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Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma

Chan-Young Ock et al. — AACR (2021)

Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma

Proceedings: AACR Annual Meeting 2021; April 10-15 and May 17-21


Introduction: Immune-excluded phenotype, defined by the existence of tumor-infiltrating lymphocytes (TIL) exclusively confined to cancer-associated stroma (CS) without protruding to tumor nest, has been suggested to be an intrinsic resistance mechanism of immune checkpoint inhibitor. However, little is known about the genomic landscape of immune-excluded phenotype across cancer types. In the current study, we analyzed genomic correlates of immune excluded phenotype in pan-carcinoma, using Lunit SCOPE IO, an artificial intelligence (AI)-powered whole slide image (WSI) software analyzer.
Methods: Lunit SCOPE IO has been trained for > 100,000 WSI across > 30 cancer types to detect lymphocyte, cancer epithelium (CE), and CS with > 95% accuracy. WSI was divided into 1 mm2 sized patch and lymphocyte density in CE or its density in CS were calculated to classify immune phenotype as follows: lymphocyte infiltration into CE was considered as inflamed, lymphocyte exclusively enriched in CS as immune-excluded, and sparse lymphocyte infiltration as immune-desert. We analyzed 7,128 The Cancer Genome Atlas (TCGA) pan-carcinoma samples across 20 cancer types excluding those of mesenchymal origin.
Results: Lunit SCOPE IO classifies WSI of TCGA pan-carcinoma into three immune phenotypes: inflamed (17.9%), immune-excluded (27.7%), immune-desert (34.2%) and remaining 20.2% of mixed phenotype. Immune-excluded phenotype is highly enriched in lung squamous cell carcinoma (61.5%), lung adenocarcinoma (53.6%), colorectal cancer (51.7%), and pancreatic cancer (42.2%) whereas inflamed phenotype is less dominant in colorectal cancer (3.4%) and pancreatic cancer (3.3%). Tumor mutational burden (TMB) is increased in both inflamed (mean 7.30/Mb) and immune-excluded (7.18/Mb) compared to that in immune-desert (3.28/Mb), however, genomic instability assessed by fraction altered loss-of-heterozygosity (LOH) is only increased in immune-excluded (mean 0.208) compared to that in inflamed (0.143) and immune-desert (0.148, P < 10-16). Consistent with this result, tumors with high TMB (> 10/Mb) and LOH of HLA genes (N = 124) is predominantly enriched by immune-excluded (45.2%), compared to inflamed (23.4%) or immune-desert (8.9%). Interestingly, a majority of oncogenic drivers such as mutations of TP53KRAS, and KEAP1 are significantly enriched in immune-excluded (fold change > 1.5, P < 10-10), and gene sets associated with epithelial-mesenchymal transition, apical junction, and TGF-beta signaling are enriched in immune-excluded.
Conclusion: AI-powered spatial analysis of WSI can classify immune phenotype in pan-carcinoma and it reveals that plausible immune intrinsic resistance pathways including genomic instability, LOH of HLA genes, and alteration of major oncogenic drivers are highly enriched in immune-excluded phenotype.

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Chan-Young Ock, Changhee Park, Kyunghyun Paeng, Donggeun Yoo, Seokhwi Kim, Sehhoon Park, Se-Hoon Lee, Tony S. Mok, Yung-Jue Bang.

Lunit Inc., Seoul, Korea, Republic of, Seoul National University Hospital, Seoul, Korea, Republic of, Ajou University School of Medicine, Suwon, Korea, Republic of, Samsung Medical Center, Seoul, Korea, Republic of, Chinese University of Hong Kong, Department of Clinical Oncology, Korea, Republic of, Bang & Ock Consulting, Seoul, Korea, Republic of

AACR (2021)

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