Nature Communications (Jul 2025)
Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC
- Maliazurina B. Saad,
- Qasem Al-Tashi,
- Lingzhi Hong,
- Vivek Verma,
- Wentao Li,
- Daniel Boiarsky,
- Shenduo Li,
- Milena Petranovic,
- 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 Jaffray,
- Justin F. Gainor,
- Yanyan Lou,
- Alessandro Di Federico,
- Federica Pecci,
- Mark Awad,
- Biagio Ricciuti,
- John V. Heymach,
- Natalie I. Vokes,
- Jianjun Zhang,
- Jia Wu
Affiliations
- Maliazurina B. Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Qasem Al-Tashi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Lingzhi Hong
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Vivek Verma
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center
- Wentao Li
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Daniel Boiarsky
- Division of Medical Oncology, Yale University School of Medicine
- Shenduo Li
- Division of Hematology and Oncology, Mayo Clinic
- Milena Petranovic
- Department of Radiology, Massachusetts General Hospital
- Carol C. Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center
- Brett W. Carter
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center
- Girish S. Shroff
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center
- Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Yasir Y. Elamin
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Mehmet Altan
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Simon Heeke
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center
- Joe Y. Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center
- Percy P. Lee
- Department of Radiation Oncology, City of Hope Orange County
- Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center
- Don L. Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Ara A. Vaporciyan
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center
- J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
- Ignacio I. Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center
- Cara Haymaker
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center
- Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley
- David Jaffray
- Office of the Chief Technology and Digital Officer, The University of Texas MD Anderson Cancer Center
- Justin F. Gainor
- Department of Medicine, Massachusetts General Hospital
- Yanyan Lou
- Division of Hematology and Oncology, Mayo Clinic
- Alessandro Di Federico
- Department of Medicine, Harvard Medical School
- Federica Pecci
- Department of Medicine, Harvard Medical School
- Mark Awad
- Department of Medicine, Harvard Medical School
- Biagio Ricciuti
- Department of Medicine, Harvard Medical School
- John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center
- Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- DOI
- https://doi.org/10.1038/s41467-025-61823-w
- Journal volume & issue
-
Vol. 16,
no. 1
pp. 1 – 11
Abstract
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.
