Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling

Nature volume 625, pages 778–787 (2024)Cite this article

Subjects

Abstract

The scarcity of malignant Hodgkin and Reed–Sternberg cells hampers tissue-based comprehensive genomic profiling of classic Hodgkin lymphoma (cHL). By contrast, liquid biopsies show promise for molecular profiling of cHL due to relatively high circulating tumour DNA (ctDNA) levels1,2,3,4. Here we show that the plasma representation of mutations exceeds the bulk tumour representation in most cases, making cHL particularly amenable to noninvasive profiling. Leveraging single-cell transcriptional profiles of cHL tumours, we demonstrate Hodgkin and Reed–Sternberg ctDNA shedding to be shaped by DNASE1L3, whose increased tumour microenvironment-derived expression drives high ctDNA concentrations. Using this insight, we comprehensively profile 366 patients, revealing two distinct cHL genomic subtypes with characteristic clinical and prognostic correlates, as well as distinct transcriptional and immunological profiles. Furthermore, we identify a novel class of truncating IL4R mutations that are dependent on IL-13 signalling and therapeutically targetable with IL-4Rα-blocking antibodies. Finally, using PhasED-seq5, we demonstrate the clinical value of pretreatment and on-treatment ctDNA levels for longitudinally refining cHL risk prediction and for detection of radiographically occult minimal residual disease. Collectively, these results support the utility of noninvasive strategies for genotyping and dynamic monitoring of cHL, as well as capturing molecularly distinct subtypes with diagnostic, prognostic and therapeutic potential.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Liquid biopsies facilitate molecular profiling of cHL.
Fig. 2: Genetic subtypes of cHL defined by LDA clustering.
Fig. 3: Genetic cHL subtypes are transcriptionally distinct.
Fig. 4: Truncating IL4R mutations enhance IL-13 signalling and are targetable through IL-4Rα antibodies.
Fig. 5: Pretreatment ctDNA levels and ctDNA MRD to prognosticate cHL.

Similar content being viewed by others

Data availability

The sequencing data are available in the database of Genotypes and Phenotypes dbGaP (study accession phs003435.v1.p1) in compliance with European General Data Protection Regulation (GDPR) and US Health Insurance Portability and Accountability Act (HIPAA) as applicable. In addition, an online tool to assign new samples to the genetic subtypes H1 and H2 is available at https://hodgkin.stanford.edu. Mutation calls can be downloaded from the same website.

Code availability

The code for LDA clustering and RePhyNER is also available at https://hodgkin.stanford.edu.

References

  1. Sobesky, S. et al. In-depth cell-free DNA sequencing reveals genomic landscape of Hodgkin’s lymphoma and facilitates ultrasensitive residual disease detection. Med 2, 1171–1193.e11 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Spina, V. et al. Circulating tumor DNA reveals genetics, clonal evolution, and residual disease in classical Hodgkin lymphoma. Blood 131, 2413–2425 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Desch, A. K. et al. Genotyping circulating tumor DNA of pediatric Hodgkin lymphoma. Leukemia 34, 151–166 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Vandenberghe, P. et al. Non-invasive detection of genomic imbalances in Hodgkin/Reed–Sternberg cells in early and advanced stage Hodgkin’s lymphoma by sequencing of circulating cell-free DNA: a technical proof-of-principle study. Lancet Haematol. 2, e55–e65 (2015).

    Article  PubMed  Google Scholar 

  5. Kurtz, D. M. et al. Enhanced detection of minimal residual disease by targeted sequencing of phased variants in circulating tumor DNA. Nat. Biotechnol. 39, 1537–1547 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Flaherty, K. T. et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N. Engl. J. Med. 363, 809–819 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lievre, A. et al. KRAS mutations as an independent prognostic factor in patients with advanced colorectal cancer treated with cetuximab. J. Clin. Oncol. 26, 374–379 (2008).

    Article  CAS  PubMed  Google Scholar 

  8. Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Soda, M. et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature 448, 561–566 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Chapuy, B. et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 24, 679–690 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Schmitz, R. et al. Genetics and pathogenesis of diffuse large B-cell lymphoma. N. Engl. J. Med. 378, 1396–1407 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Treon, S. P. et al. Ibrutinib in previously treated Waldenstrom’s macroglobulinemia. N. Engl. J. Med. 372, 1430–1440 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Reichel, J. et al. Flow sorting and exome sequencing reveal the oncogenome of primary Hodgkin and Reed–Sternberg cells. Blood 125, 1061–1072 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Tiacci, E. et al. Pervasive mutations of JAK–STAT pathway genes in classical Hodgkin lymphoma. Blood 131, 2454–2465 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wienand, K. et al. Genomic analyses of flow-sorted Hodgkin Reed–Sternberg cells reveal complementary mechanisms of immune evasion. Blood Adv. 3, 4065–4080 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Liang, W. S. et al. Comprehensive genomic profiling of Hodgkin lymphoma reveals recurrently mutated genes and increased mutation burden. Oncologist 24, 219–228 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Mata, E. et al. Analysis of the mutational landscape of classic Hodgkin lymphoma identifies disease heterogeneity and potential therapeutic targets. Oncotarget 8, 111386–111395 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Gomez, F. et al. Ultra-deep sequencing reveals the mutational landscape of classical Hodgkin lymphoma. Cancer Res. Commun. 3, 2312–2330 (2023).

