Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer

Abstract

Somatic mutations together with immunoediting drive extensive heterogeneity within non-small-cell lung cancer (NSCLC). Herein we examine heterogeneity of the T cell antigen receptor (TCR) repertoire. The number of TCR sequences selectively expanded in tumors varies within and between tumors and correlates with the number of nonsynonymous mutations. Expanded TCRs can be subdivided into TCRs found in all tumor regions (ubiquitous) and those present in a subset of regions (regional). The number of ubiquitous and regional TCRs correlates with the number of ubiquitous and regional nonsynonymous mutations, respectively. Expanded TCRs form part of clusters of TCRs of similar sequence, suggestive of a spatially constrained antigen-driven process. CD8+ tumor-infiltrating lymphocytes harboring ubiquitous TCRs display a dysfunctional tissue-resident phenotype. Ubiquitous TCRs are preferentially detected in the blood at the time of tumor resection as compared to routine follow-up. These findings highlight a noninvasive method to identify and track relevant tumor-reactive TCRs for use in adoptive T cell immunotherapy.

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Fig. 1: NSCLC tumors contain expanded TCRs that are differentially expressed in tumor as compared to nontumor lung and whose numbers correlate with tumor mutational burden.
Fig. 2: NSCLC tumors contain expanded ubiquitous and regional TCRs, which reflect the tumor mutational landscape.
Fig. 3: Expanded intratumoral TCR CDR3 sequences identify clusters of related TCRs and show enhanced convergent recombination.
Fig. 4: Expanded intratumoral ubiquitous TCRs are associated with a TH1 and CD8+ T cell transcriptional signature in the tumor and have a phenotype consistent with tumor antigen reactivity.
Fig. 5: Expanded intratumoral TCR sequences can be identified in matched blood samples at the time of primary tumor resection and can persist in the blood long term.

Data availability

The RNAseq and exome sequence data used during the study is available through the Cancer Research UK & University College London Cancer Trials Centre (ctc.tracerx@ucl.ac.uk) for non-commercial research purposes and access will be granted upon review of a project proposal that will be evaluated by a TRACERx data access committee and entering into an appropriate data access agreement subject to any applicable ethical approvals. The TCRseq Fastq data was deposited at the Short Read Archive (SRA) under accession code BioProject: PRJNA544699.

Change history

  • 03 June 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

This work was undertaken with support from the Cancer Immunotherapy Accelerator Award (CITA-CRUK; C33499/A20265), CRUK’s Lung Cancer Centre of Excellence (C5759/A20465), the National Institute for Health Research UCL Hospitals Biomedical Research Centre (B.C., C.S., S.A.Q., M.N.), a Cancer Research UK (CRUK) Project Grant (B.C.), a CRUK Senior Cancer Research Fellowship (S.A.Q.; C36463/A22246), the Sam Keen Foundation, the Royal Marsden Hospital NHS Foundation Trust and Institute of Cancer Research Biomedical Research Centre, the Royal Marsden Cancer Charity, the UCL Biomedical Research Centre (K.J.), a Cancer Research UK studentship (M.R.D.M.) and an MRC Clinical Infrastructure award (MR/M009033/1). S.A.Q. receives funding from the Rosetrees and Stoneygate Trust (A1388), a CRUK Biotherapeutics Programme grant (C36463/A20764) and a donation from the Khoo Teck Puat UK Foundation via the UCL Cancer Institute Research Trust (539288). S.R.H. was supported by the ERC grant StG 677268 NextDART. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the article. C.S. is Royal Society Napier Research Professor. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001169, FC001202), the UK Medical Research Council (FC001169, FC001202) and the Wellcome Trust (FC001169, FC001202). C.S. is funded by Cancer Research UK (TRACERx and CRUK Cancer Immunotherapy Catalyst Network), the CRUK Lung Cancer Centre of Excellence, Stand Up 2 Cancer (SU2C), the Rosetrees Trust, the Butterfield and Stoneygate Trusts, NovoNordisk Foundation (ID16584), the Prostate Cancer Foundation and the Breast Cancer Research Foundation (BCRF). The research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007-2013) Consolidator Grant (FP7-THESEUS-617844), European Commission ITN (FP7-PloidyNet 607722), an ERC Advanced Grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement 835297). Support was also provided to C.S. by the National Institute for Health Research, the University College London Hospitals Biomedical Research Centre and the Cancer Research UK University College London Experimental Cancer Medicine Centre. We thank all the patients who participated in this study and all members of the TRACERx Consortium.

