AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Amirali Darbandsari, Hossein Farahani, Maryam Asadi, Matthew Wiens, Dawn Cochrane, Ali Khajegili Mirabadi, Amy Jamieson, David Farnell, Pouya Ahmadvand, Maxwell Douglas, Samuel Leung, Purang Abolmaesumi, Steven J. M. Jones, Aline Talhouk, Stefan Kommoss, C. Blake Gilks, David G. Huntsman, Naveena Singh, Jessica N. McAlpine and Ali Bashashati ()
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Amirali Darbandsari: University of British Columbia
Hossein Farahani: University of British Columbia
Maryam Asadi: University of British Columbia
Matthew Wiens: University of British Columbia
Dawn Cochrane: British Columbia Cancer Research Institute
Ali Khajegili Mirabadi: University of British Columbia
Amy Jamieson: University of British Columbia
David Farnell: University of British Columbia
Pouya Ahmadvand: University of British Columbia
Maxwell Douglas: British Columbia Cancer Research Institute
Samuel Leung: British Columbia Cancer Research Institute
Purang Abolmaesumi: University of British Columbia
Steven J. M. Jones: British Columbia Cancer Research Center
Aline Talhouk: University of British Columbia
Stefan Kommoss: Tübingen University Hospital
C. Blake Gilks: University of British Columbia
David G. Huntsman: University of British Columbia
Naveena Singh: University of British Columbia
Jessica N. McAlpine: University of British Columbia
Ali Bashashati: University of British Columbia

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract 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 employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females.

Date: 2024
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DOI: 10.1038/s41467-024-49017-2

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