  20. Maura, F. et al. Molecular evolution of classic Hodgkin lymphoma revealed through whole-genome sequencing of Hodgkin and Reed Sternberg cells. Blood Cancer Discov. 4, 208–227 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tiacci, E. et al. Analyzing primary Hodgkin and Reed–Sternberg cells to capture the molecular and cellular pathogenesis of classical Hodgkin lymphoma. Blood 120, 4609–4620 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. Weniger, M. A. & Küppers, R. NF-κB deregulation in Hodgkin lymphoma. Semin. Cancer Biol. 39, 32–39 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Jamshidi, A. et al. Evaluation of cell-free DNA approaches for multi-cancer early detection. Cancer Cell 40, 1537–1549.e12 (2022).

    Article  CAS  PubMed  Google Scholar 

  24. Esfahani, M. S. et al. Inferring gene expression from cell-free DNA fragmentation profiles. Nat. Biotechnol. 40, 585–597 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Roschewski, M. et al. Circulating tumour DNA and CT monitoring in patients with untreated diffuse large B-cell lymphoma: a correlative biomarker study. Lancet Oncol. 16, 541–549 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Scherer, F. et al. Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA. Sci. Transl. Med. 8, 364ra155 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kurtz, D. M. et al. Circulating tumor DNA measurements as early outcome predictors in diffuse large B-cell lymphoma. J. Clin. Oncol. 36, 2845–2853 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Alig, S. et al. Short diagnosis-to-treatment interval is associated with higher circulating tumor DNA levels in diffuse large B-cell lymphoma. J. Clin. Oncol. 39, 2605–2616 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Meriranta, L. et al. Molecular features encoded in the ctDNA reveal heterogeneity and predict outcome in high-risk aggressive B-cell lymphoma. Blood 139, 1863–1877 (2022).

    Article  CAS  PubMed  Google Scholar 

  30. Newman, A. M. et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol. 34, 547–555 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chabon, J. J. et al. Integrating genomic features for non-invasive early lung cancer detection. Nature 580, 245–251 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sworder, B. J. et al. Determinants of resistance to engineered T-cell therapies targeting CD19 in large B-cell lymphomas. Cancer Cell https://doi.org/10.1016/j.ccell.2022.12.005 (2023).

  33. Han, D. S. C. et al. The biology of cell-free DNA fragmentation and the roles of DNASE1, DNASE1L3, and DFFB. Am. J. Hum. Genet. 106, 202–214 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Serpas, L. et al. Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA. Proc. Natl Acad. Sci. USA 116, 641–649 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  35. Watanabe, T., Takada, S. & Mizuta, R. Cell-free DNA in blood circulation is generated by DNase1L3 and caspase-activated DNase. Biochem. Biophys. Res. Commun. 516, 790–795 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. Stewart, B. J. et al. Spatial and molecular profiling of the mononuclear phagocyte network in classic Hodgkin lymphoma. Blood 141, 2343–2358 (2023).

    CAS  PubMed  Google Scholar 

  37. Husain, H. et al. Tumor fraction correlates with detection of actionable variants across >23,000 circulating tumor DNA samples. JCO Precis. Oncol. 6, e2200261 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).

    Google Scholar 

  39. Shukla, N. D. et al. Profiling T-cell receptor diversity and dynamics during lymphoma immunotherapy using cell-free DNA (cfDNA). Blood 136, 49–50 (2020).

    Article  Google Scholar 

  40. Steidl, C. et al. Tumor-associated macrophages and survival in classic Hodgkin’s lymphoma. N. Engl. J. Med. 362, 875–885 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wills-Karp, M. & Finkelman, F. D. Untangling the complex web of IL-4- and IL-13-mediated signaling pathways. Sci. Signal. 1, pe55 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Vigano, E. et al. Somatic IL4R mutations in primary mediastinal large B-cell lymphoma lead to constitutive JAK–STAT signaling activation. Blood 131, 2036–2046 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Duns, G. et al. Characterization of DLBCL with a PMBL gene expression signature. Blood 138, 136–148 (2021).

