构建跨癌种组织形态图谱，关联病理特征与分子机制及预后。
HistoAtlas: A Pan-Cancer Morphology Atlas Linking Histomics to Molecular Programs and Clinical Outcomes
- 从6745张H&E切片提取38个可解释的组织学特征。
- 发现多个与生存、基因表达和突变相关的形态信号，部分具分层预后价值。
- 结果可空间定位至组织区域与单细胞，适合病理与计算生物研究者使用。
我们提出HistoAtlas，一个跨21种TCGA癌症类型的计算组织学图谱，从6,745张诊断性H&E切片中提取38个可解释的组织学特征，并系统关联每个特征与生存率、基因表达、体细胞突变及免疫亚型。所有关联均经过协变量调整、多重检验校正，并按证据强度分级。图谱复现了已知生物学现象，如免疫浸润、预后、增殖与激酶信号，同时揭示了特定组织区室中的免疫信号及具有不同预后的形态亚型。所有结果可追溯至组织区室与单个细胞，经统计校准，且可公开查询。HistoAtlas使常规H&E染色实现系统性、大规模生物标志物发现，无需特殊染色或测序。数据与交互式网络图谱免费开放：https://histoatlas.com。
原文摘要 · Abstract (English)
We present HistoAtlas, a pan-cancer computational atlas that extracts 38 interpretable histomic features from 6,745 diagnostic H&E slides across 21 TCGA cancer types and systematically links every feature to survival, gene expression, somatic mutations, and immune subtypes. All associations are covariate-adjusted, multiple-testing corrected, and classified into evidence-strength tiers. The atlas recovers known biology, from immune infiltration and prognosis to proliferation and kinase signaling, while uncovering compartment-specific immune signals and morphological subtypes with divergent outcomes. Every result is spatially traceable to tissue compartments and individual cells, statistically calibrated, and openly queryable. HistoAtlas enables systematic, large-scale biomarker discovery from routine H&E without specialized staining or sequencing. Data and an interactive web atlas are freely available at https://histoatlas.com .
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