Despite the crucial role of phenotypic and genetic intratumoral heterogeneity in understanding and predicting clinical outcomes for patients with cancer, computational pathology studies have yet to make substantial steps in this area. The major limiting factor has been the bulk gene–sequencing practice that results in loss of spatial information of gene status, making the study of intratumoral heterogeneity difficult. In this issue of Cancer Research, Acosta and colleagues used deep learning to study if localized gene mutation status can be predicted from localized tumor morphology for clear cell renal cell carcinoma. The algorithm was developed using curated sets of matched hematoxylin and eosin and IHC images, which represent spatially resolved morphology and genotype, respectively. This study confirms the existence of a strong link between morphology and underlying genetics on a regional level, paving the way for further investigations into intratumoral heterogeneity.See related article by Acosta et al., p. 2792

中文翻译：

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通过深度学习研究驱动基因突变异质性的形态相关性

尽管表型和遗传瘤内异质性在理解和预测癌症患者的临床结果方面发挥着至关重要的作用，但计算病理学研究尚未在这一领域取得实质性进展。主要的限制因素是大量的基因测序实践，导致基因状态的空间信息丢失，使得肿瘤内异质性的研究变得困难。在本期《癌症研究》中，Acosta 及其同事使用深度学习来研究是否可以从透明细胞肾细胞癌的局部肿瘤形态来预测局部基因突变状态。该算法是使用精选的匹配苏木精和伊红以及 IHC 图像集开发的，这些图像分别代表空间分辨的形态学和基因型。这项研究证实了区域水平上形态学和潜在遗传学之间存在紧密的联系，为进一步研究瘤内异质性铺平了道路。参见 Acosta 等人的相关文章，第 17 页。2792