· 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

· Rapidly evolve;

· Divide continuously;

· Manipulate the surrounding tissues; 

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

· Invade other tissues;

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

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.

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.

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.

-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.

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. 

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

- 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.

- 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.

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

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.

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. 

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;

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.