted in eight complete and seven partial therapeutic responses ( Wilgenhof et al. 2016). The combination of DC vaccines and the amplification of immunity by the use of checkpoint blockade (see "T cells" below) is an attractive strategy worthy of further testing.

Overall, DCs play a key role in the priming and consolidation of anti-tumor adaptive immune response; a better understanding of such mechanisms will shed light on how the anti-tumor T-cell attack fails to eliminate and contain the tumor development. In this sense, massive parallel single-cell analysis in early lung adenocarcinoma (stage I) has identified a selective depletion of CD141⁺ DCs (compared with normal lung tissue) that correlates with impaired NK and T-cell activity, which favors tumor progression ( Lavin et al. 2017). Recently, it has been shown that in melanoma, breast, and colorectal mouse models, tumor cells impair DC recruitment to TME by secretion of prostaglandin E2, which impairs the function of tumor-associated NK cells and results in impaired NK cell-dependent DC recruitment ( Bottcher et al. 2018).

T cells

T cells are components of the adaptive immune system that act as orchestrators and effectors of immunity. Depending on the immunological context, T cells can acquire functional and effector phenotypes whose activity has direct inflammatory or anti-inflammatory consequences ( Speiser et al. 2016). As the second most frequent immune cell type found in human tumors besides TAMs, T cells are extensively studied in diverse cancer types ( Speiser et al. 2016; Donadon et al. 2017). During the early stages of tumor initiation, if enough immunogenic antigens are produced, naïve T cells will be primed in the draining lymph nodes, followed by their concomitant activation and migration to the TME. From there, they mount a protective effector immune response, eliminating immunogenic cancer cells. Histopathological analyses of human tumors show that tumor-associated T cells extend beyond the invasive edge of the tumor and also predominate in its hypoxic core ( Halama et al. 2011; Kirilovsky et al. 2016). A high level of T-cell infiltration in tumors is associated with a favorable prognosis in melanoma ( Clemente et al. 1996) and breast ( Oldford et al. 2006), lung ( Dieu-Nosjean et al. 2008), ovarian ( Kusuda et al. 2005), colorectal ( Tosolini et al. 2011), renal ( Kondo et al. 2006), prostate ( Vesalainen et al. 1994), and gastric ( Ubukata et al. 2010; Fridman et al. 2012; Kitamura et al. 2015) cancer.

CD8⁺ T cells are the most prominent anti-tumor cells. Upon priming and activation by APCs, the CD8⁺ T cells differentiate into cytotoxic T lymphocytes (CTLs) and, through the exocytosis of perforin- and granzyme-containing granules, exert an efficient anti-tumoral attack, resulting in the direct destruction of target cells ( Hanson et al. 2000; Matsushita et al. 2012). Meanwhile, the CD4⁺ T helper 1 (Th-1)-mediated anti-tumoral response-through secretion of high amounts of proinflammatory cytokines such as IL-2, TNF-α, and IFN-γ-promotes not only T-cell priming and activation and CTL cytotoxicity but also the anti-tumoral activity of macrophages and NK cells and an overall increase in the presentation of tumor antigens ( Kalams and Walker 1998; Pardoll and Topalian 1998; Shankaran et al. 2001). The presence of tumor-infiltrating CD8⁺ T cells and Th-1 cytokines in tumors correlates with a favorable prognosis in terms of overall survival and a disease-free survival in many malignancies ( Fridman et al. 2012).

If the T cells are highly effective at killing malignant transformed cells, how do some cancer cells manage to overcome the attack by these effector T cells? Preclinical investigations in patients and mouse models suggest that cancer cells exploit the immunosuppressive properties of T cells while impairing the effector functions of anti-tumor T cells, such as their ability to infiltrate tumors and their survival, proliferation, and cytotoxicity ( Fig. 4 ; Grivennikov et al. 2010). The antigen-dependent nature of the effector T cells implies that the effectiveness of the anti-tumor T-cell immune response depends on both the ability of the tumor antigen to induce an immune response (immunogenic) and the presence-or absence-of inhibitory signals that can impair the T cells' functions ( Speiser et al. 2016). Accordingly, it is widely accepted that, in a T-cell-dependent process, most neoplastic cells expressing highly immunogenic antigens will be recognized and killed during the early stages of tumor development ( Matsushita et al. 2012). The less immunogenic cancer cells escape the immune control of T cells and survive, a process termed cancer immune editing ( Teng et al. 2015). The final outcome is that the surviving cancer cells adopt an immune-resistant phenotype. In parallel, during tumor development, cancer cells evolve mechanisms that mimic peripheral tolerance and are able to prevent the local cytotoxic response of effector T cells as well as those of other cells, such as TAMs, NK cells, and TANs ( Palucka and Coussens 2016).

During immune homeostasis, a crucial mechanism of peripheral tolerance is the regulation of effector T-cell response via immune checkpoints on CTLs and activated CD4⁺ T cells to protect tissue from inflammatory damage. The two better described checkpoint molecules CTLA-4 and PD-1 act as negative regulators of T-cell function and have been associated with immune evasion in cancer ( Pardoll 2012). The involvement of CTLA-4 signaling in cancer has been described in melanoma ( Bouwhuis et al. 2010) and lung ( Khaghanzadeh et al. 2010), breast ( Erfani et al. 2006), gastric ( Hadinia et al. 2007), and colorectal ( Hadinia et al. 2007; Dilmec et al. 2008) cancer. Furthermore, the engagement of PD1 with its coreceptor, PDL-1 (expressed by other immune cells, mesenchymal cells, vascular cells, and cancer cells), results in the down-regulation of T-cell activity, which inhibits their anti-tumor activities such as T-cell migration, proliferation, secretion of cytotoxic mediators, and restriction of cell killing ( Topalian et al. 2015). Over the past few years, the use of immune checkpoint inhibitors such as anti-PD1 (pembrolizumab and nivolumab), anti-PD-L1 (MPDL3280A), and anti-CTLA4 (ipilimumab) has had remarkable success enhancing the effector anti-tumor response in different malignancies ( Gotwals et al. 2017), especially in melanoma and lung cancer ( Hamid et al. 2013; Herbst et al. 2014; Topalian et al. 2015).

As the tumor grows and the TME changes, new antigens are produced, and the ability of the immune system to prime new repertoires of T cells and direct them toward the tumor changes, thus altering the efficacy of tumor containment. As the immune system functions to stall tumor growth, cancer cells and the TME simultaneously suppress anti-tumor function by engaging immune checkpoints and the recruitment of regulatory CD4⁺ T cells (Tregs). Tregs are responsible for suppressing the priming, activation, and cytotoxicity of other effector immune cells, such as Th1 CD4 T cells, CTLs, macrophages, NK cells, and neutrophils ( Ward-Hartstonge and Kemp 2017). The Treg-mediated immunosuppression is orchestrated by contact-dependent mechanisms such as the expression of PDL-1, LAG-3, CD39/73, CTLA4, or PD1, with the latter two even enhancing suppressive activity ( Walker and Sansom 2015), and by contact-independent mechanisms, which involve the sequestration of IL-2 and production of immune-suppressive molecules such as IL-10, TGF-β, prostaglandin E2, adenosine, and galectin-1 ( Francisco et al. 2009; Campbell 2015). In squamous cell carcinoma, the inhibition of focal adhesion kinase (FAK)-a cell contact-independent mechanism-results in CCL5 secretion by cancer cells that induces the recruitment of Tregs to the tumor site, where they suppress the cytotoxic anti-tumor CD8⁺ T cells ( Serrels et al. 2015). In breast and lung adenocarcinoma, Tregs suppress T-cell activation and the anti-tumor immune response in tumor-associated tertiary structures. Notably, specific Treg depletion results in tumor cell death and increased production of IFN-γ ( Bos et al. 2013; Joshi et al. 2015). Indeed, infiltration of Tregs in breast cancer was correlated recently with worse patient outcome ( Allaoui et al. 2017).

In metastasis, CTLs exert an anti-metastatic effect in bone metastasis ( Bidwell et al. 2012), while prospective analyses of lung and breast cancer patients established an opposite correlation between the level of circulating cancer cells and T cells in peripheral blood ( Mego et al. 2016; Sun et al. 2017). These data extend to clinical trials reporting the therapeutic efficacy of immune checkpoint inhibition in metastatic carcinomas ( Di Giacomo et al. 2012; Queirolo et al. 2014; Motzer et al. 2015; Furudate et al. 2016; Goldberg et al. 2016; Pai-Scherf et al. 2017). Interestingly, checkpoint inhibitors are significantly effective in treating brain metastatic tumors from melanoma and lung cancer, especially when considering the lack of the adaptive immune response in the central nervous system ( Queirolo et al. 2014; Goldberg et al. 2016; Di Giacomo et al. 2017). Recent evidence suggests that the effectiveness of checkpoint inhibition in melanoma brain metastasis depends on extracranial disease and peripheral activation of CD8⁺ T cells ( Taggart et al. 2018). On the other hand, a high level of circulating Tregs has been associated with a higher risk of metastasis in non-small lung carcinoma patients. ( Erfani et al. 2012). Similar associations have been described in breast cancer ( Metelli et al. 2016), colorectal carcinoma metastasis ( Wang et al. 2014), and hepatocellular carcinoma ( Ye et al. 2016).

The cutting-edge technology of immunotherapy boasts impressive clinical potential and poses an opportunity to exploit the antigen specific T-cell attack to fight cancer. The challenge faced now is to better understand the mechanisms that cancer cells undergo to evade T-cell attack. Also, elucidating the mechanisms that mediate the generation of antigenic heterogeneity of tumors will lead to defining better immunotherapies for patients, expanding access to therapies to already metastasized patients.

Invariant NK T (iNKT) cells

An unconventional subset of T cells, iNKT cells, recognizes lipid antigens presented by CD1d molecules and shares some characteristics with NK cells, among which is the potential for activation in the absence of TCR stimulation. Upon activation, iNKT cells secrete effector cytokines such as IFN-γ, IL-4, and IL-17 ( Lee et al. 2015). In breast cancer cells, the down-regulation of CD1d leads to decreased iNKT-mediated cytotoxicity and metastatic progression ( Hix et al. 2011). However, in renal cell carcinoma patients, the expression of CD1b is associated with high relapse and poor prognosis ( Chong et al. 2015). The administration of retinoic acid and αGalCer (iNKT agonist) alone or the transfusion of αGalCer-loaded DCs reduces the tumor size and the number of metastatic foci in a mouse model of breast cancer by enhancing CD8⁺ T-cell cytotoxicity ( Chen and Ross 2012; Gebremeskel et al. 2015). A phase I-II study on patients with advanced lung cancer arrived at similar conclusions ( Motohashi et al. 2009). These findings are remarkable in that they illustrate the impact of iNKT activation and how the monospecific nature of iNKT cells and the therapeutic use of αGalCer can be used to reprogram the anti-tumor immune response.

B cells

Upon activation in the germinal centers in lymphoid organs, B cells expressing high-affinity antibodies differentiate into antibody-secreting plasma cells and memory B cells that mediate humoral immunity against pathogens ( De Silva and Klein 2015). Although the presence of B cells in the TME has been described in different carcinomas (including melanoma and breast, ovarian, and prostate cancer, among others) ( Chin et al. 1992; Yang et al. 2013; Woo et al. 2014; Pylayeva-Gupta et al. 2016), the role of B cells in cancer progression is much less understood than that of T cells. Accumulating evidence indicates that B cells promote and support tumor growth; for example, using a transgenic mouse model of epithelial carcinogenesis, Coussens and colleagues ( de Visser et al. 2005) demonstrated that the lack of mature B cells decreases tumor progression. Notably, the adoptive transfer of B cells restores chronic inflammation, angiogenesis, and tumor growth. Different mechanisms have been described to explain the protumor role of B cells, from immunosuppression via secretion of IL-10 ( Schioppa et al. 2011) and TGFβ ( Olkhanud et al. 2011) to direct stimulation of tumor cell proliferation by B-cell-derived IL-35 in human pancreatic neoplasia and Kras-driven pancreatic neoplasms in mice ( Pylayeva-Gupta et al. 2016). Also, by deposition of immunoglobulins in the TME, B cells indirectly stimulate angiogenesis and chronic inflammation by activating myeloid cells via FcRγ ( Andreu et al. 2010). It is unclear how many different phenotypes of B cells coexist in the TME or whether the B cells are recruited as immune suppressors or are reprogrammed into the TME. The subsets of B cells that induce immunosuppression are termed regulatory B cells due to their function, although there is no consensus about this classification due to the lack of specific markers, similar to FoxP3 and CD25 in Tregs.

Cross-talk between immune cells sculpts the response to the tumor

An aspect that has received less attention is the cross-talk between different immune cells within the TME and how it impacts the outcome of the subsequent immune response. There is growing evidence that tumor-associated immune cells act in concert to both control and promote the tumor formation. In this sense, during the phase of elimination, NK cells exert a strong tumoricidal role; secretion of CCL5 and XCL1 by NK cells promotes the recruitment of conventional DCs (cDCs) to the TME, resulting in increased priming and activation of new repertoires of anti-tumor T cells, stimulating the overall effector immune response ( Moretta et al. 2005; Bottcher et al. 2018). Additionally, the reciprocal interplay between NK cells, effector T cells, and anti-tumor macrophages by the secretion of IFN-γ and TNF-α in the tumor site boosts the differentiation of CTLs, increases macrophage phagocytosis, increases the recruitment of cytotoxic cMET⁺ neutrophils, and enhances the cytotoxic ability of NK cells ( Finisguerra et al. 2015; Showalter et al. 2017). Dectin-1, a pattern recognition receptor on macrophages and DCs, recognizes N-glycan structures on tumor cells, which activate the IRF5 pathway responsible for enhancing the killing capacity of NK cells ( Chiba et al. 2014). Moreover, CX3CR-1⁺ patrolling monocytes inhibit metastatic progression through the recruitment of NK cells to the metastatic site, and then NK cell-derived IFN-γ reprograms macrophages into a tumoricidal effector macrophage state ( O'Sullivan et al. 2012).

Once the tumors have escaped from initial tumoricidal immunity, they undergo different strategies that tip the balance toward immune tolerance, with the TAMs and tumor-associated Tregs as key orchestrators of this process, as they dampen the effect of innate and adaptive effector immune cells at various levels and through different mechanisms. For example, TAMs and Tregs boost an immune-tolerant TME by secretion of immune-suppressive molecules such as IL-10, TGF-β, and prostaglandins; they also inhibit the secretion of IL-12 by DCs, avoiding the mounting of a Th-1 response and excluding NK and effector T cells ( Ruffell et al. 2014; Speiser et al. 2016; Frydrychowicz et al. 2017; Mantovani et al. 2017; Tauriello et al. 2018). Recently, a combination immunotherapy-including a tumor antigen targeting antibody, a recombinant interleukin-2 with an extended half-life, anti-PD-1, and a T-cell vaccine-efficiently eliminated established large metastatic tumors in multiple cancer models; subsequent analysis showed that the efficacy was dependent on an orchestrated response of both innate and adaptive immune cells ( Moynihan et al. 2016). These data further highlight the importance of therapies designed to stimulate an orchestrated immune effector response.

Understanding the in situ cross-talk of different tumor-associated immune cells in the different stages of cancer progression will provide insights that could increase the efficiency of current immunotherapies. Additionally, the use of massive single-cell genomic and proteomic data will allow a system-level view of the tumor-associated immune function in the years to follow.

Cancer heterogeneity and anti-tumor immunosurveillance

Analysis of human primary and metastatic tumors has shown high levels of genomic, phenotypic, and antigenic heterogeneity ( Swanton 2012), which contribute to therapy failure and disease progression. This poses an arduous clinical and technical challenge ( Bedard et al. 2013; Kreso and Dick 2014). Various mechanisms have been proposed to explain intratumor heterogeneity: Genomic instability ( McGranahan and Swanton 2015), hierarchical organization arising from initiating cancer stem cells ( Kreso and Dick 2014), and selective pressure imposed by the immune system likely impact antigen heterogeneity of the tumor ( Quail and Joyce 2013). Through cancer immune editing, the immune system eliminates the more immunogenic cancer cells, thus promoting the development of clonal tumors and thereby decreasing the heterogeneity. In contrast, the lack of immune selection likely increases the neoantigen heterogeneity. Recent analysis of neoantigen heterogeneity in tumor samples from lung cancer and melanoma patients demonstrated that patients with clonal tumors (∼78% of clonality) are more susceptible to T-cell attack and have a more sensitive tumor checkpoint inhibition compared with more heterogeneous tumors (∼8% of clonality) ( McGranahan et al. 2016). Moreover, analysis of different areas of heterogeneous tumors showed different levels of antigen-specific CD8⁺ T cells in different tumor regions ( McGranahan et al. 2016). Increases in the mutational burden and heterogeneity of neoantigens in vivo as well as the priming of new anti-tumor T-cell repertoires ( Rizvi et al. 2015; Germano et al. 2017) result from the inactivation of the DNA repair system in colorectal, breast, and pancreatic cell lines. Interestingly, tumors with high neoantigen burden correlate with good prognosis in lung cancer patients treated with anti-PD1 ( Rizvi et al. 2015).

