Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides

Abstract

Endometrial cancer (EC) diagnosis traditionally relies on tumor morphology and nuclear grade, but personalized therapy demands a deeper understanding of tumor mutational burden (TMB), i.e., a key biomarker for immune checkpoint inhibition and immunotherapy response. Traditional TMB prediction methods, such as sequencing exomes or whole genomes, are costly and often unavailable in clinical settings. We present the first TR-MAMIL deep learning framework to predict TMB status and classify the EC cancer subtype directly from H&E-stained WSIs, enabling effective personalized immunotherapy planning and prognostic refinement of EC patients. Our models were evaluated on a large dataset from The Cancer Genome Atlas. TR-MAMIL performed exceptionally well in classifying aggressive and non-aggressive EC, as well as predicting TMB, outperforming seven state-of-the-art approaches. It also performed well in classifying normal and abnormal p53 mutations in EC using H&E WSIs. Kaplan–Meier analysis further demonstrated TR-MAMIL’s ability to differentiate patients with longer survival in the aggressive EC.