Machine learning modeling of patient health signals informs long-term survival on immune checkpoint inhibitor therapy

Abstract

System-level patient health signals, as captured by treatment-emergent adverse events (TEAEs), might contain correlates of immune checkpoint inhibitor (ICI) therapy response. Using all TEAEs and a novel machine learning modeling approach, we derived a composite signature predictive of, and potentially specific to, the response to the anti-PD-L1 ICI durvalumab in patients with non-small-cell lung cancer (NSCLC). We trained on data from the durvalumab arm and chemotherapy arm in the MYSTIC clinical trial and tested on data from four independent durvalumab-containing NSCLC trials using only the first 60days' TEAEs. We directly compared our signature performance against that of three different definitions of immune-related adverse events. Only our signature was predictive and identified longer survivors in patients treated with durvalumab but not in patients treated with chemotherapy or placebo. It also identified durvalumab-treated long survivors with stable disease at their first RECIST evaluation and a set of PD-L1-negative long survivors.