Predictive biomarkers of treatment response are lacking for metastatic clear cell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical. Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. To overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset. Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model can predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost. By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response.

翻译：对于转移性透明细胞肾细胞癌（ccRCC）——一种采用血管生成抑制剂、免疫检查点抑制剂、mTOR抑制剂及HIF2抑制剂治疗的肿瘤类型——目前尚缺乏预测治疗反应的生物标志物。Angioscore作为一种基于RNA的血管生成量化指标，可以说是预测抗血管生成（AA）治疗反应的最佳候选指标。然而，转录组检测的临床应用面临诸多挑战，包括标准化问题、时间延迟和高成本。此外，ccRCC肿瘤具有高度异质性，对多个区域进行采样测序并不现实。本文提出一种新颖的深度学习（DL）方法，能够从普遍存在的组织病理学切片中预测Angioscore。针对典型DL模型可解释性不足这一最大局限，我们的模型可生成可视化的血管网络，该网络正是模型预测的基础。为验证其可靠性，我们将该模型应用于包含临床试验数据集在内的多个队列。我们的模型在多个独立队列中准确预测了基于RNA的Angioscore（斯皮尔曼相关系数分别为0.77和0.73）。此外，其预测结果有助于揭示有意义的生物学关联，例如血管生成与肿瘤分级、分期及驱动突变状态的关系。最后，我们发现在真实世界队列和IMmotion150临床试验中，该模型均能预测AA治疗反应。我们模型的预测能力远超血管标志物CD31，并且以极低的成本实现了与基于RNA的Angioscore金标准近乎相当的预测性能（c指数0.66对0.67）。通过仅凭组织病理学切片即可提供稳健且可解释的Angioscore预测，我们的方法为血管生成生物学及AA治疗反应研究提供了新的见解。