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BackgroundBreast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRCA1/2 genes 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 patients’ gBRCA mutation status.MethodsIn 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. Since the dimensions are too large for slide-based training, 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 a designated breast cancer pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, assuming that low magnification and high magnification may provide different levels of information for classification.ResultsA trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In the 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.ConclusionIn 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 gRCA mutation risk. The results indicated that the prediction accuracy is likely to improve as the training data expand. The findings demonstrated that deep CNNs could be used to assist pathologists in the detection of gene mutation in breast cancer.

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The density functional theory (DFT) has been employed to investigate the carbon-adhering reaction on Fe 3 O 4 (111) surface, which consists of two steps: (1) the adsorption of CO onto the Fe 3 O 4 (111) surface and (2) the second CO seizes an O atom from CO, which adsorbed on the surface, to form a CO 2 molecule and the C atom left behind adheres onto the Fe 3 O 4 (111) surface. At step 1, there are five stable configurations of CO adsorbed onto Fe oct2 -terminated Fe 3 O 4 (111) surface and four stable formations of CO adsorbed onto the Fe tet1 -terminated Fe 3 O 4 (111) surface. The top configurations of these two surfaces are most stable. Moreover, a density of the state (DOS) analysis is used to investigate the bonding mechanism of CO adsorbed onto these two surfaces. The results reveal the new C-O bonds generation on two surfaces, which is important and necessary for the formation of a CO 2 molecule. Besides, the transition states (TS) are searched to analyze the energy barrier in the process of CO 2 desorption from two surfaces. The result indicates that the oxidation reaction of adsorbed CO molecule and surface O atom is feasible. For step 2, the result shows that the carbon-adhering reaction occurs only on the top site of Fe oct2 atom and the magnetite plays a catalytic role in the carbonadhering reaction process.

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