    Article  CAS  PubMed  Google Scholar 

  44. Skinnider, B. F. & Mak, T. W. The role of cytokines in classical Hodgkin lymphoma. Blood 99, 4283–4297 (2002).

    Article  CAS  PubMed  Google Scholar 

  45. Rawal, S. et al. Cross talk between follicular Th cells and tumor cells in human follicular lymphoma promotes immune evasion in the tumor microenvironment. J. Immunol. 190, 6681–6693 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Pangault, C. et al. Follicular lymphoma cell niche: identification of a preeminent IL-4-dependent TFH–B cell axis. Leukemia 24, 2080–2089 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kapp, U. et al. Interleukin 13 is secreted by and stimulates the growth of Hodgkin and Reed–Sternberg cells. J. Exp. Med. 189, 1939–1946 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Natoli, A. et al. Targeting the IL-4/IL-13 signaling pathway sensitizes Hodgkin lymphoma cells to chemotherapeutic drugs. Int. J. Cancer 133, 1945–1954 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Skinnider, B. F., Kapp, U. & Mak, T. W. Interleukin 13: a growth factor in hodgkin lymphoma. Int. Arch. Allergy Immunol. 126, 267–276 (2001).

    Article  CAS  PubMed  Google Scholar 

  50. Skinnider, B. F. et al. Interleukin 13 and interleukin 13 receptor are frequently expressed by Hodgkin and Reed–Sternberg cells of Hodgkin lymphoma. Blood 97, 250–255 (2001).

    Article  CAS  PubMed  Google Scholar 

  51. Kurtz, D. M. et al. Leveraging phased variants for personalized minimal residual disease detection in localized non-small cell lung cancer. J. Clin. Oncol. 39, 8518 (2021).

    Article  Google Scholar 

  52. Buedts, L. et al. The landscape of copy number variations in classical Hodgkin lymphoma: a joint KU Leuven and LYSA study on cell-free DNA. Blood Adv. 5, 1991–2002 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hu, Z., Chen, H., Long, Y., Li, P. & Gu, Y. The main sources of circulating cell-free DNA: apoptosis, necrosis and active secretion. Crit. Rev. Oncol. Hematol. 157, 103166 (2021).

    Article  PubMed  Google Scholar 

  54. Sin, S. T. K. et al. Identification and characterization of extrachromosomal circular DNA in maternal plasma. Proc. Natl Acad. Sci. USA 117, 1658–1665 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  55. Deng, Z. et al. DNASE1L3 as a prognostic biomarker associated with immune cell infiltration in cancer. Onco Targets Ther. 14, 2003–2017 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Shi, G., Abbott, K. N., Wu, W., Salter, R. D. & Keyel, P. A. Dnase1L3 regulates inflammasome-dependent cytokine secretion. Front. Immunol. 8, 522 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Chang, H. Y. & Nadeau, K. C. IL-4Rα inhibitor for atopic disease. Cell 170, 222 (2017).

    Article  CAS  PubMed  Google Scholar 

  58. Bauckneht, M., Piva, R., Sambuceti, G., Grossi, F. & Morbelli, S. Evaluation of response to immune checkpoint inhibitors: Is there a role for positron emission tomography? World J. Radiol. 9, 27–33 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Ferrari, C. et al. Early evaluation of immunotherapy response in lymphoma patients by 18F-FDG PET/CT: a literature overview. J. Pers. Med. 11, 217 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Castellino, S. M. et al. Brentuximab vedotin with chemotherapy in pediatric high-risk Hodgkin’s lymphoma. N. Engl. J. Med. 387, 1649–1660 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. US Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03755804 (2023).

  62. Casasnovas, R. O. et al. PET-adapted treatment for newly diagnosed advanced Hodgkin lymphoma (AHL2011): a randomised, multicentre, non-inferiority, phase 3 study. Lancet Oncol. 20, 202–215 (2019).

    Article  PubMed  Google Scholar 

  63. Fornecker, L. M. et al. Brentuximab vedotin plus AVD for first-line treatment of early-stage unfavorable Hodgkin lymphoma (BREACH): a multicenter, open-label, randomized, phase II trial. J. Clin. Oncol. 41, 327–335 (2023).

    Article  CAS  PubMed  Google Scholar 

  64. Lynch, R. C. et al. Concurrent pembrolizumab with AVD for untreated classic Hodgkin lymphoma. Blood 141, 2576–2586 (2023).

    CAS  PubMed  Google Scholar 

  65. Ghesquieres, H. et al. Prednisone, vinblastine, doxorubicin and bendamustine (PVAB) regimen in first line therapy for older patients with advanced-stage classical Hodgkin lymphoma: results of a prospective multicenter phase II trial of the Lymphoma Study Association (LYSA). Blood 134, 2832 (2019).

    Article  Google Scholar 

  66. Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Canisius, S., Martens, J. W. & Wessels, L. F. A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol. 17, 261 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Jiang, P. et al. Plasma DNA end-motif profiling as a fragmentomic marker in cancer, pregnancy, and transplantation. Cancer Discov. 10, 664–673 (2020).

