Clonal replacement of tumor-specific T cells following PD-1 blockade

Abstract

Immunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of patients with cancer1. However, whether the T cell response to checkpoint blockade relies on reinvigoration of pre-existing tumor-infiltrating lymphocytes or on recruitment of novel T cells remains unclear2,3,4. Here we performed paired single-cell RNA and T cell receptor sequencing on 79,046 cells from site-matched tumors from patients with basal or squamous cell carcinoma before and after anti-PD-1 therapy. Tracking T cell receptor clones and transcriptional phenotypes revealed coupling of tumor recognition, clonal expansion and T cell dysfunction marked by clonal expansion of CD8+CD39+ T cells, which co-expressed markers of chronic T cell activation and exhaustion. However, the expansion of T cell clones did not derive from pre-existing tumor-infiltrating T lymphocytes; instead, the expanded clones consisted of novel clonotypes that had not previously been observed in the same tumor. Clonal replacement of T cells was preferentially observed in exhausted CD8+ T cells and evident in patients with basal or squamous cell carcinoma. These results demonstrate that pre-existing tumor-specific T cells may have limited reinvigoration capacity, and that the T cell response to checkpoint blockade derives from a distinct repertoire of T cell clones that may have just recently entered the tumor.

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Fig. 1: Characterization of the BCC TME pre- and post-PD-1 blockade by scRNA-seq.
Fig. 2: Exhausted CD8+ tumor-infiltrating T cells are clonally expanded and express markers of tumor specificity.
Fig. 3: Clonal dynamics and phenotype transitions of tumor-infiltrating T cells.
Fig. 4: Clonal replacement of exhausted CD8+ T cells following PD-1 blockade.

Data availability

All ensemble and scRNA-seq data have been deposited in the GEO and are available under accession number GSE123814. Exome-sequencing data have been deposited in the Sequence Read Archive (SRA) and are available under accession number PRJNA533341. Bulk TCR-seq data can be accessed through the ImmuneACCESS database of Adaptive Biotechnologies (https://doi.org/10.21417/KY2019NM; https://clients.adaptivebiotech.com/pub/yost-2019-natmed). All other relevant data are available from the corresponding authors upon reasonable request.

Code availability

All custom code used in this work is available from the corresponding authors upon reasonable request.

References

  1. 1.

    Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).

  2. 2.

    Sakuishi, K. et al. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J. Exp. Med. 207, 2187–2194 (2010).

  3. 3.

    Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

  4. 4.

    Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

  5. 5.

    Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949 (2017).

  6. 6.

    Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Preprint at https://www.biorxiv.org/content/10.1101/409722v1 (2018).

  7. 7.

    Bonilla, X. et al. Genomic analysis identifies new drivers and progression pathways in skin basal cell carcinoma. Nat. Genet. 48, 398–406 (2016).

  8. 8.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  9. 9.

    Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).

  10. 10.

    Tellechea, O., Reis, J. P., Domingues, J. C. & Baptista, A. P. Monoclonal antibody Ber EP4 distinguishes basal-cell carcinoma from squamous-cell carcinoma of the skin. Am. J. Dermatopathol. 15, 452–455 (1993).

  11. 11.

    Bircan, S., Candir, O., Kapucoglu, N. & Baspinar, S. The expression of p63 in basal cell carcinomas and association with histological differentiation. J. Cutan. Pathol. 33, 293–298 (2006).

  12. 12.

    Bernemann, T. M., Podda, M., Wolter, M. & Boehncke, W. H. Expression of the basal cell adhesion molecule (B-CAM) in normal and diseased human skin. J. Cutan. Pathol. 27, 108–111 (2000).

  13. 13.

    Ransohoff, K. J., Tang, J. Y. & Sarin, K. Y. Squamous change in basal-cell carcinoma with drug resistance. N. Engl. J. Med. 373,1079–1082 (2015).

  14. 14.