As genomic tumor heterogeneity increases, so too does the probability of subclonal generations escaping immune attack. In this sense, metastatic progression and therapy resistance usually proceed from rare clones in primary tumors ( Gupta and Massague 2006). Consistent with this view, a deep analysis of intrapatient metastases in a patient with ovarian cancer showed that regressing metastatic tumors were associated with an immune infiltrate characterized by CD4⁺ and CD8⁺ T cells and higher tumor mutation and neoepitope load compared with progressing lesions that are associated with T-cell exclusion ( Jimenez-Sanchez et al. 2017). This case study provides evidence of the clinical impact of the relationship between tumor heterogeneity and anti-tumor immune surveillance. It is unclear whether the cancer heterogeneity observed in patients is the end result the immune system's inability to stop tumor progression or whether the mutational burden promotes heterogeneity that leads to immune evasion. Furthermore, the evolving mutation burden, the selective pressure of chemotherapy, and the rapid turnover of inflammatory cells within the primary and metastatic tumor, in conjunction with the nonuniform distribution of immune cells throughout the tumor, likely promote differential selective pressures in disparate tumor regions, allowing for the development of heterogeneous tumors ( McGranahan et al. 2016). The success of current immunotherapies depends on the ability of the immune system, particularly T cells, to recognize and eliminate tumors with multiclonal or subclonal neoantigens. These findings highlight the importance of better understanding the complex relationships between cancer cells and immune attack and how these relationships drive the development of cancer heterogeneity.

How tumor cells evade the immune response

Tumor cells evade the immune attack using two main strategies: avoiding the immune recognition and instigating an immunosuppressive TME. In the first, cancer cells may lose the expression of tumor antigens on the cell surface, thus avoiding the recognition by cytotoxic T cells. For example, 40% of non-small cell lung cancers hold a loss of heterozygosity in human leukocyte antigens (HLAs), which leads to immune escape by presenting fewer antigens ( McGranahan et al. 2017). Notably, HLA loss has been associated with resistance to T-cell transfer therapy in metastatic colorectal cancer ( Tran et al. 2016) and poor outcome response to checkpoint blockade immunotherapy in melanoma and lung cancer patients ( Chowell et al. 2018). In this sense, mutations and deletions may result in down-regulation of the antigen-presenting machinery and likely confer resistance to T-cell effector molecules such as TNF-α and IFN-γ ( Patel et al. 2017). Additionally, to overcome the attack of NK cells in experimental metastasis, breast and lung cancer cells down-regulate cell surface NK activators, becoming invisible to detection by NK cells ( Malladi et al. 2016).

In the second, cancer cell-derived factors instigate an immune-tolerant TME by (1) secretion of suppressive molecules such as IL-10, TGF-β, prostaglandin E2, and VEGF ( Gabrilovich et al. 1996; Massague 2008; Dominguez-Soto et al. 2011; Bottcher et al. 2018); (2) expression of inhibitory checkpoint molecules such as PD-L1, CTLA-4, and V domain immunoglobulin suppressor of T-cell activation (VISTA) ( Topalian et al. 2012; Snyder et al. 2014; Boger et al. 2017); and (3) induction of the recruitment of TAMs, MDSCs, and Tregs by tumor-derived chemokines such as CCL2, CSF1, CCL5, CCL22, CXCL5, CXCL8, and CXCL12 ( Weitzenfeld and Ben-Baruch 2014; Kumar et al. 2016; Mantovani et al. 2017; Tanaka and Sakaguchi 2017). Combined, these strategies result in a complex and efficient system for immune evasion. Therefore, multimodal therapies aimed at disrupting different aspects of the immune-tolerant apparatus in cancer may improve the efficiency of current immunotherapies. In this regard, two studies showed recently that the TGF-β blocking increases the therapeutic response of anti-PD-L1 therapy, resulting in tumor regression in EMT6 breast carcinoma models ( Mariathasan et al. 2018) and complete elimination of established liver metastases from a colorectal cancer model ( Tauriello et al. 2018).

Challenges in studying cancer-associated inflammatory cells

Developing a reliable experimental model that accurately resembles the cancer-associated immune surveillance observed in patients poses the biggest challenge. Xenograft models generated by injecting immune-deficient mice with patient-derived human cancer cell lines are practical models to study therapeutics. Despite their ease of use and consistent tumor growth, these models have some important limitations, such as being unable to mount an effective immune response; the inability to represent cancer cell heterogeneity present in human tumors accurately, which is known to impact tumor evolution; and resistance to chemotherapy and subsequent chemotherapy-induced anti-tumor immune response. In this sense, the generation of PDX models overcomes some limitations, as surgically derived human tumor samples are implanted in mice ( Hylander et al. 2013). For example, fat pad transplanted breast cancer PDX samples grown in NOD-SCID mice resemble the architecture of the original tumors, promote vascularization, and mimic metastatic behavior observed in patients ( DeRose et al. 2011). Successful PDX engraftments have also been described for colorectal, lung, pancreatic, gastric, renal, and prostate cancer ( Jung et al. 2018). However, the use of immune-deficient mice hinders their utility for immune-based therapies; also, the cross-talk between tumor and host is limited due to species mismatch ( Byrne et al. 2017). Conclusions related to immune oncology generated using xenograft or PDX models should be restricted to questions related to innate immune responses, and the validation in immune-competent models is needed.

Genetically engineered mouse models (GEMMs) that express known oncogenes or inactivate tumor suppressor genes combined with genetic perturbations in stromal cells have proved to be useful to study tumor immune surveillance in a genetically controlled system. Examples of GEMMs are the MMTV-PyMT breast cancer model and Kras- and Trp53-driven mouse models of lung, pancreatic, and colorectal cancer ( Gopinathan et al. 2015; Zitvogel et al. 2016), among others. These models develop spontaneous tumors, metastasis, and immune-sufficient response and display levels of cellular and antigenic heterogeneity. However, they have some limitations, such as the long lead time of generation and poor tropism of metastasis compared with patients (for example, bone and brain metastasis). More recently, the generation of humanized PDX models offers a more sophisticated alternative. In this model, irradiated immune-deficient mice are reconstituted with a human immune system ( Rongvaux et al. 2014; Verma et al. 2017)). As an example, human hepatocellular carcinoma (HCC) PDX tumors implanted in irradiated NOD- SCID Il2rg−/− (NSG) mice reconstituted with human CD34⁺ hematopoietic stem cells exhibit human leukocytes infiltrated in tumors, phenotypes associated with immune exhaustion, and therapeutic response to pembrolizumab and ipilimumab ( Zhao et al. 2018). Although there is no perfect experimental model, the ideal preclinical model to study tumor-associated cancer cells should maintain the cellular and antigenic heterogeneity present in human tumors and mimic not only their innate and adaptive immune surveillance but also the metastatic behavior observed in patients.

Another major challenge is determining whether the current insights into cancer-associated immune response from primary tumors can be used to infer molecular mechanisms that explain the cancer cell-immune cell cross-talk in metastatic tumors. Those cells that successfully reach the post-dissemination phase of the metastatic cascade differ in some respects from their counterparts in primary tumors; for example, cancer cells can acquire mutations and evolve independently ( Yates et al. 2017). Also, it is likely that cancer cells that have successfully colonized a distant organ have already been selected as therapy-resistant ( Doherty et al. 2016). While it is clear that metastatic cells must develop mechanisms to avoid immune attack in the new colonized organ, little is known about the immune-related mechanisms operating at this level. The lack of reliable preclinical models of spontaneous metastasis leaves current research dependent mainly on the better described lung metastasis models, while other metastases-bone, brain, liver, intestine, and adrenal-remain woefully understudied. Furthermore, depending on the type of metastatic carcinoma and the level of its spread, the current treatments include chemotherapy, hormone therapy, and radiotherapy, leaving surgery as an option for few patients, thus limiting the access to metastatic specimens for further characterization. With the advent of new single-cell technologies in transcriptomics and proteomics, future efforts should be focused on understanding the landscapes of immune infiltration in human metastatic tumors, as dissecting the complex interactions between the immune population and disseminated cancer cells could lead to the description of predictive biomarkers to identify dormant micrometastases in patients and, ultimately, the development of new immunotherapies to target metastatic tumors.

Concluding remarks

Immunological experiments during the last two decades have answered many important questions related to the causal relationship between chronic inflammation and carcinogenesis. Today, oncoimmunology is a field of rapid evolution and maturation. The development of new preclinical models and high-resolution technologies has provided otherwise inaccessible data, opening up exciting new avenues, exemplified by how targeting the immune system to fight cancer is becoming a reality, as evidenced by the relative success of current immunotherapies.

There clearly remains much work to be done, and there are many challenges to face. First, it will be necessary to develop high-fidelity immuno-sufficient preclinical models that incorporate the cellular and antigenic heterogeneity of cancer cells observed in patients. Second, a better understanding of the immune cross-talk that results in tumoricidal immunity will lead to the rational design of targeted therapies that will improve the efficiency of the current immunotherapies. Third, since >90% of cancer-associated deaths result from development of metastasis ( Jemal et al. 2008; Siegel et al. 2016), we need to verify whether the data generated in primary tumors can be used to infer molecular insights into metastatic tumors. Last, a significant effort into learning more about immune surveillance associated with less-studied metastasic sites, including liver, bone, and brain metastasis, is needed.

Acknowledgments

We apologize to those scientists whose studies were not cited here owing to space constraints. We thank Isabella Robles for her assistance in writing the manuscript. This study was supported by funds from the National Cancer Institute (R01 CA057621 and U01 CA190851) and the Parker Institute for Cancer Immunotherapy (to Z.W.), a Becas Chile post-doctoral fellowship (to H.G.), and funds from the Tegger Foundation, the Wenner-Gren Foundations, the Sweden-America Foundation, the Swedish Society of Medicine, and the Swedish Society for Medical Research (to C.H.).

References

Allaoui R, Hagerling C, Desmond E, Warfvinge CF, Jirstrom K, Leandersson K. 2017. Infiltration of γδ T cells, IL-17[[⁺ T cells and FoxP3⁺ T cells in human breast cancer. Cancer Biomark 20: 395-409.PMC free article] [PubMed] Google Scholar]

Chen Q, Ross AC. 2012. All-[[trans-retinoic acid and the glycolipid α-galactosylceramide combined reduce breast tumor growth and lung metastasis in a 4T1 murine breast tumor model. Nutr Cancer 64: 1219-1227.PubMed] PMC free article] [Google Scholar]

Roberts EW, Broz ML, Binnewies M, Headley MB, Nelson AE, Wolf DM, Kaisho T, Bogunovic D, Bhardwaj N, Krummel MF. 2016. Critical role for CD103[[⁺/CD141⁺ dendritic cells bearing CCR7 for tumor antigen trafficking and priming of T cell immunity in melanoma. Cancer Cell 30: 324-336.PMC free article] [PubMed] Google Scholar]

Articles from Genes & Development are provided here courtesy of Cold Spring Harbor Laboratory Press

# data[].attributes.attachment_link.title
Roles of the immune system in cancer: from tumor initiation to metastatic progression

# data[].attributes.attachment_link.description
The presence of inflammatory immune cells in human tumors raises a fundamental question in oncology: How do cancer cells avoid the destruction by immune attack? In principle, tumor development can be controlled by cytotoxic innate and adaptive immune cells; however, as the tumor develops from neoplastic tissue to clinically detectable tumors, cancer cells evolve different mechanisms that mimic peripheral immune tolerance in order to avoid tumoricidal attack.

# data[].attributes.attachment_link.readable_text
Immune checkpoint inhibitors can cause side effects that affect people in different ways. The side effects you may have and how they make you feel will depend on how healthy you are before treatment, your type of cancer, how advanced it is, the type of immune checkpoint inhibitor you are receiving, and the dose.

Doctors and nurses cannot know for sure when or if side effects will occur or how serious they will be. So, it is important to know which signs to look for and what to do if they occur.

Common side effects of immune checkpoint inhibitors include:

Rarer side effects of immune checkpoint inhibitors can include widespread inflammation. Depending on the organ of your body that is affected, inflammation can lead to:

changes in skin color, rash, and feeling itchy, caused by skin inflammation

cough and chest pains, caused by inflammation in the lungs

belly pain and diarrhea, caused by inflammation in the colon

diabetes, caused by inflammation in the pancreas

hepatitis (inflammation of the liver)

hypophysitis (inflammation of the pituitary gland)

myocarditis (inflammation of the heart muscle)

nephritis (inflammation of the kidney) and impaired kidney function

overactive or underactive thyroid

nervous system problems such as muscle weakness, numbness, and trouble breathing

Learn more about organ inflammation and immunotherapy.

# data[].attributes.attachment_link.description
Immune checkpoint inhibitors can cause side effects that affect people in different ways. The side effects you may have and how they make you feel will depend on how healthy you are before treatment, your type of cancer, how advanced it is, the type of immune checkpoint inhibitor you are receiving, and the dose.

# data[].attributes.body
Bioinformatic analysis of RNA sequencing data has been used to uncover global tumour and microenvironment features associated with ICI response or resistance. This allows the discover and cataloguing of pre-therapy RNA signatures that can be used often with statistics as a mean of comparison between patients, to discover useful information about the cancer type.

Below, two cases that led to recent discoveries are presented:

Case 1 – Unique features in cohorts

- It was identified a pre-therapy signature comprising 26 transcriptomic sub-signatures related with anti-PD-L1 resistance from 28 patients with melanoma.

- However, two other cohorts did not associate anti-CTLA4 nor anti-PD1 response, suggesting that it may reflect features unique to the original cohort analysed.

Case 2 – Predictive signatures of therapeutic outcomes

- Another study of patients with melanoma, treated with anti-PD1 identified a pre-therapy RNA expression signature associated with on-therapy changes of tumour clonal architecture. This led to the hypothesis of relation between changes of tumour clonal structure (associated with response and overall survival), and that pre-therapy RNA signature may also be predictive of therapeutic outcomes.

# data[].attributes.attachment_link.readable_text
Highlights

*

Biomarkers are key to maximize clinical benefit of treatment with anti-CTLA4, anti-PD1 and anti-PDL1 drugs.

*

Genomic correlates of neoantigen load, such as defective mismatch repair and tumour mutation burden, are clinically applicable, but fail to completely explain differences in response patterns to immune checkpoint inhibitors.

*

HLA genotype, interferon expression and copy number variation are other promising biomarkers related to immune response pathways.

*

Somatic mutations in genes such as TP53, PTEN, PBRM1 and ARID1A may also help identify responders to immunotherapy.

Immune checkpoint inhibitors (ICI) aim to restore the immune system anti-tumor function by blocking two inhibitory axes: CTLA-4/CD28 and PD1/PDL1. ICI is established as a treatment option for multiple cancers, but their remarkable clinical impact is observed only in a fraction of patients. Together with their adverse effects and high cost, it's imperative to identify patients who are likely to benefit from this type of treatment. Genomic features represent promising candidates as predictive biomarkers of response to ICI, with agnostic FDA-approvals of an anti-PD1 drug for tumors with microsatellite instability and tumors with a high mutational burden. Other genomic markers are also emerging to help refine patient selection. In this review, we discuss recent progress in genomic biomarkers development and its challenges, with a focus on alterations in the neoantigen burden, immune, and oncogenic pathways.

View full text

# data[].attributes.attachment_link.description
Biomarkers are key to maximize clinical benefit of treatment with anti-CTLA4, anti-PD1 and anti-PDL1 drugs. * Genomic correlates of neoantigen load, such as defective mismatch repair and tumour mutation burden, are clinically applicable, but fail to completely explain differences in response patterns to immune checkpoint inhibitors.

# data[].attributes.attachment_link.readable_text
Abstract

Cancer growth and progression are associated with immune suppression. Cancer cells have the ability to activate different immune checkpoint pathways that harbor immunosuppressive functions. Monoclonal antibodies that target immune checkpoints provided an immense breakthrough in cancer therapeutics. Among the immune checkpoint inhibitors, PD-1/PD-L1 and CTLA-4 inhibitors showed promising therapeutic outcomes, and some have been approved for certain cancer treatments, while others are under clinical trials. Recent reports have shown that patients with various malignancies benefit from immune checkpoint inhibitor treatment. However, mainstream initiation of immune checkpoint therapy to treat cancers is obstructed by the low response rate and immune-related adverse events in some cancer patients. This has given rise to the need for developing sets of biomarkers that predict the response to immune checkpoint blockade and immune-related adverse events. In this review, we discuss different predictive biomarkers for anti-PD-1/PD-L1 and anti-CTLA-4 inhibitors, including immune cells, PD-L1 overexpression, neoantigens, and genetic and epigenetic signatures. Potential approaches for further developing highly reliable predictive biomarkers should facilitate patient selection for and decision-making related to immune checkpoint inhibitor-based therapies.

Introduction

The development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immuno-oncology. Tumor cells evade immunosurveillance and progress through different mechanisms, including activation of immune checkpoint pathways that suppress antitumor immune responses. ICIs reinvigorate antitumor immune responses by interrupting co-inhibitory signaling pathways and promote immune-mediated elimination of tumor cells.

Ipilimumab, which targets cytotoxic T-lymphocyte antigen-4 (CTLA-4), was the first approved immune checkpoint inhibitor for treating patients with advanced melanoma. This antibody prevents T-cell inhibition and promotes the activation and proliferation of effector T cells. Following the approval of ipilimumab, other antibodies that target immune checkpoints were examined. Currently, hundreds of phase I and II clinical trials and phase III/IV clinical trials are being carried out across the globe to evaluate the efficacy of multiple ICIs as monotherapy or in combination (details of phase III/IV trials are given in Table 1).