    Article  CAS  PubMed  Google Scholar 

  69. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  70. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  71. O’Shea, J. P. et al. pLogo: a probabilistic approach to visualizing sequence motifs. Nat. Methods 10, 1211–1212 (2013).

    Article  PubMed  Google Scholar 

  72. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Aoki, T. et al. Single-cell transcriptome analysis reveals disease-defining T-cell subsets in the tumor microenvironment of classic Hodgkin lymphoma. Cancer Discov. 10, 406–421 (2020).

    Article  PubMed  Google Scholar 

  74. Sanz, I. et al. Challenges and opportunities for consistent classification of human B cell and plasma cell populations. Front. Immunol. 10, 2458 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Holmes, A. B. et al. Single-cell analysis of germinal-center B cells informs on lymphoma cell of origin and outcome. J. Exp. Med. 217, e20200483 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhang, L. et al. Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer. Signal Transduct. Target. Ther. 7, 9 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Foley, J. W. et al. Gene expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ. Genome Res. 29, 1816–1825 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Grossiord, E. et al. in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) 1118–1121 (IEEE, 2015).

  80. Boellaard, R. Quantitative oncology molecular analysis suite: ACCURATE. J. Nucl. Med. 59, 1753 (2018).

    Google Scholar 

  81. Hirata, K. et al. A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS ONE 9, e105682 (2014).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Cancer Institute (R01CA257655 and R01CA233975 to A.A.A. and M.D., the Department of Defense (CA210872, award number W81XWH-22-1-0336) the Virginia and D.K. Ludwig Fund for Cancer Research (to A.A.A. and M.D.), Hanna and Michael Murphy family gift (to A.A.A.), the Stanford Cancer Institute (to A.A.A.), the Damon Runyon Cancer Research Foundation (DR-CI#71-14 to A.A.A.), the American Society of Hematology Scholar Award (to A.A.A.), V Foundation for Cancer Research Abeloff Scholar Award (to A.A.A.), the Emerson Collective Cancer Research Fund (to A.A.A.), the Stinehart/Reed Award (to A.A.A.), the CRK Faculty Scholar Fund (to M.D.), the Lung Cancer Research Foundation (to M.S.E.), K08CA241076 (to D.M.K.) and the SDW/DT, the Shanahan Family Foundations (to A.A.A. and D.M.K.), the Terry Fox Research Institute (grants 1061 and 1108 to C.S.) and the Canadian Institutes of Health Research (180613 to C.S.). A.A.A. is the Moghadam Family Professor of Medicine at Stanford University. S.K.A. is a scholar of the German Cancer Aid (Deutsche Krebshilfe, Dr. Mildred Scheel scholarship 57406718) and A.A.A. is a scholar of the Leukemia & Lymphoma Society. M.Y.L. is a PhD candidate at the University of British Columbia, Canada and is supported by an Elizabeth C. Watters Research Fellowship. T.F. and J.E.F. were supported by the American Syrian Lebanese Associated Charities and the US National Institutes of Health Cancer Support Core grant (R03CA21765). The Children Oncology Group (COG) was supported by the NCTN Operations Center grant U10CA180886, the NCTN Statistics & Data Center grant U10CA180899 and the NCTN HM-ITSC grant UG1CA233249. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health.

Author information

Author notes

  1. These authors contributed equally: Stefan K. Alig, Mohammad Shahrokh Esfahani, Andrea Garofalo, Michael Yu Li

Authors and Affiliations

  1. Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA

    Stefan K. Alig, Mohammad Shahrokh Esfahani, Andrea Garofalo, Cédric Rossi, Navika Shukla, Michael C. Jin, Mari Olsen, Jurik A. Mutter, Joseph G. Schroers-Martin, Brian J. Sworder, Daniel A. King, Andre Schultz, Jan Bögeholz, Karan R. Kathuria, Chih Long Liu, Xiaoman Kang, Maya J. Strohband, Feng Tian, Ranjana Advani, David M. Kurtz & Ash A. Alizadeh

  2. Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, British Columbia, Canada

