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Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade

Nature Cancer volume 3, pages 1123–1136 (2022)Cite this article

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Abstract

Immune-checkpoint blockade (ICB) therapies represent a paradigm shift in the treatment of human cancers; however, it remains incompletely understood how tumor-reactive T cells respond to ICB across tumor types. Here, we demonstrate that measuring CXCL13 expression could effectively identify both precursor and terminally differentiated tumor-reactive CD8+ T cells within tumors. Applying this approach, we performed meta-analyses of published single-cell data for CXCL13+CD8+ T cells in 225 samples from 102 patients treated with ICB across five cancer types. We found that CXCL13+CD8+ T cells were correlated with favorable responses to ICB, and the treatment further increased such cells in responsive tumors. In addition, CXCL13+ tumor-reactive subsets exhibited variable responses to ICB in distinct contexts, likely due to different degrees of exhaustion-related immunosuppression. Our integrated analyses provide insights into mechanisms underlying ICB and suggest that bolstering precursor tumor-reactive CD8+ T cells might provide an effective therapeutic approach to improve cancer treatment.

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Fig. 1: CXCL13 as a key marker of tumor-specific CD8+ T cells.
Fig. 2: CXCL13+ tumor-reactive T cells correlate with response to ICB.
Fig. 3: CXCL13+ tumor-reactive T cell subsets.
Fig. 4: The subset distribution of CXCL13+ tumor-reactive T cells.
Fig. 5: Clonal revival and peripheral T cell dynamics.
Fig. 6: Dynamics of tumor-infiltrating CD4+ T cell subsets following treatment.

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Data availability

Newly generated bulk TCR-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE17994. Previously published scRNA-seq, scTCR-seq and bulk TCR-seq data reanalyzed here are available under accession codes GSE179994 (NSCLC data by Liu et al.6), GSE173321 (NSCLC data by Caushi et al.13), GSE123814 (BCC and SCC data4), GSE169246 (breast cancer data by Zhang et al.7), GSE180268 (HNSCC data by Eberhardt et al.14) and SRZ190804 (RCC data by Krishna et al.20). The breast cancer data by Bassez et al.5 were downloaded from http://biokey.lambrechtslab.org. The RCC data by Bi et al.19 were download from https://singlecell.broadinstitute.org/single_cell/study/SCP1288. An interactive web server for analyzing and visualizing the scRNA-seq data is available at http://metaICB.cancer-pku.cn/. All other relevant data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

All custom code used to generate the results in this study has been deposited in a GitHub repository at https://github.com/PaulingLiu/metaICB.

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Acknowledgements

Z.Z. is supported by the National Key R&D Program of China (2020YFE0202200), the National Natural Science Foundation of China (81988101, 91959000, 91942307, 31991171), the Beijing Municipal Science and Technology Commission (Z201100005320014 and Z211100003321005), the Changping Laboratory and the Beijing Advanced Innovation Center for Genomics. Part of the analysis was performed on the High Performance Computing and National Center for Protein Sciences at Peking University.

Author information

Authors and Affiliations

  1. Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China

    Baolin Liu, Dongfang Wang & Zemin Zhang

  2. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Yuanyuan Zhang & Zemin Zhang

  3. Analytical Biosciences Limited, Beijing, China

    Xueda Hu

  4. Changping Laboratory, Beijing, China

    Zemin Zhang

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Contributions

Z.Z. conceived and designed this study. B.L. conceived and performed bioinformatic data analysis and interpreted the results. Y.Z., D.W. and X. H. assisted with the data analysis and provided valuable discussion. B.L. and Z.Z. wrote the manuscript with input from all the authors.

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Correspondence to Zemin Zhang.

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Z.Z. is a founder of Analytical Biosciences and an advisor for InnoCare Pharma and ArsenalBio. All financial interests are unrelated to this study. The other authors declare no competing interests.

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Nature Cancer thanks Benjamin Izar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of tumor-specific CD8 T cells.

a, Volcano plot showing differentially expressed genes between bystander CD8 T cells and tumor-reactive CD8 T cells from post-ICB non-responsive tumors; N = 6 tumors. Each red dot denotes an individual gene with adjusted P value < 0.01 (two-sided t-test) and fold change ≥ 0.2. b, Volcano plot showing differentially expressed genes between bystander CD8 T cells and tumor-reactive CD8 T cells from N = 13 treatment-naïve tumors. Each red dot denotes an individual gene with adjusted P value < 0.01 (two-sided t-test) and fold change ≥ 0.2. c, Gene expression heatmap for tumor-specific and bystander CD8 T cells. The tumor-reactive CD8 T cells shown here were from N = 13 treatment-naive tumors. d, Average CXCL13 expression in bystander CD8 T cell clones and qualified (clonal size > 3; left) and all (right) tumor-specific CD8 T cell clones from treatment-naïve and post-ICB non-responsive (NR) tumors; N = 19 tumors. e, Performance summary of the CXCL13-selection-based strategy in identifying tumor-reactive CD8 T cells in terms of the clone and cell numbers, corresponding to d. f-h, UMAP plots showing the expression levels of certain signature genes in the three CD8 T cell groups; N = 7 tumors. i, Gene expression heatmap for tumor-specific and bystander CD8 T cells from pre- and post-treatment responsive lung tumors (Liu et al. NSCLC data); N = 7 tumors for both left and right panels.

