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Resident and circulating memory T cells persist for years in melanoma patients with durable responses to immunotherapy

Nature Cancer volume 2, pages 300–311 (2021)Cite this article

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Abstract

While T-cell responses to cancer immunotherapy have been avidly studied, long-lived memory has been poorly characterized. In a cohort of metastatic melanoma survivors with exceptional responses to immunotherapy, we probed memory CD8+ T-cell responses across tissues, and across several years. Single-cell RNA sequencing revealed three subsets of resident memory T (TRM) cells shared between tumors and distant vitiligo-affected skin. Paired T-cell receptor sequencing further identified clonotypes in tumors that co-existed as TRM in skin and as effector memory T (TEM) cells in blood. Clonotypes that dispersed throughout tumor, skin and blood preferentially expressed an IFNG/TNF-high signature, which had a strong prognostic value for patients with melanoma. Remarkably, clonotypes from tumors were found in patient skin and blood up to 9 years later, with skin maintaining the most focused tumor-associated clonal repertoire. These studies reveal that cancer survivors can maintain durable memory as functional, broadly distributed TRM and TEM compartments.

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Fig. 1: Overlapping transcriptional signatures of CD8+ T cells from skin and tumor of long-term melanoma survivors.
Fig. 2: TRM cells in skin and tumor are composed of three subpopulations with discrete features and prognostic signatures.
Fig. 3: Promiscuously distributed CD8+ T-cell clonotypes show a propensity to form TRM-IFNG cells in skin and tumor.
Fig. 4: Melanoma antigen-specific T cells accumulate in skin and blood and are capable of long-lived functional recall.
Fig. 5: Tumor-associated T-cell clonotypes persist for up to 9 years, with skin sustaining a focused repertoire.
Fig. 6: CD8+ T-cell clones from tumors persist as TRM cells in skin and as TEM cells in blood.

Data availability

Single-cell RNA-seq and TCR-seq data that support the findings of this study have been deposited in the Database of Genotypes and Phenotypes (dbGaP) under the accession code phs002309.v1.p1. Bulk TCR-seq data can be accessed through the ImmuneACCESS database of Adaptive Biotechnologies (https://doi.org/10.21417/JH2021NC; https://clients.adaptivebiotech.com/pub/han-2021-natcancer). The published microarray datasets used to generate the comprehensive CD8+ TRM signature for the GSEA analysis were accessible at the Gene Expression Omnibus (GEO) under accession codes GSE47045, GSE15907 and GSE37448. The remaining gene sets used in the GSEA analysis were accessible through the MSigDB database (https://www.gsea-msigdb.org/gsea/msigdb). The published TCGA skin cutaneous melanoma (SKCM) RNA-seq data used to perform the survival analysis are available at Firehose (http://gdac.broadinstitute.org/). Two additional previously published stage III/IV melanoma patient RNA-seq datasets are available at the GEO database with the following accession numbers: GSE54467 and GSE19234. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The open-source code is available at GitHub. Codes for gene expression analyses, including single-cell RNA-seq data analysis, survival analysis and GSEA analysis, are publicly available on GitHub (https://github.com/TrmMelanoma/Gene-expression-related-analysis). Codes for TCR analyses, including single-cell TCR-seq data analysis and bulk TCR-seq data analysis, are publicly available on GitHub (https://github.com/TrmMelanoma/TCR-analysis).

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Acknowledgements

We are grateful for the generosity of all the patients who volunteered their time and tissue for this study. We thank the nursing staff of the General Surgery clinic at Dartmouth-Hitchcock Medical Center led by L. O’Rourke, and the Norris Cotton Cancer Center melanoma research team, especially B. Highhouse and M. Stannard, for assistance in patient recruitment and coordination. We appreciate the effort from the core facilities—the Dartlab and the Single Cell Genomics Core—at Dartmouth. We thank G. Ward at the Dartlab for FACS sorting expertise. We thank M. Pasca di Magliano for discussion and advice. This work was funded by The Dartmouth CTSA (grant NIH KL2TR0010), the American Cancer Society (grant CSDG 18-167-01), the Dow-Crichlow Career Development Award in Surgery and the Society of Surgical Oncology Clinical Investigator Award to C.V.A.; grant NIH R01 CA225028 and The Knights of the York Cross of Honour Philanthropic Fund to M.J.T.; a Borroughs Welcome BDLS Training Grant to J.H.; grant NIH F31CA232554 to A.M.; and support from grant 5P30 CA023108-40 (Immune Monitoring and Genomics and Molecular Biology Shared Resources). Single-cell sequencing was conducted at the Dartmouth Center for Quantitative Biology with support from NIGMS (grant P20GM130454) and NIH S10 (grant S10OD025235) awards. The views expressed are those of the authors and not necessarily those of the NIH or the American Cancer Society.