    Michael Yu Li, Adèle Telenius, Shinya Rai & Christian Steidl

  3. Hematology Department, University Hospital F. Mitterrand and Inserm UMR 1231, Dijon, France

    Cédric Rossi & Olivier Casasnovas

  4. Department of Infectious Diseases, St Jude Children’s Research Hospital, Memphis, TN, USA

    Tim Flerlage, Kristine Faye Pobre-Piza & Sherri Surman

  5. Department of Oncology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Jamie E. Flerlage

  6. Department of Pediatrics, Division of Pediatric Hematology and Oncology, Stanford University, Stanford, CA, USA

    Ragini Adams, Lianna J. Marks & Michael P. Link

  7. Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA, USA

    Michael S. Binkley, Shengqin Su, Richard Hoppe & Maximilian Diehn

  8. Department of Bone Marrow Transplant and Cellular Therapy, St Jude Children’s Research Hospital, Memphis, TN, USA

    Deanna Langfitt

  9. Laboratory of Molecular Diagnostics, Department of Medical Genetics EOLAB, Bellinzona, Switzerland

    Valeria Spina

  10. Department of Imaging and Pathology, KU Leuven, Leuven, Belgium

    Thomas Tousseyn

  11. Department of Human Genetics, KU Leuven, Leuven, Belgium

    Lieselot Buedts & Peter Vandenberghe

  12. Department of Pathology, Stanford University, Stanford, CA, USA

    Yasodha Natkunam

  13. Institut de Cancérologie Strasbourg Europe (ICANS) and University of Strasbourg, Strasbourg, France

    Luc-Matthieu Fornecker

  14. Department of Pediatrics, Emory University, Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, Atlanta, GA, USA

    Sharon M. Castellino

  15. Clinic of Hematology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland

    Davide Rossi

  16. Laboratory of Experimental Hematology, Institute of Oncology Research, Bellinzona, Switzerland

    Davide Rossi

  17. Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland

    Davide Rossi

  18. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Ryan Lynch

  19. Department of Hematology, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Benite, France

    Hervé Ghesquières

  20. Department of Haematology, Université Catholique de Louvain, CHU UCL Namur, Yvoir, Belgium

    Marc André

  21. Department of Hematology, University Hospitals Leuven, Leuven, Belgium

    Peter Vandenberghe

Authors
  1. Stefan K. Alig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Shahrokh Esfahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Garofalo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Yu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cédric Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tim Flerlage
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jamie E. Flerlage
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ragini Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Michael S. Binkley
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Navika Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Michael C. Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mari Olsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Adèle Telenius
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jurik A. Mutter
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Joseph G. Schroers-Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Brian J. Sworder
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Shinya Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Daniel A. King
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Andre Schultz
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jan Bögeholz
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Shengqin Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Karan R. Kathuria
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Chih Long Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Xiaoman Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Maya J. Strohband
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Deanna Langfitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Kristine Faye Pobre-Piza
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Sherri Surman
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Feng Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Valeria Spina
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Thomas Tousseyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Lieselot Buedts
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Richard Hoppe
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Yasodha Natkunam
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Luc-Matthieu Fornecker
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Sharon M. Castellino
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Ranjana Advani
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Davide Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Ryan Lynch
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Hervé Ghesquières
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Olivier Casasnovas
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. David M. Kurtz
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. Lianna J. Marks
    View author publications

    You can also search for this author in PubMed Google Scholar

  44. Michael P. Link
    View author publications

    You can also search for this author in PubMed Google Scholar

  45. Marc André
    View author publications

    You can also search for this author in PubMed Google Scholar

  46. Peter Vandenberghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  47. Christian Steidl
    View author publications

    You can also search for this author in PubMed Google Scholar

  48. Maximilian Diehn
    View author publications

    You can also search for this author in PubMed Google Scholar

  49. Ash A. Alizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.K.A., M.S.E., A.G. and M.Y.L. contributed equally. S.K.A., M.S.E., A.G., A.A.A. and M.D. developed the concept, designed the experiments, analysed the data and wrote the initial draft of the manuscript. M.Y.L., A.T., S.R. and C.S. designed and performed the in vitro experiments. T.F., J.E.F., D.L., K.F.P.-P. and S. Surman planned, performed and analysed the single-cell experiments. S.K.A., A.G., C.R., R. Adams, M.S.B., M.C.J., M.O, S. Su, X.K. and M.J.S. performed and analysed the sequencing studies and assembled data. S.K.A. and M.S.E. developed all the machine learning modules in this study with input from A.A.A. and M.D. S.K.A., M.S.E., J.G.S.-M. and J.A.M. conducted the experiments and analysed the sequencing data related to EPIC-seq. S.K.A., M.S.B., S. Su, M.O., X.K. and Y.N. performed the laser microdissection experiments, and tumour and RNA sequencing. S.K.A. and N.S. analysed the plasma TCR rearrangements. S.K.A., M.S.E., A.G., T.F., B.J.S., J.G.S.-M., D.A.K., A.S., J.B., K.R.K., C.L.L., F.T. and D.M.K. performed the bioinformatics analyses. C.R., J.E.F., V.S., T.T., L.B., R.H., Y.N., L.-M.F., S.M.C., R. Advani, D.R., R.L., H.G., O.C., L.J.M., M.P.L., M.A. and P.V. provided patient samples and assembled clinical data. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Maximilian Diehn or Ash A. Alizadeh.

