Abstract 7398: Unveiling the tumor microenvironment of hepatocellular carcinoma using AI trained by spatial transcriptomics: A preliminary study to predict response to immunotherapy

Abstract Background. Immune checkpoint inhibitors (ICIs), particularly targeting the PD-1 pathway, are promising in treating hepatocellular carcinoma (HCC). However, their variable effectiveness among individuals calls for a better understanding of the tumor microenvironment (TME) and reliable predictive biomarkers. Here, we employ spatial transcriptomics (ST) to develop a deep-learning model that aims to investigate the TME in HCC using Hematoxylin and eosin (H. 01). Upon stratifying patients based on stromal CAF prediction, those with low stromal CAF levels exhibited better overall survival (Log-rank test, p. 05). Conclusions. We present a deep learning model to analyze TME in HCC solely on H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 7398.