Author information

Author notes

  1. These authors contributed equally: Mary Jo Turk, Christina V. Angeles.

Authors and Affiliations

  1. Departments of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

    Jichang Han, Aleksey Molodtsov & Mary Jo Turk

  2. Departments of Molecular and Systems Biology, The Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

    Yanding Zhao

  3. Norris Cotton Cancer Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

    Keisuke Shirai, Fred W. Kolling, Peisheng Zhang, Tyler G. Searles, Justin M. Bader, Jiang Gui, Mary Jo Turk & Christina V. Angeles

  4. Departments of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA

    Keisuke Shirai & Jan L. Fisher

  5. Departments of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA

    Shaofeng Yan

  6. Baylor School of Medicine, Houston, TX, USA

    Chao Cheng

  7. Roswell Park Cancer Institute, Buffalo, NY, USA

    Marc S. Ernstoff

  8. Departments of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA

    Christina V. Angeles

  9. University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA

    Christina V. Angeles

  10. Department of Surgery, University of Michigan, Ann Arbor, MI, USA

    Christina V. Angeles

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  1. Jichang Han
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Contributions

C.V.A. and M.J.T. conceived and supervised the study. J.H., M.J.T. and C.V.A. drafted the paper and figures. J.H. and Y.Z. carried out the primary analysis. C.V.A., M.S.E. and K.S. carried out patient recruitment. J.H., J.L.F., P.Z. and T.G.S. processed tissues and carried out flow cytometry and FACS. J.H. and T.G.S. extracted the DNA for bulk TCR sequencing. S.Y. provided dermatopathology expertise and assisted with IHC analysis. F.W.K. carried out scRNA and scTCR library preparation and sequencing. C.C., Y.Z. and A.M. provided bioinformatic support. C.V.A., K.S. and J.M.B. collated the clinical data. J.G. provided statistical support. M.J.T. provided scientific and infrastructural support. All authors reviewed and edited the final paper.

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Correspondence to Mary Jo Turk or Christina V. Angeles.

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Peer review information Nature Cancer thanks Adil Daud, Ansuman Satpathy 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 Summary of melanoma patient treatments and specimens.

Detailed summary of the melanoma clinical stage, types and durations of immunotherapy treatments, and specimens collected per patient. Large arrow indicates the timeline for individual patient (not to scale). The time between date of last treatment and tissue biopsy is annotated in black. Small arrows show the timepoints of each specimen collection along with the experiments done for each specimen annotated in parentheses. scRNA=scRNA/scTCR-seq; flow = flow cytometry; Adaptive = TCR Vb DNA sequencing. Different colors indicate different types of immunotherapy as depicted in the legend.

Extended Data Fig. 2 FACS gating strategy.

Pseudocolor graphs show the gating strategy for FACS of each tissue. CD45+CD8+ and CD45+CD4+ cells were sorted by FACS into the same well. Values of x and y axis represent fluorescence intensities. Percentages of cell populations were labelled in each graph.

Extended Data Fig. 3 Resident memory CD8+ T cells expressing CD69 reside in both the epidermis and dermis in long-term metastatic melanoma survivors with immunotherapy-associated vitiligo.

a, Representative pseudocolor dot plots showing the gating strategy for the live CD45+ CD3+ CD8+ population. b, Contour plots show the expression of CD69, CD103 and CD62L on pre-gated CD8+ population by flow cytometry. The number represents the proportion out of total CD8+ T cells. c, Immunohistochemistry staining for CD8+ CD69+ resident memory CD8+ T cells in the skin from patient PT628. Images are representative of multiple fields from at least three skin sections taken from each of 4 individual patients. CD8 was stained in green and CD69 was stained in red. CD8+ and CD69+ single-stain cells are indicated by green and red arrows, respectively. Black cells (indicated by black arrows) are CD8+CD69+ co-stained cells. Original magnification: 10X (left) and 40X (right).

