Abstract 644: Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma

Introduction: Deep learning-based H&E analyzer can classify the tumor microenvironment as three immune phenotypes: the immune-inflamed, excluded and desert. Our previous study demonstrated a distinct transcriptomic and immunologic landscape amongst the phenotypes in non-small cell lung cancer (NSCLC). However, it has not been fully investigated in other cancers. Here, we explore the immune profiles and clinical outcomes between the three immune phenotypes in uterine corpus endometrial carcinoma (UCEC). Methods: Tissue H&E slide images, sequencing data, and clinical data were utilized from The Cancer Genome Atlas (TCGA). Lunit-SCOPE IO was trained with multi-cancer 3,166 H&E whole slide images annotated by pathologists. Based on the proportion of tumor infiltrating lymphocytes (TIL) highly conserved either in cancer epithelium (CE) or cancer stroma (CS), Lunit-SCOPE IO classifies tumors as immune-inflamed and excluded, respectively. Also, it classifies tumors with low TIL density in CE and CS as immune-desert. Results: Among 486 patients with UCEC, the frequency of immune-inflamed, excluded and desert was 174 (35.8%), 160 (32.9%), and 156 (32%), respectively. In the three subgroup comparison, immune-inflamed was associated with the best survival outcome and -excluded was associated with the worst survival outcome (Inflamed vs excluded, HR 0.30 95% CI 0.17-0.55, p Conclusion: The three tissue phenomic subtypes showed distinct immune profiles and clinical outcomes, with immune-inflamed having the best overall survival outcome. In particular, non-inflamed group was associated with worse overall survival even in MSI-H tumors deemed to have more favorable prognosis compared to MSS tumors. Given the definite differences in the survival outcome, tissue H&E based tumor microenvironment classification may serve as a potential prognostic biomarker in UCEC. Citation Format: Horyun Choi, Leeseul Kim, Jinah Kim, Yeun Ho Lee, Hyung-Gyo Cho, Na Hyun Kim, Gahyun Gim, Sanghoon Song, Gahee Park, Soo Ick Cho, Sergio Pereira, Donggeun Yoo, Kyunghyun Paeng, Chan-Young Ock, Young Kwang Chae. Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 644.