Published in:
01-02-2020 | Checkpoint Inhibitors | PHASE II STUDIES
Germinal Immunogenetics predict treatment outcome for PD-1/PD-L1 checkpoint inhibitors
Authors:
Sadal Refae, Jocelyn Gal, Nathalie Ebran, Josiane Otto, Delphine Borchiellini, Frederic Peyrade, Emmanuel Chamorey, Patrick Brest, Gérard Milano, Esma Saada-Bouzid
Published in:
Investigational New Drugs
|
Issue 1/2020
Login to get access
Summary
Background Checkpoint inhibitors bring marked benefits but only in a minority of patients and may also be associated with severe adverse events. Treatment outcome still cannot be faithfully predicted. The following study hypothesized that host genetics could be applied as predictive biomarkers for checkpoint inhibitor response and immune-related adverse events. We conducted a study based on germinal polymorphisms from genes coding for proteins involved in immune regulation. Methods Germinal DNA was obtained from advanced cancer patients treated with anti-PD-1/PD-L1 checkpoint inhibitors. DNA was genotyped using a custom panel of 166 single nucleotide polymorphisms covering 86 preselected immunogenetic-related genes. Computational analysis using a GTEX portal was made to determine potential expression Quantitative Trait Loci in tissues. Results Ninety-four consecutive patients were included. Objective response rate (complete or partial response) was significantly correlated to tumor microenvironment-related SNPs concerning CCL2, NOS3, IL1RN, IL12B, CXCR3 and IL6R genes. Toxicity were linked to target-related gene SNPs including UNG, IFNW1, CTLA4, PD-L1 and IFNL4 genes. The Area Under the ROC curve (AUC) was 0.81 (95% CI: 0.72–0.9) for response and 0.89 (95% CI: 0.76–1.00) for toxicity. In silico functionality exploring pointed rs4845618 (IL6R), rs10964859 (IFNW1) and rs3087243 (CTLA4) as potentially impacting gene expression. Conclusion These results strongly support a role for distinct immunogenetic-related gene SNPs able to predict efficacy and safety of anti-PD1/PD-L1 therapies. The results highlight the existence of patient-specific, germinal biomarkers able predict response to checkpoint inhibitor efficacy and, possibly, to predict treatment-related adverse events.