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Contributions

B.C., S.A.Q. and C.S. conceived the project. B.C., S.A.Q., C.S., K.J., M.I. and M.R.D.M. designed the experiments and analysis and wrote the manuscript. B.C., S.A.Q., C.S., T.E., M.N. and K.S.P. contributed to project management and supervision, as well as providing valuable critical discussion. K.J., J.L.R., I.U., A.W., T.O., V.T., A.J.S.F., A.G., Y.N.S.W., A.B.A., M.W.S., S.R.H. and E.H. contributed to the wet lab experiments. R.R., T.P., T.R., N.J.B., G.A.W., J.A.G.-A., J.H., E.G. and N.M. contributed to the bioinformatics analysis. M.J.-H., S.V., C.T.H., C.S., A.H. and the TRACERx Consortium coordinated clinical trials and provided patient samples and patient data.

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Correspondence to Charles Swanton or Sergio A. Quezada or Benny Chain.

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Competing interests

C.S. receives grant support from Pfizer, AstraZeneca, BMS, Roche-Ventana and Boehringer-Ingelheim. C.S. has consulted for Pfizer, Novartis, GlaxoSmithKline, MSD, BMS, Celgene, AstraZeneca, Illumina, Genentech, Roche-Ventana, GRAIL, Medicxi and the Sarah Cannon Research Institute and is an adviser for Dynamo Therapeutics. C.S. is a shareholder of Apogen Biotechnologies, Epic Bioscience, GRAIL, and has stock options in and is co-founder of Achilles Therapeutics. S.A.Q. is a co-founder of Achilles Therapeutics. R.R., N.M. and G.A.W. have stock options in and have consulted for Achilles Therapeutics. J.L.R. has consulted for Achilles Therapeutics.

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Peer review information Joao Monteiro was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Patient selection, mutational burden and clinical characteristics.

a, CONSORT diagram showing the selection of TRACERx patients for TCR sequencing. b, The total number of nonsynonymous mutations (clonal and subclonal) and patient clinical characteristics (histology, stage, smoking status and clinical outcome) for the TCR sequencing cohort are shown.

Extended Data Fig. 2 Tumor and nontumor regions contain a highly diverse polyclonal TCR repertoire.

ac, Graphs depicting the total number of TCR α-chain and β-chain segments sequenced (left), the number of unique TCR sequences detected (middle) and the correlation between the total number of TCR α-chain and β-chain segments sequenced (right) in multiregion tumors (n = 220) (a), nontumor lung (n = 64) (b) and PBMCs (n = 56) (c). Spearman’s rank correlation P values are shown. d, The relationship between the total number of TCRs in each region (expressed as log2) and the transcriptional score for a set of genes specifically expressed in T cells (see Methods). The Spearman’s rank correlation coefficient and P value are shown; n = 99.

Extended Data Fig. 3 NSCLC tumors contain expanded TCR β-chain sequences that are differentially expressed in tumor as compared to nontumor lung and whose numbers correlate with tumor mutational burden.

a, The frequency distribution of TCR β-chain abundance was fitted to a power law (f = kα) with maximum likelihood. The figure shows a representative plot (patient CRUK0046) for β-chain sequences from pooled tumor regions (red circles) and the matched nontumor lung sample (blue circles). The average power law parameter α, which corresponds to the slope on a log–log plot, was 2.5 ± 0.05 for tumor and 2.6 ± 0.03 for nontumor. The x axis refers to TCR abundance (size of clone), and the y axis refers to the proportion of the repertoire. b, The number of β-chain sequences detected above a given frequency threshold is shown for tumor (n = 72, multiple tumor regions were pooled from an individual patient; red circles) and matched nontumor lung samples (n = 64; blue circles). c, A volcano plot showing the likelihood (–log10 (P value)) of a β-chain sequence being sampled from two populations of equal mean in tumor and nontumor lung, plotted against the differential expression in tumor versus nontumor lung. If the log likelihood was >120, it was given a value of 120 for plotting purposes. Blue circles represent β-chain sequences expanded (>0.002) in nontumor lung; red circles represent β-chain sequences expanded in tumor lung. d, The proportion of expanded tumor α-chain sequences (T) or expanded nontumor lung β-chain sequences (NTL) that are specific to their respective tissue; this is defined on the volcano plot as TCRs that have a P value <0.01 and a differential abundance of at least two between the tissues. The two proportions are significantly different, with the Mann–Whitney P value shown; n(tumor) = 72; n(nontumor lung) = 64. e, The correlation between the number of unique intratumoral expanded β-chain sequences (frequency ≥ 2/1,000) and the number of nonsynonymous mutations is shown for all patients. The Spearman’s rank correlation and P value are shown (n = 62). f, The Spearman’s rank correlation coefficient and P value (shown above each point; n = 62) are shown for the relationship between the number of unique intratumoral expanded β-chain sequences at different frequencies (ranging from all TCRs (threshold of zero) up to those found at frequency ≥ 8/1,000) and the number of nonsynonymous mutations.

