Chromosomal instability (CIN) is a hallmark of human cancer that involves mis-segregation of chromosomes during mitosis, leading to aneuploidy and genomic copy number heterogeneity. CIN is a prognostic marker in a variety of cancers, yet, gold-standard experimental assessment of chromosome mis-segregation is difficult in the routine clinical setting. As a result, CIN status is not readily testable for cancer patients in such setting. On the other hand, the gold-standard for cancer diagnosis and grading, histopathological examinations, are ubiquitously available. In this study, we sought to explore whether CIN status can be predicted using hematoxylin and eosin (H&E) histology in breast cancer patients. Specifically, we examined whether CIN, defined using a genomic aneuploidy burden approach, can be predicted using a deep learning-based model. We applied transfer learning on convolutional neural network (CNN) models to extract histological features and trained a multilayer perceptron (MLP) after aggregating patch features obtained from whole slide images. When applied to a breast cancer cohort of 1,010 patients (Training set: n=858 patients, Test set: n=152 patients) from The Cancer Genome Atlas (TCGA) where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve (AUC) of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor spatial heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of spatial heterogeneity. Overall, we demonstrated the ability of deep learning methods to predict CIN status based on histopathology slide images. Our model is not breast cancer subtype specific and the method can be potentially extended to other cancer types.

中文翻译：

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使用组织病理学图像预测乳腺癌的染色体不稳定：一种深度学习方法

染色体不稳定性（CIN）是人类癌症的标志，涉及有丝分裂过程中染色体的错误分离，导致非整倍性和基因组拷贝数异质性。CIN是多种癌症的预后标志物，然而，在常规临床环境中，很难对染色体错误分离进行金标准实验评估。结果，在这种情况下，癌症患者的CIN状态不容易得到检验。另一方面，癌症诊断和分级的金标准，组织病理学检查无处不在。在这项研究中，我们试图探讨是否可以使用苏木精和曙红（H＆E）组织学来预测乳腺癌患者的CIN状态。具体来说，我们研究了使用基于深度学习的模型是否可以预测使用基因组非整倍体负担法定义的CIN。我们在卷积神经网络（CNN）模型上应用了转移学习，以提取组织学特征，并在汇总从整个幻灯片图像获得的斑块特征后训练了多层感知器（MLP）。当将其应用于癌症基因组图集（TCGA）的1,010名乳腺癌患者（培训组：n = 858名患者，测试组：n = 152名患者）中，其中485名患者的CIN状况较高时，我们的模型可以对CIN状况进行准确分类，在测试集中，曲线下面积（AUC）达到0.822，灵敏度为81.2％，特异性为68.7％。CIN状态的补丁级别预测表明幻灯片内肿瘤内空间异质性。此外，与幻灯片的平均CIN得分相比，在整个幻灯片中具有较高的CIN预测值的补丁对临床结果的预测性更高，因此强调了空间异质性的临床重要性。总体而言，我们展示了深度学习方法根据组织病理学幻灯片图像预测CIN状态的能力。我们的模型不是特定于乳腺癌的亚型，该方法可以潜在地扩展到其他癌症类型。