Table 1 Immune checkpoint inhibitors in phase III and IV clinical trials

Pembrolizumab and nivolumab, ICIs that target programmed death-1 (PD-1), showed promising results in melanoma and non-small cell lung carcinoma (NSCLC) patients, with an objective response rate (ORR) of 40-45%. Additionally, urothelial bladder cancer patients treated with PD-1/PD-L1 inhibitors showed an increase in overall response rate, between 13 and 24%7. In triple-negative breast cancer (TNBC) patients, the response to PD-1 inhibitors was relatively moderate (19%)8. In contrast, in relapsed or refractory Hodgkin's lymphoma, nivolumab showed an objective response rate of 87% with 17% complete response9. Pembrolizumab and nivolumab are currently under phase IV clinical trials for treating various malignancies (Table 1).

Despite the success of anti-CTLA-4 and anti-PD-1/PD-L1 therapies, only a fraction of patients benefit from ICIs. Antitumor immunity, regulated through complex factors in the tumor microenvironment (TME), could create variable immune responses. The TME is segregated into three major types based on the infiltration of immune cells: immune desert, immune excluded and immune inflamed10. These phenotypes have their own mechanisms for preventing immune responses from eradicating tumor cells10. Immune deserts are characterized by the absence of T cells in the TME and the lack of suitable T cell priming or activation. The immune excluded phenotype exhibits the presence of multiple chemokines, vascular factors or mediators and stromal-based inhibition; however, accumulated T cells are unable to infiltrate the TME. Immune inflamed tumors demonstrate infiltration of multiple immune cell subtypes10.

Accumulating evidence suggests that only a fraction of cancer patients benefit from checkpoint inhibitors, and severe immune-related adverse events (irAEs) are seen in some patients undergoing ICI therapy11. irAEs are due to the inhibition of immune checkpoints that reinforce the normal physiological barriers against autoimmunity, leading to various local and systemic autoimmune responses. Therefore, the development of predictive biomarkers is critical for differentiating responders and nonresponders to avoid any adverse effects. Ongoing clinical studies are aiming to develop predictive biomarkers for better treatment outcomes and less irAEs.

Predictive biomarkers could determine the outcome of therapy in a patient before the initiation of a proposed therapy. These biomarkers should indicate whether a patient would benefit from a particular checkpoint monotherapy or if there is a need for combination therapy. In this review, we discuss biomarkers that predict the response to various ICI therapies in cancer.

Immune cells

Immune inflamed tumors have a high degree of response to immunotherapy. Reports suggest that immune inflamed tissues are more sensitive because ICIs can activate immune reactions and inhibit immune evasion/suppression. Studies confirmed that the response to ICI therapy is related to tumor-infiltrating lymphocytes (TILs) and other immune cells in the TME12.

Analyses of peripheral blood is a noninvasive method with good potential to predict treatment outcomes after immune therapies. Reports have shown that in various malignancies, increased tumor-infiltrating immune cells and peripheral blood absolute lymphocyte count (ALC) can be utilized as predictive biomarkers. The role of ALC as a predictive biomarker has been validated in metastatic melanoma patients treated with ipilimumab. Patients with 1.35-fold higher ALC values from baseline in the first 2 weeks of treatment had significantly higher overall survival16. In ipilimumab-treated patients, overall progression-free survival was associated with a low serum lactate dehydrogenase value (LDH ≤ 1.2-fold), a low absolute monocyte count (AMC < 650 cells/µL), a low myeloid-derived suppressor cell count (MDSCs < 5.1%), a high absolute eosinophil count (eosinophils ≥ 50 cells/µL), a relative lymphocyte count < 10.5% and baseline CD4⁺CD25⁺FOXP3⁺ Tregs ≥ 1.5% in the peripheral blood15,17. Multiple studies validating the applicability of LDH as a predictive biomarker showed that patients with elevated levels of LDH also responded to ICIs18. Studies have reported that LDH can be used as a potential predictive biomarker for overall survival but not as a prognostic biomarker. CyTOF-based immune profiling of peripheral blood samples collected from anti-CTLA-4 and anti-PD-1-treated melanoma patients showed a distinct set of biomarkers in response to therapy20. This study suggested that the abundance of CD4⁺ and CD8⁺ memory T cells was a predictive biomarker for anti-CTLA-4 therapy and the abundance of CD69⁺ and MIP1β⁺ NK cells was a predictive biomarker for anti-PD-1 therapy20. CyTOF analyses of anti-PD-1-treated melanoma patients showed an involvement of CD14⁺CD16 −HLA-DR hi cells in therapy response and progression-free survival (PFS)21. An increase in circulating CD4⁺, CD8⁺ T cells and ALC, 2 to 8 weeks after treatment initiation with ipilimumab, was reported in melanoma patients with better clinical outcomes22. Apart from the circulating CD8⁺ T cells, CD8⁺ effector memory type-1 T cells were also reported as predictive biomarkers for ipilimumab-treated stage IV melanoma patients23,24.

The presence of TILs in various malignancies can be used as potent predictive biomarkers for response to ICIs13,14. Tumors with increased TILs are a major hallmark of the immune inflamed phenotype, and they exhibit improved immune-mediated elimination of tumor cells. In ipilimumab-treated melanoma patients, TILs were significantly increased from baseline in a therapy-responsive group, confirming their significance in response to ICIs25. To explain the role of immune cells in the treatment response, a study was carried out using 52 lymph nodes and 34 cutaneous/subcutaneous metastatic surgical samples collected from 30 metastatic melanoma patients receiving ipilimumab26. In this study, Balatoni et al.26 examined 11 immune cell subsets in the TME and their post-therapy responses. Interestingly, 7 out of 11 immune subsets positively correlated with an increase in the overall survival rate. These subsets included CD4⁺ T cells, CD8⁺ T cells, CD20⁺ B cells, cells expressing CD134⁺ and CD137⁺ activation markers, FOXP3⁺ T cells and NKp46⁺ cells. Notably, subcutaneous and cutaneous metastatic tissues, compared to lymph nodes, showed distinct immune cell infiltration. In subcutaneous and cutaneous samples, the presence of CD16⁺ and CD68⁺ cells positively correlated with therapy response as well as prolonged survival. In contrast, in the lymph nodes, CD45RO⁺, PD-1⁺, CD16⁺, and CD68⁺ cells correlated only with increased survival26. In addition to the abundance of FOXP3⁺ Tregs, the ratio of effector T cells (Teffs) to Tregs is reported to be a more specific predictive biomarker for anti-CTLA-4 immune therapies27,28. Immune profiling of TILs using multiparametric flow cytometry in metastatic melanoma patients showed that PD-1 hiCTLA-4 hi in CD8⁺ T cells predict the response to anti-PD-1 therapy29. This study is supported through the identification of both transcriptionally and functionally distinct CD8⁺PD-1⁺ T-cell subpopulations in NSCLC patients, showing predictive potential for anti-PD-1 therapy30. Additionally, intratumoral and peripheral CD4⁺FOXP3 −PD-1 hi nonconventional Tregs in NSCLC as well as melanoma patients were reported as prognostic biomarkers for anti-PD-1 and anti-CTLA-4 therapies31. Anti-CTLA-4 therapy induced an immune inflamed phenotype via expansion of intratumoral and systemic CD4⁺FOXP3 −PD-1 hi Tregs that were reduced with anti-PD-1 therapy and improved the overall antitumor response31. Moreover, PD-L1⁺CD4⁺CD25⁺ Tregs predict responses to PD-1/PD-L1 blockade in NSCLC patients32. Figure 1 shows an overview of how the presence of various immune cell subsets in the TME may contribute to the differential responses to ICIs in responders and nonresponders.

The response to immune checkpoint inhibitors varies depending on the TME. In the responders, tumors have a high neoantigen load, high levels of TILs, especially effector cells, a high Teff to Treg ratio, low MDSC levels and increased secretion of IFN-γ and other cytokines ( a). In nonresponders, the TME contains high levels of immunosuppressive cells, such as Tregs and MDSCs, and very low levels of NK cells and activated lymphocytes ( b)

Pembrolizumab in advanced melanoma patients showed that pre-existing CD8⁺ T cells in the TME are required for better tumor regression33. The presence of an immune excluded phenotype with an abundance of immune cells at invasive margins or stroma is also associated with clinical benefits. The spatiotemporal dynamics of CD8⁺ T cells are also an important factor for better treatment outcomes. Analysis of pretreatment samples collected from patients undergoing PD-1/PD-L1 therapy showed a relatively higher abundance of CD8⁺ T cells at the invasive margins in therapy responders. These pretreatment samples show an immune excluded phenotype through increased accumulation of T cells on the invasive margin without effective infiltration. Moreover, serially sampled tumors during therapy showed an increase in CD8⁺ T cells at the invasive margin and then in parenchyma in the response group33. This increase in CD8⁺ T cells may be due to the negative regulation of PD-1/PD-L1 by ICIs, which resulted in either the infiltration of immune cells or the enhanced proliferation of CD8⁺ T cells33. Additionally, it has been reported that in lung cancer patients, CD3⁺, CD4⁺ and CD8⁺ T-cell infiltration to deep tissues significantly correlated with longer overall survival34. Metastatic breast cancer patients treated with atezolizumab showed an increased ORR related to stromal TILs35. The predictive potentials of stromal TILs were confirmed in the KEYNOTE-086 study; significantly higher levels of stromal TILs were associated with the anti-PD-1 therapy response in metastatic triple-negative breast cancer patients36.

CTLA-4 blockade activates T cells to target malignant cells. CTLA-4 is constitutively expressed in T cells and attenuates immune responses when bound to CD80 or CD86 on the surface of antigen-presenting cells (APCs) (Fig. 2). Analyses of pre- and post-treated surgical tissues and peripheral blood showed that the inducible costimulator (ICOS) pathway is activated upon anti-CTLA-4 therapy37. This overexpression of ICOS (CD28/CTLA-4 Ig superfamily) resulted in an increase in ICOS⁺ T cells in both tumor and blood samples37. In tremelimumab-treated breast cancer patients, increased CD4⁺ICOS⁺ and CD8⁺ICOS⁺ T cells were observed in peripheral blood37. The ratio of FOXP3⁺ Treg cells to ICOS⁺ T cells was also increased in therapy-responsive patients37. Moreover, in patients exhibiting clinical benefits, there was an increase in the frequency of CD4⁺ICOS⁺ Teff cells. These cells express T-bet and produce IFN-γ, strengthening immune responses in anti-CTLA-4 therapy.

Immune checkpoints, including PD-1 and CTLA-4, expressed on activated T cells lead to inhibition of T-cell activation upon binding to their ligands on tumor cells/antigen-presenting cells. These interactions can be blocked using monoclonal antibodies, leading to the activation of T cells targeting tumor cells through the release of effector cytokines and cytotoxic granules.

PD-L1 overexpression

Interactions between PD-1 and its ligands, B7-H1/PD-L1 and B7-DC/PD-L2, lead to T-cell inactivation to maintain immune homeostasis and prevent autoimmunity. PD-1/PD-L1 pathway activation is related to the immune inflamed phenotype41. IC ligands are commonly found on tumor cells, and these interactions work in tandem with elevated tumor infiltration of immunosuppressive cells to support tumor escape from active T-cell responses42. Therefore, blocking the PD-1/PD-L1 inhibitory pathway can activate T cells in the TME, releasing inflammatory cytokines and cytotoxic granules to eliminate tumor cells (Fig. 2).

The direct approach to check responsiveness to PD-1/PD-L1 therapy in patients is to detect the expression levels of PD-L1 in tumor tissues. Teng et al.43 proposed four different classifications of TME based on the presence of TILs and PD-L1 expression. They classified PD-L1-positive tumors with TILs as a type I tumor microenvironment and proposed it to be most likely to respond to immune checkpoint blockade.

IHC analyses performed on patients with metastatic melanoma, NSCLC, colon cancer, renal cell carcinoma and prostate cancer who underwent PD-1/PD-L1 targeting therapy suggested PD-L1 overexpression as a potential biomarker. An open-label Phase II clinical trial of pembrolizumab in NSCLC reported that progression-free survival and overall survival were higher in patients with PD-L1 expression in at least 50% of tumor cells44. Notably, elevated levels of PD-L1 expression in the TME do not correlate with worse differentiation and poor prognosis. High PD-L1 expression is often accompanied by IFN-γ-secreting TILs in some cancers45. However, Aguiar et al. suggested that PD-L1 overexpression may not be a robust biomarker for the response to ICIs in all cancers, as PD-L1-negative tumors can also respond to mAbs targeting PD-1/PD-L1 interactions. Therefore, to date, PD-L1 overexpression as a prerequisite for initiation of PD-1/PD-L1 checkpoint blockade is not established as a potent biomarker for determining responsiveness to anti-PD-1/PD-L1 based immunotherapy.

Investigating PD-L1 expression has some limitations that need to be considered. PD-L1 expression is known to be both spatial and temporal, and it is also expressed on other immune cells, including antigen-presenting cells. One plausible approach to counter these limitations is to perform PD-L1 expression analyses on circulating tumor cells (CTCs) in peripheral blood samples from cancer patients. Interestingly, PD-L1⁺ CTCs were found to be higher than PD-L1⁺ cells in the TME of NSCLC patients (83% vs. 41%), and no correlation was observed between tissue and CTC PD-L1 expression46. Therefore, further investigations are warranted to establish PD-L1 expression on CTCs as a predictive biomarker.

Neoantigens

Acquired mutations during cancer progression have promise in detecting efficiency of and resistance to therapy. Mutations in the protein-coding regions of DNA generate truncated proteins termed ''neoantigens.'' Neoantigens result in a higher degree of foreignness to cells, which helps immune cells readily target and eliminate tumor cells. Various neoantigens that confer therapy efficacy could be potential biomarkers for predicting the clinical activity of ICIs. A retrospective study on stage I/II and stage III/IV lung cancer samples showed that high neoantigen burden is associated with the longest overall survival ( P = 0.025)47. Moreover, intratumoral heterogeneity analyses showed that high neoantigen-expressing clones were homogenous with the highest differential expression of PD-L1 and IL-647. Additionally, CD8α and β, STAT1, TAP-1 and 2, CXCL-10, CXCL-9, granzyme-B, -H, and -A, and IFN-γ were upregulated in the high neoantigen-expressing clones47. Overexpression of IFN-γ, IDO, and Th1-associated markers was reported in ipilimumab-treated patients with favorable clinical outcomes. Resistance to CTLA-4 therapy was observed with a loss in IFN-γ signaling in CD8⁺ T cells. These findings confirm that the immune-mediated elimination of tumor cells could be proportional to the neoantigen load. Neoantigens exhibiting high-affinity binding with MHC and TCR are highly eliminated neoantigens48. Moreover, acquired resistance to ICI can also be predicted through neoantigen landscapes48. Screening of these neoantigens has the potential to predict clinical activity as well as therapeutic resistance.

Tumor tissues from melanoma patients treated with ipilimumab or tremelimumab were used to study the role of somatic mutations as predictive biomarkers for clinical response. Whole genome somatic neoepitope analyses and patient-specific HLA typing were performed in tumors and whole blood samples from 64 patients. It was reported that the neoantigen landscape, as defined through IHC analyses, has a strong association with the treatment response to CTLA-4 blockade49. This study strengthens high-throughput IHC analyses using biopsy specimens to clinically validate therapeutic outcomes.

Recent studies revealed that the evolution of the neoantigen profile in NSCLC patients is associated with the response to ICIs. Acquired resistance to immunotherapy was observed in a cohort of 42 patients with NSCLC who were treated with a PD-1 inhibitor alone or in combination with a CTLA-4 inhibitor48. The whole genome of paired tissues collected before and after therapy was analyzed for the neoantigen landscape related to therapy resistance. This study reported that loss-of-function mutations coding for neoantigens either by the elimination of tumor clones or by chromosomal truncated gene alteration can result in therapy resistance48. Additionally, tumor cells alter the expression of immune suppressive proteins and multiple transcription factors involved in immune functions to acquire resistance against ICIs50. Whole-genome analyses performed on tissues obtained from baseline and relapsed tumors of metastatic melanoma patients undergoing pembrolizumab treatment revealed that acquired resistance to ICIs are associated with loss-of-function mutations51. Truncated mutations in IFN-receptor-associated JAK1 or JAK2 that cause the loss of IFN-γ function and mutations in the B2M gene, resulting in the loss of MHC-I expression and antigen presentation, are also reported in acquired ICI therapy-resistant samples51.

Genetic signatures

In a retrospective study conducted with a cohort of breast cancer patients with 1- to 5-year tumor relapse versus those with up to 7-year relapse-free survival, Ascierto et al.52 screened more than 299 immune-related genes and found that five genes (IGK [IGKC], GBP1, STAT1, IGLL5, and OCLN) were highly overexpressed in patients with relapse-free survival, highlighting their potential as predictive biomarkers. Similarly, RNA expression studies in ipilimumab-treated patients revealed that the number of immune-related genes involved in both innate and adaptive responses were overexpressed in patients with better clinical activity compared with nonresponsive patients. This suggests the importance of a pre-existing immune-active TME for better clinical response to ipilimumab. PD-L1 and PD-L2 copy number alterations (CNA) are also considered potential biomarkers53. Budczies et al.54 reported PD-L1 CNA in 22 major cancers and found a strong correlation between PD-L1 CNA and mRNA expression levels. The mutation load also correlated with PD-L1 copy number gains.