    Atwood, S. X. et al. Smoothened variants explain the majority of drug resistance in basal cell carcinoma. Cancer Cell 27, 342–353 (2015).

  15. 15.

    Hoang, V. L. T. et al. RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers. PeerJ 5, e3631 (2017).

  16. 16.

    Wei, S. C. et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170, 1120–1133 (2017).

  17. 17.

    Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).

  18. 18.

    Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).

  19. 19.

    Kumar, B. V. et al. Human tissue-resident memory T cells are defined by core transcriptional and functional signatures in lymphoid and mucosal sites. Cell Rep. 20, 2921–2934 (2017).

  20. 20.

    Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).

  21. 21.

    Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

  22. 22.

    Duhen, T. et al. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat. Commun. 9, 2724 (2018).

  23. 23.

    Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789 (2019).

  24. 24.

    Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018).

  25. 25.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

  26. 26.

    Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

  27. 27.

    Zemmour, D. et al. Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR. Nat. Immunol. 19, 291–301 (2018).

  28. 28.

    Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).

  29. 29.

    Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

  30. 30.

    Kurtulus, S. et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1CD8+ tumor-infiltrating T cells. Immunity 50, 181–194 (2019).

  31. 31.

    Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 (2019).

  32. 32.

    Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

  33. 33.

    Huang, A. C. et al. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat. Med. 25, 454–461 (2019).

  34. 34.

    Kamphorst, A. O. et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc. Natl Acad. Sci. USA 114, 4993–4998 (2017).

  35. 35.

    Ghoneim, H. E. et al. De novo epigenetic programs inhibit PD-1 blockade-mediated T cell rejuvenation. Cell 170, 142–157 (2017).

  36. 36.

    Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502 (2017).

  37. 37.

    Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400–404 (2012).

  38. 38.

    Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).

  39. 39.

    Gee, M. H. et al. Antigen identification for orphan T cell receptors expressed on tumor-infiltrating lymphocytes. Cell 172, 549–563 (2018).

  40. 40.

    Li, J. et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy. Immunity 49, 178–193 (2018).

  41. 41.

    Chang, A. L. S. et al. Pembrolizumab for advanced basal cell carcinoma: an investigator-initiated, proof-of-concept study. J. Am. Acad. Dermatol. 80, 564–566 (2019).

  42. 42.

    Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

  43. 43.

    Wang, C. et al. High-throughput, high-fidelity HLA genotyping with deep sequencing. Proc. Natl Acad. Sci. USA 109, 8676–8681 (2012).

  44. 44.

    Thorstenson, Y. R. et al. Allelic resolution NGS HLA typing of class I and class II loci and haplotypes in Cape Town, South Africa. Hum. Immunol. 79, 839–847 (2018).

  45. 45.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  46. 46.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  47. 47.

    Birger, C. et al. FireCloud, a scalable cloud-based platform for collaborative genome analysis: strategies for reducing and controlling costs. Preprint at https://www.biorxiv.org/content/10.1101/209494v1 (2017).

  48. 48.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  49. 49.

    Skidmore, Z.L. et al. GenVisR: Genomic Visualizations in R. Bioinformatics 32, 3012–3014 (2016).

  50. 50.

    Xie, C. et al. Fast and accurate HLA typing from short-read next-generation sequence data with xHLA. Proc. Natl Acad. Sci. USA 114, 8059–8064 (2017).

  51. 51.

    Hundal, J. et al. pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).

  52. 52.

    Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution Nat. Genet. 49, 1015–1024 (2017).

  53. 53.

    Ha, G. et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 24, 1881–1893 (2014).

  54. 54.

    Li, B. & Li, J. Z. A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data. Genome Biol. 15, 473 (2014).

  55. 55.

    Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

  56. 56.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

  57. 57.

    McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  58. 58.

    Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

  59. 59.

    Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  60. 60.

    Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).

  61. 61.

    Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

  62. 62.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

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Acknowledgements

We thank members of the Chang laboratory for discussions; X. Ji, D. Wagh and J. Coller at the Stanford Functional Genomics Facility, J. Sung and S. Fitch at the Stanford Clinical and Translational Research Unit Biobank and D. D. Hiraki at the Stanford Blood Center Histocompatibility, Immunogenetics, and Disease Profiling Laboratory; A. D. Colevas, S. Reddy, L. Van Der Bokke and R. Patel for clinical collaboration; A. Pague and A. Valencia for assistance with clinical specimens; K. Quema-Yee at the Parker Institute for Cancer Immunotherapy for assistance with illustrations. This work was supported by the Parker Institute for Cancer Immunotherapy (A.T.S., D.K.W., R.K., S.L.B., M.M.D. and H.Y.C.), the Michelson Foundation (A.T.S.) and the National Institutes of Health (NIH) (P50HG007735, R35CA209919 (H.Y.C.), K08CA230188 (A.T.S.), K23CA211793 (K.Y.S.), R01CA182514, DP1-CA238296 (C.C.) and 5U19AI057229 (M.M.D.)). K.E.Y. was supported by the National Science Foundation Graduate Research Fellowship Program (NSF DGE-1656518) and a Stanford Graduate Fellowship. A.T.S. was supported by a Bridge Scholar Award from the Parker Institute for Cancer Immunotherapy, a Career Award for Medical Scientists from the Burroughs Wellcome Fund and the Human Vaccines Project Michelson Prize for Human Immunology and Vaccine Research. Cell sorting for this project was done on instruments in the Stanford Shared FACS Facility. Sequencing was performed by the Stanford Functional Genomics Facility (supported by NIH grant S10OD018220). M.M.D. and H.Y.C. are investigators of the Howard Hughes Medical Institute.

Author information

K.E.Y., A.T.S., A.L.S.C. and H.Y.C. conceived the project. K.E.Y., A.T.S., Y.Q., R.K.G., R.A.B. and K.Y.S. performed experiments. K.E.Y., A.T.S., D.K.W., R.K., C.W., K.M., J.M.G., R.A.B. and K.Y.S. analyzed data. S.L.B., C.C., M.M.D., A.L.S.C. and H.Y.C. guided data analysis. K.E.Y., A.T.S. and H.Y.C. wrote the manuscript with input from all authors.

Correspondence to Ansuman T. Satpathy or Anne Lynn S. Chang or Howard Y. Chang.

Ethics declarations

Competing interests

H.Y.C. is a co-founder of Accent Therapeutics, Pretzel Therapeutics, and is an advisor for 10x Genomics, Arsenal Biosciences, and Spring Discovery. A.L.S.C. was an advisory board member and clinical investigator for studies sponsored by Merck, Regeneron, Novartis, Galderma and Genentech Roche. A.T.S. and D.K.W. are advisors for Immunai.

Additional information

Peer review information: Saheli Sadanand and Joao Monteiro were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Mutational landscape of BCC tumors following PD-1 blockade.

Related to Fig. 1. a, Summary of mutation burden, potential driver mutations and mutation frequencies detected in whole-exome sequencing data. Potential driver mutations were selected based on frequently mutated genes in BCCs that have been identified previously7. Del, deletion; Ins, insertion. b, Bar plots of nonsynonymous mutation burdens before and after treatment detected by exome sequencing (top) and predicted neoepitope burden using only the predicted binding strength of the mutant peptide, for peptides with a binding strength of less than 500 nM (bottom left) or less than 50 nM (right). c, Changes in the composition of clonal mutations detected in exome sequencing data following treatment, with persistent mutation clusters in gray, mutation clusters decreasing in cellular prevalence following treatment in blue or green and mutation clusters increasing in cellular prevalence following treatment in red. For clonal composition analysis, variant allele information from matched pre- and post-treatment tumor samples was leveraged to rescue shared low-frequency variants that did not pass standard variant filtering (Methods). Bar plots of the ratio of predicted neoepitopes to nonsynonymous mutations in each mutation cluster (right), with two novel tumor subclones emerging after treatment that were devoid of predicted neoepitopes. Predicted neoepitopes were based on binding strength of less than 500 nM for the mutant peptide and greater than 500 nM for the corresponding wild-type peptide (as in Fig. 1c). d, Representative flow cytometry staining of dissociated BCC cells. Similar results were obtained for each sorted sample (including SCC samples, n = 32). Cells were stained for expression of the indicated markers, and two-color histograms are shown for cells pre-gated as indicated by the arrows and above each diagram. Numbers represent the percentage of cells within the indicated gate. Bottom panels demonstrate cell size differences between tumor and stromal cells, immune cells (non-T cells) and T cells.

