Supporting data for "Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma"

Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, three molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.<br>We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital H&amp;E slides with molecular subtype annotation, and an independent TCGA-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&amp;E-based mapping of molecular subtypes (AUC for classical, mesenchymal, proneural = 0.84, 0.81, and 0.71, respectively; p &lt; 0.001) and regions associated with worse outcome (univariable survival model p &lt; 0.001, multivariable p = 0.01). The latter were characterized by higher tumor cell density (p &lt; 0.001), phenotypic variability of tumor cells (p &lt; 0.001), and decreased T-cell infiltration (p = 0.017).<br>We modify a well-known CNN architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of AI-enabled image mining in brain cancer