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.

中文翻译：

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TISCH：一个全面的网络资源，可实现肿瘤微环境的交互式单细胞转录组可视化

摘要

通过检查点阻断来靶向共抑制途径的癌症免疫疗法在多种癌症类型中显示出显着的疗效。然而，由于肿瘤微环境（TME）的随机异质性，只有少数患者对治疗有反应。单细胞 RNA 测序技术的最新进展使肿瘤免疫系统异质性的全面表征成为可能，但在整合和利用大量已发表的数据集为免疫治疗提供信息方面提出了计算挑战。在这里，我们推出肿瘤免疫单细胞中心（TISCH，http://tisch.comp-genomics.org），这是一个大型精选数据库，集成了来自 76 个高质量肿瘤数据集的近 200 万个细胞的单细胞转录组图谱涵盖 27 种癌症。所有数据均采用标准化工作流程进行统一处理，包括质量控制、批次效应去除、聚类、细胞类型注释、恶性细胞分类、差异表达分析和功能富集分析。TISCH 在单细胞水平或簇水平上提供跨多个数据集的交互式基因表达可视化，允许在不同细胞类型、患者、组织起源、治疗和反应组甚至不同癌症类型之间进行系统比较。总之，TISCH 提供了一个用户友好的界面，用于系统地可视化、搜索和下载多种癌症类型的 TME 中的基因表达图谱，从而实现快速、灵活和全面的 TME 探索。