文章目录

泛癌计算组织病理学揭示突变、肿瘤组成和预后

原文发布日期：2020-07-27

英文摘要：

摘要翻译：

原文链接：

文章：

泛癌计算组织病理学揭示突变、肿瘤组成和预后

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

原文发布日期：2020-07-27

英文摘要：

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.

摘要翻译：

我们使用深度跨域迁移学习，对28种癌症类型共17,355张哈希图染色图像进行组织学模式量化，结合匹配的基因组、转录组及生存数据进行相关分析。该方法能准确分类癌症类型，并提供空间定位的肿瘤与正常组织区分。自动学习的计算组织学特征与多种癌症类型相关的大量遗传异常存在关联，其中包括全基因组重复（显示跨癌症类型的一致性特征）、个别染色体不整、局部增殖和缺失，以及关键基因突变。 bulk基因表达水平与组织学特征呈现广泛关联，反映肿瘤组成并可定位转录学定义的肿瘤浸润淋巴细胞位置。计算组织学能基于组织学亚型及分级提高预后预测能力，并突出具有诊断意义的区域（如坏死或淋巴细胞聚集）。这些发现表明计算机视觉在分子描述肿瘤组织学中的巨大潜力。