Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)–stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87–0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77–0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications. Significance: This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity. See related commentary by Song et al., p. 2672

中文翻译：

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使用深度学习解决肾癌驱动基因突变异质性的瘤内

肿瘤进化产生的瘤内异质性在生物学和临床上提出了重大挑战。剖析这种复杂性可能会受益于深度学习 (DL) 算法，该算法可以从普遍存在的苏木精和伊红 (H&E) 染色的组织切片中推断出分子特征。尽管已经开发了深度学习算法来预测 H&E 图像中的一些驱动突变，但这些深度学习算法在亚克隆空间分辨率下解决肿瘤内突变异质性的能力尚未得到探索。在这里，我们将深度学习应用于肿瘤内异质性的范例，透明细胞肾细胞癌（ccRCC），这是最常见的肾癌类型。利用匹配的 IHC 和 H&E 图像来开发 DL 模型，用于预测三个最常突变的 ccRCC 基因（BAP1、PBRM1 和 SETD2）的瘤内遗传异质性。DL 模型是在一个大型队列 (N = 1,282) 上生成的，并在几个独立队列中进行测试，包括 TCGA 队列（N = 363 名患者）和两个组织微阵列 (TMA) 队列（N = 118 和 365 名患者）。这些模型还扩展到患者来源的异种移植（PDX）TMA，提供肿瘤和基质的同位和异位相互作用的分析。所有三个基因的状态都可以通过 DL 推断，其中 BAP1 在组织样本内和组织样本之间显示出最高的灵敏度和性能（AUC = 0.87–0.89，保留）。BAP1 结果在独立人类 (AUC = 0.77–0.84) 和 PDX (AUC = 0.80) 队列中得到验证。最后，BAP1 预测与疾病特异性生存等临床输出相关。总的来说，这些数据表明深度学习模型可以解决癌症的瘤内异质性，具有潜在的诊断、预后、和生物学影响。意义：这项工作展示了组织病理学图像的深度学习分析作为评估遗传肿瘤内异质性的快速、低成本方法的潜力。参见 Song 等人的相关评论，第 17 页。2672