Abstract 617: Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer
Authors: Kim Hyojin , Kim Eun Sun , Lee Song Kook , Lee Jeong Hoon , Paeng Kyunghyun , Ock Chan-Young , Suh Dong Hoon , Kim Kidong , No Jae Hong , Kim Yong-Beom 
Published: July 2021

Introduction: Based on molecular classification of endometrial cancer (EC) of The Cancer Genome Atlas (TCGA) and Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), EC has been classified into four novel prognostic groups: POLE-mutated (POLE-mt), mismatch repair-deficient (dMMR), copy number-high (p53abn), and copy number-low (no specific molecular profile, NSMP). We hypothesized that spatial distribution of tumor-infiltrating lymphocyte (TIL) using an artificial-intelligence (AI)-powered tissue analyzer, Lunit SCOPE, would be distinct according to the molecular classification.

Methods: We analyzed EC of TCGA database (N=224) and EC tissues retrospectively collected from Seoul National University Bundang Hospital (SNUBH, N=236). EC from SHUBH were molecularly classified in which MMR and p53 status were determined by immunohistochemistry (IHC) and POLE mutation by digital droplet polymerase chain reaction of six hotspot mutations in exon 9, 13 and 14 (P286R, S297F, V411L, V424I, L424V, and A456P). Lunit SCOPE analysis to detect lymphocyte, cancer epithelium (CE), and cancer-associated stroma (CS) was performed using x40 scanned images of TCGA and SNUBH. Cox proportional hazard model were used for survival analysis.

Results: Composition of molecular classification were comparable in both cohorts: 7.6%, 28.6%, 24.6%, and 39.3% for POLE-mt, dMMR, CN-high, and CN-low in TCGA cohort, and 8.9%, 19.5%, 17.4%, and 54.2% for POLE-mt, dMMR, p53abn, and NSMP in SNUBH cohort, respectively. CN-high subtype in TCGA and p53abn in SNUBH cohort significantly correlated with poor prognosis (TCGA: adjusted hazard ratio 2.54, p value 0.0383; SNUBH: adjusted hazard ratio 7.47, p value 2.38 x 10-4). TIL density calculated by total number of lymphocytes in CE and CS area was significantly increased in POLE and MSI groups of TCGA-cohort (p value = 2.24 x 10-4) and SNUBH-cohort (p value = 1.74 x 10-6). Moreover, uneven TIL enrichment in CS was observed in CN-high or TP53-expressor compared to CN-low or TP53-wildtype (ratio of TIL in CS/TIL in CE, TCGA: 6.96 versus 5.33; SNUBH: 11.4 versus 8.83). These findings suggested that the CN-high/p53abn and POLE/dMMR subtypes might be associated with immune-excluded and immune-inflamed tumor microenvironment (TME) phenotype, respectively.

Conclusion:The distribution of TIL in EC differs according to the molecular subtypes. Our data suggest the possibility of predicting subtypes through TIL analysis, and provide insight into treatment through TME modulation.

Citation Format: Hyojin Kim, Eun Sun Kim, Song Kook Lee, Jeong Hoon Lee, Kyunghyun Paeng, Chan-Young Ock, Dong Hoon Suh, Kidong Kim, Jae Hong No, Yong-Beom Kim. Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 617.