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Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.

Chan-Young Ock et al. — ASCO 2021

Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.



Abstract


Background: Novel immuno-oncology (IO) agents are promising but showing their efficacy in early phase clinical trials has been challenging due to limited enrichment strategies using practical biomarker platforms. We hypothesize that an artificial intelligence (AI)-powered spatial analysis of TIL using practically feasible H&E slides, can reflect a specific target gene expression derived from RNA sequencing. This enhances its potential application in early development of novel IO agents.


Methods: An AI-powered spatial TIL analyzer, namely Lunit SCOPE IO, was developed with data from 2.8 x 109 micrometer2 H&E-stained tissue regions and 5.9 x 106 TILs from 3,166 whole slide images of multiple cancer types, annotated by board-certified pathologists. Inflamed Score and Immune-Excluded Score was defined as the proportion of all tumor-containing 1 mm2-size tiles within a WSI classified as being of inflamed immune phenotype (high TIL density within cancer epithelium) and immune-excluded phenotype (low TIL density within cancer epithelium, but high TIL density within stroma), respectively. We used RNA sequencing data and H&E images from The Cancer Genome Atlas database, excluding those of mesenchymal origin (n = 7,467). Spearman's rank correlation between each gene expression and IS or IES, respectively, was calculated. Correlation coefficient > 0.2 and false discovery rate (FDR) < 1% was considered as a significant correlation.


Results: In a total of 20,304 genes, 871 (4.3%) and 1,155 (5.7%) genes were significantly correlated with Inflamed Score (IS) and Immune-Excluded Score (IES), respectively. The IS was highly related to genes reflecting immune cytolytic activity and targets of approved immune checkpoint inhibitors (Table). Interestingly, it was also significantly correlated with target genes of novel IO such as TIGIT, LAG3, TIM3, IDO, Adenosine receptor A2A, OX40, ICOS, M-CSF, IL2, IL7, and IL12. Moreover, the IES was exclusively correlated with the target genes of CEACAM, TGFB, and IL1.


Conclusions: Expression levels of novel I-O target genes are correlated with three scores derived from AI-powered TIL analysis using H&E slides, which can be easily applied to clinical research.

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AUTHORS

Chan-Young Ock, Seunghwan Shin, Wonkyung Jung, Sangheon Ahn, Haejoon Kim, Chunggi Lee, Jaehong Aum, Daim Tak, Ahyoung Ryu, Taiwon Chung, Eunji Baek, Jisoo Shin, Seungje Lee, Jiwon Shin, Minuk Ma, Seonwook Park, Sergio Pereira, Jeongseok Kang, Donggeun Yoo, Kyunghyun Paeng

PUBLISHED
ASCO 2021

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