Sample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma

Yin, Liangwei ; Traversa, Pietro (Universidad de Zaragoza) ; Elati, Mohamed ; Moreno, Yamir (Universidad de Zaragoza) ; Marek-Trzonkowska, Natalia ; Battail, Christophe

Resumen: Immunotherapies have recently emerged as a standard of care for advanced cancers, offering remarkable improvements in patient prognosis. However, only a small subset of patients benefit, and robust molecular predictors remain elusive. We present a computational framework leveraging sample-specific gene coexpression networks to identify features predictive of immunotherapy response in kidney cancer. Our results reveal that patients with similar clinical outcomes exhibit comparable gene co-expression patterns. Notably, increased gene connectivity and stronger negative gene-gene associations are hallmarks of poor responders. We further developed sample-specific pathway-level network scores to detect dysregulated biological pathways linked to treatment outcomes. Finally, incorporating these sample-level network features improves the predictive performance of gene expression-based machine learning models. This work highlights the value of personalized gene network features for stratifying patients with cancer and optimizing immunotherapy strategies.

Idioma: Inglés

DOI: 10.1016/j.isci.2025.113061

Año: 2025

Publicado en: ISCIENCE 28, 8 (2025), 113061
ISSN: 2589-0042

Financiación: info:eu-repo/grantAgreement/EC/H2020/101017453/EU/Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity/KATY

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Física Teórica (Dpto. Física Teórica)

Dataset asociado: Sample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma ( https://zenodo.org/records/15723818)