Ethics declarations

Competing interests

S.K.A. reports speaker honoraria from Takeda. M.S.E. reports consultancy for Foresight Diagnostics. J.E.F. reports research funding from Seattle Genetics. B.J.S. reports consultancy for Foresight Diagnostics. A.S. is currently employed by Foresight Diagnostics. Y.N. reports consultancy for Roche & Leica Biosystems and research funding from Kite Pharma. D.R. reports consultancy, honoraria, research funding and travel support from AstraZeneca, Janssen, AbbVie, Gilead, MSD, BMS and BeiGene. R.L. reports research funding from TG Therapeutics, Incyte, Bayer, Cyteir, Genentech, SeaGen and Rapt, as well as consultancy for Cancer Study Group, SeaGen, Foresight Diagnostics and AbbVie. D.M.K. reports consultancy for Roche, Adaptive Biotechnologies and Genentech, equity ownership interest in Foresight Diagnostics, and patent filings, including patent issued, licensed and with royalties paid from Foresight. M.A. reports consultancy for Takeda, BMS, Karyopharm, Gilead and Incyte, research funding from Roche, Johnson & Johnson and Takeda, as well as travel support from Roche, BMS, Celgene, Gilead, AbbVie, AstraZeneca and Takeda. C.S. has performed consultancy for Bayer, and has received research funding from Epizyme and Trillium Therapeutics. M.D. reports research funding from AstraZeneca, Genentech, Varian Medical Systems and Illumina, ownership interest in CiberMed and Foresight Diagnostics, consultancy from AstraZeneca, Boehringer Ingelheim, BMS, Genentech, Gritstone Oncology, Illumina, Regeneron and Roche, and multiple issued and pending patents including patents licensed to Foresight Diagnostics and Roche. A.A.A. reports consultancy for Celgene, Chugai, Genentech, Gilead, Janssen, Pharmacyclics and Roche, scientific advisory board membership in the Lymphoma Research Foundation, and Professional Affiliations with the American Society of Hematology, American Society of Clinical Oncology, American Society of Clinical Investigation, Leukemia & Lymphoma Society, Research Funding from the National Cancer Institute, the National Heart, Lung, and Blood Institute, the US National Institutes of Health, Celgene, BMS and Pfizer, patent filings including patent issued, licensed and with royalties paid from FortySeven, a patent pending and Licensed to Foresight, a patent pending relating to MARIA, a patent issued and licensed to CiberMed, a patent issued and a patent pending to CiberMed, patents issued to Idiotype Vaccines, and a patent issued, licensed and with royalties paid from Roche, and equity ownership interests in CiberMed Inc., ForeSight Diagnostics, FortySeven Inc. and CARGO Therapeutics. S.K.A., M.S.E., M.Y.L., B.J.S., D.M.K., C.S., M.D. and A.A.A. also report patent filings related to cancer biomarkers. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Noninvasive profiling of cHL.

(A) Study overview. MRD: Minimal residual disease; PET2: Positron emission tomography after 2 cycles of chemotherapy; preTx: pretreatment; AF: allelic fraction; SNV: single nucleotide variants; LDA: Latent Dirichlet Allocation; SCNA: somatic copy number aberration; EPIC-Seq: epigenetic expression inference from cell-free DNA-sequencing. (B) Line plot summarizing sensitivity of the Gradient Boosting Machine (GBM) model to call exome-wide SNVs as a function of plasma VAF in 2 non-Hodgkin lymphoma validation samples as compared with a conventional workflow. Sensitivity was calculated considering mutations with VAFs ranging from x-1 to x + 1%. Tumour mutation calls were considered ground truth for sensitivity estimation. (C) Line plot summarizing positive predictive values (PPV) of the GBM model as a function of plasma VAF in 2 non-Hodgkin lymphoma validation samples as compared with a conventional workflow. PPV was calculated considering mutations with VAFs ranging from x-1 to x + 1%. The union of tumour and deep plasma exome mutation calls were considered ground truth for PPV estimation. (D) Bar plot summarizing MutSig2CV −log10 Q values of 41 genes found to be significantly mutated in targeted or whole exome sequencing (Q value < 0.05). The heat of the bars reflects mutation recurrence frequency. Genes that have not been recurrently described in the cHL literature (i.e. ≤ 1 study comprising at least 50 patients) are highlighted in bold. (E) Cophenetic coefficients (sample, feature and final) by number of clusters (k) when applied to the Latent Dirichlet Allocation (LDA) framework to identify genetic subtypes in 293 cHL cases.

Extended Data Fig. 2 Oncoprint visualizing targeted SNV and indel variant calls.

Genes with mutation frequencies ≥5% are visualized. If more than 1 variant per gene was identified in a patient, colour coding was derived using the following hierarchy: nonsense > start codon mutation > frameshift indel > nonstop > splice-site > missense > inframe indel. N = 293 pretreatment plasma samples were included in the analysis.

Extended Data Fig. 3 Oncoprint visualizing whole exome SNV and indel variant calls.

