Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer (original) (raw)

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

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.

Author information

Author notes

  1. These authors contributed equally: Kroopa Joshi, Marc Robert de Massy, Mazlina Ismail.

Authors and Affiliations

  1. Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
    Kroopa Joshi, Marc Robert de Massy, James L. Reading, Emine Hatipoglu, Andrew J. S. Furness, Andrew Georgiou, Yien Ning Sophia Wong, Assma Ben Aissa, Mariana Werner Sunderland, Ehsan Ghorani, Karl S. Peggs, Andrew Georgiou, Mariana Werner Sunderland, James L. Reading, Karl S. Peggs, Ehsan Ghorani, Marc Robert de Massy, Emine Hatipoglu, Sergio A. Quezada & Sergio A. Quezada
  2. Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, London, UK
    Kroopa Joshi, Emine Hatipoglu, Andrew J. S. Furness & Emine Hatipoglu
  3. Division of Infection and Immunity, University College London, London, UK
    Mazlina Ismail, Imran Uddin, Annemarie Woolston, Theres Oakes, Thomas Peacock, Tahel Ronel, Mahdad Noursadeghi, Benny Chain & Benny Chain
  4. Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
    Rachel Rosenthal, Assma Ben Aissa, Mariam Jamal-Hanjani, Selvaraju Veeriah, Gareth A. Wilson, Crispin T. Hiley, Allan Hackshaw, Nicholas McGranahan, Charles Swanton, Charles Swanton, Mariam Jamal-Hanjani, Sergio A. Quezada, Nicholas McGranahan, Allan Hackshaw, Crispin T. Hiley, Selvaraju Veeriah, Rachel Rosenthal, Gareth A. Wilson & Sergio A. Quezada
  5. Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
    Rachel Rosenthal, José Afonso Guerra-Assunção, Javier Herrero, Rachel Rosenthal & Javier Herrero
  6. Computation, Mathematics and Physics in the Life Sciences and Experimental Biology, Department of Computer Science, University College London, London, UK
    Thomas Peacock
  7. Department of Cancer Biology, University College London Cancer Institute, London, UK
    Virginia Turati
  8. Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
    Nicolai J. Birkbak, Gareth A. Wilson, Charles Swanton, Charles Swanton, Gareth A. Wilson & Nicolai J. Birkbak
  9. University College London Cancer Institute, London, UK
    Tariq Enver, Christopher Abbosh, Yin Wu, Marcin Skrzypski, Robert E. Hynds, Teresa Marafioti, John A. Hartley, Pat Gorman, Helen L. Lowe, Leah Ensell, Victoria Spanswick, Angeliki Karamani, David Moore, Dhruva Biswas, Maryam Razaq, Stephan Beck, Ariana Huebner, Michelle Dietzen, Cristina Naceur-Lombardelli, Mita Afroza Akther, Haoran Zhai, Nnennaya Kannu, Elizabeth Manzano, Supreet Kaur Bola, Elena Hoxha & Stephanie Ogwuru
  10. Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
    Sine R. Hadrup
  11. Department of Computer Sciences, University College London, London, UK
    Benny Chain & Benny Chain
  12. Cancer Research UK & UCL Cancer Trials Centre, London, UK
    Yenting Ngai, Abigail Sharp, Cristina Rodrigues, Oliver Pressey, Sean Smith, Nicole Gower & Harjot Dhanda
  13. The Francis Crick Institute, London, UK
    Thomas B. K. Watkins, Mickael Escudero, Aengus Stewart, Andrew Rowan, Jacki Goldman, Peter Van Loo, Richard Kevin Stone, Tamara Denner, Emma Nye, Sophia Ward, Emilia Lim, Stefan Boeing, Maria Greco, Maise Al Bakir, Kevin Litchfield, Jerome Nicod, Clare Puttick, Katey Enfield, Emma Colliver & Brittany Campbell
  14. University College London Hospitals, London, UK
    David Lawrence, Martin Hayward, Nikolaos Panagiotopoulos, Robert George, Davide Patrini, Mary Falzon, Elaine Borg, Reena Khiroya, Asia Ahmed, Magali Taylor, Junaid Choudhary, Penny Shaw, Sam M. Janes, Martin Forster, Tanya Ahmad, Siow Ming Lee, Dawn Carnell, Ruheena Mendes, Jeremy George, Neal Navani, Dionysis Papadatos-Pastos, Marco Scarci, Elisa Bertoja, Robert C. M. Stephens, Emilie Martinoni Hoogenboom, James W. Holding & Steve Bandula
  15. Aberdeen Royal Infirmary, Aberdeen, UK
    Gillian Price, Sylvie Dubois-Marshall, Keith Kerr, Shirley Palmer, Heather Cheyne, Joy Miller, Keith Buchan, Mahendran Chetty & Mohammed Khalil
  16. Ashford and St Peter’s Hospitals NHS Foundation Trust, Chertsey, UK
    Veni Ezhil & Vineet Prakash
  17. Barnet Hospital and Chase Farm Hospital, London, UK
    Girija Anand & Sajid Khan
  18. Barts Health NHS Trust, London, UK
    Kelvin Lau, Michael Sheaff, Peter Schmid, Louise Lim & John Conibear
  19. Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
    Roland Schwarz
  20. German Cancer Consortium (DKTK), partner site Berlin, Berlin, Germany
    Roland Schwarz
  21. German Cancer Research Center (DKFZ), Heidelberg, Germany
    Roland Schwarz
  22. Cancer Research UK Manchester Institute, University of Manchester, Manchester, UK
    Jonathan Tugwood, Jackie Pierce, Caroline Dive, Ged Brady, Dominic G. Rothwell, Francesca Chemi & Elaine Kilgour
  23. Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
    Caroline Dive, Ged Brady, Dominic G. Rothwell, Francesca Chemi, Elaine Kilgour, Fiona Blackhall, Lynsey Priest, Matthew G. Krebs & Philip Crosbie
  24. Christie NHS Foundation Trust, Manchester, UK
    Fiona Blackhall, Lynsey Priest, Matthew G. Krebs, Mathew Carter, Colin R. Lindsay & Fabio Gomes
  25. Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
    Philip Crosbie, Yvonne Summers, Raffaele Califano, Paul Taylor, Rajesh Shah, Piotr Krysiak, Kendadai Rammohan, Eustace Fontaine, Richard Booton, Matthew Evison, Stuart Moss, Juliette Novasio, Leena Joseph, Paul Bishop, Anshuman Chaturvedi, Helen Doran, Felice Granato, Vijay Joshi, Elaine Smith & Angeles Montero
  26. Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
    Philip Crosbie
  27. Cancer Research Centre, University of Leicester, Leicester, UK
    John Le Quesne, Joan Riley, Lindsay Primrose, Luke Martinson, Nicolas Carey, Jacqui A. Shaw & Dean Fennell
  28. Leicester University Hospitals, Leicester, UK
    Dean Fennell, Apostolos Nakas, Sridhar Rathinam, Louise Nelson, Kim Ryanna, Mohamad Tuffail, Amrita Bajaj & Jan Brozik
  29. Cardiff & Vale University Health Board, Cardiff, UK
    Fiona Morgan, Malgorzata Kornaszewska, Richard Attanoos, Haydn Adams & Helen Davies
  30. Danish Cancer Society Research Center, Copenhagen, Denmark
    Zoltan Szallasi
  31. Department of Pathology, GZA-ZNA Antwerp, Antwerp, Belgium
    Roberto Salgado
  32. Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
    Istvan Csabai & Miklos Diossy
  33. Departments of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
    Hugo Aerts
  34. Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
    Hugo Aerts
  35. Golden Jubilee National Hospital, Clydebank, UK
    Alan Kirk, Mo Asif, John Butler, Rocco Bilanca & Nikos Kostoulas
  36. Independent Cancer Patients’ Voice, London, UK
    Mairead MacKenzie & Maggie Wilcox
  37. University of Leicester, Leicester, UK
    Sara Busacca, Alan Dawson & Mark R. Lovett
  38. Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
    Michael Shackcloth, Sarah Feeney & Julius Asante-Siaw
  39. Royal Liverpool University Hospital, Liverpool, UK
    John Gosney
  40. Manchester Cancer Research Centre Biobank, Manchester, UK
    Angela Leek, Nicola Totten, Jack Davies Hodgkinson, Rachael Waddington, Jane Rogan & Katrina Moore
  41. National Institute for Health Research Leicester Respiratory Biomedical Research Unit, Leicester, UK
    William Monteiro & Hilary Marshall
  42. NHS Greater Glasgow and Clyde, Glasgow, UK
    Kevin G. Blyth, Craig Dick & Andrew Kidd
  43. Royal Brompton and Harefield NHS Foundation Trust, London, UK
    Eric Lim, Paulo De Sousa, Simon Jordan, Alexandra Rice, Hilgardt Raubenheimer, Harshil Bhayani, Morag Hamilton, Lyn Ambrose, Anand Devaraj, Hema Chavan, Sofina Begum, Aleksander Mani, Daniel Kaniu, Mpho Malima, Sarah Booth, Andrew G. Nicholson, Nadia Fernandes, Jessica E. Wallen & Pratibha Shah
  44. Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
    Sarah Danson, Jonathan Bury, John Edwards, Jennifer Hill, Sue Matthews, Yota Kitsanta, Jagan Rao, Sara Tenconi, Laura Socci, Kim Suvarna, Faith Kibutu, Patricia Fisher, Robin Young, Joann Barker, Fiona Taylor & Kirsty Lloyd
  45. The Princess Alexandra Hospital NHS Trust, Harlow, UK
    Teresa Light, Tracey Horey, Dionysis Papadatos-Pastos & Peter Russell
  46. The Whittington Hospital NHS Trust, London, UK
    Sara Lock & Kayleigh Gilbert
  47. University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
    Babu Naidu, Gerald Langman, Andrew Robinson, Hollie Bancroft, Amy Kerr, Salma Kadiri, Charlotte Ferris, Gary Middleton, Madava Djearaman & Akshay Patel
  48. University Hospital Southampton NHS Foundation Trust, Southampton, UK
    Christian Ottensmeier, Serena Chee, Benjamin Johnson, Aiman Alzetani & Emily Shaw
  49. Velindre Cancer Centre, Cardiff, UK
    Jason Lester

Authors

  1. Kroopa Joshi
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  2. Marc Robert de Massy
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  3. Mazlina Ismail
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  4. James L. Reading
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  5. Imran Uddin
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  6. Annemarie Woolston
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  7. Emine Hatipoglu
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  8. Theres Oakes
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  9. Rachel Rosenthal
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  10. Thomas Peacock
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  11. Tahel Ronel
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  12. Mahdad Noursadeghi
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  13. Virginia Turati
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  14. Andrew J. S. Furness
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  15. Andrew Georgiou
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  16. Yien Ning Sophia Wong
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  17. Assma Ben Aissa
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  18. Mariana Werner Sunderland
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  19. Mariam Jamal-Hanjani
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  20. Selvaraju Veeriah
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  21. Nicolai J. Birkbak
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  22. Gareth A. Wilson
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  23. Crispin T. Hiley
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  24. Ehsan Ghorani
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  25. José Afonso Guerra-Assunção
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  26. Javier Herrero
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  27. Tariq Enver
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  28. Sine R. Hadrup
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  29. Allan Hackshaw
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  30. Karl S. Peggs
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  31. Nicholas McGranahan
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  32. Charles Swanton
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  33. Sergio A. Quezada
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  34. Benny Chain
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Consortia

TRACERx consortium

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.

Corresponding authors

Correspondence toCharles Swanton, Sergio A. Quezada or Benny Chain.

Ethics declarations

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.

Additional information

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.

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.

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 = k α) 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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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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