Extended Data Fig. 4 The heterogeneity of TCR repertoires across different regions of tumors differs between patients and correlates with genomic heterogeneity.

a, The heat maps show the abundance (log2 of the number of times each TCR is found) of expanded intratumoral β-chain sequences (frequency ≥ 2/1,000) in different tumor regions for several patients. Patient ID is shown above each heat map. Each row represents one unique sequence. Each column represents one tumor region. b, The TCR repertoire of multiple regions of a patient’s tumor were sequenced and a pairwise comparison of the repertoires of different regions of the same tumor was performed by using the cosine similarity (see Methods). The pairwise intratumoral TCR repertoire similarity (β-chain sequences) is shown for each patient. Each circle represents a comparison between two regions of the same patient’s tumor. Patients are ordered by descending rank of mean intratumoral TCR similarity. c, TCR repertoire (β-chain sequences) diversity plotted against genomic diversity for each patient. The diversity measurement is calculated as the normalized Shannon entropy as described in the Methods. The Spearman’s rank correlation and P value are shown; n = 41. d,e, TCR repertoire for α-chain (d) and β-chain (e) sequence pairwise similarity plotted against genomic similarity for each pair of tumor regions (within patient comparison). The TCR and mutational pairwise similarities are both measured as cosine similarity, as described in the Methods. The Spearman’s rank correlation and P value are shown; n = 226. Dashed lines represent median values.

Extended Data Fig. 5 Mutation prevalence defines ubiquitous and regional mutations in NSCLC.

a, The frequency histogram of corrected mutation prevalence for all mutations in the TRACERx patient cohort analyzed in this paper. Mutation prevalence (number of mutant reads/number of wild-type reads) was corrected for tumor purity and local genomic copy number as described in the Methods. The distribution is bimodal, with peaks at zero (0–10%, very few mutant reads) and 1 (corresponding to every cell in a tumor region carrying the mutation on one chromosome). b, The number of ubiquitous mutations defined as described in the Methods is plotted against the number of clonal mutations, calculated as described in Jamal-Hanjani et al.39 for all patients analyzed in this study. c, The number of regional mutations defined as described in the Methods is plotted against the number of subclonal mutations, calculated as described in Jamal-Hanjani et al.39.

Extended Data Fig. 6 The number of ubiquitous and regional TCRs correlates with the number of ubiquitous and regional nonsynonymous mutations, respectively.

a, The numbers of expanded (frequency ≥ 2/1,000) ubiquitous (red circles) and regional TCR (β-chain) sequences (gray circles) is shown for each tumor region. The number of ubiquitous mutations is greater than the number of regional mutations, with the Mann–Whitney P value shown; n = 52. b, The frequency distribution of the intratumoral expanded β-chain ubiquitous (red circles) and regional (gray circles) TCRs is shown. The two distributions were not significantly different when compared by the Kolmogorov–Smirnov test, P = 0.78. c, The number of expanded ubiquitous (top) or regional (bottom) β-chain sequences is plotted against the number of ubiquitous or regional nonsynonymous mutations for each tumor region. The Spearman’s rank correlation coefficient and associated P value are shown; the dashed lines indicate median values. n = 42. d, Patients were stratified according to the number of ubiquitous mutations. The red line indicates a ratio above the top quartile and the blue line indicates a ratio below the top quartile. The Kaplan–Meier statistical P value is shown.

