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Darbandsari, Amirali

University of British Columbia

Background: In contrast to histopathological assessment, molecular subtyping of Endometrial Cancer (EC) provides a reproducible classification system with significant prognostic value. Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) was developed as a practical, cost-efficient, and therapeutically beneficial molecular classifier, replacing complex genomic tests. ProMisE stratifies EC into four subtypes: (1) POLE mutant, (2) Mismatch repair deficient, (3) p53 abnormal (p53abn) by immunohistochemistry, and (4) No Specific Molecular Profile (NSMP), which lacks any of the distinguishing features of the other three subtypes. Although ProMisE has provided significant prognostic value, there are clinical outliers within its four subtypes. This is especially evident in the largest ProMisE subtype, NSMP, accounting for about half of all ECs, where a fraction of patients encounter a very aggressive disease course, similar to the behavior of patients diagnosed with p53abn. Method: We considered the problem of refining the EC NSMP subtype using ubiquitous histopathology images. We hypothesized that evaluating the digital hematoxylin and eosin-stained images of NSMP could discern clinical outcome outliers. To this end, we designed an image analysis framework utilizing Artificial Intelligence (AI) to detect NSMP patients with comparable histological characteristics to the p53abn subtype. The analysis included various preprocessing steps, deep neural networks classifying the subtype of images, and survival and genomic analyses. Finding: Exploiting an AI-based methodology, we have expanded the NSMP subtype into two subgroups: ‘p53abn-like’ NSMPs and the rest of the NSMP cases. The former consists of patients diagnosed with NSMP by ProMisE, yet our AI-based analysis labeled them as p53abn due to morphological similarities. With following similar trends in two independent datasets, ‘p53abn-like’ NSMPs displayed comparable clinical behavior to p53abn, where they had markedly unfavorable outcomes in comparison with the remainder of the NSMP cases. In addition, the extensive genomic analysis suggested that ‘p53abn- like’ NSMPs had significantly higher fractions of genome altered than NSMPs in both datasets, validating our initial hypothesis in a different domain of data. We also discovered that ‘p53abn-like’ NSMPs patients might not benefit from hormone therapy. These findings emphasize the potential of AI screening as a stratification tool within ProMisE.

Text

2023-09-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82284

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/