TISCH: a comprehensive web resource enabling interactive single-cell...

Paper

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.

Analysis

Paper intelligence

Summary

TISCH is a curated database integrating single-cell transcriptomic profiles from numerous tumor datasets to facilitate the exploration and visualization of the tumor microenvironment (TME) for immunotherapy research.

Key findings

Integrates single-cell transcriptomic profiles from nearly 2 million cells across 76 tumor datasets and 27 cancer types.
Provides uniform processing, including quality control, batch effect removal, and cell-type annotation.
Enables interactive visualization of gene expression at single-cell or cluster levels.
Facilitates systematic comparison across different cell types, patients, and cancer types.

Methods

Integration of single-cell RNA-seq data.
Standardized computational workflow for data processing.
Interactive gene expression visualization tools.
Cell-type annotation and malignant cell classification.

Limitations

Data is limited to published datasets available at the time of curation.
The accuracy of cell-type annotation depends on the quality of the input data and annotation algorithms.

Applications

Understanding tumor microenvironment heterogeneity.
Informing cancer immunotherapy strategies.
Comparative analysis of TME across different cancer types.
Identifying potential therapeutic targets.

Confidence100%

Discovery

Related papers

2026Crossref0 citations

Evaluation of Computational Neoantigen Prioritization Strategies for Personalized Cancer Vaccines

Vladimir Zyrin; Evgeniy Mozheiko; Ivan Valiev; Alexey Lazarev; Tigran Gevorkyan; Matvey Murashko; Konstantin Okonechnikov

Sep 28, 2026Vaccines

2026Crossref0 citations

First mRNA‑based flu vaccine wins US approval, Moderna’s Ebola candidate reaches the clinic, and early‑phase cancer vaccine trials broaden across personalized and off‑the‑shelf platforms

Ashling Cannon

Sep 10, 2026Vaccine Insights

2026arXiv

Optimizing RNA yield using deep neural networks coupled to massively parallel screening

Dinghai Zheng; Justin Hong; Jun Wang; Adrien Villain; Mickaël Costallat; Fernando Ulloa Montoya; Vikram Agarwal

Aug 24, 2026

2026Crossref0 citations

Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction

Petar Brlek; Jan Kolić; Luka Bulić; Vedrana Škaro; Dragan Primorac

Jul 29, 2026Frontiers in Genetics

2026Crossref0 citations

Neoantigen-Encoded Oncolytic Viruses as Personalized Cancer Vaccines

Almohanad A. Alkayyal

Feb 26, 2026Pharmaceuticals

2026PubMed

Longer-term response to SARS-CoV-2 vaccination after allogeneic blood stem cell transplantation.

Crodel CC; Gork L; Göpel W; Linke P; Sinn K; Miethke J; Hochhaus A; Hilgendorf I

Jan 1, 2026Journal of cancer research and clinical oncology

Source record

Publication metadata

Authors: Dongqing Sun; Jin Wang; Ya Han; Xin Dong; Jun Ge; Rongbin Zheng; Xiaoying Shi; Binbin Wang; Ziyi Li; Pengfei Ren; Liangdong Sun; Yilv Yan; Peng Zhang; Fan Zhang; Taiwen Li; Chenfei Wang
Published: Oct 16, 2020
Journal or venue: Nucleic Acids Research
DOI: 10.1093/nar/gkaa1020
Paper type: journal article
Source: Openalex

DOI OpenAlex