Extended Data Fig. 2 Characterization of cell types present in BCC TME.

Related to Fig. 1. a, Heat map of differentially expressed genes (rows) between cells belonging to each cell-type cluster (columns). All malignant cells were treated as one cluster. b, Correlation between aggregated expression profiles from immune-cell-type clusters identified in BCC TME and bulk RNA-seq profiles from sorted reference populations (data were obtained from a previously published study6, n = 1–4 biologically independent samples from different donors). c, UMAP of all BCC TME cells colored by cell-type-specific markers. d, Bar plots indicating relative proportions of markers used for sorting that were detected in each cluster (excluding cells that were not sorted using any markers), proportions of cells for which a TCR sequence was detected in each cluster, relative proportions of each non-malignant cell type detected per patient, relative proportions of cells from each patient detected in each cluster, and proportions of cells detected pre- and post-treatment in each cluster.

Extended Data Fig. 3 Copy number alterations and gene expression of individual BCC tumors.

Related to Fig. 1. a, Inferred CNV profiles for malignant cells separated by patient based on scRNA-seq (scCNV) and whole-exome sequencing (WES). Dashed line indicates a potential subclone identified by scCNV highlighted for su005. For all patients, pre- and post-treatment malignant cells were analyzed together and exhibited similar CNV profiles, with the exception of su006. For su006, differences between time points were apparent in CNV profiles obtained from both scRNA-seq and exome sequencing, analogous to the changes in mutation composition identified in Extended Data Fig. 1c. b, Heat map of differentially expressed genes (rows, n = 577) across malignant BCC cells (n = 3,548) aggregated by patient (columns, n = 8). Cut-offs for differential expression were adjusted P < 0.01 (Bonferroni-corrected, two-tailed Wilcoxon rank-sum test), log-transformed average fold change > 0.3 and difference in fraction of positive cells > 0.3. Core BCC genes that are differentially expressed between all malignant cells and other TME cells are shown in the top cluster. Genes differentially expressed between patients are shown in the bottom clusters. Specific genes associated with cancer-associated pathways are highlighted.

Extended Data Fig. 4 Characterization of T cell subtypes present in BCC TME.

Related to Fig. 2. a, Enrichment of bulk T cell subtype signatures for each T cell cluster identified in the BCC TME. T cell subtype signatures were derived from bulk datasets (from this study and a previously published study21, n = 3–7 biologically independent samples from different donors) and single T cells from a BCC dataset were scored for signature enrichment. Heat maps represent the z-scored average signature enrichment for each cluster. b, Heat map of Pearson correlation between T cell clusters based on first 20 principal components used for clustering (n = 33,106 cells). c, UMAP of all T cells colored according to marker gene expression. d, UMAP of all T cells separated by patient and colored according to anti-PD-1 treatment status.

Extended Data Fig. 5 Characterization of diffusion map trajectories and increase in TFH cell clonality accompanied by B cell expansion.

