Abstract
e21110 Background: Immune checkpoint inhibitor (ICI) therapy represents one of the most promising cancer treatments to date. However, despite unprecedented rates of durable response, only a small proportion of patients benefits from this approach. Major efforts are therefore required to characterize treatment resistance mechanisms, as well as to identify reliable biomarkers for response. We have previously shown that in response to various types of cancer therapy, including ICIs, the host may induce pro-tumorigenic processes that can promote therapy resistance. Here we examined systemic host-response proteomic profiles in non-small cell lung cancer (NSCLC) patients, aiming to discover biomarkers for response to ICI therapy and to unravel underlying resistance mechanisms. Methods: As part of our ongoing PROPHETIC clinical trial (NCT04056247), plasma samples were obtained at baseline (T0) and early-on treatment (T1; following the first treatment) from 120 NSCLC patients receiving ICI therapy. Proteomic profiling of the plasma samples was performed using proximity-extension assay (PEA) technology; validation was carried out for a fraction of the samples using ELISA-based arrays. To identify a proteomic signature that predicts clinical outcome, machine learning algorithms were applied following a random separation of the cohort into a discovery set and a validation set. Results: A proteomic signature predictive of response to treatment was identified and validated. Bioinformatic analysis identified potential mechanisms of resistance based on differentially expressed proteins associated with pro-tumorigenic biological processes. Statistical analysis of the clinical data identified multiple novel differential clinical parameters between responders and non-responders, either at baseline or by comparing T0 to T1, which may suggest host-mediated effects. Conclusions: Our study demonstrates the potential clinical utility of analyzing the host response to ICI therapy, in particular for the discovery of novel predictive biomarkers for NSCLC patient stratification. Clinical trial information: NCT04056247.
Published Version
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