Variables of prospective response rates of PD-1/PD-L1 based therapies across cancers

Variables of prospective response rates of PD-1/PD-L1 based therapies across cancers

Last Updated on August 28, 2019 by Joseph Gut – thasso

August 28, 2019 – Immune checkpoint inhibitor (ICI) therapy is a form of cancer immunotherapy. The therapy targets immune checkpoints, key regulators of the immune system that when stimulated can dampen the immune response to an immunologic stimulus. Some cancers can protect themselves from attack by stimulating immune checkpoint targets. Checkpoint therapy can block inhibitory checkpoints, thereby restoring the immune system’s repressed function. The first anti-cancer drug targeting an immune checkpoint was Ipilimumab (Yervoy), a CTLA4 blocker approved in the United States in 2011.

Thus, currently approved checkpoint inhibitors target the molecules CTLA4, PD-1, and PD-L1. PD-1 is the transmembrane programmed cell death 1 protein (also called PDCD1 or CD279), which interacts with PD-L1 (PD-1 ligand 1, or CD274). PD-L1 on the cell surface binds to PD-1 on an immune cell surface, which inhibits immune cell activity. Among PD-L1 functions is a key regulatory role on T cell activities. It appears that (cancer-mediated) upregulation of PD-L1 on cancer cell surfaces may inhibit T cells that otherwise might attack the very cancer cells. Antibodies that bind to either PD-1 or PD-L1 and therefore block this interaction may allow the T-cells to attack cancer cells (i.e., a given tumor or tumors). This could be seen as activation of a autoimmune reaction towards a person’s own tumor.

While ICIs are arguably the most important development in cancer therapy over the past decade, one of the costs of these advances is the emergence of a new spectrum of immune-related adverse events (irAEs), which are often distinctly different from the classical chemotherapy-related toxicities. A recent review article indicates that regardless of the ICI used, toxicities with fatal outcomes tend to occur early in the course of treatment and evolve rapidly, especially in patients receiving combinations of agents. The median time to the onset of a fatal toxic event is ~14.5 days for ICI combinations, whereas the onset of such events tends to be delayed to 40 days in patients receiving ICI monotherapies (P < 0.001). The spectrum of fatal irAEs differs widely between regimens, as demonstrated in a meta-analysis published in 2018. In this analysis, colitis was the most frequent cause of death as an irAE in patients receiving anti-CTLA-4 antibodies (135 (70%) of 193 deaths), whereas fatalities in patients receiving anti-PD-1 or anti-PD-L1 antibodies were mainly attributed to pneumonitis (115 (35%) of 333), hepatitis (75 (22%) of 333) and neurotoxic effects (50 (15%) of 333). In patients receiving combination therapies, ICI-related deaths were mainly attributed to colitis (32 (37%) of 87) or myocarditis (22 (25%) of 87). Of note, patients who develop myocarditis as an irAE have the highest fatality rate (52 (39.7%) of 131 events reported) across all treatment groups.

On the other side of the coin, the development of ICIs has led to the advancement of tumor agnostic therapies, which means the a biomarker (i.e., a molecular target such as, for example, a particular genetic variant) defines the tumour rather than the organ (tissue) from where the tumor originated, as has typically been the case in the past. Here, that would mean that any tumor, irrespective of its tissue origin, becomes treatable by one of the drugs that target the PD-1 / PD-L1 dependent immunosuppression. In fact, in 2017, the FDA has approved for the first time a cancer therapy  drug, i.e., Pembrolizumab (Keytruda) along these conceptual lines for the treatment of a wide variety of cancers, i.e., in a tumor agnostic way.

In clinical practice, PD-1/PD-L1 based ICIs show potent and durable anti-tumor effects, particularly in some refractory tumors. However, the primary problem for application of PD-1/PD-L1 inhibitors is the at best unsatisfactory response rate in overall cancer patients. Despite of this limitations, PD-1/PD-L1 inhibitors attract extensive attention with clinicians and patients alike. Therefore, it would be of great and urgent value if patient selection could be implemented prior to PD-1/PD-L1 inhibitors therapy, most likely based on genetic predictors of therapy response. Identifying predictive biomarkers to distinguish patients most likely to respond to immunotherapy from overall individuals would decrease treatment cost and avoid immune-related adverse events.

In a new article just published online in  the Journal JAMA Oncology, a research team now addressed these question: Can cancer patient responses to PD-1/PD-L1 based therapies? And what are most important variables that predict the response to therapy to inhibit PD-1 and PD-L1  across different cancer types?  The analysis of multiomics data from the Cancer Genome Atlas cohort and objective response rates to therapy data across 21 cancer types found that estimated CD8+ T-cell abundance is the most predictive, followed by tumor mutational burden and the fraction of samples with high programmed cell death 1 gene expression. This would mean that immune, neoantigen, and checkpoint target variables are required in combination for accurately predicting the response to therapy to inhibit programmed cell death 1 and its ligand across multiple cancers.

Specifically, the  research team analysed a broad range of data from whole-exome and RNA sequencing of 7187 patients from the publicly available Cancer Genome Atlas and the objective response rate (ORR) data of 21 cancer types obtained from a collection of clinical trials. Thirty-six variables of 3 distinct classes considered were associated with 1) tumor neoantigens, 2) tumor microenvironment and inflammation, and 3) the checkpoint targets. The performance of each class of variables and their combinations in predicting the overall response rate (ORR) to anti–PD-1/PD-L1 therapy was evaluated. Accuracy of predictions was quantified with Spearman correlation measured using a standard leave-one-out cross-validation, a statistical method of evaluating a statistical model by dividing data into 2 segments: one to train the model and the other to validate the model. Data were collected from October 19 through 31, 2018, and were analyzed from November 1 through December 14, 2018. Thus, among the 36 variables, estimated CD8+ T-cell abundance was the most predictive of the response to anti–PD-1/PD-L1 therapy across cancer types (Spearman R = 0.72; P < 2.3 × 10−4), followed by the tumor mutational burden (Spearman R = 0.68; P < 6.2 × 10−4), and the fraction of samples with high PD1 gene expression (Spearman R = 0.68; P < 6.9 × 10−4). Notably these top 3 variables cover the 3 classes considered, and their combination is highly correlated with response (Spearman R = 0.90; P < 4.1 × 10−8), explaining more than 80% of the ORR variance observed across different tumor types.

Overall, these findings suggest that the 3 key variables can explain most of the observed cross-cancer response variability, but their relative explanatory roles may vary in specific cancer types. If fully developed, these predictive approaches for therapy outcomes constitutes a perfect example what theragenomic medicine is all about.

See here a short sequence on immunossupression and cancer:

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Ph.D.; Professor in Pharmacology and Toxicology. Senior expert in theragenomic and personalized medicine and individualized drug safety. Senior expert in pharmaco- and toxicogenetics. Senior expert in human safety of drugs, chemicals, environmental pollutants, and dietary ingredients.

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