Molecular alterations in cancer can cause phenotypic changes in tumor cells and their microenvironment. Routine histopathology tissue slides, which are ubiquitously available, can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5,000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype–phenotype links in cancer.

癌症中的分子改变可导致肿瘤细胞及其微环境的表型变化。随处可见的常规组织病理学组织切片可以反映这种形态学变化。在这里，我们表明深度学习可以直接从常规组织学中推断出广泛的基因突变、分子肿瘤亚型、基因表达特征和标准病理生物标志物。我们开发、优化、验证并公开发布了一站式工作流程，并将其应用于跨越多个实体瘤的 5,000 多名患者的组织切片。我们的研究结果表明，可以训练一个单一的深度学习算法来预测来自用苏木精和伊红染色的常规石蜡包埋组织学载玻片的广泛分子变化。这些预测推广到其他人群并在空间上得到解决。我们的方法可以在移动硬件上实现，有可能实现个性化癌症治疗的即时诊断。更一般地说，这种方法可以阐明和量化癌症中的基因型-表型联系。