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Immune awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy

Abstract

Our understanding of how checkpoint inhibitors (CPIs) affect T cell evolution is incomplete, limiting our ability to achieve full clinical benefit from these drugs. Here, we analyzed peripheral T cell populations after one cycle of CPI treatment and identified a dynamic awakening of the immune system, as revealed by T cell evolution in response to treatment. We sequenced T cell receptors in plasma cell-free DNA and peripheral blood mononuclear cells and performed phenotypic analysis of peripheral T cell subsets from patients with metastatic melanoma treated with CPIs. We found that early peripheral T cell turnover and T cell receptor repertoire dynamics identified which patients would respond to treatment. Additionally, the expansion of a subset of immune effector peripheral T cells we call TIE cells correlated with response. These events are prognostic and occur within 3 weeks of starting immunotherapy, raising the potential for monitoring patients’ responses by using minimally invasive liquid biopsies.

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Fig. 1: CPI treatment induced peripheral TCR repertoire divergence.
Fig. 2: TIL CDR3 sequences also present in peripheral PBMCs and cfDNA for patient 12 at t0 and week 3.
Fig. 3: CPI treatment induced peripheral T cell turnover.
Fig. 4: Identification of TIE cells.
Fig. 5: TIE cells infiltrate tumors that respond to immunotherapy.
Fig. 6: Peripheral TIE cell expansion in response to first-line CPI treatment.
Fig. 7: Expansion of a peripheral regulatory T cell subset associated with toxicity.
Fig. 8: TCR repertoire evolution after immune stimulation.

Data availability

Sample metadata files and custom scripts are available from GitLab (https://gitlab.com/cruk-mi/tcell-immune-awakening). The data from all TCR sequencing performed in this study are deposited in the immuneACCESS repository immunoSEQ (https://doi.org/10.21417/SV2020NM). The RNA sequencing data for patient 12 can be downloaded from EGA (accession code EGAS00001004043). TCR sequencing data for matched pre-treatment and week-3 melanoma biopsy and PBMC samples of patients with locally advanced melanoma18 re-analyzed here were downloaded from referenced accession EGAS00001003178 (EGA study accession dataset EGAD00010001608). TCR sequencing data of matched pre-treatment and week-3 PBMCs of patients with melanoma from Huang et al.7 re-analyzed here were made available by the authors. TCR sequencing data of matched pre-treatment and week-3 PBMCs for the cohort of patients with locally advanced treatment-naïve melanoma from referenced accession Amaria et al.18 re-analyzed here were downloaded from the EGA (accession code EGAS00001003178 (EGA study accession dataset EGAD00010001608)) and the patient clinical history metadata file was downloaded from the EGA via accession dataset EGAD00001004352. PBMC and biopsy CyTOF data from Krieg et al.8 and Greenplate et al.24 re-analyzed here were downloaded from referenced accessions https://flowrepository.org/experiments/1124 and http://flowrepository.org/id/FR-FCM-ZZMC, respectively. PBMC REAP-Seq data from Peterson et al.27 re-analyzed here were downloaded from referenced Gene Expression Omnibus accession GSE100501. The authors confirm that for approved reasons (UK Data Protection Act 2018), some access restrictions apply to the data containing patient medical records (specifically, dates of birth). Source data for Figs. 17 and Extended Data Figs. 36 are provided with the paper. Additional data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

ImmunoSEQ Analyzer (Adaptive Biotechnologies), FlowJo (version 10; Tree Star), Cytobank54, Trimmomatic (version 0.36)48 and STAR (version 2.5.1) aligner49 are published or commercial codes and software programs. Diversity was calculated using the Rényi index (α = 1) as per Spreafico et al.51. Differential marker expression analysis was performed on CyTOF data using the custom workflow described by Nowicka et al.52. Custom R scripts are available from GitLab (https://gitlab.com/cruk-mi/tcell-immune-awakening).

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Acknowledgements

We are grateful to the patients who participated to this study and their families. We thank P. Serra-Bellver for help with the collection of patient samples, R. Cox for assistance with preparing the figures, and J. Allen, S. Zelenay and the Molecular Oncology Group for advice. We thank G. Williams for assistance during the TCR sequencing. This work was supported by CRUK (A27412 and A22902), the Harry J. Lloyd Charitable Trust (Career Development Award to S.V.) and the Wellcome Trust (100282/Z/12/Z). The role of the MCRC Biobank is to distribute samples; therefore, it cannot endorse the studies performed or assist in interpretation of the results.

