Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images - Wang, Xiaoxiao; Zou, Chong; Zhang, Yi; Li, Xiuqing; Wang, Chenxi; Ke, Fei; Chen, Jie; Wang, Wei; Wang, Dian; Xu, Xinyu; Xie, Ling; Zhang, Yifen (Frontiers in Genetics, 2021)
Background Breast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. Genetic predisposition to breast cancer may be associated a mutation in particular gene such as BRCA1/2. Patients who carry a germline pathogenic mutation in the BRCA1/2 have a significantly increased risk of developing breast cancer and might benefit from targeted therapy. However, genetic testing is time consuming and costly. This study aims to predict the risk of gBRCA mutation by using the whole slide pathology features of breast cancer H&E stains and the patient's gBRCA mutation status. Methods In this study, we trained a deep convolutional neural network (CNN) of ResNet on whole-slide images (WSIs) to predict the gBRCA mutation in breast cancer. We divided WSI into smaller tiles with the original resolution. The tile-based classification was then combined by adding the positive classification result to generate the combined slide-based accuracy. Models were trained based on the annotated tumor location and gBRCA mutation status labeled by pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, which assuming low-magnification and high-magnification may provide different levels of information for classification. Results Trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In external validation dataset, AUCs (95%CI) of DL models that used 40×, 20×, 10× and 5× magnification tiles among all cases were 0.766 (0.763-0.769), 0.763 (0.758-0.769), 0.750 (0.738-0.761) and 0.551 (0.526-0.575), respectively, while the corresponding magnification slides among all cases were 0.774 (0.642−0.905), 0.804 (0.676−0.931), 0.828 (0.691−0.966) and 0.635 (0.471-0.798), respectively. The study also identified the influence of histological grade to the accuracy of the prediction. Conclusions In this paper, the combination of pathology and molecular omics was used to establish the gBRCA mutation risk prediction model, revealing the correlation between the whole slide histopathological images and gBRCA mutation risk. The results indicated that the prediction accuracy is likely to improve as the training data expands. The findings demonstrated that deep convolutional neural networks could be used to assist pathologists in the detection of gene mutation in breast cancer.