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基于泛癌图像的临床可操作基因改变检测

原文发布日期：2020-07-27

英文摘要：

摘要翻译：

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文章：

基于泛癌图像的临床可操作基因改变检测

Pan-cancticer image-based deteon of clinically actionable genetic alterations

原文发布日期：2020-07-27

英文摘要：

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.

摘要翻译：

癌症中的分子改变可能导致肿瘤细胞及其微环境的表型变化。常规的组织切片 histopathology tissue slides，因其普遍可用性，可以反映这些形态学的变化。在这里，我们展示了深度学习可以从染色后的常规组织切片中一致推断出一系列基因突变、分子肿瘤亚类、基因表达图谱以及标准病理标志物。我们开发、优化、验证并公开发布的统一工作流程能够应用于超过5000名患者的多种实体瘤组织切片。我们的发现表明，一个深度学习算法可以训练以从常规的、经 paraffin 嵌入的组织切片中预测大量分子改变（这些切片被染色为血红素和/eosin）。这些预测在其他群体中具有普适性，并且具有空间分辨率。该方法可以在移动设备上实现，从而可能促进个性化癌症治疗的点对点诊断。更广泛地说，这种方法可以揭示和量化基因与表型之间的联系。