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Contributions

S.V. and R.M. conceived of and designed the study. S.V., N.D. and R.M. developed the methodology. S.V., E.G., P.A.M., A.B., P.M., J.B., J.T., G.G., A.G., S.M., Z.S., F.B., J.W., N.D., P.C.L. and R.M. acquired the data (managed the patients, provided facilities, provided bioinformatics supervision, performed the experiments and so on). S.V., P.A.M., N.D. and R.M. analyzed and interpreted the data (for example, they performed statistical analysis, biostatistics and computational analysis). S.V. and A.B. prepared the figures. S.V., N.D. and R.M. wrote the manuscript with input and final approval from all authors.

Corresponding authors

Correspondence to Paul C. Lorigan or Richard Marais.

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Competing interests

R.M. is a consultant for Pfizer and has a drug discovery program with Basilea Pharmaceutica. P.L. serves as a paid advisor/speaker for Bristol-Myers Squibb, Merck Sharp and Dohme, Roche, Novartis, Amgen, Pierre Fabre, Nektar and MelaGenix. P.L. reports travel support from Bristol-Myers Squibb and Merck Sharp and Dohme, and receives research support from Bristol-Myers Squibb. A.G. received honoraria and consultancy fees from Bristol-Myers Squibb and Novartis. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Schematic summarizing T cell maturation and life-cycle.

a Pro-T cells undergo sequential somatic recombination of their T cell Receptor β (TCR) loci in attempts to generate functional TCR with unique CDR3 antigen binding regions. Cells that fail to generate a functional TCRβ at the first attempt can recombine their second TCR allele, but cells which fail to produce a functional TCR at the end of the process (crossed red box) are eliminated (β-selection) and their DNA, which encodes the CDR3 unique regions, enters the blood as circulating cell-free DNA (cfDNA). Surviving cells retain the T cell receptor excision circle (TREC) generated during TCR locus rearrangement as an episome in the nucleus. The TREC does not replicate so is diluted during subsequent cell divisions. b T cells with a functional TCR undergo positive and negative selection (± selection) for HLA and self-antigen recognition. The CDR3 DNA from T cells eliminated during this step is released into the blood. c Naive T cells enter the circulation as early thymic emigrants (ETE). d T cells primed by antigen presenting cells (APC) in the lymphatic system undergo clonal expansion, which dilutes the TREC amongst the daughter cells. e T cell homeostasis is maintained by subsequent contraction (turnover cycles), releasing further CDR3 DNA into the blood.

Extended Data Fig. 2 Gating strategy for the identification of T cell subsets in peripheral blood of melanoma patients.

Multiparametric fluorescence activated cell sorting analysis using the indicated gates. a Lymphocyte gate on side scatter/forward scatter; b single cell gate to exclude doublets; c live gating to exclude dead cells from subsequent gates; d CD3+ gate for T cells; e,f CD4+ and CD8+ gates for “helper” and “killer” T cell subsets, CD8 was detected with a PE-Cy7 labelled antibody for the Treg panel (e) and with a FITC labelled antibody for the T maturation panel (f); g CD4+/CD25+/CD127−/low regulatory T cells (Treg); h naive (top left) and memory (bottom right) gates total T cells; i ETE (top) and CD31 naive (bottom) gates for naive T cells; j naive (top left) and memory (bottom right) gates for CD8+ T cells; k CD8+ memory T cell subsets, the left bottom subset (CCR7/CD27) represents the TIE cells.

Extended Data Fig. 3 Clonal relatedness in tumor infiltrating T cells and PBMC.

a Clonal relatedness changes in PBMC-private and TIL-private TCR pools; comparison of week 3 (W3) CDR3 clonal relatedness in patients with progressive disease (PD, n=11 patients) and disease control at week 12 (DC, n=7 patients) in the PBMC-private (P=0.724, median=0.6x10−6 and 0.6x10-6, respectively; two-sided Mann-Whitney U test) and TIL-private pools (P=0.246, median= 0.5x10-4 and 0.8x10-5, respectively; two-sided Mann-Whitney U test). Dot represents one patient; green indicates DC; orange indicates PD; error bar is standard deviation. Source data

Extended Data Fig. 4 Identification of TIE in CPI-treated patient PBMC.

