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

Abstract

Immunotherapies have recently emerged as a standard of care for advanced cancers, offering remarkable improvements in patient prognosis. However, only a subset of patients benefits and robust molecular predictors remain elusive. We present a computational framework leveraging sample-specific gene co-expression 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 expressionbased machine learning models. This work highlights the value of personalized gene network features for stratifying cancer patients and optimizing immunotherapy strategies.

Code repository

This repository contains data and code in order to reproduce the data analysis from the article entitled "Sample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma" (bioRxiv 2024.11.02.621068; doi: https://doi.org/10.1101/2024.11.02.621068).