Machine learning-guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance.

ABSTRACT: Immune checkpoint blockade (ICB) has transformed non-small cell lung cancer (NSCLC) treatment, but durable clinical responses remain limited, underscoring the need for robust predictive biomarkers. We integrated multiomics profiling with machine learning to systematically identify determinants of ICB efficacy. Comparative evaluation of 22 survival algorithms across four NSCLC cohorts (n=156) led to the development of an Accelerated Oblique Random Survival Forest model, which outperformed conventional Cox regression and deep learning methods in predictive accuracy (training C-index=0.864; test C-index=0.748). Single-cell RNA sequencing of an immunotherapy-treated cohort revealed that high-risk tumors harbor malignant epithelial subclusters expressing growth differentiation factor 15 (GDF15)

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12266572 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature