TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification

Cancer treatment involves decisions across multiple clinical outcomes, yet pathway-informed deep learning models are typically evaluated in isolation, making their relative benefits unclear. We present a harmonized benchmark of three biologically informed architectures, BINN, GraphPath, and PATH, for predicting treatment exposure and short-term survival across five TCGA cancer cohorts comprising 2,622 patients represented by Reactome pathway activity scores. Treatment labels indicate recorded exposure in TCGA rather than therapeutic response. All models jointly predict targeted molecular therapy (TMT), radiation therapy (RT), and six-month overall survival (OS) from a shared pathway representation and are evaluated on identical stratified folds using five repeated splits and paired-bootstrap testing. Under this controlled evaluation, most differences between architectures fall within 95 percent confidence intervals, indicating that rankings suggested by isolated evaluations are largely not statistically resolved. The main exception is survival prediction: the sparse-hierarchy BINN significantly outperforms both graph models on breast-cancer OS, with an AUROC improvement of up to 0.14 and p less than or equal to 0.01, and leads on lung and prostate OS. For treatment exposure, TMT is best discriminated in prostate cancer, with AUROC approximately 0.80 for all models, but no architecture significantly outperforms another on any TMT cohort. RT prediction remains weak across models, suggesting that its determinants may be more clinical than transcriptomic. Overall, architecture choice has limited impact under a unified evaluation, while short-term survival provides the clearest differentiation among pathway-informed models.