Extended Data Fig. 7 Expanded intratumoral ubiquitous TCRs are associated with a TH1 and CD8+ T cell transcriptional signature in the tumor and have a phenotype consistent with tumor antigen reactivity.

a, Correlation between the numbers of expanded intratumoral ubiquitous and regional TCR β-chain sequences and the transcriptional expression score (geometric mean) for various immune-related gene sets, characterizing cell types or functional states (names indicated above heat map). Details of how the transcriptional scores are calculated are in the Methods. The area and color of the circles correspond to the magnitude of the correlation coefficient. The color key indicates Spearman’s rank correlation coefficient. *P < 0.05; **P < 0.01; after Bonferroni correction. b, CD8+ TILs from CRUK0291 and CRUK0099 were sorted into two populations, PD-1+CD103+ and PD-1+CD103 cells. The flow cytometry gating strategy for a representative patient is shown (pre-gated on live > singlets > CD3+ > CD8+ T cells). RNA was extracted and sequenced from sorted populations as described in the Methods. c, The RNA-seq data were mined for the presence of expanded ubiquitous and regional α-chain and β-chain sequences. The heat maps show the number of times each expanded ubiquitous or regional TCR CDR3 sequence was found in each of the RNA-seq data from PD-1+CD103+ or PD-1+CD103 cells, as a proportion of the number of times a constant region sequence of the same length was detected. These proportions are scaled for each row and color coded. Each row represents a distinct expanded TCR sequence.

Extended Data Fig. 8 Network diagram of clusters of intratumoral CDR3 β-chain sequences shown for all patient CDR3 repertoires.

All panels show the network of TCR CDR3 β-chain sequences that are connected to at least one other expanded intratumoral ubiquitous TCR (shown as red circles). Clusters are defined as networks with at least two nodes. Only those patients with at least one cluster are shown.

Extended Data Fig. 9 Further analysis of TCR clusters.

a, The clustering algorithm was run on all patients, and the number of distinct clusters containing expanded ubiquitous and regional TCRs are shown. The number is normalized for the number of expanded TCRs of each type. The Mann–Whitney P value is shown; n = 46. b, A full alignment of the cluster shown in Fig. 3b,c. c, The GLIPH (https://github.com/immunoengineer/gliph) clustering algorithm was run on all patients. The panels show the number of distinct GLIPH clusters containing expanded ubiquitous, expanded regional and randomly selected CDR3 β-chain sequences. The number is normalized for the number of TCRs of each type. The ubiquitous TCRs show greater clustering than randomly selected TCRs (left), with the Mann–Whitney P value shown; n = 46. There was no significant difference between GLIPH clustering of normalized ubiquitous and regional expanded TCRs (right), with the Mann–Whitney P value shown; n = 46. d, The cluster Shannon diversity (see Methods) for all clusters containing ubiquitous or regional expanded TCRs. The Mann–Whitney P value is shown; n = 46. e, As an additional control in the TCR clustering analysis, we took expanded ubiquitous TCRs from patients CRUK0041 and CRUK0322 and mixed them in silico, and we then looked to see whether the resulting clusters were primarily composed of TCRs from individual patients. We analyzed three pairs of patients in whom we observed prominent clustering in this way. One representative example is shown.

Extended Data Fig. 10 Dynamic occurrence of expanded intratumoral ubiquitous TCRs in blood.

a, The proportion of expanded intratumoral ubiquitous (red circles) and regional (gray circles) TCRs (β-chain) detected within the blood for all patients (the Mann–Whitney P value is shown; n = 45). b, The frequency (number of TCR sequences detected, as a proportion of the total number of TCRs) of expanded intratumoral ubiquitous (red circles) and regional (gray circles) TCRs (β-chain) in the peripheral blood at the time of primary NSCLC surgery (the Mann–Whitney P value is shown; n = 42 for ubiquitous, n = 22 for regional). c, The proportion of expanded intratumoral ubiquitous (left), expanded intratumoral regional (middle) and expanded nontumor lung (right) TCRs (β-chain) that were detected in the blood at the time of primary NSCLC surgery and at routine follow-up (the median time to follow-up was just under 2 years) (the Mann–Whitney P value is shown; n = 14 for ubiquitous, regional and nontumor lung). d, The proportion of expanded intratumoral ubiquitous (left) and regional (right) α-chain (top) and β-chain (bottom) sequences that were detected in the blood at the time of primary NSCLC surgery and at disease recurrence (the median time to first recurrence was 350 d) (the Mann–Whitney P value is shown; n = 14 for α-chains and n = 15 for β-chains).

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Supplementary Table 1

Gene lists for the RNA-seq gene module analysis.

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Joshi, K., de Massy, M.R., Ismail, M. et al. Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer. Nat Med 25, 1549–1559 (2019). https://doi.org/10.1038/s41591-019-0592-2

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