Source data

Extended Data Fig. 2 Identification of tumor-specific CD8 T cell clones.

a, Determination and classification of n = 31 tumor-specific and n = 57 bystander CD8 T cell clones by using CXCL13 expression and exhaustion score. Data were summarized from pre-treatment responsive BCC and SCC tumors; N = 15 patients. b, ROC curves for the performance of individual exhaustion-related genes and CXCL13 in discriminating tumor-specific from bystander CD8 T cell clones from post-treatment responsive tumors. The antigen specificities of post-treatment CD8 T cell clones were determined based on matched pre-treatment clones in a. The sample size is the same as in a. c, Determination and classification of n = 44 tumor-specific and n = 29 bystander CD8 T cell clones by using CXCL13 expression and exhaustion score. Data were summarized from pre-treatment responsive breast tumors; N = 4 patients. d, ROC curves for the performance of individual exhaustion-related genes and CXCL13 in discriminating tumor-specific from bystander CD8 T cell clones from post-treatment responsive tumors. The antigen specificities of post-treatment CD8 T cell clones were determined based on matched pre-treatment clones in c. The sample size is the same as in c.

Source data

Extended Data Fig. 3 Isolation of CXCL13 + CD8 T cell clones and T cell expansion.

a, Density plots showing the distribution of CXCL13 and CD8A expression across all T cell clones. For each dataset, the threshold for identifying CXCL13- or CD8A-positive clones was determined as the inflection point of the density curve (the vertical line). Data were summarized from n = 20 (the Liu et al. data), 15 (the Caushi et al. data), 22 (BCC), 7 (SCC), 53 (breast cancer cohort 1), 22 (breast cancer cohort 2), 12 (breast cancer cohort 3), 7 (RCC; Krishna et al.) and 4 (RCC; Bi et al.) tumor samples. b, Comparison of the number of expanded CXCL13+ clonotypes (clonal size ≥ 2 and ≥ 20, respectively) between treatment responses. Samples analyzed here were the same as in a, presented as mean ± s.e.m. Two-sided Wilcoxon test.

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Extended Data Fig. 4 Clustering of CXCL13 + tumor-reactive T cells.

a, UMAP plot of 27,155 CXCL13+ CD8 T cells from N = 15 post-ICB lung tumor samples from the Caushi et al. data, color-coded by clusters. b, UMAP plots showing the expression levels of certain signature genes in different cell clusters from a. c, TOX gene expression in the tumor-specific CD8 T cell subsets from N = 20 tumor samples from 10 patients from the Liu et al. NSCLC dataset. d, UMAP plots of CXCL13+ CD8 T cells from pre- and post-ICB tumors for other immunotherapy datasets. N = 20, 22, 7, 53, 22, 7, and 4 tumor samples for the 7 datasets, respectively (from left to right and top to bottom). e, Comparison of the stem-like T cell signature scores and terminally differentiated (TD) signature scores in different cell clusters from a and d, color-coded by clusters. Precursor-like T cells were not identified in the two RCC datasets, and we therefore included the reference data as comparisons when analyzing TD cells. The reference data were from the Eberhardt et al. study and included experimentally validated stem-like and TD cells. Each dot represents one cell while the center line indicates the median value. Data were summarized from n = 13,538, 27,155, 1,944, 4,360, 3,325, 542, 7,290, 3,305 cells from the 8 datasets, respectively. The tumor sample size for each dataset is the same as in a and d. The lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range. ***P < 2.2×10−16, two-sided t-test.

Source data

Extended Data Fig. 5 Epigenetic features of CXCL13 + tumor-reactive T cell subsets.

a, ATAC-seq tracks showing the chromatin accessibility in the CD8A, CCR7 and CXCR4 loci for the three CXCL13+ CD8 T cell clusters from N = 4 patients. b, Correlation between the residual tumor size and the proportion of precursor-like cells in all CXCL13+ tumor-reactive CD8 T cells after treatment. Data were summarized from n = 15 patients. P value was determined by two-tailed linear regression t-test.

Source data

Extended Data Fig. 6 Clonal replacement and CXCL13 + tumor-reactive T cell clones in blood.

a, Relationship of clonal replacement with baseline T cell infiltration (left) and the gap of the time between two matched samplings before and after treatment (right). Data were from n = 7 patients from the Liu et al. NSCLC study. P value was determined by a two-tailed linear regression t-test. b, c, Scatter plots of the clone size of each detected CXCL13+ CD8 T cell clone in tumor (y axis) and blood (x axis) for patients with NSCLC (b) and BCC (c); N = 14, 7 and 63 clones for samples NSCLC (P010), NSCLC (P013) and BCC (su001), respectively. Each dot represents one clone. d, Comparison of the number of CXCL13+ tumor-reactive CD8 T cell clonotypes detected in pre- and post-treatment blood for the 3 responsive patients from the Liu et al. NSCLC study. P value was determined by a two-sided paired t-test.

Source data

Extended Data Fig. 7 Dynamics of CD4 T cell subsets following treatment.

a-c, Comparison of the frequency of Tregs (a), other Th (b) and naïve (c) CD4 T cells in all CD4 T cells from pre- and post-treatment responsive and non-responsive tumors. Data were summarized from n = 20, 15, 22, 7, 53, 22, 12, 4 and 7 tumor samples from datasets 1-9, respectively. R, responsive; NR, non-responsive; MPR, major pathologic response. Each dot represents one sample while the center line indicates the median value. The lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5× interquartile range.

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Liu, B., Zhang, Y., Wang, D. et al. Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade. Nat Cancer 3, 1123–1136 (2022). https://doi.org/10.1038/s43018-022-00433-7

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