TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

TLDR

Cancer immunotherapy via checkpoint blockade is effective in many cancers, yet only a minority of patients respond because of stochastic heterogeneity in the tumor microenvironment, and although single‑cell RNA‑seq has enabled detailed immune profiling, integrating the vast published data remains computationally challenging. Here, we present Tumor Immune Single Cell Hub (TISCH), a large‑scale curated database that integrates nearly 2 million single‑cell transcriptomes from 76 high‑quality tumor datasets across 27 cancer types. All data were uniformly processed with a standardized workflow—quality control, batch‑effect removal, clustering, cell‑type annotation, malignant cell classification, differential expression and functional enrichment—and TISCH offers interactive gene‑expression visualization at single‑cell and cluster levels, enabling systematic comparison across cell types, patients, tissues, treatments, responses, and cancer types. TISCH provides a user‑friendly interface for visualizing, searching, and downloading a comprehensive gene‑expression atlas of the tumor microenvironment across multiple cancer types, facilitating rapid, flexible, and thorough exploration of the TME.

Abstract

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.