The mutational loads in exomes also have potential roles as predictive biomarkers for ICIs. Studies have shown that patients with higher mutational loads have greater responsiveness to ICIs49,55. Genetic mutations that lead to the expression of immune-related peptides that expand pre-existing T cells or that can be generated in response to immune or other stimuli can increase the efficacy of ICIs49,56. JAK3, a member of the Janus kinase signaling pathway, generally found in leukocytes, was reported to have a regulatory role in PD-L1 expression in lymphomas57. Mutations that activate JAK3 can cause overexpression of PD-L1 in lymphomas and make them responsive to PD-L1 inhibitors58,59.

Mismatch-repair mechanisms are the machinery that protects cells by repairing mutations during DNA replications. A high neoantigen load and high mutational load are associated with an improper mismatch-repair system. The identification of defective mismatch-repair mechanisms may therefore be exploited as potential predictive biomarkers. Mismatch-repair deficiency in pembrolizumab-treated patients with hereditary nonpolyposis colorectal cancer resulted in a high positive response, highlighting the potential of mismatch-repair deficiency as a predictive biomarker60,61. Additionally, in a recent study with 53 cancer patients, the objective response rate was 50% in patients with mismatch-repair deficiency, compared to 0% in patients with mismatch-proficient tumors60. The mismatch-deficient group, compared with the other group, also showed a longer progression-free survival61. Advances in NGS and microarray technologies have made genome-wide screening of potential markers comparatively easier. The accurate prediction of these biomarkers and their use in clinical conditions are suboptimal. However, the development of simple algorithms to read these potential gene signatures from patient DNA is necessary to make these findings clinically applicable. A PanCancer IO 360™ assay was developed by nanoString; the assay profiles TME interactions using a 770 gene panel. This panel evaluates multiple immune processes, including simultaneous assessment of immune evasion in the context of all three immune phenotypes (immune desert, immune excluded and immune inflamed) and supports the prediction of patient responses to a variety of immunotherapies, including ICIs41.

Epigenetic signatures

Epigenetic modifications are complex cellular processes that can modify cellular functions in response to the prevailing environment without altering genetic codes. Multiple epigenetic marks are involved in these complex mechanisms, including DNA methylation, post-transcriptional histone tail modifications, and short noncoding RNAs62. Although the association of multiple epigenetic regulatory mechanisms was evaluated in response to immune checkpoint expression and their applicability in combination therapy for synergistic combination, studies on the evaluation of epigenetic modifications as predictive biomarkers are warranted.

The transcriptomic and epigenetic studies on NSCLC show that the hypomethylation of the CTLA-4, PD-1, and PD-L1 promoter regions may be associated with the upregulation of these genes in the TME63. It has been shown that in chronic lymphocytic leukemia (CLL), the mRNA and protein levels of PD-1 were elevated and significantly hypomethylated in both promoter and enhancer regions compared to healthy B-cell controls64.

miRNAs are small single-stranded RNA sequences that have a critical role in various diseases, including cancer65. Reports have shown that five members of the miR-200 family, miR-200a, 200b, 200c, 141, and 429, play pivotal roles in tumor suppression by restricting the epithelial-to-mesenchymal transition (EMT). In human breast cancer cells, it has been reported that expression of PD-L1 decreases with overexpression of miR-20069. These reports rationalize the hypothesis that miR-200 might be a promising biomarker for responders treated with anti-PD-L1 antibodies (atezolizumab or durvalumab). A recent study showed that serum miRNA levels correlated with progression-free survival and overall survival in a phase II clinical study on patients with esophageal squamous cell carcinoma (ESCC) treated with nivolumab70. Eight miRNAs were found to be associated with a better clinical response, out of which four miRNAs were positively associated with progression-free survival70. In contrast, overexpression of miR-34a has been reported as an inducer of CD8⁺ TILs by repressing PD-L1 expression in colorectal carcinoma and NSCLC patients71,72. These data suggest that the miRNA-PD-L1 axis might be a promising therapeutic/diagnostic biomarker target in ICI therapy.

Concluding remarks

Immunological response to ICIs is a complex process. Biomarkers that predict the efficacy of ICI therapy and irAEs should help in patient selection and decision-making by distinguishing between responders and nonresponders. Numerous studies on predictive biomarkers focusing on immune cell infiltration, peripheral blood analyses, PD-L1 overexpression, copy number alterations, neoantigen clonality, mutational landscape, mismatch-repair deficiency, SNPs, transcription factors, and miRNA are currently available (Table 2).

Table 2 Predictive biomarkers for progression-free survival and overall survival in patients treated with immune checkpoint inhibitors

Major issues in the development of predictive biomarkers are the dynamic variations in cancer biomarker types and a patient's genetic makeup. Biopsies obtained from multiple sites of the same patient showed variation in biomarker levels owing to intratumoral heterogeneity. Intense research will develop combination biomarker sets to predict ICI therapy outcomes and avoid irAEs73.

Although several predictive biomarker studies are completed and many are underway, the clinical validation of the identified biomarkers is necessary. More integrated approaches should be developed to identify patient-specific choices for checkpoint monotherapies or combination therapies. Moreover, next-generation sequencing techniques should become clinically applicable through the development of simple algorithms to process large quantities of clinical data. In conclusion, biomarker-driven prediction of immune therapy outcomes has the potential to make dramatic changes in cancer immunotherapy.

Acknowledgements

This work was supported by a start-up grant [VR04] for Dr. Eyad Elkord from Qatar Biomedical Research Institute, Qatar Foundation.

Author information

Affiliations

1. Cancer Research Center, Qatar Biomedical Research Institute, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Pramod Darvin, Salman M. Toor, Varun Sasidharan Nair & Eyad Elkord

2. Institute of Cancer Sciences, University of Manchester, Manchester, UK

Eyad Elkord

Corresponding author

Correspondence to Eyad Elkord.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and Permissions

About this article

Cite this article

Darvin, P., Toor, S.M., Sasidharan Nair, V. et al. Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp Mol Med 50, 1-11 (2018). https://doi.org/10.1038/s12276-018-0191-1
Download citation

Received:

Revised:

Accepted:

Published:

Issue Date:

DOI: https://doi.org/10.1038/s12276-018-0191-1

# data[].attributes.attachment_link.title
Immune checkpoint inhibitors: recent progress and potential biomarkers - Experimental & Molecular Medicine

# data[].attributes.attachment_link.description
Biomarkers that predict a patient's response to cancer immunotherapy are being developed, and may allow individual tailoring of therapies. Cancer cells can suppress the immune system by activating immune checkpoints, signals that stop the immune system from attacking the host. Immunotherapy, a recently developed treatment, inactivates immune checkpoints, restoring the patient's immune response against the tumor.

# data[].attributes.attachment_link.readable_text
Affiliations

¹ Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

² Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

³ Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

⁴ NewYork-Presbyterian/Columbia University Medical Center, 177 Fort Washington Avenue, New York, NY 10032, USA.

⁵ IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA.

⁶ Laboratory of Cellular Biophysics, The Rockefeller University, New York, NY 10065, USA.

⁷ Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

⁸ Tisch Cancer Institute, Departments of Medicine, Oncological Sciences, and Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

⁹ David Geffen School of Medicine, University of California, Los Angeles, 2825 Santa Monica Boulevard, Suite 200, Santa Monica, CA 90404, USA.

¹⁰ Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

¹¹ Weill Cornell School of Medicine, New York, NY 10065, USA.

¹² Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

¹³ Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

¹⁴ Department of Chemistry, Columbia University, New York, NY 10027, USA.

¹⁵ NewYork-Presbyterian/Columbia University Medical Center, 177 Fort Washington Avenue, New York, NY 10032, USA. chant@mskcc.org nar2144@cumc.columbia.edu.

¹⁶ Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. chant@mskcc.org nar2144@cumc.columbia.edu.

Free PMC article

Free PMC article

Affiliations

¹ Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

² Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

³ Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

⁴ NewYork-Presbyterian/Columbia University Medical Center, 177 Fort Washington Avenue, New York, NY 10032, USA.

⁵ IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA.

⁶ Laboratory of Cellular Biophysics, The Rockefeller University, New York, NY 10065, USA.

⁷ Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

⁸ Tisch Cancer Institute, Departments of Medicine, Oncological Sciences, and Pathology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

⁹ David Geffen School of Medicine, University of California, Los Angeles, 2825 Santa Monica Boulevard, Suite 200, Santa Monica, CA 90404, USA.

¹⁰ Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

¹¹ Weill Cornell School of Medicine, New York, NY 10065, USA.

¹² Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

¹³ Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

¹⁴ Department of Chemistry, Columbia University, New York, NY 10027, USA.

¹⁵ NewYork-Presbyterian/Columbia University Medical Center, 177 Fort Washington Avenue, New York, NY 10032, USA. chant@mskcc.org nar2144@cumc.columbia.edu.

¹⁶ Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. chant@mskcc.org nar2144@cumc.columbia.edu.

Abstract

CD8⁺ T cell-dependent killing of cancer cells requires efficient presentation of tumor antigens by human leukocyte antigen class I (HLA-I) molecules. However, the extent to which patient-specific HLA-I genotype influences response to anti-programmed cell death protein 1 or anti-cytotoxic T lymphocyte-associated protein 4 is currently unknown. We determined the HLA-I genotype of 1535 advanced cancer patients treated with immune checkpoint blockade (ICB). Maximal heterozygosity at HLA-I loci ("A," "B," and "C") improved overall survival after ICB compared with patients who were homozygous for at least one HLA locus. In two independent melanoma cohorts, patients with the HLA-B44 supertype had extended survival, whereas the HLA-B62 supertype (including HLA-B*15:01) or somatic loss of heterozygosity at HLA-I was associated with poor outcome. Molecular dynamics simulations of HLA-B*15:01 revealed different elements that may impair CD8⁺ T cell recognition of neoantigens. Our results have important implications for predicting response to ICB and for the design of neoantigen-based therapeutic vaccines.

USP8 inhibition reshapes an inflamed tumor microenvironment that potentiates the immunotherapy.

Xiong W, Gao X, Zhang T, Jiang B, Hu MM, Bu X, Gao Y, Zhang LZ, Xiao BL, He C, Sun Y, Li H, Shi J, Xiao X, Xiang B, Xie C, Chen G, Zhang H, Wei W, Freeman GJ, Shu HB, Wang H, Zhang J. Xiong W, et al. Nat Commun. 2022 Mar 31;13(1):1700. doi: 10.1038/s41467-022-29401-6. Nat Commun. 2022. PMID: 35361799

# data[].attributes.attachment_link.keywords[]
ctla-4 antigen / antagonists & inhibitors*

# data[].attributes.attachment_link.keywords[]
histocompatibility antigens class i / chemistry

# data[].attributes.attachment_link.keywords[]
histocompatibility antigens class i / genetics*

# data[].attributes.attachment_link.keywords[]
programmed cell death 1 receptor / antagonists & inhibitors*

# data[].attributes.attachment_link.keywords[]
national center for biotechnology information

# data[].attributes.attachment_link.title
Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy - PubMed

# data[].attributes.attachment_link.description
CD8 + T cell-dependent killing of cancer cells requires efficient presentation of tumor antigens by human leukocyte antigen class I (HLA-I) molecules. However, the extent to which patient-specific HLA-I genotype influences response to anti-programmed cell death protein 1 or anti-cytotoxic ...

# data[].attributes.attachment_link.readable_text
Highlights

*

LOHHLA enables estimation of allele-specific HLA loss from sequencing data

*

LOH of the HLA locus occurs in 40% of early stage non-small-cell lung cancers

*

HLA LOH is associated with a high subclonal neoantigen burden and immune activity

*

HLA LOH is an immune escape mechanism subject to strong selection pressures

Introduction

Immune evasion represents a hallmark of cancer (). The majority of cancer immunotherapies, including immune checkpoint blockade therapy, aim to counteract immune evasion by shifting the balance in favor of immune activation, enabling T cell-mediated cancer cell elimination (). However, only a subset of patients benefit from immunotherapies, emphasizing the need to identify the genomic and molecular determinants underpinning immune evasion.

Recent work has highlighted the importance of cancer-specific neoantigens in determining cytolytic and T cell activity as well as predicting efficacy of immune checkpoint inhibition (

, ,

, , ). A critical step in neoantigen presentation and cytolytic T cell response is governed by class I human leukocyte antigen (HLA), which presents intra-cellular peptides on the cell surface for recognition by T cell receptors. Each individual's genome contains up to six distinct HLA class I alleles, encoded by three genes ( HLA-A, HLA-B, and HLA-C), located on the homologous paternal and maternal chromosome 6.

Downregulation of HLA genes may result in reduced antigen presentation and thus facilitate immune evasion. HLA downregulation, characterized by immunohistochemistry or monoclonal antibodies, has been found to be prevalent across a range of cancer types and has also been linked to poor outcome (,

, ,

). Loss of either the maternal or paternal HLA haplotype may also impact upon the efficacy of immunotherapy. An intriguing report documented loss of heterozygosity (LOH) at the HLA locus, with loss of HLA-C ∗08:02 in the resistant lesion from a tumor treated with tumor-infiltrating lymphocytes composed of T cell clones targeting KRAS G12D (). Because the presence of the HLA-C ∗ 08:02 allele was required for presentation of the KRAS G12D neoantigen and tumor recognition by T cells, its loss was proposed to directly enable immune evasion.

Inferences of HLA LOH and Imbalance in Tumor Samples Using LOHHLA

In order to determine allele-specific copy number, the majority of copy-number tools rely on the relative coverage and variant allele frequency of single nucleotide polymorphisms (SNPs) in the tumor and matched normal across the genome or exome (, , ,

, ). However, inferring copy number status at the HLA locus is problematic due to poor coverage and the polymorphic nature of the region. SNPs cannot readily be identified at the HLA locus using sequencing data that has been aligned to the human reference genome, as reads that are highly polymorphic will not align and will therefore be discarded. Indeed, despite being one of the most polymorphic regions of the human genome, an average of <1 (mean 0.84, range 0-7) informative heterozygous SNP in the three HLA class I genes was identified in 96 patients where copy-number analysis was possible from the TRACERx cohort () using the state-of-the-art SNP caller Platypus (

). These data suggest that conventional copy-number calling algorithms are not suited to directly infer haplotype-specific copy number of the HLA locus.

We reasoned that, by leveraging the reads that map specifically to an individual's germline HLA alleles rather than the human reference genome, it would be possible to accurately determine HLA haplotype-specific copy number. To achieve this, we developed the computational tool LOHHLA ( Figure 1A). Implementation of LOHHLA relies upon five steps. First, tumor and germline reads that map to the HLA region of the genome and chromosome 6, including contigs, are extracted. Second, tumor and germline HLA allele-specific .bam files are generated by aligning reads to patient-specific HLA alleles (obtained from HLA serotyping or an inference tool, e.g., Polysolver [] or Optitype [

]). Third, polymorphic sites between homologous HLA alleles are identified. Fourth, tumor coverage relative to germline (logR) and b-allele frequencies (BAF) are inferred at each HLA locus, making use of identified polymorphic sites. Finally, HLA allele-specific copy number is determined for each HLA gene, accounting for stromal contamination.

To the best of our knowledge, no other computational method currently exists to infer haplotype-specific copy number of the HLA locus, and as such, there is no gold-standard with which we can compare LOHLA copy-number estimation or inference of which HLA haplotype is subject to loss. Therefore, to test the accuracy of HLA copy-number estimation, we made the assumption that genomic segments adjacent to the HLA locus will often exhibit the same copy-number profile as the HLA locus itself, which holds for cases without a highly focal HLA event ( Figure S1 D). We therefore used ASCAT () to estimate the frequency of allelic imbalance and LOH in the genomic regions surrounding the HLA locus in 288 TRACERx non-small-cell lung cancer (NSCLC) exomes from 96 patients () and compared these to LOHHLA copy-number estimation. Notably, given that ASCAT is not designed to infer which HLA haplotype is subject to loss or imbalance, for this analysis, we could only compare whether ASCAT and LOHHLA exhibited concordant copy-number profiles not whether concordant haplotypes were predicted to be lost.

We observed a highly significant relationship between the minor and major allele copy-number estimates obtained from LOHHLA and ASCAT (p = 1.36e-115, rho = 0.70, Spearman's rank test; Figures 1B and S1A), supporting the utility of LOHHLA to accurately estimate copy number and LOH. We found concordant allelic imbalance estimates in 246/288 tumor regions ( Figures 1C, S1B, and S1C). Thirty-four additional allelic imbalance events in tumor regions were uncovered using LOHHLA while only 8 tumor regions exhibited evidence of allelic imbalance using ASCAT and not LOHHLA. In many cases, the discrepancies between ASCAT and LOHHLA could be explained by the fact that ASCAT cannot directly infer haplotype-specific copy number at the HLA locus, and thus, the copy number of either the 5′ or 3′ adjacent segment is erroneously assumed to cover the HLA locus ( Figure S1 D).

Concordant LOH inference, where either the maternal or paternal allele was deleted, was observed in 258/288 tumor regions, with additional LOH defined by LOHHLA identified in 21 tumor regions, while 9 tumor regions were identified as harboring a lost haplotype by ASCAT and not LOHHLA ( Figures 1D and S1 C).

