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Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning.

Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify the TMB-related histopathologic features from hematoxylin and eosin slides and explore their prognostic value in gliomas. We detected the TMB-related features using a graph convolutional neural network from whole-slide images of patients from The Cancer Genome Atlas data set (619 patients), and evaluated the correlation between features and TMB in an external validation set (237 patients). Then, we used TMB-related features for predicting overall survival (OS) of patients to investigate whether these features have potential for prognostic prediction. Moreover, biological pathways underlying the prognostic value of the features were further explored. Our results demonstrated histopathologic features derived from whole-slide images were significantly associated with patient TMB (P = 0.007 in the external validation set). TMB-related features showed excellent performance for OS prediction, and patients with lower-grade gliomas could be further stratified into different risk groups according to the features (P = 0.00013; hazard ratio, 4.004). Pathways involved in the cell cycle and execution of immune response are enriched in patients with higher OS risk. The TMB-related features could be used to estimate TMB and aid in prognostic risk stratification of patients with glioma with dysregulated biological pathways.

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