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:

Dynamic molecular monitoring reveals that SWI–SNF mutations mediate resistance to ibrutinib plus venetoclax in mantle cell lymphoma

Nature Medicine volume 25, pages 119–129 (2019)Cite this article

Subjects

Abstract

Ibrutinib plus venetoclax is a highly effective combination in mantle cell lymphoma. However, strategies to enable the evaluation of therapeutic response are required. Our prospective analyses of patients within the AIM study revealed genomic profiles that clearly dichotomized responders and nonresponders. Mutations in ATM were present in most patients who achieved a complete response, while chromosome 9p21.1–p24.3 loss and/or mutations in components of the SWI–SNF chromatin-remodeling complex were present in all patients with primary resistance and two-thirds of patients with relapsed disease. Circulating tumor DNA analysis revealed that these alterations could be dynamically monitored, providing concurrent information on treatment response and tumor evolution. Functional modeling demonstrated that compromise of the SWI–SNF complex facilitated transcriptional upregulation of BCL2L1 (Bcl-xL) providing a selective advantage against ibrutinib plus venetoclax. Together these data highlight important insights into the molecular basis of therapeutic response and provide a model for real-time assessment of innovative targeted therapies.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Genomic characterization of MCL patients enrolled in the AIM clinical trial.
Fig. 2: Modeling resistance to ibrutinib and venetoclax.
Fig. 3: Genomic characterization of a non-AIM patient with MCL.
Fig. 4: Bcl-xL inhibition overcomes ibrutinib and venetoclax resistance.
Fig. 5: SMARCA4 regulates the expression of ATF3.
Fig. 6: ctDNA analysis allows monitoring of treatment response and emerging resistance in patients treated with ibrutinib and venetoclax.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon request. The sequencing data that supports the findings of this study has been deposited into the sequence read archive, which is hosted by the National Centre for Biotechnology Information. The BioProject accession number is PRJNA489753.

References

  1. Cheah, C. Y., Seymour, J. F. & Wang, M. L. Mantle cell lymphoma. J. Clin. Oncol. 34, 1256–1269 (2016).

    Article  CAS  Google Scholar 

  2. Le Gouill, S. et al. Rituximab after autologous stem-cell transplantation in mantle-cell lymphoma. N. Engl. J. Med. 377, 1250–1260 (2017).

    Article  Google Scholar 

  3. Martin, P., Ruan, J. & Leonard, J. P. The potential for chemotherapy-free strategies in mantle cell lymphoma. Blood 130, 1881–1888 (2017).

    Article  CAS  Google Scholar 

  4. Souers, A. J. et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat. Med. 19, 202–208 (2013).

    Article  CAS  Google Scholar 

  5. Wang, M. L. et al. Targeting BTK with ibrutinib in relapsed or refractory mantle-cell lymphoma. N. Engl. J. Med. 369, 507–516 (2013).

    Article  CAS  Google Scholar 

  6. Davids, M. S. et al. Phase I first-in-human study of venetoclax in patients with relapsed or refractory non-Hodgkin lymphoma. J. Clin. Oncol. 35, 826–833 (2017).

    Article  CAS  Google Scholar 

  7. Dreyling, M. et al. Ibrutinib versus temsirolimus in patients with relapsed or refractory mantle-cell lymphoma: an international, randomised, open-label, phase 3 study. Lancet 387, 770–778 (2016).

    Article  CAS  Google Scholar 

  8. Zhao, X. et al. Combination of ibrutinib with ABT-199: synergistic effects on proliferation inhibition and apoptosis in mantle cell lymphoma cells through perturbation of BTK, AKT and BCL2 pathways. Br. J. Haematol. 168, 765–768 (2015).

    Article  CAS  Google Scholar 

  9. Axelrod, M. et al. Combinatorial drug screening identifies synergistic co-targeting of Bruton’s tyrosine kinase and the proteasome in mantle cell lymphoma. Leukemia 28, 407–410 (2014).

    Article  CAS  Google Scholar 

  10. Li, Y. et al. FBXO10 deficiency and BTK activation upregulate BCL2 expression in mantle cell lymphoma. Oncogene 35, 6223–6234 (2016).

    Article  CAS  Google Scholar 

  11. Beltran, E. et al. A cyclin-D1 interaction with BAX underlies its oncogenic role and potential as a therapeutic target in mantle cell lymphoma. Proc. Natl Acad. Sci. USA 108, 12461–12466 (2011).

    Article  CAS  Google Scholar 

  12. Tam, C. S. et al. Ibrutinib plus venetoclax for the treatment of mantle-cell lymphoma. N. Engl. J. Med. 378, 1211–1223 (2018).

    Article  CAS  Google Scholar 

  13. Bea, S. et al. