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.

翻译：癌症治疗规划需同时协调多个临床维度的决策。临床医生必须判断患者是否应接受靶向分子治疗、放射治疗，以及患者是否可能存活超过六个月。现有基于通路信息的深度学习模型均为独立开发与测试，无法实现跨架构的公平比较。我们提出了首个面向通路引导疗法反应建模的统一基准，利用Reactome通路活性评分编码的2,622例患者数据，评估了BINN、GraphPath和PATH三种生物学信息架构在癌症基因组图谱中五个癌症队列上的表现。每个模型均在相同数据与评估条件下联合训练全部三个临床结局，这是首次将通路结构化深度学习视为联合疗法与生存预测问题的研究。结果显示，无单一架构可通吃所有任务：PATH在整体靶向分子疗法预测中表现最佳，BINN在生存预测中最可靠，而所有模型对放射疗法均未产生有效预测——该决策的关键驱动因素为基因表达数据未捕获的临床变量。最引人注目的是，GraphPath在前列腺癌靶向分子疗法预测中达到0.92的AUROC值，为整个基准测试的最高分，证明即使在极端类别不平衡（阳性率仅11%）条件下，侧向协同调控结构仍能在匹配到窄靶向驱动程序的队列时产生卓越判别能力。