1. Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer.
- Author
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Captier N, Lerousseau M, Orlhac F, Hovhannisyan-Baghdasarian N, Luporsi M, Woff E, Lagha S, Salamoun Feghali P, Lonjou C, Beaulaton C, Zinovyev A, Salmon H, Walter T, Buvat I, Girard N, and Barillot E
- Subjects
- Humans, Female, Male, Middle Aged, Aged, Machine Learning, Treatment Outcome, B7-H1 Antigen metabolism, B7-H1 Antigen genetics, Positron-Emission Tomography methods, Gene Expression Profiling, Carcinoma, Non-Small-Cell Lung genetics, Carcinoma, Non-Small-Cell Lung pathology, Carcinoma, Non-Small-Cell Lung therapy, Carcinoma, Non-Small-Cell Lung diagnostic imaging, Lung Neoplasms genetics, Lung Neoplasms pathology, Lung Neoplasms therapy, Lung Neoplasms diagnostic imaging, Lung Neoplasms immunology, Immunotherapy methods, Transcriptome, Biomarkers, Tumor genetics, Biomarkers, Tumor metabolism
- Abstract
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information. Testing multiple integration strategies, most of them yield multimodal models surpassing both the best unimodal models and established univariate biomarkers, such as PD-L1 expression. Additionally, several multimodal combinations demonstrate improved patient risk stratification compared to models built with routine clinical features only. Our study thus provides evidence of the superiority of multimodal over unimodal approaches, advocating for the collection of large multimodal NSCLC datasets to develop and validate robust and powerful immunotherapy biomarkers., Competing Interests: Competing interests: Nicolas Girard has a consulting or advisory role for the following companies: Abbvie, AMGEN, AstraZeneca, BeiGene, Bristol-Myers Squibb, Daiichi Sankyo/Astra Zeneca, Gilead Sciences, Ipsen, Janssen, LEO Pharma, Lilly, MSD, Novartis, Pfizer, Roche, Sanofi, Takeda. The other authors declare no competing interests., (© 2025. The Author(s).)
- Published
- 2025
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