To further validate LOHHLA using an approach independent of exome sequencing, we performed PCR-based fragment analysis of highly polymorphic stretches of DNA in close proximity to the HLA locus in 82 tumor regions from 27 tumors ( Figure S2). Tumor regions analyzed were either predicted to have all loci (HLA-A, HLA-B, and HLA-C) subject to LOH, or no loci affected. Supporting the utility of LOHHLA to accurately classify LOH, we observed significant differences in normalized allelic ratio between tumors classified as exhibiting LOH, allelic imbalance without LOH, or no observable imbalance (p = 1.07e-19 [LOH versus no imbalance], p = 4.57e-05 [LOH versus allelic imbalance]; Figure S2). Furthermore, the distinction between these three categories was clearer using LOHHLA than the copy-number tools ASCAT (), Sequenza (), or TITAN () ( Figure S2).

Prevalence and Timing of HLA Imbalance and Loss across NSCLC

HLA mutations, which have the ability to disrupt neoantigen-MHC binding, have been previously described in many cancer types, including NSCLC (). However, despite being linked to cancer and immune escape, mutations in HLA genes are infrequently detected (, ). In our cohort of 90 lung adenocarcinoma or lung squamous cell carcinoma TRACERx patients, only tumors from three patients were found to harbor nonsynonymous mutations in HLA genes using Polysolver () ( Figure 2 A). One lung adenocarcinoma tumor had also acquired a mutation in β-2 microglobulin (B2m), which is vital for MHC class I expression and peptide binding stability. No further mutations predicted to disrupt antigen presentation or the MHC class I complex were identified in this cohort. Likewise, a broader study of 174 lung squamous cell and 223 lung adenocarcinoma patients from TCGA only classified 8% and 5% of tumors as harboring HLA mutations, respectively ().

To investigate whether HLA allele-specific loss was an early event in the tumor's evolution, present clonally in every cancer cell, or whether it was present subclonally, in only a subset of cancer cells, indicating an occurrence later in evolution and potentially in response to a shift in the equilibrium between immune recognition and evasion, we utilized the high-depth and multi-region nature of the TRACERx dataset. In this cohort of early stage NSCLC tumors, HLA LOH appeared to frequently occur subclonally in both histological subtypes, with 13/17 lung adenocarcinoma and 9/17 lung squamous cell carcinomas exhibiting loss of an HLA allele in a subset of cancer cells ( Figures 2C and 2D). Clonality of the HLA LOH event could not be determined for two lung squamous cell carcinoma patients with only a single region available for copy-number analysis. Phylogenetic analysis permitted us to map HLA LOH events to probable subclones from the tumor's evolutionary tree ( Figures 2 E and 2F) (). These data suggest that selective pressure from the immune system may increase during tumor development and also that without multi-region sequencing, the prevalence of HLA LOH may be significantly underestimated.

HLA Loss Is under Positive Selection in NSCLC

Given the relevance to immune evasion and high incidence of both clonal and subclonal LOH in HLA genes, we asked whether HLA LOH was significantly more frequent than expected by chance. Taking the frequency of LOH in every tumor into account, we simulated the expected frequencies of both focal and arm-level events. The observed incidence of focal, but not arm-level, HLA LOH occurred at a significantly greater frequency than expected by chance ( Figures 3, p < 0.001, and S3). Indeed, we observed a clear peak in focal LOH centered around the HLA locus for both histological subtypes. This peak was more pronounced when restricting the analysis to subclonal LOH ( Figure S3). Thus, while chromosomal instability may lead to LOH at the HLA locus, facilitating immune escape, the high prevalence of HLA LOH, beyond that expected by chance, suggests it is subject to significant positive selection in tumor evolution.

Moreover, in keeping with a strong selective pressure later in tumor evolution, in four tumors we observed losses of HLA haplotypes occurring as distinct events on separate branches of the tumors' phylogenetic trees, indicative of parallel evolution with convergence upon HLA loss ( Figure 3 C). Of note, in all four cases where we observed parallel evolution, the same alleles were subject to loss on distinct branches, suggesting that loss of these alleles specifically may have been required for subclonal expansions. We also noted that in certain cases (e.g., CRUK0051) only one HLA gene was subject to allele-specific loss, implying a selective benefit of perturbations to neoantigen presentation associated with that gene specifically.

HLA Loss Reflects Immune Editing and Is Associated with an Enrichment of Subclonal Mutations

Conceivably, if one of the homologous chromosomes harboring the HLA haplotypes were subject to copy-number loss, the number of putative neoantigens presented to T cells would be reduced. Thus, we hypothesized that loss of an HLA haplotype may be permissive for subclonal expansions and would be associated with an elevated mutation/neoantigen burden.

We first compared the number of non-synonymous mutations and neoantigens present in tumor samples with and without LOH at the HLA locus, without taking into account timing or clonal nature of the HLA LOH event. While overall, we observed a significant increase in the number of non-synonymous mutations ( Figure 4A) and neoantigens ( Figure S4 A) in tumor samples exhibiting any HLA LOH, this did not remain significant when the subtypes were considered separately (NSCLC p = 0.016; lung adenocarcinoma p = 0.07; lung squamous cell carcinoma p = 0.82, Wilcoxon test). However, we observed only 3/36 tumors with HLA LOH that exhibited a low mutational burden (as defined by the lowest quartile of NSCLC mutation burden), compared to 21/54 tumors without HLA LOH.

When we considered the clonal nature of mutations, we found that among tumors with HLA LOH there was a significant increase in the number of subclonal, but not clonal, non-synonymous mutations ( Figures 4B and 4C) (NSCLC p = 0.008; lung adenocarcinoma p = 0.01; lung squamous cell carcinoma p = 0.6, Wilcoxon test) and neoantigens ( Figures S4B and S4C). This observation is consistent with HLA LOH frequently occurring as a branched, subclonal event and indicates that HLA LOH may allow for the accumulation of potentially antigenic subclonal mutations. Consistent with this, we found that when HLA LOH occurred as a clonal event, on the trunk of a tumor's phylogenetic tree, this was significantly associated with both an elevated clonal (NSCLC p = 0.002; lung adenocarcinoma p = 0.01; lung squamous cell carcinoma p = 0.29, Wilcoxon test) and subclonal (NSCLC p = 0.03; lung adenocarcinoma p = 0.004; lung squamous cell carcinoma p = 0.89, Wilcoxon test) non-synonymous mutation and neoantigen burden ( Figures 4B, 4C, S4 B, and S4C).

When we considered HLA LOH events at the region-level, we also observed a significant increase in subclonal mutations between tumor regions exhibiting HLA loss compared to tumor regions from patients without any evidence for HLA LOH ( Figure S4D; NSCLC p = 1.9e-05; lung adenocarcinoma p = 0.009; lung squamous cell carcinoma p = 0.07). Interestingly, even in tumor regions without HLA LOH, but evidence for HLA LOH in other regions from the same tumor, we observed a significantly higher burden of subclonal mutations compared to tumor regions derived from tumors without any evidence for HLA LOH ( Figure S4 D). Thus, while HLA LOH may allow for subsequent subclonal expansion, a tumor with a high mutational burden may be under increased selective pressure for the HLA LOH event.

We next considered the specific cancer subclones in which HLA LOH events occurred, allowing us to more directly assess the impact of HLA LOH on non-synonymous mutation and neoantigen burden in cancer cells ( Figure S4E). In tumors with subclonal HLA LOH, we directly compared the mutational burden of the cancer subclone harboring HLA loss with its sister subclone, descended from the same ancestral cancer cell, but without HLA loss. Among the 36 tumors exhibiting any HLA LOH, we identified 19 instances where the event was subclonal and not on a terminal node for which a comparison between sister subclones could be made. Subclones with HLA LOH consistently showed a higher non-synonymous mutational burden than their counterparts without HLA LOH, regardless of histological subtype ( Figure 4 D; NSCLC p = 4e-04; lung adenocarcinoma p = 0.018; lung squamous cell carcinoma p = 0.008). Indeed, there were only 2/19 instances of the subclone with HLA LOH having fewer non-synonymous mutations than its sister subclone without HLA LOH. This result suggests that HLA LOH may contribute to the observed increase in subclonal non-synonymous mutations among tumors harboring HLA LOH.

While there were only three instances of low mutational burden in tumors harboring an HLA LOH event ( Figure 4 A) and an increase in mutation burden in subclones harboring HLA LOH was observed in both cancer types, we noted that a significant increase in subclonal non-synonymous mutation burden in tumors with loss of an HLA allele compared to those without HLA LOH was only observed among the lung adenocarcinomas. These data suggest that while HLA LOH may allow for acquisition of subclonal mutations in lung squamous cell carcinomas, there are likely to be additional mechanisms contributing to the observed high subclonal mutational burden in tumors without HLA LOH in this subtype.

Only neoantigens binding to the kept HLA alleles will be presented to the immune system. We reasoned that if HLA LOH reflects cancer immune-editing one would expect to observe an enrichment of subclonal neoantigens predicted to bind with high affinity to the lost HLA alleles compared to the kept HLA alleles. We therefore investigated tumors with six distinct HLA alleles and loss of one HLA haplotype (HLA-A, HLA-B, and HLA-C) in at least one tumor region (n = 20; 9 lung adenocarcinomas and 11 lung squamous cell carcinoma). Consistent with LOH at the HLA locus representing immune editing and facilitating accumulation of subclonal neoantigens, we observed a significant enrichment for subclonal neoantigens predicted to bind to the lost HLA alleles compared to the kept alleles ( Figure 4 E) (NSCLC p = 0.0083; lung adenocarcinoma p = 0.29; lung squamous cell carcinoma p = 0.02, paired Wilcoxon test). In one extreme example, tumor CRUK0020, a lung adenocarcinoma, we observed a total of 1,220 mutations predicted to yield neoantigens, of which 92% were predicted to bind to lost HLA alleles.

To determine more generally the impact HLA LOH might have on which neoantigens are presented to the immune system, we identified neoantigens predicted to bind to lost alleles in the full cohort of 36 patients exhibiting any HLA LOH ( Figure 4 F). We found that all patients harbored mutations predicted to bind to a now lost HLA allele, highlighting the potential impact HLA LOH could have on the targeting of putative neoantigens in a clinical setting, such as through personalized neoantigen vaccine approaches (, ).

HLA Loss and Immune Phenotype

Next, to investigate whether HLA loss might be associated with an immune replete tumor microenvironment, we performed immunohistochemistry analysis to determine the expression of PD-L1 on both tumor and immune cells. PD-L1 is a ligand to the immune inhibitory receptor PD1 and its expression may reflect a cancer adaptive immune response to an active immune system.

We found tumors exhibiting clonal HLA LOH were characterized by significantly elevated PD-L1 staining of immune cells compared to tumors without any HLA LOH (p = 0.029, Cochrane Armitage test), and a trend was observed for elevated PD-L1 staining on tumor cells (p = 0.14, Cochrane Armitage test). These data are consistent with the notion that HLA LOH may facilitate immune escape in response to an active immune microenvironment ( Figures 5 A and 5B ).

In keeping with results from the TRACERx cohort, we found HLA LOH was highly prevalent in lung squamous-cell carcinomas (133/309) and lung adenocarcinomas (118/383) tumors and significantly enriched in lung squamous cell carcinomas compared to adenocarcinomas (p = 0.001, Fisher's exact test) ( Figure S5A). Additionally, we again observed a significantly higher non-synonymous mutation burden in lung adenocarcinomas tumors exhibiting HLA LOH (p = 0.0001, Wilcoxon test), regardless of whether the HLA LOH affected a single locus (p = 0.002, Wilcoxon test) or all three HLA loci (p = 0.003, Wilcoxon test) ( Figure S5 B), a factor we could now consider due to the increased sample size from TCGA.

Previous work has identified immune signatures indicative of immune activity and/or immune cell infiltrates (

, , ). By using these signatures, we were able to further investigate whether HLA loss was associated with a specific immune phenotype. Consistent with the immunohistochemistry results, in both lung adenocarcinoma and lung squamous cell carcinomas harboring HLA LOH, we identified a significantly elevated cytolytic activity score, which measures the levels of two genes upregulated upon CD8⁺ T cell activation, granzyme A (GZMA) and perforin (PRF1) () ( Figure 5C). In lung adenocarcinoma with HLA LOH at all three loci, we observed an increase in abundance of CD8⁺ T cells and expression profiles associated with improved checkpoint blockade response (, , , , , ). Additionally, we identified an increase in NK cells, suggesting that HLA LOH alone may interrupt inhibitory NK cell/MHC interactions ( Figure 5C). Differential expression analysis between tumors with and without LOH confirmed an increase of PD-L1 and effector molecules such as granzymes-A, -B, and -H, as well as STAT1 and interferon (IFN)-γ, in lung adenocarcinoma with HLA LOH but not lung squamous cell carcinoma ( Table S1).

These data suggest that lung tumors with HLA loss have a more active immune predatory microenvironment and disruption of antigen presentation may act as a mechanism to evade the immune system.

Discussion

Losing the ability to present productive tumor neoantigens could facilitate evasion from immune predation. An integral part of neoantigen presentation is the HLA class I molecule, which presents epitopes to T cells on the cell surface. Thus, loss of an HLA allele, resulting in HLA homozygosity, may be a mechanism of immune escape ( Figure 6).

However, the polymorphic nature of the HLA locus precludes accurate copy-number calling using conventional copy-number tools. Here, we present LOHHLA, a computational tool to systematically evaluate the prevalence and importance of HLA loss in lung cancer evolution using next-generation sequencing data ( Figure 1).

In both lung adenocarcinomas and lung squamous cell carcinomas, subclones harboring HLA LOH were associated with a significantly elevated non-synonymous mutation/neoantigen burden compared to subclones descended from the same ancestral cancer cell but without HLA LOH. Tumors with HLA LOH were found to exhibit an enrichment of neoantigens predicted to bind to the lost HLA alleles and were associated with significantly elevated PD-L1 staining on immune cells and RNA signatures of immune activation. These data suggest that loss of HLA alleles, under the selective pressure of immune predation, may be permissive for subclonal expansions and result in previously antigenic mutations becoming effectively invisible to the immune system.

The high mutational load and low levels of HLA expression in lung squamous cell tumors (), even in tumors without HLA LOH suggests alternative mechanisms of immune evasion and/or disruption of neoantigen presentation through other mechanisms (e.g., mutations to B2M or NLRC5) (, ). In this regard, we note that LOHHLA could be extended to perform haplotype-specific copy number on any genomic segment that has been subject to haplotyping. For instance, if HLA class II typing has been performed, LOHHLA can be implemented to assess the extent to which loss of HLA class II occurs in tumor evolution and which haplotype is subject to loss.

Further work is warranted to explore the extent to which HLA LOH represents a pan-cancer immune evasion mechanism. Immunohistochemistry analysis has documented loss of HLA expression in many cancers (,

,

), however, the extent to which allele-specific loss of HLA molecules is a pervasive mechanism of immune evasion in tumor evolution across cancer types remains unclear. Furthermore, as more data pre- and post-therapy emerges, it will be possible to investigate the extent to which HLA LOH represents a common mechanism of resistance within the context of checkpoint blockade (and other immune-targeted) therapies.

In conclusion, LOHHLA enables accurate estimation of haplotype-specific HLA loss from sequencing data, revealing that HLA LOH is a common feature of NSCLC, facilitating immune escape and subclonal genome evolution.

STAR★Methods

Contact for Reagent and Resource Sharing

Further information and requests for reagents may be directed to, and will be fulfilled by the Lead Contact, Charles Swanton ( [email protected] ).

Experimental Model and Subject Details

The TRACERx 100 cohort comprises the first 100 patients prospectively analyzed by the lung TRACERx study ( https://clinicaltrials.gov/ct2/show/NCT01888601, approved by an independent Research Ethics Committee, 13/LO/1546) and mirrors the prospective 100 patient cohort described in .

Method Details

LOHHLA (Loss Of Heterozygosity in Human Leukocyte Antigen) algorithm

To call HLA LOH, LOHHLA relies upon five computational steps:

Step 1: extract HLA reads

First, tumor and germline reads that map to the HLA region of the genome (chr6:29909037-29913661, chr6:31321649-31324964, and chr6:31236526-31239869) as well as chromosome 6 contigs (chr6_cox_hap2, chr6_dbb_hap3, chr6_mann_hap4, chr6_mcf_hap5, chr6_qbl_hap6, chr6_ssto_hap7) are extracted using samtools view. Unpaired mates from this step are removed and the output is converted to FASTQ format.

Step 2: create HLA allele specific BAM files

For each of the patient's heterozygous HLA alleles, a patient-specific reference fasta is created. The FASTQ files generated in the previous step are used to generate HLA specific BAM files,using similar mapping parameters to those previously published that allow for reads to map to multiple HLA alleles (

). Post-alignment filtering is subsequently performed such that reads whose mates map to a different allele are discarded, as well as any reads that contain more than one insertion, deletion, or mismatch event compared to the reference HLA allele. For each filtered tumor/germline HLA allele-specific BAM file, coverage is then calculated using samtools mpileup.

Step 3: determine coverage at mismatch positions between homologous HLA alleles

For each HLA locus, a local pairwise alignment is performed between the two homologous HLA alleles, using the R Biostrings package. From the pairwise alignment, all of the mismatch positions between the two homologs are extracted. The HLA-specific coverage calculated in Step 2 is then used to determine differences in coverage at each of the mismatch positions. An additional file is also generated containing the coverage at every mismatch position, counting each read only once, as to avoid over-counting reads that span more than one mismatch position.

Step 4: obtain HLA specific logR and BAF

LogR across each HLA gene is then obtained by binning the coverage across both homologous alleles at 150 base pair intervals, for both tumor and normal. For each bin, the tumor/normal coverage ratio is multiplied by the multiplication factor, M, corresponding to number of unique mapped reads in the germline, divided by the number of unique mapped reads in the tumor region.

