Artificial intelligence-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes.

Authors

null

Gahee Park

Lunit Inc., Seoul, South Korea

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 , Chan-Young Ock

Organizations

Lunit Inc., Seoul, South Korea

Research Funding

No funding received

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<0.001) and stromal TIL density (R=0.42, p<0.001), but it was negatively correlated with TCP (R=-0.27, p<0.001). Lunit SCOPE IO analyzed the images from ST analysis and calculated intra-tumoral TIL, stromal TIL, and TCP of each region of interest, containing 2 (IQR 0-7) cells. Interestingly, at a microscopic, cell-level, TIGIT expression was still higher in areas of enriched stromal TIL (P < 0.001) and lower in tumor cell-dense areas, but it was not significantly correlated with enriched intra-tumoral TIL areas, meaning that TIGIT expression is likely derived from the excluded TILs in the CS area. Conclusions: Interactive analysis of spatial transcriptomics with AI-powered pathology image analysis revealed that TIGIT expression in the tumor microenvironment is exclusive to confined areas with stromal TIL enrichment, reflecting the exclusion of TIL from the tumor nest.

Fold change of TIGIT expression
P value
TCGA: iTIL < mean versus iTIL ≥ mean
1.58
< 0.001
TCGA: sTIL < mean versus sTIL ≥ mean
1.55
< 0.001
TCGA: TCP < mean versus TCP ≥ mean
0.75
< 0.001
ST: iTIL < mean versus iTIL ≥ mean
0.47
< 0.001
ST: sTIL < mean versus sTIL ≥ mean
2.39
< 0.001
ST: TCP < mean versus TCP ≥ mean
0.28
< 0.001

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Abstract Details

Meeting

2022 ASCO Annual Meeting

Session Type

Poster Session

Session Title

Developmental Therapeutics—Immunotherapy

Track

Developmental Therapeutics—Immunotherapy

Sub Track

New Targets and New Technologies (IO)

Citation

J Clin Oncol 40, 2022 (suppl 16; abstr 2570)

DOI

10.1200/JCO.2022.40.16_suppl.2570

Abstract #

2570

Poster Bd #

225

Abstract Disclosures