Abstract 2445: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images

Abstract

Abstract Introduction: Tumor purity and deconvolution of cell subsets in tumor environments has been developed based on DNA sequencing data or gene expression profiles. We have developed the artificial intelligence named Lunit SCOPE, identifying and quantifying various histologic components using deep learning from H&E-stained whole slide images. The Lunit SCOPE can define the proportions of cancer epithelium, cancer stroma and immune cells infiltration. We hypothesized that cell proportions analyzed by Lunit SCOPE can accurately predict cell subsets in tumor microenvironment with biologically-reliable results. Method: Deep learning-based H&E image analyzer, Lunit SCOPE, has been trained by 1,191 H&E-stained breast cancer whole slide images with histological components such as cancer epithelium, cancer stroma, normal cells, fat, necrosis, lymphocyte, and fibroblast, annotated by over 10 pathologists. Lunit SCOPE trained by the annotated dataset can profile the cancer stroma ratio and tumor infiltrating lymphocyte (TIL) density in cancer epithelium and cancer stroma. We have selected 21 types of cancers excluding mesenchymal origin cancer in The Cancer Genome Atlas (TCGA). Finally, H&E slides from 7,837 cases have been analyzed by Lunit SCOPE. Result: Pan-cancer analysis has shown that the cancer stroma ratio from Lunit SCOPE was the highest in pancreatic adenocarcinoma. Especially, Consensus Molecular Subtype 4 (CMS4) has shown the highest cancer stroma ratio among colorectal cancer (P < 2.2 x 10-16). The density of tumor infiltrating lymphocyte (TIL) was highest in renal clear cell carcinoma, melanoma and bladder cancer in that order, and lung cancer, esophageal cancer, and head and neck cancer have also presented high TIL density. Moreover, high mutational burden (> 10/Mb) was significantly related to higher TIL density across all cancer types (P = 4.06 x 10-5). In addition, the stromal TIL density was higher in basal type than other subtypes of breast cancer (P = 2.45 x 10-7). Pan-cancer survival analysis showed that higher cancer stroma ratio (HR 1.14, 95% CI 1.04-1.25, P = 0.00419) and higher stromal TIL density (HR 1.17, 95% CI 1.07-1.28, P = 0.000732) were associated with poor prognosis, but higher intratumoral TIL density had good prognosis (HR 0.89, 95% CI 0.82-0.98, P = 0.0126). Conclusion: In this study, TCGA pan-cancer dataset have been analyzed by deep learning-based H&E image analyzer, Lunit SCOPE. As a result, it has been proven that the ratio of cancer stroma is high in pancreatic cancer and CMS4 colorectal cancer, and immunogenic tumors and high mutational burden is related to high TIL density. In summary, Lunit SCOPE has significantly correlated with previously well-defined biological features in pan-cancer analysis. Furthermore, Lunit SCOPE can be developed for the novel tissue-agnostic predictive biomarkers of cancer immunotherapy. Citation Format: Kyunghyun Paeng, Geunyoung Jung, Sarah Lee, Soo Youn Cho, Eun Yoon Cho, Sang Yong Song. Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2445.