The BAF, corresponding to the coverage of HLA allele 1 divided by the coverage of HLA allele 1 + coverage of HLA allele 2, is subsequently calculated at each polymorphic site.

Step 5: determine HLA haplotype specific copy number

Finally, at each polymorphic site, an estimate of the major and minor allele copy number is obtained using the following equations:

where = tumor purity and = tumor ploidy, which are input at the start. The logR value from the corresponding bin in which the polymorphic site was found to reside is used as well as the BAF of the polymorphic site.

For each bin, the median Allele 1 and Allele 2 copy number is then determined. To estimate copy number of Allele 1, the median value across bins is calculated. Likewise, to estimate the copy number of Allele 2, the median value across bins is calculated.

A copy number < 0.5, is classified as subject to loss, and thereby indicative of LOH. To avoid over-calling LOH, we also calculate a p value relating to allelic imbalance for each HLA gene. This p value corresponds to the pairwise difference in logR values at mismatch sites between the two HLA homologs, adjusted to ensure each sequencing read is only counted once. Allelic imbalance is determined if p < 0.01 using the paired Student's t-Test between the two distributions.

TRACERx mutation and copy number data

TRACERx mutation data was obtained from . In brief, mutations were called using VarScan2 () and MuTect (1.1.4) (). To estimate whether mutations were clonal or subclonal, a modified version of PyClone was implemented (). ASCAT () segmented copy number data, purity and ploidy estimates were obtained from .

To compare LOHHLA to additional tools, we also implemented Sequenza (), and TITAN (). In both cases, default settings were used. For TITAN, the purity estimates from ASCAT were used as input.

Comparison of ASCAT and LOHHLA

In order to compare ASCAT and LOHHLA we treated each tumor region as a separate sample, and ran it through the LOHHLA pipeline with default settings. Note, for this analysis we used all TRACERx samples available, including NSCLCs that were not classified as lung adenocarcinomas or lung squamous cell carcinomas.

Given that it was not possible to directly infer the copy number of the HLA alleles using ASCAT, the segment overlapping the HLA locus was used. In twenty-five tumor regions from seven tumors no segment overlapped the HLA locus, and in these cases, the closest genomic segment was used.

To compare our allelic imbalance estimates, we considered a tumor region to be concordant if ASCAT predicted allelic imbalance across the locus and at least one HLA gene using LOHHLA was found to harbor allelic imbalance. Likewise, for LOH, we considered ASCAT and LOHHLA estimates to be concordant if ASCAT predicted a minor allele of 0 and this was also predicted for at least one HLA gene.

Conversely, allelic imbalance estimates were classified as discordant if allelic imbalance was predicted in any HLA gene using LOHHA and not with ASCAT. Similarly, LOH was classified as discordant if any HLA gene using LOHHLA was classified as exhibiting a minor allele of 0 and no LOH was identified using ASCAT.

Fragment analysis validation of LOHHLA results

Allelic imbalance was validated using four polymorphic Sequence-Tagged Site (STR) markers located on the short arm of chromosome 6, close to the HLA locus - (D6S2852, D6S2872, D6S248 and D6S1022). 20ng of patient germline and tumor region DNA was amplified using the PCR. The PCR comprised of 35 cycles of denaturing at 95C for 45 s, followed by an annealing temperature of 55C for 45 s and then a PCR extension at 720C for 45 s. PCR products were separated on the ABI 3730xl DNA analyzer. Fragment length and area under the curve of each allele was determined using the Applied Biosystems software GeneMapper v5. When two separate alleles were identified for a particular marker, the fragments could be analyzed for allelic imbalance using the formula (A tumor/B tumor)/(A normal/B normal). The output of this formula was defined as the normalized allelic ratio.

HLA Type, HLA Mutations, and Predicted NeoAntigen Binders

Novel 9-11-mer peptides that could arise from identified non-silent mutations present in the sample () were determined. The predicted IC ₅₀ binding affinities and rank percentage scores, representing the rank of the predicted affinity compared to a set of 400,000 random natural peptides, were calculated for all peptides binding to each of the patient's HLA alleles using netMHCpan-2.8 and netMHC-4.0 (

, , ). Putative neoantigen binders were those peptides with a predicted binding affinity < 500nM or rank percentage score < 2%.

Mapping HLA LOH to phylogenetic trees and identification of parallel evolution

LOH events detected in every tumor region tested were considered to be clonal events and mapped to the trunk of the phylogenetic tree. For heterogeneous LOH events, the regional copy number of the HLA allele lost was used in conjunction with the patient tree structure and subclone cancer cell fractions in a quadratic programming approach, using the R package quadprog, to determine the best placement of the LOH event.

This was achieved by solving a quadratic programming equation:

with the constraints:

The LOH event was tested at each branch. For each possibility, the phylogenetic tree was broken into two, one containing all clones after the LOH event and the other consisting of the remainder of the tree. A 2xn matrix, where n is the number of regions sampled, was constructed containing the regional sum of the cancer cell fractions for each subclone in the subtree and the regional sum of cancer cell fractions from subclones in the remaining tree. The regional cancer cell fraction matrix was multiplied by the transpose of itself to generate a 2x2 matrix for input ( Dmat) into the quadprog function, solve.QP. The vector to be minimized ( dvec) was obtained by multiplying the LOHHLA calculated HLA allele copy number for each region by the transpose of the regional cancer cell fraction matrix. Finally, the solve.QP function was called with Dmat and dvec, using a constraint matrix, Amat, such that all results had to be positive and a constraint vector, bvec, such that the estimated copy number of HLA allele for the remaining tree was at least 0.5. The errors between observed and predicted copy number values from placing LOH event on each branch were output and the solution providing the least error was selected.

Each mapped event was inspected and events that did not fit the phylogenetic tree or had large error values, either indicating the presence of an additional subclone or multiple independent HLA LOH events, were manually adjusted. Patients CRUK0013, CRUK0061, CRUK0082, and CRUK0084 had HLA LOH events that did not fit the current phylogentic tree, so additional nodes (indicated in gray) were included to contain the HLA LOH event. Patients CRUK003, CRUK0032, CRUK0051, and CRUK0062 had multiple independent HLA LOH events which were manually mapped.

Assessing significance of focal and arm-level LOH

In order to assess whether HLA LOH occurred more than expected by chance, we considered whether each LOH event was focal or arm-level in nature. In brief, to classify LOH as arm-level or focal, we focused on the minor allele frequency across the genome. First, any segments (as predicted by ASCAT) with identical minor allele copy numbers were merged. Subsequently, segments that spanned > = 75% the length of a given chromosome arm, were classified as 'arm-level', while segments that were < 75% were considered focal.

To assess the significance of focal events, for each tumor, the proportion of the genome subject to focal minor allele loss was determined. This value was assumed to reflect the probability for focal minor allele loss in each tumor. Based on this probability, we generated an aberration state (loss or no loss) for each sample separately and determined the proportion of samples exhibiting loss. We repeated this process 10,000 times to obtain a background distribution reflecting the likelihood of observing losses given the probability of loss in each sample. A p value reflecting the likelihood of observing the level of minor allele loss seen at the HLA locus was determined by counting the percentage of simulations showing a higher proportion loss than that observed.

The same procedure was conducted for arm-level events, using the observed frequency of arm-level allele specific loss in each tumor.

Mutational signature analysis

Mutational signatures were estimated using the deconstructSigs R package (

). Signature 1A, 2, 4, 5, 13 were considered.

Assessing whether neoantigens preferentially bind to loss HLA alleles

To assess whether neoantigens preferentially bind to lost HLA alleles, we focused on tumors exhibiting six distinct HLA alleles (i.e., no homozygosity for any allele in the germline) and loss of one HLA haplotype (HLA-A, HLA-B and HLA-C) in at least one tumor region.

Neoantigens (as defined above), were ranked according to IC ₅₀ binding scores. Duplicate mutations were removed to ensure each neoantigen reflected the highest binding score (lowest IC ₅₀ value) for any given mutation. We further filtered the mutation list to only include subclonal mutations (defined as previously described ()) occurring in the tumor regions harboring loss events (> 5% VAF). The number of subclonal neoantigens binding to each haplotype was then determined for each tumor. A paired wilcoxon test was used to compare the number of subclonal neoantigens binding to the lost haplotype compared to the kept haplotype.

PD-L1 immunohistochemistry

Analysis of TCGA mutation data

TCGA tumor and matched germline exome sequencing BAM files for both lung adenocarcinoma (LUAD, n = 397) and lung squamous cell carcinoma (LUSC, n = 350), were obtained from the Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/) via https://cghub.ucsc.edu. The data was processed as previously described ().

RNA-seq expression analysis using TCGA

RNA-sequencing data was downloaded from the TCGA data portal. For each LUAD and LUSC sample, all available 'Level_3′ gene-level data was obtained. Previously defined measures of immune infiltration and activity were used to compare the immune microenvironment between tumors exhibiting HLA LOH at all HLA loci and those without any evidence for HLA LOH (

, ,

). Additionally the expression level of PD-L1, CTLA4, and an IFN score were compared (, , , ). Significance was determined using a Wilcoxon test and FDR correction. To determine the degree of change between the HLA LOH groups, a ratio of the medians was calculated. For differential expression analysis, the raw RNA-seq read counts were used as input into the R package DESeq2 for analysis. An FDR cutoff of 0.05 was used to determine genes significantly differentially expressed.

Quantification and Statistical Analysis

All analysis was performed in the R statistical environment version > = 3.2.1. All statistical tests were two-sided and statistical significance was determined if p value was less than 0.05, unless otherwise stated. Comparisons were made using the Fisher's exact test Figure 2B, as described above for Figure 3, unpaired Wilcoxon test for Figures 4A-4C, and paired Wilcoxon test for Figures 4 D and 4E.

Data and Software Availability

Consortium

TRACERx Consortium: Charles Swanton, Mariam Jamal-Hanjani, Selvaraju Veeriah, Seema Shafi, Justyna Czyzewska-Khan, Diana Johnson, Joanne Laycock, Leticia Bosshard-Carter, Rachel Rosenthal, Pat Gorman, Robert E. Hynds, Gareth Wilson, Nicolai J. Birkbak, Thomas B.K. Watkins, Nicholas McGranahan, Stuart Horswell, Richard Mitter, Mickael Escudero, Aengus Stewart, Peter Van Loo, Andrew Rowan, Hang Xu, Samra Turajlic, Crispin Hiley, Christopher Abbosh, Jacki Goldman, Richard Kevin Stone, Tamara Denner, Nik Matthews, Greg Elgar, Sophia Ward, Marta Costa, Sharmin Begum, Ben Phillimore, Tim Chambers, Emma Nye, Sofia Graca, Maise Al Bakir, Kroopa Joshi, Andrew Furness, Assma Ben Aissa, Yien Ning Sophia Wong, Andy Georgiou, Sergio Quezada, John A. Hartley, Helen L. Lowe, Javier Herrero, David Lawrence, Martin Hayward, Nikolaos Panagiotopoulos, Shyam Kolvekar, Mary Falzon, Elaine Borg, Teresa Marafioti, Celia Simeon, Gemma Hector, Amy Smith, Marie Aranda, Marco Novelli, Dahmane Oukrif, Sam M. Janes, Ricky Thakrar, Martin Forster, Tanya Ahmad, Siow Ming Lee, Dionysis Papadatos-Pastos, Dawn Carnell, Ruheena Mendes, Jeremy George, Neal Navani, Asia Ahmed, Magali Taylor, Junaid Choudhary, Yvonne Summers, Raffaele Califano, Paul Taylor, Rajesh Shah, Piotr Krysiak, Kendadai Rammohan, Eustace Fontaine, Richard Booton, Matthew Evison, Phil Crosbie, Stuart Moss, Faiza Idries, Leena Joseph, Paul Bishop, Anshuman Chaturved, Anne Marie Quinn, Helen Doran, Angela Leek, Phil Harrison, Katrina Moore, Rachael Waddington, Juliette Novasio, Fiona Blackhall, Jane Rogan, Elaine Smith, Caroline Dive, Jonathan Tugwood, Ged Brady, Dominic G. Rothwell, Francesca Chemi, Jackie Pierce, Sakshi Gulati, Babu Naidu, Gerald Langman, Simon Trotter, Mary Bellamy, Hollie Bancroft, Amy Kerr, Salma Kadiri, Joanne Webb, Gary Middleton, Madava Djearaman, Dean Fennell, Jacqui A. Shaw, John Le Quesne, David Moore, Apostolos Nakas, Sridhar Rathinam, William Monteiro, Hilary Marshall, Louise Nelson, Jonathan Bennett, Joan Riley, Lindsay Primrose, Luke Martinson, Girija Anand, Sajid Khan, Anita Amadi, Marianne Nicolson, Keith Kerr, Shirley Palmer, Hardy Remmen, Joy Miller, Keith Buchan, Mahendran Chetty, Lesley Gomersall, Jason Lester, Alison Edwards, Fiona Morgan, Haydn Adams, Helen Davies, Malgorzata Kornaszewska, Richard Attanoos, Sara Lock, Azmina Verjee, Mairead MacKenzie, Maggie Wilcox, Harriet Bell, Allan Hackshaw, Yenting Ngai, Sean Smith, Nicole Gower, Christian Ottensmeier, Serena Chee, Benjamin Johnson, Aiman Alzetani, Emily Shaw, Eric Lim, Paulo De Sousa, Monica Tavares Barbosa, Alex Bowman, Simon Jordan, Alexandra Rice, Hilgardt Raubenheimer, Chiara Proli, Maria Elena Cufari, John Carlo Ronquillo, Angela Kwayie, Harshil Bhayani, Morag Hamilton, Yusura Bakar, Natalie Mensah, Lyn Ambrose, Anand Devaraj, Silviu Buderi, Jonathan Finch, Leire Azcarate, Hema Chavan, Sophie Green, Hillaria Mashinga, Andrew G. Nicholson, Kelvin Lau, Michael Sheaff, Peter Schmid, John Conibear, Veni Ezhil, Babikir Ismail, Melanie Irvin-sellers, Vineet Prakash, Peter Russell, Teresa Light, Tracey Horey, Sarah Danson, Jonathan Bury, John Edwards, Jennifer Hill, Sue Matthews, Yota Kitsanta, Kim Suvarna, Patricia Fisher, Allah Dino Keerio, Michael Shackcloth, John Gosney, Pieter Postmus, Sarah Feeney, Julius Asante-Siaw, Hugo J.W.L. Aerts, Stefan Dentro, and Christophe Dessimoz

Author Contributions

N.M. jointly conceived the project, wrote LOHHLA code to perform allele-specific copy number, conducted bioinformatics analysis, supervised the study, and wrote the manuscript. R.R. wrote LOHHLA code, conducted bioinformatics analysis, and wrote the manuscript. C.H. generated the PD-L1 immunohistochemistry data. A.J.R. performed fragment analysis to validate LOHHLA. T.B.K.W., G.A.W., and N.J.B. helped with data analysis. S.V. performed DNA extraction. P.V.L. provided expertise in copy-number analysis. J.H. provided data analysis support and supervision. C.S. jointly conceived the project, supervised the study, and wrote the manuscript with N.M. and R.R. All co-authors contributed to manuscript preparation and research progress discussion.

Acknowledgments

We thank the members of the TRACERx consortium for participating in this study. The results published here are in part based upon data generated by The Cancer Genome Atlas pilot project established by the NCI and the National Human Genome Research Institute. The data were retrieved through database of Genotypes and Phenotypes (dbGaP) authorization (accession no. phs000178.v9.p8). Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at https://cancergenome.nih.gov/. N.M. receives funding from Cancer Research UK , Rosetrees , and the University College London Hospitals Biomedical Research Centre . T.B.K.W. is funded by the European Union Seventh Framework Programme (FP7-People-2013-ITN) under grant agreement (2013)607722-PloidyNet . P.V.L. is a Winton Group Leader in recognition of the Winton Charitable Foundation's support towards the establishment of The Francis Crick Institute. C.S. is Royal Society Napier Research Professor. This work was supported by the Francis Crick Institute that receives its core funding from Cancer Research UK ( FC001169 , FC001202 ), the UK Medical Research Council ( FC001169 , FC001202 ), and the Wellcome Trust ( FC001169 , FC001202 ). C.S. is funded by Cancer Research UK (TRACERx and CRUK Cancer Immunotherapy Catalyst Network), the CRUK Lung Cancer Centre of Excellence , Stand Up 2 Cancer (SU2C), the Rosetrees Trust , NovoNordisk Foundation (ID 16584 ), the Prostate Cancer Foundation , the Breast Cancer Research Foundation (BCRF), the European Research Council (THESEUS), and Marie Curie Network PloidyNet . Support was also provided to C.S. by the National Institute for Health Research , the University College London Hospitals Biomedical Research Centre , and the Cancer Research UK University College London Experimental Cancer Medicine Centre . The TRACERx study ( Clinicaltrials.gov no: NCT01888601) is sponsored by University College London ( UCL/12/0279 ) and has been approved by an independent Research Ethics Committee (13/LO/1546). TRACER is funded by Cancer Research UK ( C11496/A17786 ) and coordinated through the Cancer Research UK and UCL Cancer Trials Centre. C.S. is a founder of Achilles Therapeutics.

Supplemental Information

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens.

