Integrating transcriptomics and histopathology can improve cancer risk modelling, yet practical use is constrained by the limited availability of RNA profiling in routine settings. Here we introduce Mixture of Pathway Experts (MoPE), a knowledge-distillation framework that reframes multimodal learning as privileged distillation for histology-only inference. MoPE is motivated by the partial observability between RNA profiles and whole-slide images: histology can capture morphology-linked consequences of certain molecular programmes, but cannot be expected to reconstruct the full transcriptomic state. MoPE encodes RNA-derived pathways and transfers the molecular supervision to pathway-indexed pathology experts through memory-usage alignment. Across diverse public benchmarks and two independent breast cancer cohorts, MoPE consistently improved WSI-only inference performance relative to baseline methods. Pathway-usage analyses and human-audited visual inspection provide bounded inspection of model behaviour and candidate morphology-linked readouts. These results support pathway-structured privileged distillation as a promising route to using molecular information during training while preserving RNA-free inference.

翻译：整合转录组学与组织病理学可改善癌症风险建模，但实际应用中RNA谱分析在日常检测中的有限可用性构成制约。本文提出混合路径专家（Mixture of Pathway Experts, MoPE）框架——一种将多模态学习重构为组织学推理特权蒸馏的知识蒸馏框架。MoPE的提出基于RNA谱与全切片图像之间的部分可观测性：组织学可捕获特定分子程序相关的形态学后果，但无法完整重建转录组状态。MoPE通过编码RNA衍生通路，并借助显存占用对齐技术将分子监督信号传递至通路索引病理学专家。在多个公开基准数据集及两个独立乳腺癌队列中，相较于基线方法，MoPE持续提升了仅基于全切片图像的推理性能。通路使用分析与人机协同视觉检查提供了模型行为的边界化审查及候选形态学相关读数。这些结果表明，路径结构特权蒸馏是在训练阶段利用分子信息、同时保持无RNA推理能力的可行方案。