用基因组信息训练病理图像模型，实现仅凭切片预测分子特征
Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images
- 训练时引入转录组数据，生成与基因组对齐的病理嵌入
- 49项任务中14项生物标志物预测AUC≥0.80，5种癌症生存预测C-index≥0.70
- 可揭示肿瘤细胞形态与特定基因型的关联，适合临床病理研究
精准肿瘤学需要准确的分子信息，但直接获取基因组数据成本高、耗时长，难以广泛应用于临床。当前深度学习方法仍难以从常规全幻灯片图像（WSI）中准确预测复杂分子特征和患者预后。本文提出PathLUPI，利用训练期间的转录组特权信息，提取与基因组对齐的组织学嵌入，使推理阶段仅需WSI即可实现有效的分子预测。在涵盖20个队列、共11,257例样本的49项分子肿瘤学任务中，PathLUPI表现优于仅使用WSI训练的传统方法。关键结果包括：14项生物标志物预测和分子分型任务的AUC ≥ 0.80，5种主要癌症类型的生存队列中C-index ≥ 0.70。此外，PathLUPI嵌入揭示了与特定基因型及生物通路相关的细胞形态特征。通过将分子上下文编码至WSI表示中，该模型克服了现有方法的关键局限，为连接分子洞察与常规病理流程提供了新策略。
原文摘要 · Abstract (English)
Precision oncology requires accurate molecular insights, yet obtaining these directly from genomics is costly and time-consuming for broad clinical use. Predicting complex molecular features and patient prognosis directly from routine whole-slide images (WSI) remains a major challenge for current deep learning methods. Here we introduce PathLUPI, which uses transcriptomic privileged information during training to extract genome-anchored histological embeddings, enabling effective molecular prediction using only WSIs at inference. Through extensive evaluation across 49 molecular oncology tasks using 11,257 cases among 20 cohorts, PathLUPI demonstrated superior performance compared to conventional methods trained solely on WSIs. Crucially, it achieves AUC $\geq$ 0.80 in 14 of the biomarker prediction and molecular subtyping tasks and C-index $\geq$ 0.70 in survival cohorts of 5 major cancer types. Moreover, PathLUPI embeddings reveal distinct cellular morphological signatures associated with specific genotypes and related biological pathways within WSIs. By effectively encoding molecular context to refine WSI representations, PathLUPI overcomes a key limitation of existing models and offers a novel strategy to bridge molecular insights with routine pathology workflows for wider clinical application.
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