Pan-cancer single-cell landscape of tumor-infiltrating T cells

By Liangtao Zheng, Shishang Qin, Wen Si, Anqiang Wang, B. Xing, Ranran Gao, Xianwen Ren, Li Wang, Xiaojiang Wu, Ji Zhang and 9 more. Science, 2021

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Description An atlas of cancer-associated T cells The tumor microenvironment contains many different kinds of immune cells, the composition, function, and roles of which are unclear. Using single-cell RNA sequencing of T cells in 21 cancer types from more than 300 patients, Zheng et al. identified differences in transcript composition that could be used to catalog different T cell types (see the Perspective by van der Leun and Schumacher). These annotations identified the different roles of specific types of CD4+ and CD8+ T cells among the different tumor types. Some of these clusters revealed evidence for two developmental paths for T cells, one of which shows a trajectory toward the “exhausted” T cell state, knowledge of which may be useful in developing future cancer immunotherapies. —LMZ Single-cell RNA sequencing of cancer-associated T cells elucidates links between immune cell types and tumor characteristics. INTRODUCTION Cancer immunotherapies that target tumor-specific T cells have benefited many cancer patients, but the clinical efficacy varies greatly among different cancer types. Tumor-infiltrating T cells often enter a dysfunctional state, widely known as T cell exhaustion, and the antitumor functions of effector T cells are regulated by multiple factors, including the presence of regulatory T cells (Treg cells). The states and abundances of T cells vary across tumor microenvironments (TMEs) of different cancer types, which may fundamentally influence different clinical parameters such as drug response to immunotherapies. RATIONALE To build a high-resolution pan-cancer T cell atlas, we performed single-cell RNA sequencing (scRNA-seq) on tumors, paracancerous tissues, and blood samples from patients of various cancer types and collected additional published scRNA-seq datasets. The diverse data were integrated after correcting confounding factors and batch effects. This atlas was composed of scRNA-seq data from 316 patients across 21 cancer types. T cell receptor (TCR) sequences of individual T cells with gene expression profiles were assembled to characterize the expansion and dynamics of T cells. Various computational methods were applied to investigate the features and abundance of T cells across cancer types. RESULTS We identified multiple potentially tumor-reactive T cell (pTRT) populations in cancer patients. The states of the pTRTs varied dramatically in the tumor microenvironment of different cancer types. For CD8+ T cells, the major pTRTs were exhausted T cells and exhibited high heterogeneity. We computationally inferred two major developmental paths to T cell exhaustion, through effector memory T cells and tissue-resident memory T cells, respectively, and both were prevalent among cancer types. We also noted the state transitions between terminal exhausted T cells and cells such as natural killer (NK)–like T cells, Type 17 CD8+ T cells (Tc17 cells) cells, and CD8+ Treg cells, but such transitions tend to occur in specific cancer types. For CD4+ T cells, follicular helper T cell (TFH)/T helper 1 (TH1) dual-functional T cells, which appeared to originate from TFH cells, were also notable pTRTs and correlated with the tumor mutation burden. We also found that the transcriptional programs of pTRTs could be affected by transforming growth factor–β (TGF-β) and interferons in the TMEs. The abundances of T cell states vary dramatically depending on cancer types. On the basis of tumor-infiltrating T cell compositions, cancer patients could be immune-typed as a group with high frequencies of terminal exhausted CD8+ T cells and another group with high frequencies of tissue-resident memory CD8+ T cells, and the immune types were associated with clinical traits such as patient survival and responses to immune checkpoint blockade. CONCLUSION We depicted the pan-cancer landscape of T cell heterogeneity and dynamics in the TME and established a baseline reference for future temporal or spatial studies associated with cancer treatments. The systematic comparison across cancer types revealed the commonalities and differences of T cell states in different TMEs. Our detailed signature, dynamics, and regulations of tumor-infiltrating T cells will facilitate the development of immunotherapies, and our proposed immune-typing can aid the therapeutic and diagnostic strategies that target T cells. Systematic analysis of a human pan-cancer T cell atlas. We analyzed approximately 390,000 T cells from 316 patients of 21 cancer types by means of scRNA-seq. Combining gene expression profiles and T cell receptor sequences, we investigated the heterogeneity and dynamics of tumor-infiltrating T cells and performed a systematic comparison of T cells among cancer types. Additionally, we provided a T cell composition–based immune-typing scheme. KIR, killer cell immunoglobulin-like receptor; IL26, interleukin-26. T cells play a central role in cancer immunotherapy, but we lack systematic comparison of the heterogeneity and dynamics of tumor-infiltrating T cells across cancer types. We built a single-cell RNA-sequencing pan-cancer atlas of T cells for 316 donors across 21 cancer types and revealed distinct T cell composition patterns. We found multiple state-transition paths in the exhaustion of CD8+ T cells and the preference of those paths among different tumor types. Certain T cell populations showed specific correlation with patient properties such as mutation burden, shedding light on the possible determinants of the tumor microenvironment. T cell compositions within tumors alone could classify cancer patients into groups with clinical trait specificity, providing new insights into T cell immunity and precision immunotherapy targeting T cells.

Subfields: Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/eda76900f3954e1930080779b5cfb92274a11da3

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