1. Artificial intelligence-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes
- Author
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Gahee Park, Sanghoon Song, Sukjun Kim, Sangheon Ahn, Hyunjoo Kim, Jaegeun Lee, Juneyoung Ro, Woomin Park, Taiwon Chung, Cholmin Kang, Chunggi Lee, Huijeong Kim, Jisoo Shin, Seungje Lee, Eunji Baek, Sumin Lee, Melody SeungHui Seo, Hyojung Choi, Donggeun Yoo, and Chan-Young Ock
- Subjects
Cancer Research ,Oncology - Abstract
2570 Background: TIGIT is a promising emerging immunotherapeutic target. However, the specific sources of TIGIT expression within the tumor microenvironment are largely unknown. Here, we present an AI-powered spatial tumor-infiltrating lymphocyte (TIL) analyzer, Lunit SCOPE IO, to integrate image analysis from whole slide images with single-cell molecular profiling. Methods: We used The Cancer Genome Atlas (TCGA) RNA expression data across 23 cancer types (n=6,930). Lunit SCOPE IO was developed, trained, and validated based on >17k H&E whole-slide images, to segment cancer area (CA) and cancer-associated stroma (CS) and to detect tumor cells and TILs. The intra-tumoral TIL, stromal TIL, and tumor cell purity (TCP) in the CA+CS area were calculated. The public spatial transcriptomics (ST) dataset for breast cancer was downloaded from the 10X Visium web page. Lunit SCOPE IO was applied to the associated H&E WSIs to match distinct TIGIT expression to single cells identified in the WSIs. Results: TIGIT was highly expressed in TGCT (3.45±0.11; median±SEM), LUAD (3.07±0.05), and HNSC (2.89±0.06), and was highly enriched in samples with microsatellite instability-high or tumor mutational burden-high (≥ 10/Mb) compared to those without them (fold change = 1.30, p < 0.001). At a macroscopic, bulk-level in the TCGA dataset, TIGIT expression was positively correlated with intra-tumoral TIL density (R=0.37, p
- Published
- 2022