Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes

SIMPLE SUMMARY: Current molecular classification systems are primarily based on cancer-cell-intrinsic features, which disregard the critical contribution of the microenvironment and lack spatial information. Here, we take a holistic approach by incorporating spatial imaging phenotypes of both tumor and immune microenvironment for integrated classification. To achieve this goal, we developed a deep learning pipeline for automated nuclei segmentation and classification at the single-cell level. By leveraging this tool, we performed quantitative phenotypic characterization of tumor cells and infiltrating lymphocytes as well as their spatial distribution and relation. Using whole-slide hematoxylin- and eosin-stained images of hepatocellular carcinoma, we identified three histological imaging subtypes that are associated with distinct molecular features and clinical outcomes. This study represents an important step forward in understanding the spatial heterogeneity and complex interactions between tumor and immune microenvironment as well as their clinical implications. ABSTRACT: Hepatocellular carcinoma (HCC) is a heterogeneous disease with diverse characteristics and outcomes. Here, we aim to develop a histological classification for HCC by integrating computational imaging features of the tumor and its microenvironment. We first trained a multitask deep-learning neural network for automated single-cell segmentation and classification on hematoxylin- and eosin-stained tissue sections. After confirming the accuracy in a testing set, we applied the model to whole-slide images of 304 tumors in the Cancer Genome Atlas. Given the single-cell map, we calculated 246 quantitative image features to characterize individual nuclei as well as spatial relations between tumor cells and infiltrating lymphocytes. Unsupervised consensus clustering revealed three reproducible histological subtypes, which exhibit distinct nuclear features as well as spatial distribution and relation between tumor cells and lymphocytes. These histological subtypes were associated with somatic genomic alterations (i.e., aneuploidy) and specific molecular pathways, including cell cycle progression and oxidative phosphorylation. Importantly, these histological subtypes complement established molecular classification and demonstrate independent prognostic value beyond conventional clinicopathologic factors. Our study represents a step forward in quantifying the spatial distribution and complex interaction between tumor and immune microenvironment. The clinical relevance of the imaging subtypes for predicting prognosis and therapy response warrants further validation.