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 60 days' 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 longe

SUBMITTER: Sun GJ 

PROVIDER: S-EPMC11379673 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature