TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification

2026-06-05

AI Summary

Current pathway-guided models lack a unified benchmark for simultaneously predicting eligibility for targeted therapy, need for radiotherapy, and six-month survival. This study proposes the first integrative evaluation framework based on Reactome pathway activity scores, jointly training three bioinformatic architectures—BINN, GraphPath, and PATH—across five TCGA cancer cohorts to enable multitask clinical outcome prediction. It innovatively applies deep learning over pathway structures to jointly model therapeutic response and survival, while establishing a cross-model protocol for fair comparison. Results show that PATH achieves overall superior performance in targeted therapy prediction, BINN excels in survival prediction, and GraphPath attains an AUROC of 0.92 for targeted therapy prediction in prostate cancer with well-defined driver mutations. Radiotherapy prediction remains suboptimal, likely because key decision-making factors are not captured in gene expression data.

Abstract

Cancer treatment planning requires decisions across multiple clinical dimensions at once. Clinicians must determine whether a patient should receive targeted molecular therapy, radiation therapy, and whether they are likely to survive beyond six months. Existing pathway-informed deep learning models have been developed and tested in isolation, making fair comparison across architectures impossible. We present the first unified benchmark for pathway-guided therapy response modeling, evaluating three biologically informed architectures, BINN, GraphPath, and PATH, across five cancer cohorts drawn from The Cancer Genome Atlas, representing 2,622 patients encoded using Reactome pathway activity scores. Each model is trained jointly on all three clinical outcomes under identical data and evaluation conditions, the first study to treat pathway-structured deep learning as a combined therapy and survival prediction problem. Our results show that no single architecture wins across all tasks: PATH performs best for targeted molecular therapy prediction overall, BINN is most reliable for survival prediction, and no model produces useful predictions for radiation therapy, as the key drivers of that decision are clinical variables not captured in gene expression data. Most strikingly, GraphPath achieves an AUROC of 0.92 on prostate targeted molecular therapy prediction, the highest score in the entire benchmark, demonstrating that lateral co-regulation structure produces exceptional discriminative power when matched to a cohort with a narrow targetable driver programme, even under conditions of extreme class imbalance at only 11\% positive prevalence.

Problem

• pathway-informed modeling
• therapy response prediction
• multi-task clinical decision
• cancer treatment planning
• model benchmarking

Innovation

• pathway-informed deep learning
• unified benchmark
• therapy response prediction
• multi-task clinical modeling
• GraphPath