Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC

作者：Maliazurina B. Saad, Qasem Al-Tashi, Lingzhi Hong, Vivek Verma, Wentao Li, Daniel Boiarsky, Shenduo Li, Milena Petranović, Carol C. Wu, Brett W. Carter, Girish S. Shroff, Tina Cascone, Xiuning Le, Yasir Y. Elamin, Mehmet Altan, Simon Heeke, Ajay Sheshadri, Joe Y. Chang, Percy P. Lee, Zhongxing Liao, Don L. Gibbons, Ara A. Vaporciyan, J. Jack Lee, Ignacio I. Wistuba, Cara Haymaker, Seyedali Mirjalili, David A. Jaffray, Justin F. Gainor, Yanyan Lou, Alessandro Di Federico, Federica Pecci, Mark M. Awad, Biagio Ricciuti, John V. Heymach, Natalie I. Vokes, Jianjun Zhang, Jia Wu · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-61823-w · 被引用次数：8 · 研究领域：Cancer Immunotherapy and Biomarkers、Lung Cancer Treatments and Mutations、Cancer Genomics and Diagnostics

Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remains limited, and single-feature biomarkers like PD-L1 prove inadequate. We develop a machine learning model using clinicogenomic data from four cohorts (MD Anderson n = 750; Mayo Clinic n = 80; Dana-Farber n = 1077; Stand Up To Cancer n = 393) to predict individual benefit from adding chemotherapy. Benefit scores are calculated using five distinct functions derived from 28 genomic and 6 clinical features. Our integrated model, A-STEP (Attention-based Scoring for Treatment Effect Prediction), estimates heterogeneous treatment effects and achieves the largest reduction in 3-month progression risk, improving weighted risk reduction by 13-23% over stand-alone models. A-STEP recommends treatment changes for over 50% of patients, most often favoring ICI-Chemo. In simulation on external cohort, patients treated in accordance with A-STEP recommendations show improved 2-year progression-free survival (HR = 0.60 for ICI-Mono treatment arm; HR = 0.58 for ICI-Chemo treatment arm). Predictive features include FBXW7, APC, and PD-L1. In this study, we demonstrate how machine learning can fill critical gaps in immunotherapy selection for NSCLC, by modeling treatment heterogeneity with real-world clinicogenomic data, driving precision medicine beyon...