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

• Published in: Nature communications 2024-06, Vol.15 (1), p.4973-12, Article 4973
• Main Authors: Darbandsari, Amirali, Farahani, Hossein, Asadi, Maryam, Wiens, Matthew, Cochrane, Dawn, Khajegili Mirabadi, Ali, Jamieson, Amy, Farnell, David, Ahmadvand, Pouya, Douglas, Maxwell, Leung, Samuel, Abolmaesumi, Purang, Jones, Steven J. M., Talhouk, Aline, Kommoss, Stefan, Gilks, C. Blake, Huntsman, David G., Singh, Naveena, McAlpine, Jessica N., Bashashati, Ali
• Format: Article
• Language: English
• Subjects: 45/23; 631/114/1305; 631/67/1517/1931; 631/67/2321; 631/67/69; 692/53/2422; Abnormalities; Aged; Artificial Intelligence; Cancer; Cohort Studies; Copy number; DNA Copy Number Variations; Endometrial cancer; Endometrial Neoplasms - genetics; Endometrial Neoplasms - pathology; Endometrium; Female; Gene sequencing; Histopathology; Humanities and Social Sciences; Humans; Image analysis; Image classification; Image processing; Image Processing, Computer-Assisted - methods; Medical imaging; Middle Aged; multidisciplinary; Prognosis; Science; Science (multidisciplinary); Subgroups; Tumor Suppressor Protein p53 - genetics; Uterine cancer; Whole Genome Sequencing
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-49017-2

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. Endometrial cancer (EC) has four molecular subtypes; of these, the No Specific Molecular Profile (NSMP) subtype encompasses patients with heterogeneous outcomes. Here, the authors use artificial intelligence and histopathology images to differentiate p53abn and NSMP subtypes in EC, and identify one distinct subgroup within NSMP with unfavourable outcome.