Identification of a novel subtype of endometrial cancer with unfavorable outcome using artificial intelligence-based histopathology image analysis.

Abstract

5594 Background: Molecular subtyping of endometrial cancer (EC), unlike histopathological evaluation, offers an objective and reproducible classification system that has strong prognostic value and therapeutic implications. The Proactive Molecular risk classifier for Endometrial cancer (ProMisE) was developed by our team as a pragmatic, cost-effective, and clinically applicable molecular classifier for EC patients. ProMisE has four subtypes: (i) POLE mutant ( POLEmut), (ii) mismatch repair deficient (MMRd), (iii) p53 abnormal (p53abn) by immunohistochemistry, and (iv) NSMP (No Specific Molecular Profile), lacking any of the defining features of the other three subtypes. While ProMisE subtypes are associated with clinical outcomes, within each subtype, there are clinical/prognostic outliers. This is particularly true within the largest ProMisE subtype; NSMP (representing ̃50% of ECs), where a subset of patients experience a very aggressive disease course, comparable to what is observed in patients with p53abn ECs. Methods: We hypothesized that objective assessment of the digitized hematoxylin and eosin (H&E)-stained histopathology slides of the largest and most diverse EC subset, NSMP, could potentially identify clinical outcome outliers. As such, we developed an artificial intelligence (AI)-based image analysis model to identify the NSMP cases that had similar histopathological features to the p53abn subtype, as assessed by H&E stain. We used a discovery cohort of 182 and an external validation cohort of 195 NSMP ECs. Results: Our AI-based image analysis model, based on deep convolutional neural networks, identified 21 (11.5%) out of the 182 NSMP cases with similar histopathological features as p53abn cases. We refer to these cases as ‘p53abn-like’ NSMPs. Compared to the rest of the NSMP cases, these cases had markedly inferior disease-specific survival (DSS) (10-year DSS 58.9% vs. 93.1% ( p<3.44e-8)) and progression-free survival (PFS) (10-year PFS 55.1% vs. 91.4% ( p<3.76e-6)). These findings were confirmed in our validation cohort, with 10.7% of the 195 patients categorized as ‘p53abn-like’ tumors with 10-year DSS of 82% vs. 51.3% ( p<5.28e-5) and PFS of 89.3% vs. 56.6% (p<2.15e-4). Conclusions: Utilizing an AI-based approach for histopathology image analysis, we have discovered ‘p53abn-like’ NSMPs, a novel subtype of NSMP ECs with morphological features similar to p53abn cases. ‘p53abn-like’ NSMPs exhibit similar clinical behavior as p53abn, having noticeably inferior outcome compared to the rest of the NSMP cases in two independent cohorts. These findings warrant further molecular investigation of this novel subtype of EC to identify the biological underpinning and future therapeutic strategies.