Extended Data Fig. 4 Patient contributions to each CD8+ T-cell cluster.

Pie charts depicting the proportion of cells from each patient to the total number of cells in each cluster (C1-C10). Each color represents one individual patient, with the absolute number of cells from each patient labeled in the corresponding slice of the pie chart.

Extended Data Fig. 5 Transcriptional profiles of clusters C8-C10 are enriched in consensus TRM gene lists while the transcription profiles of clusters C1-C7 are not enriched for core TRM genes.

a, GSEA analysis showing that the upregulated genes of TRM were enriched in the downregulated genes in clusters C1-C7 demonstrating that only clusters C8-C10 have key features of TRM. NESs and FDR q-values are shown for each gene set. The statistics were performed by the two-sample Kolmogorov-Smirnov test. b, Heatmaps depict the z-transformed mean expression of published consensus skin or tumor TRM gene lists across CD8+ T cell clusters C1-C10. c, Venn diagrams show the number of genes that overlap between the marker gene list of each cluster (red circle) with the published human cancer TRM gene list (blue circle). The overlap levels between two gene lists were evaluated by two-sided fisher exact test. Enrichment scores and P-values are labeled accordingly. d, Venn diagrams show the number of genes that overlap between the marker gene list of each cluster (red circle) with the published mouse skin/gut/lung TRM gene list (blue circle). The overlap levels between two gene lists were evaluated by two-sided fisher exact test. Enrichment scores and P-values are labeled accordingly. e, Heatmap depicts the z-transformed mean expression of a published melanoma infiltrating dysfunctional CD8+ gene list across clusters C1-C10.

Extended Data Fig. 6 TRM-IFNG signature is superior in predicting the survival of stage III/IV melanoma patients.

a, Kaplan–Meier overall survival curves of melanoma patients from two different published datasets, GSE54467 (top, n=75 patients) and GSE19324 (bottom, n=44 patients), stratified by enrichment of signatures derived from each of three TRM clusters. High and low groups were separated by the median value of the Z-transformed normalized mean expression of each gene set (top: N= 37 patients high and N=38 patients low; bottom: N=22 patients high and N=22 patients low). P-values were calculated by two-sided log-rank test. OR P-values were calculated by multivariate Cox regression. b, Multivariable cox survival regression model evaluating the individual contribution of different variables to the prognosis of melanoma patients from the above datasets, GSE54467 (left) and GSE19324 (right). Forest plots show the means of hazard ratios (HRs) represented by blue squares, the 95% confidence intervals of HRs represented by horizontal bars, and p-values calculated by the two-sided Wald test for each variable.

Extended Data Fig. 7 Tumor-associated TCR clonotypes in the skin and blood of long-term metastatic melanoma survivors.

a, Gini indexes of each tissue (left) or each TRM cluster (right) calculated for n=4 individual patients, showing no significant differences in baseline clonal expansion among different tissues or different TRM clusters. The lines indicate the average Gini index across all four patients for each cluster. Two-sided Wilcoxon test. b, Venn diagrams showing the number of matched TCR clonotypes between different specimens from individual patients. Colors indicate different tissue origins. The number of TCR clonotypes belonging to each group was labeled accordingly. c, The distribution of the remaining Resident/Circulating clonotypes (11/15) to the UMAP plot. Dots indicate CD8+ T cells from the same clone. Colors designate different specimen types. d, The distribution of the remaining Resident-Only clonotypes (14/18) to the UMAP plot. Dots indicate CD8+ T cells from the same clone. Colors designate different specimen types. e, The distribution of the remaining Circulation-Capable clonotypes (7/11) to the UMAP plot. Dots indicate CD8+ T cells from the same clone. Colors designate different specimen types.

Source data

Extended Data Fig. 8 Tumor-associated clonotypes in the skin and blood of patients.

Venn diagrams showing the number of matched TCR clonotypes between skin, blood and historically banked tumors of each patient. The number of TCR clonotypes belonging to each group was labeled accordingly.

Supplementary information

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Supplementary Tables 1–7.

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Han, J., Zhao, Y., Shirai, K. et al. Resident and circulating memory T cells persist for years in melanoma patients with durable responses to immunotherapy. Nat Cancer 2, 300–311 (2021). https://doi.org/10.1038/s43018-021-00180-1

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