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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The RNAseq and exome sequence data used during the study is available through the Cancer Research UK & University College London Cancer Trials Centre (email@example.com) 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.
Anagnostou, V. et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017).
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).
McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
Miao, D. et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat. Genet. 50, 1271–1281 (2018).
Jamal-Hanjani, M. et al. Tracking genomic cancer evolution for precision medicine: the lung TRACERx study. PLoS Biol. 12, e1001906 (2014).
Feng, L. et al. Heterogeneity of tumor-infiltrating lymphocytes ascribed to local immune status rather than neoantigens by multi-omics analysis of glioblastoma multiforme. Sci. Rep. 7, 6968 (2017).
Wang, T. et al. The different T-cell receptor repertoires in breast cancer tumors, draining lymph nodes, and adjacent tissues. Cancer Immunol. Res. 5, 148–156 (2017).
Kuang, M. et al. A novel signature for stratifying the molecular heterogeneity of the tissue-infiltrating T-cell receptor repertoire reflects gastric cancer prognosis. Sci. Rep. 7, 7762 (2017).
Lin, K. R. et al. T cell receptor repertoire profiling predicts the prognosis of HBV-associated hepatocellular carcinoma. Cancer Med. 7, 3755–3762 (2018).
Reuben, A. et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. NPJ Genom. Med. 2, 10 (2017).
Gerlinger, M. et al. Ultra-deep T cell receptor sequencing reveals the complexity and intratumour heterogeneity of T cell clones in renal cell carcinomas. J. Pathol. 231, 424–432 (2013).
Sherwood, A. M. et al. Tumor-infiltrating lymphocytes in colorectal tumors display a diversity of T cell receptor sequences that differ from the T cells in adjacent mucosal tissue. Cancer Immunol. Immunother. 62, 1453–1461 (2013).
Emerson, R. O. et al. High-throughput sequencing of T-cell receptors reveals a homogeneous repertoire of tumour-infiltrating lymphocytes in ovarian cancer. J. Pathol. 231, 433–440 (2013).
Jiménez-Sánchez, A. et al. Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170, 927–938 (2017).
Cui, J. H. et al. TCR repertoire as a novel indicator for immune monitoring and prognosis assessment of patients with cervical cancer. Front. Immunol. 9, 2729 (2018).
Bai, X. et al. Characteristics of tumor infiltrating lymphocyte and circulating lymphocyte repertoires in pancreatic cancer by the sequencing of T cell receptors. Sci. Rep. 5, 13664 (2015).
Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).
Cui, C. et al. T cell receptor β-chain repertoire analysis of tumor-infiltrating lymphocytes in pancreatic cancer. Cancer Sci. 110, 61–71 (2019).
Jin, Y. B. et al. TCR repertoire profiling of tumors, adjacent normal tissues, and peripheral blood predicts survival in nasopharyngeal carcinoma. Cancer Immunol. Immunother. 67, 1719–1730 (2018).
Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).
Pasetto, A. et al. Tumor- and neoantigen-reactive T-cell receptors can be identified based on their frequency in fresh tumor. Cancer Immunol. Res. 4, 734–743 (2016).
Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765 (2017).
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).
Ahmadzadeh, M. et al. Tumor-infiltrating human CD4+ regulatory T cells display a distinct TCR repertoire and exhibit tumor and neoantigen reactivity. Sci Immunol. 4, eaao4310 (2019).
Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).
Zhang, C. et al. TCR repertoire intratumor heterogeneity of CD4+ and CD8+ T cells in centers and margins of localized lung adenocarcinomas. Int J. Cancer 144, 818–827 (2019).
Reuben, A. et al. TCR repertoire intratumor heterogeneity in localized lung adenocarcinomas: an association with predicted neoantigen heterogeneity and postsurgical recurrence. Cancer Discov. 7, 1088–1097 (2017).
Robert, L. et al. CTLA4 blockade broadens the peripheral T-cell receptor repertoire. Clin. Cancer Res. 20, 2424–2432 (2014).
Cha, E. et al. Improved survival with T cell clonotype stability after anti-CTLA-4 treatment in cancer patients. Sci. Transl. Med. 6, 238ra270 (2014).
Kvistborg, P. et al. Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+ T cell response. Sci. Transl. Med. 6, 254ra128 (2014).
Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).
Snyder, A. et al. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: an exploratory multi-omic analysis. PLoS Med. 14, e1002309 (2017).
Yusko, E. et al. Association of tumor microenvironment T-cell repertoire and mutational load with clinical outcome after sequential checkpoint blockade in melanoma. Cancer Immunol. Res 7, 458–465 (2019).
Hogan, S. A. et al. Peripheral blood TCR repertoire profiling may facilitate patient stratification for immunotherapy against melanoma. Cancer Immunol. Res. 7, 77–85 (2019).
Hopkins, A. C. et al. T cell receptor repertoire features associated with survival in immunotherapy-treated pancreatic ductal adenocarcinoma. JCI Insight 3, 122092 (2018).
Oakes, T. et al. Quantitative characterization of the T cell receptor repertoire of naïve and memory subsets using an integrated experimental and computational pipeline which is robust, economical, and versatile. Front. Immunol. 8, 1267 (2017).
Uddin, I. et al. An economical, quantitative, and robust protocol for high-throughput T cell receptor sequencing from tumor or blood. Methods Mol. Biol. 1884, 15–42 (2019).
Best, K., Oakes, T., Heather, J. M., Shawe-Taylor, J. & Chain, B. Computational analysis of stochastic heterogeneity in PCR amplification efficiency revealed by single molecule barcoding. Sci. Rep. 5, 14629 (2015).
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).
Thomas, N., Heather, J., Ndifon, W., Shawe-Taylor, J. & Chain, B. Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine. Bioinformatics 29, 542–550 (2013).
Sun, Y. et al. Specificity, privacy, and degeneracy in the CD4 T cell receptor repertoire following immunization. Front. Immunol. 8, 430 (2017).
Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).
Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).
Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).
Bengsch, B. et al. Epigenomic-guided mass cytometry profiling reveals disease-specific features of exhausted CD8 T cells. Immunity 48, 1029–1045 (2018).
Bengsch, B. et al. Coexpression of PD-1, 2B4, CD160 and KLRG1 on exhausted HCV-specific CD8+ T cells is linked to antigen recognition and T cell differentiation. PLoS Pathog. 6, e1000947 (2010).
Ganesan, A. P. et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat. Immunol. 18, 940–950 (2017).
Danaher, P. et al. Gene expression markers of tumor infiltrating leukocytes. J. Immunother. Cancer 5, 18 (2017).
Clauset, A., Shalizi, C. R. & Newman, M. E. Power law distributions in empirical data. J. Soc. Ind. Appl. Math. 54, 661–703 (2009).
Karatzoglou, A., Smola, A., Hornik, K. & Achim, Z. kernlab—an S4 package for kernel methods in R J. Stat. Software 11 (2004).
Csardi, G. & Nepusz, T. The igraph software package for complex network research. Complex Systems 1695, 1–9 (2006).
Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32, D493–D496 (2004).
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
Cheng, J. et al. Single-cell copy number variation detection. Genome Biol. 12, R80 (2011).
Pollara, G. et al. Validation of Immune cell modules in multicellular transcriptomic data. PLoS One 12, e0169271 (2017).
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.
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.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
a–c, 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.
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.
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.
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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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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