A Serum Protein Classifier Identifying Patients with Advanced Non–Small Cell Lung Cancer Who Derive Clinical Benefit from Treatment with Immune Checkpoint Inhibitors

Mirte Muller, Karlijn Hummelink, Daan P. Hurkmans, Anna-Larissa N. Niemeijer, Kim Monkhorst, Joanna Roder, Carlos Oliveira, Heinrich Roder, Joachim G. Aerts, Egbert F. Smit*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Purpose: Pretreatment selection of patients with non–small cell lung cancer (NSCLC) who would derive clinical benefit from treatment with immune checkpoint inhibitors (CPIs) would fulfill an unmet clinical need by reducing unnecessary toxicities from treatment and result in substantial health care savings. Experimental Design: In a retrospective study, mass spectrometry (MS)-based proteomic analysis was performed on pretreatment sera derived from patients with advanced NSCLC treated with nivolumab as part of routine clinical care (n ¼ 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 was 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
Original languageEnglish
Pages (from-to)5188-5197
JournalClinical Cancer Research
Volume26
Issue number19
DOIs
Publication statusPublished - 1 Oct 2020

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