Senescence-aware filtering facilitates robust prediction of cancer immunotherapy outcomes

Predictive biomarkers for immune-checkpoint inhibitor (ICI) therapy response often fail to show stable predictive performance because tumor microenvironment (TME) heterogeneity can create biologically confounding tumor states, where similar molecular patterns can lead to divergent therapeutic outcomes. When analyzed together, these biologically distinct states can act as out-of-distribution (OOD) samples, complicating model training and limiting predictive consistency. Here, we present a stepwise framework that improves target-based ICI-response prediction by excluding senescence-associated tumor states prior to model training, thereby reducing biological heterogeneity that can obscure the relationship between checkpoint activity and therapeutic response. The framework leverages network-based representations of immune-checkpoint and senescence pathways to identify senescence-associated non-responders (SNRs). Across multiple melanoma, gastric, bladder, and lung cancer cohorts, this approach improved accuracy, AUROC, precision, and specificity in within-cohort evaluations, and showed consistent performance in external validation using independent melanoma cohorts. The excluded tumors were enriched for senescence-associated markers, suggesting that they exhibit senescence-related transcriptional features that may act as biological confounders limiting the predictive capacity of standard target-based models. These results suggest that confounder-aware filtering of tumor microenvironment states can provide a practical approach to improving ICI therapy response prediction.