Video Abstract

# data[].attributes.attachment_link.title
Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

# data[].attributes.attachment_link.description
LOHHLA enables estimation of allele-specific HLA loss from sequencing data * LOH of the HLA locus occurs in 40% of early stage non-small-cell lung cancers * HLA LOH is associated with a high subclonal neoantigen burden and immune activity * HLA LOH is an immune escape mechanism subject to strong selection pressures Immune evasion represents a hallmark of cancer ().

# data[].attributes.attachment_link.title
A genomic mutation signature predicts the clinical outcomes of immunotherapy and characterizes immunophenotypes in gastrointestinal cancer - npj Precision Oncology

# data[].attributes.attachment_link.description
npj Precision Oncology - A genomic mutation signature predicts the clinical outcomes of immunotherapy and characterizes immunophenotypes in gastrointestinal cancer

# data[].attributes.attachment_link.readable_text
Highlights

*

Immune-based combinations have led to marked improvements in OS in mRCC.

*

One major challenges is to identify a predictive biomarker of response to immunotherapy-based treatments in mRCC.

*

Anticancer immune response is extremely complex, thus finding a unique biomarker for patient's selection may not be feasible.

*

The development of a reproducible and reliable biomarker will require the integration of different elements, e.g. checkpoints' expression, TILs, mutational load, and probably many others emergent biological events.

Abstract

A major breakthrough in cancer immunotherapy was the development of monoclonal antibodies targeting inhibitory immune checkpoint proteins. This approach demonstrated significant antitumor activity and efficacy in different cancer types, including metastatic renal cell carcinoma (mRCC). In the majority of patients, this drug is able to restore the patient's tumour-specific T-cell-mediated response thus improving both overall survival and objective response rate. However, a lack of clinical response occurs in a number of patients, raising questions about how to predict and increase the number of patients who receive long-term clinical benefit from immune checkpoint therapy or not. The aim of this review is to summarize available data about immune biomarkers in patients with mRCC treated with immunotherapy.

View full text

# data[].attributes.attachment_link.title
Finding predictive factors for immunotherapy in metastatic renal-cell carcinoma: What are we looking for?

# data[].attributes.attachment_link.description
Immune-based combinations have led to marked improvements in OS in mRCC. * One major challenges is to identify a predictive biomarker of response to immunotherapy-based treatments in mRCC. * Anticancer immune response is extremely complex, thus finding a unique biomarker for patient's selection may not be feasible.

# data[].attributes.attachment_link.readable_text
Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/journal/jco/site/ifc.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians ( Open Payments).

Aurelien Marabelle

Stock and Other Ownership Interests: Pegascy

Honoraria: Bristol-Myers Squibb, AstraZeneca, MedImmune, Oncovir, Merieux

Consulting or Advisory Role: Lytix Biopharma, Eisai, Pierre Fabre, Merck Serono, Novartis, Amgen, Pfizer, Symphogen, AstraZeneca, Servier, Gritstone Oncology, Molecular Partners, Bayer, Partner Therapeutics, Sanofi, Roche, Boehringer Ingelheim, Redx Pharma, Sotio, Innate Pharma, Imcheck, Cerenis Therapeutics, MSD, OSE Immunotherapeutics, Bioncotech

Speakers' Bureau: MSD, Bristol-Myers Squibb, AstraZeneca, Roche

Research Funding: Mersu (Inst), Bristol-Myers Squibb (Inst), Boehringer Ingelheim (Inst), Transgene (Inst), MSD (Inst)

Patents, Royalties, Other Intellectual Property: Monoclonal antibodies against CD81 (Stanford University)

Travel, Accommodations, Expenses: Bristol-Myers Squibb, MSD, Roche, AstraZeneca

Other Relationship: Pegascy

Dung T. Le

Honoraria: Merck

Consulting or Advisory Role: Merck, Bristol-Myers Squibb

Research Funding: Merck, Bristol-Myers Squibb, Aduro Biotech, Curegenix, Medivir

Patents, Royalties, Other Intellectual Property: Inventor of technology, Microsatellite Instability as a Pharmacogenomic Marker of Therapeutic Response to Immune Checkpoint Inhibition

Paolo A. Ascierto

Stock and Other Ownership Interests: PrimeVax

Consulting or Advisory Role: Bristol-Myers Squibb, Genentech, Merck Sharp & Dohme, Novartis, Amgen, Array BioPharma, Merck Serono, Pierre Fabre, Newlink Genetics, Genmab, Incyte, MedImmune, AstraZeneca, Syndax, Sun Pharma, Sanofi, Idera, Ultimovacs, Sandoz, Immunocore, 4SC

Research Funding: Bristol-Myers Squibb (Inst), Genentech (Inst), Array BioPharma (Inst)

Travel, Accommodations, Expenses: Merck Sharp & Dohme

Anna Maria Di Giacomo

Consulting or Advisory Role: Bristol-Myers Squibb, Incyte, Pierre Fabre, MSD Oncology

Ana De Jesus-Acosta

Consulting or Advisory Role: Merck

Research Funding: Merck (Inst), AstraZeneca (Inst)

Jean-Pierre Delord

Consulting or Advisory Role: Novartis, Genentech, Bristol-Myers Squibb, MSD Oncology

Research Funding: Genentech (Inst), Bristol-Myers Squibb (Inst), MSD Oncology (Inst)

Ravit Geva

Leadership: Pyxis

Honoraria: Bristol-Myers Squibb, MSD, Medison, Roche, Novartis, Janssen, Pfizer, Takeda

Consulting or Advisory Role: Bayer, Novartis, MSD, BOL Pharma

Patents, Royalties, Other Intellectual Property: Options, BOL Pharma

Travel, Accommodations, Expenses: Bristol-Myers Squibb, Merck Serono, Bayer, Medison

Maya Gottfried

Consulting or Advisory Role: Pfizer, Boehringer Ingelheim, Roche

Travel, Accommodations, Expenses: Pfizer, Roche, Boehringer Ingelheim

Nicolas Penel

Research Funding: Bayer (Inst)

Travel, Accommodations, Expenses: Astellas Pharma, Janssen-Cilag

Other Relationship: PharmaMar

Aaron R. Hansen

Honoraria: Merck, AstraZeneca, MedImmune, Pfizer, GlaxoSmithKline, Novartis, Bristol-Myers Squibb

Consulting or Advisory Role: Genentech, Merck, GlaxoSmithKline, Bristol-Myers Squibb, Novartis, Boston Biomedical, Boehringer Ingelheim

Research Funding: Karyopharm Therapeutics (Inst), Merck (Inst), Bristol-Myers Squibb (Inst), Boehringer Ingelheim (Inst), GlaxoSmithKline (Inst), Novartis (Inst), Genentech (Inst), Janssen (Inst)

Sarina A. Piha-Paul

Research Funding: AbbVie (Inst), Aminex (Inst), BioMarin Pharmaceutical (Inst), Boehringer Ingelheim (Inst), Bristol-Myers Squib (Inst), Cerulean Pharma (Inst), Chugai Pharmaceutical (Inst), Curis (Inst), Five Prime Therapeutics (Inst), Genmab (Inst), GlaxoSmithKline (Inst), Helix BioPharma (Inst), Incyte (Inst), Jacobio Pharmaceuticals (Inst), MedImmune (Inst), Medivation (Inst), Merck Sharp & Dohme (Inst), New Link Genetics Corp (Inst), Blue Link Pharmaceuticals (Inst), Novartis Pharmaceuticals (Inst), Pieris Pharmaceuticals (Inst), Pfizer (Inst), Principia Biopharma (Inst), Puma Biotechnology (Inst), Rapt Therapeutics (Inst), Seattle Genetics (Inst), Taiho Oncology (Inst), Tesaro (Inst), TransThera Bio (Inst), XuanZhu Biopharma (Inst)

Toshihiko Doi

Consulting or Advisory Role: MSD, Daiichi Sankyo, Amgen, Sumitomo Dainippon, Taiho Pharmaceutical, Takeda, AbbVie, Novartis, Bayer, Boehringer Ingelheim, Rakuten Medical

Research Funding: Taiho Pharmaceutical (Inst), Novartis (Inst), Merck Serono (Inst), MSD (Inst), Boehringer Ingelheim (Inst), Pfizer (Inst), Eli Lilly (Inst), Sumitomo Group (Inst), Kyowa Hakko Kirin (Inst), Daiichi Sankyo (Inst), Bristol-Myers Squibb (Inst), AbbVie (Inst), Quintiles (Inst), Eisai (Inst)

Bo Gao

Consulting or Advisory Role: MSD

Hyun Cheol Chung

Consulting or Advisory Role: Taiho Pharmaceutical, Celltrion, MSD, Eli Lilly, Quintiles, Bristol-Myers Squibb, Merck Serono

Speakers' Bureau: Merck Serono, Eli Lilly, Foundation Medicine

Research Funding: Eli Lilly, GlaxoSmithKline, MSD, Merck Serono, Bristol-Myers Squibb, Taiho Pharmaceutical

Jose Lopez-Martin

Stock and Other Ownership Interests: PharmaMar

Consulting or Advisory Role: Novartis, Eli Lilly, Roche, GlaxoSmithKline, MSD Oncology, Celgene, Caris Life Sciences, Bayer, Pfizer, Pierre Fabre, Bristol-Myers Squibb, Bristol-Myers Squibb (Inst), Roche Molecular Diagnostics (Inst), Amgen, PharmaMar

Patents, Royalties, Other Intellectual Property: PharmaMar

Travel, Accommodations, Expenses: Roche, MSD Oncology, Roche, Bristol-Myers Squibb

Yung-Jue Bang

Consulting or Advisory Role: Samyang, BeiGene, Green Cross, Taiho Pharmaceutical, Merck Serono, AstraZeneca, MedImmune, Novartis, MSD Oncology, Bayer, Hanmi, Genentech, Eli Lilly, Daiichi Sankyo, Astellas Pharma, Bristol-Myers Squibb, Genexine

Research Funding: AstraZeneca (Inst), MedImmune (Inst), Novartis (Inst), Genentech (Inst), MSD (Inst), Merck Serono (Inst), Bayer (Inst), GlaxoSmithKline (Inst), Bristol-Myers Squibb (Inst), Pfizer (Inst), Eli Lilly (Inst), Boehringer Ingelheim (Inst), Macrogenics (Inst), Boston Biomedical (Inst), Five Prime Therapeutics (Inst), CKD (Inst), Ono Pharmaceutical (Inst), Taiho Pharmaceutical (Inst), Takeda (Inst), BeiGene (Inst), Curis (Inst), Green Cross (Inst), Daiichi Sankyo (Inst), Astellas Pharma (Inst), Genexine (Inst)

Ronnie Shapira Frommer

Honoraria: MSD Oncology, Bristol-Myers Squibb, Novartis, Roche, AstraZeneca, Medison, Neopharm

Consulting or Advisory Role: VBL Therapeutics, Clovis Oncology, MSD Oncology

Manisha Shah

Consulting or Advisory Role: Eisai, Loxo, Novartis, Ignyta

Research Funding: Eisai, Merck, Loxo

Razi Ghori

Employment: Merck

Andrew K. Joe

Employment: Merck Sharp & Dohme, Sanofi

Stock and Other Ownership Interests: Merck Sharp & Dohme, Sanofi

Travel, Accommodations, Expenses: Merck Sharp & Dohme, Sanofi

Scott K. Pruitt

Employment: Merck Sharp & Dohme

Stock and Other Ownership Interests: Merck Sharp & Dohme

Luis A. Diaz Jr

Leadership: Personal Genome Diagnostics, Jounce Therapeutics

Stock and Other Ownership Interests: PapGene, Personal Genome Diagnostics, Jounce Therapeutics, Zydecom Corporation, Thrive Detect, Neophore, Amgen (I)

Consulting or Advisory Role: Merck, Personal Genome Diagnostics, Cell Design Labs, Lyndra, Caris Life Sciences, Genocea Biosciences, Zydecom, Neophore

Research Funding: Merck (Inst)

Patents, Royalties, Other Intellectual Property: US-2010041048-A1, Circulating mutant DNA to assess tumor dynamics; US-2015344970-A1, Personalized tumor biomarkers; WO-2010118016-A2, Digital quantification of DNA methylation; US-2005202465-A1, Thymidylate synthase gene and metastasis; US-2014227271-A1, Somatic mutations in ATRX in brain cancer; WO-2012094401-A2, Genes frequently altered in pancreatic neuroendocrine tumors; US-2013323167-A1, Detecting and treating solid tumors through selective disruption of tumor vasculature; EP-2912468-B1, Papanicolaou test for ovarian and endometrial cancers; US-9976184-B2, Mutations in pancreatic neoplasms; US-2017267760-A1, Checkpoint blockade and microsatellite instability; US-2018171413-A1, Head and neck squamous cell carcinoma assays; US-2018171413-A1, Head and neck squamous cell carcinoma assays; US-2018171413-A1, Head and neck squamous cell carcinoma assays; US-2018086832-A1, HLA-restricted epitopes encoded by somatically mutated genes; US-2018258490-A1, Assaying ovarian cyst fluid; US-2016208340-A1, TERT promoter mutations in urothelial neoplasia; US-2015252415-A1, Arid1b and neuroblastoma; WO-2018071796-A2, Compositions and methods for identifying functional antitumor T-cell responses; EP-3322824-A1, Detection of tumor-derived DNA in cerebrospinal fluid; US-2016273049-A1, Systems and methods for analyzing nucleic acid (Inst); US-2018135044-A1, Nonunique barcodes in a genotyping assay (Inst); and US-2017016075-A1, Neoantigen analysis (Inst)

Travel, Accommodations, Expenses: Merck

No other potential conflicts of interest were reported.

# data[].attributes.attachment_link.keywords[]
immunological / administration & dosage*

# data[].attributes.attachment_link.keywords[]
programmed cell death 1 receptor / antagonists & inhibitors

# data[].attributes.attachment_link.keywords[]
programmed cell death 1 receptor / immunology

# data[].attributes.attachment_link.keywords[]
national center for biotechnology information

# data[].attributes.attachment_link.title
Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study - PubMed

# data[].attributes.attachment_link.description
Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution.

# data[].attributes.attachment_link.title
Signatures of mutational processes in human cancer - Nature

# data[].attributes.attachment_link.description
An analysis of mutations from over 7,000 cancers of diverse origins reveals the diversity of mutational processes underlying the development of cancer; more than 20 distinct mutational signatures are described, some of which are present in many cancer types, notably a signature attributed to the APO…

# data[].attributes.attachment_caption
Schematic example of the MMRd effect in specific mutations

# data[].attributes.body
The current understanding of the clinical response to ICI therapy unequivocally indicates that the development of a predictive model that takes into account the different components that affect tumour–host interactions is needed. Predictive models will require a combination of different types of data for training and evaluation.

These variables may include, for example:

· DNA sequencing data of the tumour to estimate TMB, detect presence or absence of specific genetic alterations, assess ITH and estimate the fraction of SCNAs;

· RNA sequencing data to evaluate whether the immune phenotype will favour sensitivity to ICIs;

· Germline DNA sequencing data to detect patient genetic polymorphisms (for example, HLA diversity);

· IHC for PDL1 expression and expression of other checkpoint molecules and features of the tumour microenvironment;

# data[].attributes.body
Commensal microorganisms, collectively known as the microbiota, influence human immune responses in health and disease. Importantly, it appears that the diversity and composition of the gut microbiota but not the oral microbiota can influence ICI response in humans.

Studies in which baseline faecal samples were analysed have found an association between specific gut bacteria and ICI response in melanoma, NSCLC, RCC and urothelial cancer.

An important implication of these findings is that factors affecting the gut microbiota will likely affect ICI therapy response. Indeed, patients treated with antibiotics, but not proton-pump inhibitors, during the course of ICI therapy had decreased antitumour response, whether this response was due to depletion of the gut microbiota or simply a result of the infections that necessitated antibiotic use, it is not known.

Nonetheless, these results implicate the gut microbiota as an influential factor in antitumour immunity and ICI response.

# data[].attributes.body
Routine clinical biomarkers are measurable biological entities which respond to a certain therapy and should be assessed in the least invasive manner. Therefore, there is great interest in developing whole blood-derived or serum-derived predictive biomarkers of ICI (immune checkpoint inhibitors) response.

Several features of peripheral blood composition have been associated with ICI response, including:

Neutrophil to lymphocyte ratio values greater than five, were associated with decreased PFS (progression-free survival) and OS (overall survival) in multiple studies of anti-CTLA4 and anti-PD1 across a wide range of cancer types,

Total lymphocyte count,

T cell clonality,

Monocyte count,

Circulating Treg cell levels,

Cytokine levels,

Relative eosinophil count,

Circulating monocytes or MDSCs (Myeloid-derived suppressor cells)

Lactate dehydrogenase (LDH activity).

There is also a potential association between tumour genomic factors and ICI response, determined from blood-derived cfDNA (circulating free dna), where the number of mutations was positively associated with ICI response and patient overall survival, and cfDNA-derived TMB (tumour mutational burden) with improved survival in patients with NSCLC treated with anti-PDL1.

# data[].attributes.body
Genomic features are promising candidates as predictive biomarkers of ICI response, specificly:
· Mutation signatures
· Germline and somatic alterations
· Tumor infiltrating lymphocytes characteristics,
· Systemic biomarkers
· Individual microbiota

Detecting defects in the DNA repair machinery (MMRd), tumor mutation burden (TMB) and indel burden in each type of cancer can lead to a truly personalized treatment in the future.

# data[].attributes.body
These predictive models will require a continuous process of model update and re-evaluation as more knowledge on the molecular determinants of response to ICIs is unravelled.

Such quantitative models will have profound implications in the area of precision immuno-oncology.

The ongoing work to establish and understand biomarkers for ICI response prediction holds great promise for maximizing patient benefit from these transformative therapies, and there is a lot that can already be done, but we are not there yet, a lot more research needs to be done in this filed.

Here we focussed on the role of genomics in ICI and cancer response, however, predictive genomics can have an important role in many other heath related conditions, where we can predict a patients response to treatment and decide the best course of action without invasive and time consuming procedures, saving time and money to both patients and health care providers, and potentiating more effective personalized treatments while minimizing side effects.

# data[].attributes.body
PD-L1 Immunohistochemistry has been approved by FDA as a companion diagnostic for anti-PD1 therapy for patients with Non-Small Cell Lung Cancer (NSCLC), however PD-L1 expression remains an imperfect predictor of ICI. Multiple studies in different types of tumours have found a correlation while others have detected no association.

Potential reasons for this phenomenon include:

· The use of different detection assays

· Temporal and Intratumoural Heterogeneity (ITH) of PD-L1 expression

· Non-standardized criteria and cut-offs for assessing positivity (e.g. percent PD-L1⁺ vs Staining intensity)

Even when the correlation exists, there are many patients with low to no detectable PD-L1 expression who experience durable clinical benefit

Shortly, PD-L1 status is likely not a sufficiently comprehensive standalone biomarker for therapeutic decisions in the clinic.

# data[].attributes.attachment_caption
Germline Mutations vs. Somatic Mutations

# data[].attributes.body
The Germline are cells that pass on their genetic material to an offspring, meaning that changes in the germline, unlike in a somatic cell, may be passed on to the next generation. In humans, these cells form the eggs and sperm, which will eventually join to form an embryo.
Studys are starting to find correlations between germline genetics and the body's response to tumours and cancer therapies, showing that a host's germline may determine its survival.

# data[].attributes.attachment_caption
Chromossomic distribution of HLA genes in the MHC

# data[].attributes.body
The Human Leukocyte Antigen, or HLA, is the human form of the MHC complex. These highly polymorphic genes are located in chromosome 6, and encode a critical component for immunosurveillance, which connects them to the diversity of immune responses to diseases and other health conditions.

Of the two classes of HLA, we will focus on HLA class I (HLA-I), made up of HLA-A, HLA-B and HLA-C, which grant the human genome up to six primary HLA-I alleles. This means that there is a considerable amount of sequence variation in the peptide-binding region because each variant presents a distinct, though often overlapping, collection of peptides, constituting the human immunopeptidome.

# data[].attributes.attachment_caption
Activation of a CD8+ T cell by the MHC of an Antigen presenting cell

# data[].attributes.body
Studys have shown that the heterozygosity of HLA-I molecules in a patient is associated with their increased survival on ICI treatments. The HLA-I molecules bind specific peptides (for presentation on the cell surface) to CD8+ T cells, molecules on which ICI depends, making this an activity dependent on HLA-I. This way, the higher diversity of HLA-I, due to its heterozygosity, gives its host the ability to present a wider array of tumour antigens to the T cells. Therefore, a patient homozygous in at least one HLA-I locus would present a smaller and less diverse range of tumour antigens to T cells when compared with the latter. The same logic can be posited for HLA-I Supertypes, which are groups of HLA molecules with similar peptide binding features, meaning that if a peptide is able to bind to one allele within a supertype, it can also bind to all other alleles in this supertype.

# data[].attributes.attachment_caption
Loss of hererozygosity vs. Immune evasion

# data[].attributes.body
Yet another situation is during tumour progression, when cancer cells resort to immune evasion to avoid their demise, doing so through the disruption of the antigen presentation pathway by downregulating HLA-I expression, resulting in a loss of their heterozygosity (LOH). The LOH in HLA leads to fewer HLA-I expression and, therefore, a considerable decrease of tumour-specific peptide antigens on the surface of cells, meaning that CD8+ T cells are unable to do proper vigilance of the tumour due to a lack of antigens they can recognize. This way, the LOH not only allows for growth of the tumour but also leads to a decreased response to ICI therapy.

# data[].attributes.body
The immune system has the inherent potential to destroy and damage the body’s cells, as such it is kept on a “tight leash”. There are several membrane receptors in Immune cells that work as immune checkpoints, if triggered they supress the immune cell’s function or even lead the cell’s apoptosis (programmed death).

# data[].attributes.attachment_caption
A T cell being deactivated by a cancer cell expressing PD-L1

# data[].attributes.body
One of these receptor proteins, programmed cell death protein 1 or PD-1, is found on B and T cells and is activated upon binding to programmed death-ligand 1 or PD-L1, which is usually found on the membranes of cells like pAPCs or in the placenta during pregnancy, preventing the immune targeting of these cells. Cancer cells often begin to express PD-L1, acquiring the ability to repress T cell function via PD-1 inactivation.

# data[].attributes.attachment_caption
Suppression of T cell and Dendritic cell by a "tumour manipulated" T regulatory cell by CTLA-4 inhibition.

# data[].attributes.body
Another important checkpoint protein is the cytotoxic T-lymphocyte-associated protein 4 or CTLA-4 which is found in large amounts on the membranes of activated T cells. CTLA-4 is homologous to the co-stimulatory protein CD28 but has a strong inhibitory effect if activated and a stronger affinity for the same ligands, CD80 and CD86. These ligands are found in pAPC and regulatory T cells (Tregs). Cancer cells often manipulate these cells to inhibit the action of Cytotoxic T cells via CTLA-4 inactivation.

# data[].attributes.attachment_link.title
Tregs Suppress Dendritic Cells and Effector T Cells

# data[].attributes.attachment_caption
Left: Normal T cell activation by cancer cell or pAPC. Right: Inhibition of T cell by cancer cell and by a "manipulated" immune cell. Bottom: ICI antibodies

# data[].attributes.body
Having these Immune checkpoints in mind and their respective “abuse” by cancer cells, several companies developed immunotherapies that blocked their activation. Most of these therapies consist in the injection in the bloodstream of antibodies that either bind to PD-1, PD-L1 or to CTLA-4, though often these therapies are combined. The antibodies bind to the receptors, but don’t activate them, working as immune checkpoint inhibitors (ICI), blocking the receptors to bind to their respective ligands and preventing the inhibition of the T cell response.

# data[].attributes.headline
No 2 People are Alike, No 2 Cancers are Alike

# data[].attributes.subject
No 2 People are Alike, No 2 Cancers are Alike

# data[].attributes.body
Not every cancer relies on checkpoint inhibition to evade the immune system, and as such some patients derive great benefit from these therapies, and others see no effect at all. Further despite form having less side effects than chemotherapy, ICI can provoke autoimmune pathologies and other toxicity effects, so it is crucial to determine which patients can benefit from the therapy while minimizing potential side effects. A great tool to achieve this, not just in ICI but with medicine in general is with tumour and patient genomics.

# data[].attributes.attachment_caption
RNA signatures processing and evaluation (for patogens). The same principle is applied to different types of tumours, but without external stimulation.

# data[].attributes.attachment_caption
 Some factors that predict response to immune checkpoint inhibitor therapy

# data[].attributes.attachment_link.title
TMB PD-L1 expression Copy number variation HLA class I LOH at HLA class I aleles T cell repertoire clonality change T cell-inflamed microenvironment SERPINB34 mutations Gut microbial diversity

# data[].attributes.body
Cancer cells are very similar to the host cells, as such the native Immune system has limited capability to detect and fight cancer cells, further, monocytes, macrophages and neutrophils are known to be manipulated by cytokines produced by cancer cells and often exhibit pro tumour activity by promoting inflammation and angiogenesis, shaping the extracellular matrix, and suppressing the response of other immune cells. The adaptive Immune system exhibits antigen specificity having a higher capacity of detecting and as such combating the aberrant cancer cells.

# data[].attributes.headline
Immune Cells in the Tumor Microenvironment

# data[].attributes.subject
Immune Cells in the Tumor Microenvironment

# data[].attributes.body
Th: T helper cells
NKT: Natural killer T cells and T cells
NK: Natural killer Cells
M: macrophages
N: Neutrophils
DC: Dendritic cells
ILC: Innate Lymphoid cells
Treg: T regulatory cells
MDSC: Myeloid-derived suppressor cells

# data[].attributes.attachment_link.title
Pro- and Anti-Tumor Immune Cells in the Tumor Microenvironment

# data[].attributes.attachment_caption
Comparison between MHC I and MHC II pathways 

# data[].attributes.body
Antigen presentation is central to the adaptive immune system.

A Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that holds a peptide and is capable of presenting said peptide to a T cell by the T cell receptor (TCR). There are two main types of MHCs, class I and class II:

-MHC I are present in every cell of the Human body and hold an intracellular peptide of the cell which is on. They Function as “cellular IDs” and are crucial for the identification of “self” or “non self” by the adaptive immune cells.

-MHC II are found in professional antigen presenting cells (pAPC) (macrophages, dendritic cells and B cells) and hold peptides degraded from phagocyted or pinocyted material, allowing for the activation of T cells from extracellular antigen.

Every cell in the Human body also possesses a proteosome, a proteolytic system that degrades intracellular proteins into short peptides which are transported to the Rough Endoplasmic reticulum where they are bound to newly formed MHC I proteins. These MHC I bound to peptides are then transported to the cell’s membrane where they can bind to TCRs form T cells.

# data[].attributes.body
T cells are perhaps the most important immune cells in the fight against cancer, they express a unique antigen binding receptor, the TCR that only recognises antigen bound to MHCs. Each T cell has a specific TCR that can only bind to a random “non self” peptide.  There exist two main types of T cells:

-Cytotoxic T cells browse cell membranes in search of MHC I . If the T cell binds to an MHC I and receives the costimulatory signals it activates, proliferating and destroying the cells with the MHCs I that activated it by releasing apoptosis-inducing cytokines and cytotoxic substances such as perforin.

-Helper T cells browse cell membranes of pAPCs in search of MHC II. If the T cell binds to an MHC II and receives the costimulatory signals it activates, proliferating and differentiating into a variety of subtypes specialized in different functions. Generally, these subtypes coordinate and regulate the immune response, being very important for the function of T and NK cells, for the activation of the anti-tumor activity of macrophages (phagocytosis, cytotoxicity and recruitment) and the stimulation of the continuous presentation of antigens.

# data[].attributes.attachment_link.description
Watch and share Science GIFs and Cancer GIFs on Gfycat

# data[].attributes.body
NK cells (natural killer) also have a crucial role against cancer. One way that cancer cells evade the immune system is by reducing/stopping the expression of MHC I or it’s associated antigen presenting machinery. NK cells destroy cells that fail to present the MHC I thus “forcing” the tumour cells to present antigens remaining vulnerable to T cell action. NK cells induce cell death by releasing cytokines that induce self-destruction and cytotoxic substances.

# data[].attributes.attachment_link.title
Natural killer cell(White blood cells) killing cancer cells

# data[].attributes.attachment_link.description
Watch and share Natural Killer Cell(White Blood Cells) Killing Cancer Cells GIFs on Gfycat

# data[].attributes.body
NKT cells have characteristics of cytotoxic T and NK cells, they detect exposed lipid antigens on CD1ds (MHC-like membrane proteins) and destroy cells that present foreign antigens or that do not have CD1ds, releasing cytokines that induce self-destruction and cytotoxic substances.

# data[].attributes.headline
The Biggest Challenge in the 21st Century

# data[].attributes.subject
The Biggest Challenge in the 21st Century

# data[].attributes.body
Cancer is an umbrella term encompassing a collection of similar diseases all consisting of cells that, due to mutations, acquire genetic instability and therefore the ability to:

· Rapidly evolve;

· Divide continuously;

· Manipulate the surrounding tissues;

· Evade the action of their host's immune system;

· Invade other tissues;

# data[].attributes.attachment_caption
Schematic representation of the formation of a malignus tumour and it’s respective metastization from a epithelial cell.

# data[].attributes.body
Cancer usually kills the host by stealing resources from surrounding tissues, taking up space and blocking blood vessels, thus preventing the normal functioning of the tissues in question. Depending on the type of cells that give rise to cancer and the mutations that occur, these diseases can manifest in very different ways, and due to their similarity to the host cells, their genetic instability and the usual cellular heterogenicity, effective treatment of the disease is often difficult.

# data[].attributes.body
Almost all cells in the human body have the potential to become cancerous, but the probability of a cell giving rise to cancer is negligible due to a whole cascade of specific mutations that must occur. However, as the average human being has about 30 trillion cells (30 × 10 ¹²), these diseases are quite common, with cancer being the second most common cause of death worldwide, affecting approximately 40% of people and currently causing approximately 1 in every 6 deaths.

# data[].attributes.attachment_caption
Common Causes of DNA Damage. DNA Damage often results in Mutations.

# data[].attributes.body
Mutations that can cause cancer occur naturally over years, but occur more often when cells are exposed to mutagens or stress such as ionizing radiation, compounds present in tobacco, certain viruses, metabolic syndromes such as diabetes and obesity, autoimmune diseases... As such, one's lifestyle can dramatically increase the chances of developing cancer, but this likelihood increases exponentially with age.

# data[].attributes.body
Somatic copy number alterations (SCNA) are another variant that can interfere in ICI response since SCNAs can result in the loss of genes necessary for immunological activity.

Both SCNAs and tumour aneuploidy are the most common observed alterations in cancer cells. These genomic alterations are apparently associated with gene expression signatures on immune evasion. This suggests potential as biomarkers for the prediction of the response of patients to ICI treatment.
TMB, indels and SCNAs have a better prognostic value when all are considered together.

# data[].attributes.body
Small insertion or deletion mutations (indel) can lead to large synthesis of mutagenic peptides, which eventually may contribute to the immunogenic phenotype of cancer.

As example renal cell carcinoma is so far the cancer with the highest rate of indel and consequently they are candidates to produce a higher rate of neoantigens than non-synonymous single nucleotide variants (nsSNVs). This tumor load does not show any association with the response in patients treated with anti-PD1 or anti-PDL1. On the other hand, the indel burden in melanomas treated with anti-CTLA4 or anti-PD1 is related to a positive clinical outcome.

# data[].attributes.body
There is evidence that suggests that specific genetic mutations can affect the formation of neoantigens and the tumour microenvironment, contributing to different immune responses.

Specific genetic aberrations such as mutations in TP53, PTEN or IFNγ signalling pathway have been observed to impact immune-cell infiltration and consequently the outcomes of treatments with ICI therapy. However, TMB rates show similar results for each gene mutation, which makes this indicator inaccurate.

As example, SERPINB3 and SERPINB4 are coding genes for protease inhibitors with high rates of mutations in melanoma patients threated with anti-CTLA4. Even though mutations on these genes are not related to survival in individuals not treated with ICI, they may be good for predicting its response.

# data[].attributes.body
In reality, most cells that begin to divide uncontrollably never become cancerous due to the improbability of developing the necessary mutations, the action of the immune system and due to “anti-cancer safety features” like senescence. As such, most people develop hundreds of “proto-cancers”, benign tumors that never become problematic.

# data[].attributes.body
Cellular senescence is a process of complex changes that lead a cell to a chronic or acute state where it is unable to replicate, commit apoptosis, alter its chromatin or its metabolism. It is thought to have important roles in the inflammatory response, wound healing, embryological development and cancer prevention. Several stimuli can trigger senescence, like inflammation, epigenetic changes, cellular stresses or DNA damage, but the most relevant trigger for cancer is telomere shortening.

# data[].attributes.body
Epigenetic modifications of genes involved in immunologic response play an important role for predicting treatment outcomes.

The methylation status of T-cell differentiation factor forkhead box P1 and P3 (FOXP1 & FOXP3) was associated with about 60% reduced hazard of deaths in a cohort of NSCLC patients treated with ICI. In the same study, a DNA methylation signature was associated with an impressive 92% lower hazard of death.
Nonetheless, these findings need prospective analyses for validation and may be different for different types of cohorts.

# data[].attributes.body
Telomeres are segments of non-coding repetitive DNA (TTAGGG in vertebrates) associated with shelterin proteins that are found at the ends of chromosomes. The telomers protect and sequester the ends of the chromosomes, avoiding their identification as DNA brakes by the cellular DNA repair machinery, additionally they solve the “End Replication Problem”: DNA polymerase cannot replicate the sequences near the 3’ ends of chromosomes, as it can only extend a sequence from a primer in the 5’ to 3’ direction, as a result, each time a human cell replicates the telomeres of its chromosomes get 20 to 50 nucleotides shorter. The telomers exist as a buffer, avoiding the loss of coding DNA from the ends of the chromosomes every time a cell divides. When the telomeres reach a critical size, the cell becomes senescent. This system is a major cause of aging and is thought to have evolved as a way of controlling tumour growth. Any tumour that does not evolve a way to overcome this mechanism, reaches a state of senescence and stops growing, not posing a threat to the host.

# data[].attributes.body
The mechanisms mentioned represent genetic barriers to ICI, influencing the treatment's sucess and its patient's survival, and have been shown in cancers such as melanoma and lung cancer. It is likely that polymorphisms in other immunity related molecules, like the case for HLA, may also influence tumour and therapy responses, which calls for further research into the effect of host genetics. It also opens new doors to treatment, as germline DNA sequencing data could provide an overview of how a patient will respond to therapies and allow the adjustment to a better course of action.