Abstract 6172: Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations
Authors: Park Gahee , Song Sanghoon , Cho Hyung-Gyo , Cho Soo Ick , Jung Wonkyung , AI team Lunit , Pereira Sergio , Yoo Donggeun , Paeng Kyunghyun , Ock Chan-Young 
Published: June 2022

Abstract
Aberrant transforming growth factor-beta(TGF-B) pathway in the tumor microenvironment has been highlighted as one of the core resistance pathways of immunotherapy, by excluding tumor-infiltrating lymphocytes (TIL) out of the tumor area. However, no studies have coupled immune phenotypes classified by spatial analysis of TIL in whole slide images (WSI) with TGF-B pathway analysis on a large-scale database. Here, we hypothesized that the immune-excluded phenotype classified by a deep-learning spatial analysis model, Lunit SCOPE IO, would be correlated with the aberrant TGF-B pathway in The Cancer Genome Atlas (TCGA) cohorts. Aberrant TGF-B pathway was measured by Trimmed Mean of M-values (TMM) normalized and transformed to log2 of counts-per-million of previously published gene sets of fibroblast-specific TGF-beta responsive gene signature, using edgeR packages from TCGA RNA-sequencing data (n=6,709) across the 23 cancer types. Lunit SCOPE IO was developed to identify immune phenotypes trained and validated from 3,166 multi-cancer H&E WSI with sections of 2.8e+9 mm2 tumor tissue containing 5.9e+6 TIL annotated by 52 board-certified pathologists. Lunit SCOPE IO classified immune phenotypes as immune-inflamed and -excluded according to the proportion of TIL density either highly conserved in cancer epithelium (CE) or cancer stroma (CS), respectively, and otherwise, classified as immune-desert with low TIL density in CE and CS. Aberrant TGF-B expression was highly enriched in multiple cancer types including pancreatic cancer, head and neck cancer, kidney clear cell carcinoma, lung squamous cell carcinoma, and breast cancer, in ascending order. TGF-B expression was increased in microsatellite-stable tumor samples (p = 7.4e-15) or samples with low tumor mutational burden (TMB, &lt; 10/megabase, p = 4.9e-8), compared to those with microsatellite instability-high or high TMB, respectively. Interestingly, TGF-B expression was highly correlated with the proportion of cancer stroma in WSI (R = 0.315, p &lt; 2.2e-16) and the proportion of immune-excluded phenotype (R = 0.115, p &lt; 2.2e-16) across multiple cancer types. Tumor samples with SMAD4 mutations (n = 161, 2.4%) had significantly higher TGF-B expression (p = 0.0190), and a higher proportion of immune-excluded phenotype (p &lt; 2.2e-16) in WSI, compared to wild-type SMAD4. Aberrant TGF-B pathway is clearly associated with increased proportion of cancer stroma, and excluded TIL, or immune-excluded phenotype in a large-scale pan-carcinoma analysis.
Citation Format: Gahee Park, Sanghoon Song, Hyung-Gyo Cho, Soo Ick Cho, Wonkyung Jung, Lunit AI team, Sergio Pereira, Donggeun Yoo, Kyunghyun Paeng, Chan-Young Ock. Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations [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 6172.