Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employed artificial intelligence (AI)-powered histopathology image analysis to identify a novel sub-group of NSMP EC patients that had markedly inferior progression free and disease free survival in a discovery cohort of 368 patients and an independent validation cohort of 290 patients from another center. Shallow whole genome sequencing revealed a higher burden of copy number abnormalities in the identified group, compared to other NSMP EC, in our discovery and validation cohorts. Taken together, our work demonstrates the power of AI to discover new knowledge, identifying a prognostically relevant subset of EC that is unrecognizable with conventional histopathological assessment, refining image-based tumor classification.

The authors have declared no competing interest.

Terry Fox Research institute, Canadian Institute for Health Research, Natural Sciences and Engineering Research Council of Canada, Michael Smith Health Research, UBC & VGH Foundation

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

University of British Columbia - BC Cancer Research Ethics Board (UBC BC Cancer REB)

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