Integrative analysis of transcriptomic data reveals a predictive gene signature for chemoradiotherapy response in rectal cancer

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemoradiotherapy (nCRT), but only a minority of patients achieve a pathological complete response (pCR). Predictive biomarkers of response could help guide treatment decisions, yet none have reached clinical practice. In this exploratory study, we integrated six publicly available transcriptomic datasets and applied machine learning to derive a 186-gene signature predictive of nCRT response. The signature showed good performance in cross-validation (AUC 0.80) and was associated with consensus molecular (CMS4) and immune (iCMS3) subtypes enriched in responders. Gene set enrichment analyses highlighted pathways involved in tumor growth, immune regulation, and resistance. Spatial transcriptomic profiling of pre-treatment biopsies further identified compartment-specific markers, with tumor-associated genes showing greater predictive value. These results provide biological insights into response mechanisms and generate hypotheses for future validation. Larger prospective studies are required to assess the clinical utility of this approach.

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Publication Details

Journal
iScience
Published
2025-12-17
DOI
https://doi.org/10.1016/j.isci.2025.114455
Primary Topic
Colorectal Cancer Surgical Treatments
Type
article
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article

Integrative analysis of transcriptomic data reveals a predictive gene signature for chemoradiotherapy response in rectal cancer

Jonathan Thévenet, Valérie Dutoit, Mikäel J. Pittet, Giacomo Puppa et al.
iScience
Colorectal Cancer Surgical Treatments
article

Integrative analysis of transcriptomic data reveals a predictive gene signature for chemoradiotherapy response in rectal cancer

Jonathan Thévenet, Valérie Dutoit, Mikäel J. Pittet, Giacomo Puppa, Stéphanie Tissot-Renaud, Paolo Angelino, Claudia Corrò, Arnaud Bakaric, André Durham, Melivoia Rapti, Joao Victor Machado Carvalho, Pratyaksha Wirapati, Frédéric Ris, Inti Zlobec, Petros Tsantoulis, Thibaud Koessler, Matthieu Tihy
article en

Abstract

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemoradiotherapy (nCRT), but only a minority of patients achieve a pathological complete response (pCR). Predictive biomarkers of response could help guide treatment decisions, yet none have reached clinical practice. In this exploratory study, we integrated six publicly available transcriptomic datasets and applied machine learning to derive a 186-gene signature predictive of nCRT response. The signature showed good performance in cross-validation (AUC 0.80) and was associated with consensus molecular (CMS4) and immune (iCMS3) subtypes enriched in responders. Gene set enrichment analyses highlighted pathways involved in tumor growth, immune regulation, and resistance. Spatial transcriptomic profiling of pre-treatment biopsies further identified compartment-specific markers, with tumor-associated genes showing greater predictive value. These results provide biological insights into response mechanisms and generate hypotheses for future validation. Larger prospective studies are required to assess the clinical utility of this approach.

iScienceVol. 29(1)
University of Geneva (CH), University of Bern (CH), SIB Swiss Institute of Bioinformatics (CH), Université Paris Cité (FR), Hôpital Beaujon (FR), Assistance Publique – Hôpitaux de Paris (FR), University Hospital of Geneva (CH), Swiss Cancer Center Léman (CH), University of Lausanne (CH)
Openalex Percentile: Top 44%
Colorectal Cancer Surgical Treatments
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