发表机构
University of Torino; University of Copenhagen; Helmholtz Munich（都灵大学; 哥本哈根大学; 慕尼黑赫尔姆霍茨中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Hist2Sig是一种深度学习框架，从H&E切片预测30种COSMIC突变特征暴露，在TCGA和CPTAC数据上验证，证明常规组织学推断突变特征的可行性。
AI 中文摘要
突变特征揭示了具有临床相关性的癌症驱动过程：MMR缺陷型肿瘤对免疫治疗反应更好，HRD型肿瘤对PARP抑制剂敏感，而POLE突变型肿瘤通常具有高突变负荷，影响治疗反应。然而，特征分析仍受限于全基因组测序（WGS）或全外显子组测序的成本、复杂性和周转时间。组织病理学广泛可用且成本效益高，H&E形态已与MSI、HRD、POLE相关过程及驱动突变相关联。组织学能否在泛癌背景下恢复更广泛的突变特征暴露图谱仍属未知。在此，我们引入Hist2Sig，一种从H&E染色切片预测30种COSMIC SBS特征暴露的深度学习框架。该框架在来自29种癌症类型的7,063名TCGA患者的配对WGS和组织学数据上训练，并与仅基于肿瘤类型的基线进行比较，以区分形态学来源的信号与组织起源先验。在内部交叉验证中，Hist2Sig在大多数肿瘤类型中恢复了相对特征组成，并在29种中的19种中实现了高于基线的平均前三重叠率，其中在COAD、PRAD、GBM和UCEC中增益最大。它还反复恢复了病因学特征不明确的特征，包括SBS8、SBS12、SBS39和SBS40a。在包含五种肿瘤类型193名患者的外部CPTAC队列中，Hist2Sig保持了与观察组成的中等一致性，并在胶质母细胞瘤和胰腺腺癌中优于基线。这些结果支持从常规组织学推断突变特征暴露的可行性，同时强调了不同肿瘤类型间的差异。Hist2Sig为肿瘤特异性模型奠定了基础，这些模型可在测序前分诊中补充测序，或在无法获得WGS时支持决策。
英文摘要
Mutational signatures reveal cancer-driving processes with clinical relevance: MMR-deficient tumors respond better to immunotherapy, HRD tumors are sensitive to PARP inhibitors, and POLE-mutant tumors often have high mutation burdens influencing treatment response. Yet signature profiling remains limited by the cost, complexity, and turnaround time of whole-genome sequencing (WGS) or whole-exome sequencing. Histopathology is widely available and cost-effective, and H&E morphology has been linked to MSI, HRD, POLE-related processes, and driver mutations. Whether histology can recover the broader landscape of mutational signature exposures in a pan-cancer setting remains unknown. Here we introduce Hist2Sig, a deep learning framework predicting exposures to 30 COSMIC SBS signatures from H&E-stained slides. Trained on matched WGS and histology data from 7,063 TCGA patients across 29 cancer types, Hist2Sig was compared with a tumor type-only baseline to separate morphology-derived signal from tissue-of-origin priors. In internal cross-validation, it recovered relative signature compositions across most tumor types and achieved higher mean top-three overlap than the baseline in 19 of 29, with the largest gains in COAD, PRAD, GBM, and UCEC. It also recurrently recovered signatures of poorly characterized etiology, including SBS8, SBS12, SBS39, and SBS40a. In an external CPTAC cohort of 193 patients across five tumor types, Hist2Sig retained moderate concordance with observed compositions and outperformed the baseline in glioblastoma and pancreatic adenocarcinoma. These results support the feasibility of inferring mutational signature exposures from routine histology, while highlighting variation across tumor types. Hist2Sig provides a foundation for tumor-specific models that could complement sequencing in pre-sequencing triage or support decisions where WGS is unavailable.
