Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer

Nicolas Captier (), Marvin Lerousseau, Fanny Orlhac, Narinée Hovhannisyan-Baghdasarian, Marie Luporsi, Erwin Woff, Sarah Lagha, Paulette Salamoun Feghali, Christine Lonjou, Clément Beaulaton, Andrei Zinovyev, Hélène Salmon, Thomas Walter, Irène Buvat, Nicolas Girard and Emmanuel Barillot ()
Additional contact information
Nicolas Captier: PSL Research University
Marvin Lerousseau: PSL Research University
Fanny Orlhac: PSL Research University
Narinée Hovhannisyan-Baghdasarian: PSL Research University
Marie Luporsi: PSL Research University
Erwin Woff: PSL Research University
Sarah Lagha: Institut Curie
Paulette Salamoun Feghali: Institut Curie
Christine Lonjou: PSL Research University
Clément Beaulaton: Institut Curie
Andrei Zinovyev: Evotec
Hélène Salmon: PSL Research University
Thomas Walter: PSL Research University
Irène Buvat: PSL Research University
Nicolas Girard: Institut Curie
Emmanuel Barillot: PSL Research University

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: 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.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-55847-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-55847-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-55847-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().