Tumor Genomic Profiles Associated with Clinical Benefit from Immune Checkpoint Inhibitor Therapy – An Exploratory Analysis of the INFINITY Precision Oncology Registry

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Background and Purpose: The multicenter precision oncology registry INFINITY investigated biomarker-driven therapy and outcomes in patients with advanced malignancies not eligible for standard therapy in routine clinical care in Germany. Although programmed cell death (ligand) 1 (PD-(L)1) antibodies have changed the treatment landscape of several malignancies and PD-(L)1 expression level is predictive and therefore often used for treatment selection, existing predictive biomarkers are insufficient to identify patients who will benefit from PD-(L)1 inhibitor-based therapy. This project aimed to identify genomic tumor profiles predicting benefit from anti-PD-(L)1 therapy. Patients and Methods: Patients from the INFINITY registry treated with anti-PD-(L)1 monotherapy were stratified into two cohorts using a case–control design: clinical benefit (treatment duration > 182 days) and no clinical benefit (treatment duration 22– 63 days). Tumor tissue samples from the virtual biobank were requested from local pathologies and sent to the central pathology for next‑generation sequencing (NGS) using the Oncomine TM Comprehensive Assay Plus panel (Thermo Fisher, NGS pan-cancer assay). Results: Of the 26 patients with clinical benefit and 24 patients without clinical benefit, NGS-based genetic tumor profiles were available for 11 and 8 patients, respectively. Only patients with clinical benefit from anti-PD-(L)1 therapy showed genetic alterations in KEAP1, PIK3CA and MRE11 . Conclusion: Our analysis revealed a set of three genes ( KEAP1, PIK3CA and MRE11 ) exclusively mutated in patients with clinical benefit from PD-(L)1-inhibitor therapy, possibly representing promising biomarker candidates. Given the small sample size and the heterogeneous tumor types included, these findings are exploratory, and validation in larger, preferably tumor-type-specific cohorts is required before any predictive value can be attributed to these genomic alterations. Keywords: registries, biomarkers, real-world, precision oncology, PD-L1

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