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

## TL;DR

Researchers developed a 186-gene signature that predicts response to chemoradiotherapy in rectal cancer, offering potential for personalized treatment decisions.

## Contribution

The study introduces a novel gene signature derived from integrating multiple transcriptomic datasets and validated across cancer subtypes.

## Key findings

- A 186-gene signature predicted chemoradiotherapy response with an AUC of 0.80 in cross-validation.
- The gene signature was associated with CMS4 and iCMS3 subtypes enriched in responders.
- Spatial transcriptomics identified compartment-specific genes with higher predictive value.

## 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.

•A 186-gene signature predicts response to nCRT in rectal cancer•Signature shows consistent performance across six GEO datasets•Associations observed with CMS4 and iCMS3 subtypes enriched in responders•Spatial transcriptomics identifies compartment-specific candidate genes

A 186-gene signature predicts response to nCRT in rectal cancer

Signature shows consistent performance across six GEO datasets

Associations observed with CMS4 and iCMS3 subtypes enriched in responders

Spatial transcriptomics identifies compartment-specific candidate genes

Oncology; Molecular biology; Transcriptomics