Related to Fig. 2. a, Violin plots of cell coordinates in diffusion components 1 and 2 separated by cluster identity (left, middle). Violin plot of pseudotime values separated by cluster identity (right). n = number of cells. b, Heat map of expression of genes with highest correlation with diffusion components 1 and 2 (rows) across cells belonging to each cell-type cluster (columns). c, Box plot of Gini indices for each CD4+ T cell cluster separated by time point, showing clonal expansion of TFH cells after treatment. Each point represents a patient with more than 10 cells belonging to a cluster at that time point; the size is proportional to the number of cells. d, UMAP of all cells detected for patient su001 colored by treatment time point (left) and relative proportions of each immune cell type (right), showing increased frequency of B cells after treatment. e, UMAP of T cells detected for patient su001 colored by treatment time point (left) and relative proportions of CD4+ phenotype (right), showing increased frequency of TFH cells after treatment. f, Bar plot of percentage AICDA+ B cells, separated by patient. g, Hematoxylin and eosin (H&E) staining of the post-treatment BCC tumor of patient su001 demonstrating islands of BCC in sclerotic stroma with a peripheral cuff of dense lymphoid tissue. Scale bars, 400 μm (top) and 100 μm (bottom). Hematoxylin and eosin staining was performed once for each sample.

Extended Data Fig. 6 Correlations between TCR clonotypes or TCR specificity groups and scRNA-seq phenotypes.

Related to Fig. 3. a, Distributions of the proportion of cells within each clone (≥3 cells) that share a common cluster identity, separated by patient (for patients with >3 clones with ≥3 cells), compared to randomly selected and size-matched groups of T cells (n = number of clones, two-tailed unpaired Student’s t-test). b, Distribution of the proportion of CD4+ cells (left) and CD8+ cells (right) within each clone or TRB clones within each TCR specificity group (≥3 cells) that share a common cluster identity, separated by treatment time point, compared to randomly selected and size-matched groups of T cells from the same sample (left, n = number of clones, two-tailed unpaired Student’s t-test). c, Bar plot of T cell cluster assignments for all clones with more than 10 cells, separated by patient and treatment status (top: pre-treatment, bottom: post-treatment). d, Bar plot of T cell cluster assignments for the largest 10 TCR specificity groups, separated by TRB clone and treatment status (top, pre-treatment; bottom, post-treatment). Conserved motifs between TRB clones identified by GLIPH highlighted in red. Representative TRB sequences shown for TCR specificity groups with more than four unique clonotypes. e, Heat map of the fraction of TCR specificity groups with clones belonging to a given primary phenotype (rows) that also contain clones belonging to a secondary phenotype (columns).

Extended Data Fig. 7 Details of clone transitions.

Related to Fig. 3. a, Heat map of TRB clonotype overlap between all samples, indicating correct pairing of samples and a significant number of overlapping clones between time points within individual patients with the exception of one pair with limited cell numbers and no clonotype overlap (su003). b, Bar plot of T cell cluster assignments for matched TRB clones between time points for the top 60 clones with at least 3 cells per time point. Related to Fig. 3e.

Extended Data Fig. 8 Clonal expansion in tumor and peripheral blood detected by bulk TCR-seq.

Related to Fig. 4. a, Scatterplots comparing TRB clone frequencies pre- and post-treatment measured by scRNA-seq and TCR-seq, separated by patient. Clones for which the majority of cells share an exhausted CD8+ phenotype (red) or a memory CD8+ phenotype (blue) are highlighted. Patient su003 without clonotype overlap between time points was excluded. In this and subsequent panels, exhausted refers to both exhausted and exhausted/activated clusters. b, Box plot of the fraction of novel TCR specificity groups within each cluster after treatment for TCR specificity groups that contain at least two distinct TRB sequences and at least three cells, separated by patient (n = number of patients). c, Bar plot of the fraction of clones with significant expansion post-treatment based on bulk TCR-seq, separated by patient and phenotype and colored according to replacement status. d, Scatterplots comparing TRB clone frequencies between time points measured by bulk TCR-seq for sequential time points in patient su001; clones for which the majority of cells share an exhausted CD8+ phenotype (red) or a memory CD8+ phenotype (blue) are highlighted. Novel clones that emerged between time points are highlighted in dark red and were detected only in pre- and post-treatment comparisons, but not in comparisons between pre-treatment time points, suggesting that replacement is primarily a result of PD-1 blockade rather than time between sampling.

