doi:10.1016/j.isci.2025.113061engYin, LiangweiTraversa, PietroElati, MohamedMoreno, YamirMarek-Trzonkowska, NataliaBattail, ChristopheSample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinomaART-2025-147831Immunotherapies 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.2025http://zaguan.unizar.es/record/16838210.1016/j.isci.2025.113061http://zaguan.unizar.es/record/168382oai:zaguan.unizar.es:168382info:eu-repo/grantAgreement/EC/H2020/101017453/EU/Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity/KATYThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101017453-KATYISCIENCE 28, 8 (2025), 113061byhttps://creativecommons.org/licenses/by/4.0/deed.esinfo:eu-repo/semantics/openAccess