Development and validation of an interpretable machine learning model for predicting progression-free survival after immunotherapy in patients with non-small cell lung cancer: a multicenter study
This study establishes a robust, interpretable ctDNA-derived machine learning algorithm for predicting PFS in NSCLC patients receiving immune checkpoint inhibitors. The identification of TP53, BRCA2, and NOTCH1 as biologically plausible predictive biomarkers advances understanding of immunotherapy response mechanisms and enables clinically actionable risk stratification to guide therapeutic decision-making. These findings underscore the need for prospective multicenter validation to facilitate translation into precision oncology practice.
Authors
- Tony He (ORCID: https://orcid.org/0000-0002-5363-7780)
- Hongyan Luo
- Benlan Li
- Shengfa Su (ORCID: https://orcid.org/0000-0002-6429-0081)
- Daobin Zhou (ORCID: https://orcid.org/0000-0002-1592-1932)
- Xingxing Zhao (ORCID: https://orcid.org/0000-0001-5045-8130)
- Dan Zou
- Fangyang Lu
- Xuzhen Liu (ORCID: https://orcid.org/0009-0008-2365-7079)
- M. Zhang
- Xia Ji (ORCID: https://orcid.org/0000-0002-2820-0405)
- Yong‐Jie Hu (ORCID: https://orcid.org/0000-0003-1500-4015)
- Zhongjun Huang
- Ya Li (ORCID: https://orcid.org/0000-0001-7246-2069)
- Chuan Zhang (ORCID: https://orcid.org/0000-0002-9311-0799)
- Min Zhang (ORCID: https://orcid.org/0009-0002-1427-6721)
- Jie Peng
- Minfang Wang
Institutions
- Guiyang Medical University (CN)
- Guizhou Cancer Hospital (CN)
- Affiliated Hospital of Guizhou Medical University (CN)
- Guiyang University (CN)
- People's Hospital of Shiyan (CN)
- Xintai People's Hospital (CN)
- Liupanshui Normal University (CN)
- The First People's Hospital of Guiyang (CN)
- Cixian People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-12-19
- DOI
- https://doi.org/10.3389/fimmu.2025.1686260
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 0.00