a Comparison of differential abundance of TIE in CD8+ memory T cells in the PBMC of The Christie NHS Foundation Trust patients with best response progressive disease (PD, orange, n=14) and disease control (DC, green, n=16) at T0 (n=30, light shade) and week 9 (W9; n=10, dark shade; PD, n=4, DC, n=6). Differences over time were not significant for PD (median=15.2 and 35.5; P=0.375; two-sided Wilcoxon test) or DC (median=7.9 and 24; P=0.219; two-sided Wilcoxon test); PD vs DC patient values did not differ at T0 (P=0.275; two-sided Mann-Whitney U test) or W9 (P=0.762; two-sided Mann-Whitney U test). b Distributions of marker intensities of the T cell surface markers in the 20 cell populations (clusters) for PBMC from a published cohort3 (n=20 patients). Cluster 5 was identified as the TIE subset. Blue densities are calculated over all the cells and serve as a reference and red densities represent marker expression for cells in a given cluster. Arrows highlight the TIE subset. c T-stochastic neighbor embedding of single cell profiles (dots) performed in an external cohort3 using the T cell surface markers CD3, CD4, CD8, CD45RA, CD45RO, CCR7 and CD27; different colors are attributed by clustering. Arrow highlights the TIE subset. d Comparison of the differential abundance of the TIE cluster in the PBMC from a published cohort3 of patients with PD (orange, n=9) or DC (green, n=11) at pre-treatment (light shade, n=20; PD, n=9; DC, n=11) and at week 12 (W12, dark shade, n=20) on treatment with pembrolizumab or nivolumab in the external cohort. Horizontal bars indicate the differences over time for the PD (median at T0=5.9 and W12=9.1; P=0.164; two-sided Wilcoxon test) or DC patients (median at T0=3.8 and W12=3.3; P=0.831; two-sided Wilcoxon test), and difference in the two response groups at T0 or W12 (P=0.37 and P=0.201, respectively; two-sided Mann-Whitney U test). Light and dark orange indicate PD for T0 and W9-W12, respectively, light and dark green indicate DC for T0 and W9-W12, respectively; n represents patients; ns means not significant P values; error bars are standard deviation. Source data

Extended Data Fig. 5 Characterization of TIE in PBMC.

Analysis of published cohort of PBMC single cell data from reference #27. a Violin plots of the expression level of selected phenotypic and transcriptomic features of the clusters identifying peripheral T cell subsets (n=7488 single cells), the cluster with TIE phenotype is indicated in red; the plots represent the density probability, the area shapes reflect the data distribution; horizontal lines represent the minima and maxima values; central dots represent the medians. Overall minima, mean and maxima values: surface CD3=0, 0.3785, 4.1396; surface CD8a=0, 0.96327, 6.21476; surface CD45RA=0, 0.8161, 4.8508; surface CD45RO=0, 0.6628, 4.6468; surface CD197/CCR7=0, 0.8961, 5.7975; surface CD69=0, 0.5219, 4.2200; surface CD279=0, 0.09787, 3.84886; surface CD25=0, 0.08653, 4.00428; surface TIGIT=0, 0.4663, 4.2381; surface CD155=0, 0.4850, 4.6679; surface CD40=0, 0.6003, 5.5083; surface CD154=0, 0.4062, 3.8159; surface CD357=0, 0.1193, 4.0316; LGALS2=0, 0.561, 6.089; TYROBP=0, 1.337, 6.662; FCN1=0, 1.290, 6.789; CST3=0, 1.404, 6.504; LST1=0, 1.042, 6.097; LYZ=0, 1.775, 6.859. b T-SNE plot showing the clusters identified by means of the antibody derived tags (ADT) targeted to surface markers (n=7488 single cells); the black arrow indicates the cluster with TIE phenotype. c Plot showing the proportion of cells with the TIE phenotype from the same published cohort after standard in vitro culture (CTRL, n=3 sorted healthy donor peripheral blood CD8+ naïve T cell samples in standard culture) or following stimulation with anti-CD3/anti-CD27 Dynabeads23 (STIM, n=3 sorted healthy donor peripheral blood CD8+ naïve T cell samples after stimulation) (P=0.0267, two-sided paired t test, two degrees of freedom) and d Volcano plot representing the transcriptomic differential expression of the cells with the TIE phenotype in PBMC presented in a (n=7488 single cells) or expanded from naive CD8+ T cells from the experiment presented in c22 (n=12217 single cells; two-sided Wilcoxon test with Bonferroni correction for multiple comparisons). Source data

Extended Data Fig. 6 Expression of Ki-67 and PD-1 in peripheral TIE cells before and after 1 cycle of CPI.

a Expression of Ki67 and PD1 in the TIE subset as measured by FACS in n=5 frozen samples of PBMC from The Christie NHS Foundation Trust metastatic melanoma patients treated with CPI, at pre-treatment (T0) and after 1 cycle of CPI (W3); horizontal line indicates median; error bar indicates standard deviation. The small sample size did not allow statistical comparison of the outcome groups. Source data

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Valpione, S., Galvani, E., Tweedy, J. et al. Immune awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy. Nat Cancer 1, 210–221 (2020). https://doi.org/10.1038/s43018-019-0022-x

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