A serum protein classifier identifying patients with advanced non-small cell lung cancer who derive clinical benefit from treatment with immune checkpoint inhibitors.

Purpose: Pretreatment selection of non-small-cell lung cancer (NSCLC) patients who derive clinical benefit from treatment with immune checkpoint inhibitors would fulfill an unmet clinical need by reducing unnecessary toxicities from treatment and result in substantial health care savings. Patients and Methods: In a retrospective study, mass spectrometry (MS) based proteomic analysis was performed on pretreatment sera derived from advanced NSCLC patients treated with nivolumab as part of routine clinical care (n=289). Machine learning combined spectral and clinical data to stratify patients into three groups with good ("sensitive"), intermediate and poor ("resistant") outcomes following treatment in the second-line setting. The test was applied to three independent patient cohorts and its biology investigated using protein set enrichment analyses (PSEA). Results: A signature consisting of 274 MS features derived from a development set of 116 patients was associated with progression free survival (PFS) and overall survival (OS) across 2 validation cohorts (n=98 and n=75). In pooled analysis, significantly better OS was demonstrated for "sensitive" relative to "not sensitive" patients treated with nivolumab, HR 0.58 (95% CI 0.38-0-87, p=0.009). There was no significant association with clinical factors including PD-L1 expression, available from 133/289 patients. The test demonstrated no significant association with PFS or OS in a historical cohort (n=68) of second-line NSCLC patients treated with docetaxel. PSEA revealed proteomic classification to be significantly associated with complement and wound healing cascades. Conclusions: This serum-derived protein signature successfully stratified outcomes in cohorts of advanced NSCLC patients treated with second line PD-1 checkpoint inhibitors and deserves further prospective study.