Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC

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 beyond conventional biomarker boundaries.

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Publication Details

Journal
Nature Communications
Published
2025-07-24
DOI
https://doi.org/10.1038/s41467-025-61823-w
Citations
5
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
5.23

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article

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC

Percy P. Lee, Carol C. Wu, Jia Wu, Xiuning Le et al.
5 citations
Nature Communications
Cancer Immunotherapy and Biomarkers
5.23
article

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC

Percy P. Lee, Carol C. Wu, Jia Wu, Xiuning Le, David A. Jaffray, Joe Y. Chang, Federica Pecci, Don L. Gibbons, Simon Heeke, Vivek Verma, Brett W. Carter, Ignacio I. Wistuba, Lingzhi Hong, Biagio Ricciuti, Mehmet Altan, Milena Petranović, Daniel Boiarsky, Maliazurina B. Saad, Girish S. Shroff, John Victor Heymach, Ara Asadur Vaporciyan, Cara Haymaker, Shenduo Li, Tina Cascone, Ajay Sheshadri, Qasem Al-Tashi, Alessandro Di Federico, Yasir Y. Elamin, Justin F. Gainor, Yanyan Lou, Mark M. Awad, Zhongxing Liao, Natalie I. Vokes, Seyedali Mirjalili, Jianjun Zhang, J. Jack Lee, Wentao Li
article en
5 citations

Abstract

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 beyond conventional biomarker boundaries.

Nature CommunicationsVol. 16(1)
The University of Texas MD Anderson Cancer Center (US), Harvard University (US), VSB - Technical University of Ostrava (CZ), Yale University (US), Massachusetts General Hospital (US), HOPE Clinic (US), Dana-Farber Cancer Institute (US), Torrens University Australia (AU), Mayo Clinic in Florida (US)
Cancer Prevention and Research Institute of Texas, National Institutes of Health, University of Texas MD Anderson Cancer Center
Openalex Percentile: Top 4%
Cancer Immunotherapy and Biomarkers
5.23
DOIOpen Access PDFOpenAlex
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