Genes with mutation frequencies ≥7.5% are visualized. If more than 1 variant per gene was identified in a patient, colour coding was derived using the following hierarchy: nonsense > start codon mutation > frameshift indel > nonstop > start gain > splice-site > missense > inframe indel. N = 119 cases profiled by plasma exome sequencing were included in the analysis.

Extended Data Fig. 4 GISTIC2 peaks identified in 61 plasma samples profiled by whole exome sequencing with AFs ≥ 5%.

Selected gene symbols of genes of interest falling within a peak are annotated with the cytobands. Top x-axis: G-score, bottom x-axis: Q value.

Extended Data Fig. 5 Mutual exclusivity/ co-association analysis using the DISCOVER R-package.

Heatmaps summarizing (A) unadjusted −log10 P values or (B) −log10 Q values for mutual exclusivity/ co-association, respectively. Alterations that tend to co-occur are visualized in blue, while those with a tendency for mutual exclusivity are depicted in red colours. Non-silent mutations and SCNA as summarized in the LDA clustering matrix observed in >20 patients among 293 cases were included in the analysis. Statistics were performed using the DISCOVER R-package.

Extended Data Fig. 6 Genetic subtypes are independent of EBV status and validate in external data.

(A) Overview figure summarizing genetic cHL subtype discovery and validation. LDA: Latent Dirichlet allocation; cfDNA: cell-free DNA; TCR: T-cell receptor. The figure in panel A was created using BioRender (biorender.com). (B-C) Boxplots and Wilcoxon P values (two-sided) summarizing the targeted SNV burden (B), and fraction of the genome affected by SCNAs (C) in cluster H1 (n = 200), H2 EBV− (n = 56) and H2 EBV+ (n = 37) in the plasma sequencing cohort. (D) Heatmap summarizing non-silent mutations and SCNAs (rows) of 61 patients with cHL (columns) as published in Maura et al.20 Clusters were assigned using the probabilistic model generated by LDA from the plasma discovery cohort as shown in Fig. 2. (E) Bar plot visualizing recurrence frequencies of features associated with subtype H1 (top) and H2 (bottom) as presented in panel D. Dark colours denote frequencies from plasma genotyping (H1: n = 200; H2: n = 93, as visualized in Fig. 2) while light colours reflect frequencies as described in Maura et al. (H1: n = 33; H2: n = 28). Spearman rhos and P values (algorithm AS 89) provided in the graphs describe the correlation of recurrence frequencies from all 30 features visualized in D between this study and Maura et al. within H1 and H2, respectively. (F) Boxplots summarizing the whole genome mutational burden in cluster H1 (n = 16), H2 EBV− (n = 5) and H2 EBV+ (n = 3) in patients with available whole genome sequencing and known EBV status from Maura et al. Wilcoxon P values (two-sided) are provided. (G) Loess regression describing the association of age and the probability of assignment to the H2 subtype in n = 292 patients from the plasma genotyping cohort (black line: mean; ribbon: standard deviation*1.96). Each dot represents a group of 10 patients from Maura et al. (n = 60 total) with x and y illustrating average age, and the fraction of H2 cases within the group, respectively. Patients were sorted by age prior to grouping. (H) Pie chart summarizing EBV status of patients from Maura et al. assigned to the H1 (n = 33) and H2 (n = 26) clusters. Two-sided Fisher’s exact test P value is provided. Panels B,C,F: each box represents the interquartile range (the range between the 25th and 75th percentile) with the median of the data, whiskers indicate the upper and lower value within 1.5 times the IQR.

Extended Data Fig. 7 Mutual exclusivity analysis in the validation cohort20.

Mutual exclusivity/co-association analysis using the DISCOVER R-package. The heatmap summarizes −log10 P values for mutual exclusivity/co-association, respectively. The mutual exclusivity of H1- and H2- defining features (black boxes) was compared to a null distribution constructed by random shuffling of the matrix while maintaining the number of cluster-defining features. To provide a single measure of mutual exclusivity, P values (one-sided, mutual exclusivity) were combined using Fisher’s method. The empirical P value provided on the top of the heatmap was defined by comparing the observed combined P value with the null distribution. Features from Extended Data Fig. 6d with greater than 4 occurrences were visualized. In addition, only one cytoband per chromosome was visualized selected by their rank in the feature list of H1/H2.

Extended Data Fig. 8 Truncating IL4R mutations enhance STAT6 signalling.