Extended Data Fig. 9 TCR overlap between peripheral blood and tumor detected by bulk TCR-seq.

Related to Fig. 4. a, Pie chart of the percentage of TRB clones detected in peripheral blood that were also detected in the tumor, expanded to show the distribution of phenotypes in the tumor, as well as the fraction of exhausted clones detected in peripheral blood, colored according to replacement status in the tumor. In this and subsequent panels, the exhausted category includes both exhausted and exhausted/activated clusters. b, Bar plot of the percentage of peripheral T cells that match tumor-infiltrating TRB clones with exhausted phenotypes post-treatment as detected by scRNA-seq. c, Violin plot of TCR specificity group enrichment (tumor frequency/PBMC frequency) detected by bulk TCR-seq, separated by phenotype and treatment status (n = number of TCR specificity groups, two-tailed unpaired Student’s t-test).

Extended Data Fig. 10 Clonal replacement analysis in SCC TILs following PD-1 blockade.

Related to Fig. 4. a, UMAP of tumor-infiltrating T cells present in SCC samples pre- and post-PD-1 blockade colored by patient (top) and anti-PD-1 treatment status (bottom). b, Heat map of correlation between averaged RNA expression between BCC and SCC T cell clusters. Ex/Act, exhausted/activated. c, Box plot of Gini indices for each CD8+ T cell cluster calculated for each patient (n = number of patients). In this and subsequent panels, exhausted refers to both exhausted and exhausted/activated clusters, unless otherwise noted. d, Abundance of the top 12 exhausted clones in sample su010-S identified by unsupervised clustering compared to the abundance of the same clones in sorted CD8+CD39+ T cells, colored by assigned phenotype. e, Distribution of the proportion of cells within each clone or TRB clones within each TCR specificity group (≥3 cells) that share a common cluster identity, separated according to treatment time point, compared to randomly selected and size-matched groups of T cells from the same sample (left, n = number of TRB clones or TCR specificity groups, two-tailed unpaired Student’s t-test). f, Heat map of the fraction of clonotypes belonging to a given primary phenotype cluster (rows) that are shared with other secondary phenotype clusters (columns). g, Heat map of all observed phenotype transitions for matched clones during PD-1 blockade for clones with at least three cells for each time point. h, TCF7+/stem-like score29 versus exhaustion score for all CD8+ T cells, colored according to gene expression (left). TCF7+/stem-like score versus exhaustion score for exhausted cells and cells of other phenotypes belonging to primarily exhausted clones, colored according to phenotype (top right). Violin plot of TCF7+/stem-like score for exhausted cells and cells of other phenotypes belonging to primarily exhausted clones, demonstrating that the highest TCF7+/stem-like score is observed in cells with an exhausted phenotype (bottom right, n = number of cells). i, Violin plot of TCF7+/stem-like score for memory and exhausted cells separated by change in clone abundance after treatment (left, n = number of cells, two-tailed unpaired Student’s t-test). Clones were defined as expanded or contracted if they significantly changed in abundance by a Fisher exact test (P < 0.05 and fold change > 0.5) and persistent if they did not significantly change in abundance and at least one cell was detected at each time point. j, Scatterplots comparing TRB clone frequencies pre- and post-treatment measured by bulk TCR-seq for SCC patients (n = 3). Clones that were significantly expanded or contracted post-treatment based on a binomial test (two-sided, Bonferroni-corrected P < 0.01) are highlighted on the left. Clones for which the majority of cells share an exhausted CD8+ phenotype (middle, red) or a memory CD8+ phenotype (right, blue) are also highlighted.

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