Integrated single-cell analysis of T cells reveals landscapes of T-cell states in different cancers.

  • Major Finding: Integrated single-cell analysis of T cells reveals landscapes of T-cell states in different cancers.

  • Concept: Systematic comparison of T-cell subsets highlights pan-cancer and cancer-specific T-cell dynamics.

  • Impact: This work enables comparison of T-cell landscapes and proposes a clinical immune-typing strategy.

Although cancer immunotherapy has provided remarkable clinical benefit to a subset of patients, response to immunotherapy varies widely. The composition of the tumor microenvironment (TME) and tumor-infiltrating immune cells differs among cancer types and may potentially underlie variation in patient response. Given that T cells play critical roles in antitumor immunity, Zheng, Qin, Si, and colleagues assembled a pan-cancer T-cell atlas to systematically compare T-cell state and dynamics across TMEs of different cancer types. Single-cell RNA sequencing and T-cell receptor (TCR) profiling data were used to analyze over 390,000 T cells from 316 patients representing 21 cancer types, revealing 17 CD8+ and 24 CD4+ clusters prevalent in the majority of cancers. Focusing on potentially tumor-reactive T cells, classified based on tumor enrichment, TCR signaling, proliferation, and clonal expansion, terminally exhausted (Tex) cells and TNFRSF9+ regulatory T (Treg) cells were the most common subsets of CD8+ and CD4+ T cells, respectively. Analysis of RNA velocity to map trajectories toward T-cell exhaustion delineated two distinct pathways by which naïve CD8+ T cells develop into Tex cells, either through GZMK+ effector memory T cells or through ZNF683+ tissue-resident memory T (Trm) cells, with some cancer types showing preference for a specific path. Similar analysis of CD4+ T cells found that naïve CD4+ T cells could develop into TNFRSF9+ Treg cells, follicular helper T cells, or terminally differentiated effector memory or effector T cells. Cancer types displayed distinct T-cell distribution patterns, and both tumor mutational burden and specific somatic mutations were associated with frequency of T-cell subsets. Grouping samples based on T-cell frequencies, tumors were classified into eight immune types, and stratification of patient data into TexhiTrmlo or TexloTrmhi immune types demonstrated better overall survival of the latter subgroup. In summary, this work constructs a T-cell atlas that enables characterization of T-cell populations, with clinical implications for therapeutic strategies targeting specific immune subsets in various cancers. ■

Zheng L, Qin S, Si W, Wang A, Xing B, Gao R, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 2021;374:abe6474.

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