(A) Immunoblot showing protein levels of IL-4Rα and GAPDH in transduced DEV and KM-H2 cells. (B) Gene set enrichment plots from RNA-Sequencing showing enrichment of canonical KEGG pathways in mutant (E684Kfs2, n = 6) vs WT (n = 6) expressing DEV cells. Normalized enrichment scores (NES) and adjusted P values (fgsea R-package) are provided. (C) Scatter plot visualizing base mean expression and log2 fold change comparing gene expression in mutant (E684Kfs2, n = 6) vs WT (n = 6) expressing DEV cells. Absolute log2 fold changes >1 are highlighted in orange or black, respectively. (D) Phospho-STAT6 levels (flow) in transduced DEV (n = 5 each) and KM-H2 (n = 4 each) cells under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. Unadjusted Wilcoxon P values (two-sided) compared to IL-13 stimulation alone are provided. (E) Representative phospho-STAT6 flow raw data for KM-H2 Empty, WT, Q666* and Q698* constructs under IL-13-stimulated conditions. (F) Phospho-STAT6 levels in WT, E684Kfs2 and I242N (PMBL hotspot) DEV cells under unstimulated, IL-4-stimulated, IL-4-stimulated + IL-4R-Ab treated as well as IL-4-stimulated + STAT6-I. treated conditions (n = 5 each). Unadjusted Wilcoxon P values (two-sided) compared to IL-4 stimulation alone are provided. (G) Representative immunoblot showing protein levels of pSTAT6, STAT6 and GAPDH in KM-H2 cells as a function of IL4R construct expression under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. All conditions were run on the same gel. Ladders run between IL4R constructs were cropped and are not shown. (H) Immunoblot showing protein levels of pSTAT6, STAT6 and GAPDH in IL4R I242N expressing KM-H2 cells under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. (I-J) CCL17 (TARC) concentrations in supernatant of transduced (I) DEV (n = 5 for unstimulated and IL-13; n = 4 for IL-4R-Ab and STAT6-I.) and (J) KM-H2 (n = 3 each) cells under unstimulated, IL-13-stimulated, IL-13-stimulated + IL-4R-Ab treated as well as IL-13-stimulated + STAT6-I. treated conditions. Unadjusted Wilcoxon P values (two-sided) are provided. (K) Boxplots and Wilcoxon P values (two-sided) comparing IL13 and IL4 expression in RNA-Sequencing of primary bulk tumour specimens visualized as normalized counts (n = 86 cHL, n = 66 LBCL32). (L) Log2 copy number ratio (L2CNR, boxplot and Wilcoxon P value, two-sided) of the 5q31.1 cytoband harboring IL13 stratified by IL4R mutation status in n = 119 patients with plasma exome sequencing (IL4R mutant: n = 12; IL4R WT: n = 107). Panels A,G-H: At least 2 independent experiments were performed for each condition. Panels D,F,I,J: mean +/− standard error (se). Panels K-L: each box represents the interquartile range (the range between the 25th and 75th percentile) with the median of the data, whiskers indicate the upper and lower value within 1.5 times the IQR.

Extended Data Fig. 9 Pretreatment ctDNA correlates with clinical risk factors and MRD is an independent prognostic factor in cHL.

(A-E) Previously untreated, adult cHL patients were considered for associations between pretreatment ctDNA levels and clinical variables (n = 309). Boxplots summarizing pretreatment ctDNA levels by (A) stage (n = 309, Kruskal-Wallis P value), (B) bulky disease (n = 184, Wilcoxon P value), (C) B-symptoms (n = 308, Wilcoxon P value), (D) EBV status (n = 309, Wilcoxon P value) and (E) histological subtype (n = 247, Wilcoxon P value). Patients with lymphocyte rich and lymphocyte depleted subtypes are not visualized due to small numbers (n = 9). All P values were derived from two-sided tests. (F-G) Waterfall plots showing log10 ctDNA changes from baseline at (F) C1D15 and (G) the >C(ycle)4/EoT (End of Treatment) timepoint. Bars are coloured by PFS event status. Top annotation visualizes PET2 readings according to the 5-point scale (5PS Deauville). (H) Kaplan-Meier curves and logrank P value showing PFS stratified by ctDNA detection at >C4/EoT. (I) Kaplan-Meier curves and logrank P values showing progression-free survival (PFS) stratified by ctDNA detection at C3D1 in PET2 negative and PET2 positive patients. Panels A-E: each box represents the interquartile range (the range between the 25th and 75th percentile) with the median of the data, whiskers indicate the upper and lower value within 1.5 times the IQR.

Extended Data Fig. 10 Model depicting the potential pathogenesis of H1 and H2 cHL subtypes.

GC: Germinal centre; SHM: Somatic hypermutation; SNV: Single nucleotide variant; SCNA: Somatic copy number aberrations. The figure was created using BioRender (biorender.com).

Supplementary information

Supplementary Information

This file contains Supplementary Note Tables 1–4; Supplementary Figs. 1–16; and Supplementary References.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1–12.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alig, S.K., Shahrokh Esfahani, M., Garofalo, A. et al. Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling. Nature 625, 778–787 (2024). https://doi.org/10.1038/s41586-023-06903-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06903-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer