We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal tissue distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations across cancer types. This includes whole-genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions, as well as driver gene mutations. There are widespread associations between bulk gene expression levels and histopathology, which reflect tumor composition and enable the localization of transcriptomically defined tumor-infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading, and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings show the remarkable potential of computer vision in characterizing the molecular basis of tumor histopathology.

我们使用深度迁移学习来量化来自 28 种癌症类型的 17,355 张苏木精和伊红染色的组织病理学切片图像的组织病理学模式，并将这些与匹配的基因组、转录组和生存数据相关联。这种方法准确地分类癌症类型并提供空间分辨的肿瘤和正常组织的区别。自动学习的计算组织病理学特征与各种癌症类型的大量复发性遗传畸变相关。这包括全基因组重复，它们显示出跨癌症类型、个体染色体非整倍体、局灶性扩增和缺失以及驱动基因突变的普遍特征。大量基因表达水平和组织病理学之间存在广泛的关联，它反映了肿瘤的组成并能够定位转录组定义的肿瘤浸润淋巴细胞。计算组织病理学增强了基于组织病理学亚型和分级的预后，并突出了与预后相关的区域，例如坏死或淋巴细胞聚集。这些发现显示了计算机视觉在表征肿瘤组织病理学的分子基础方面的巨大潜力。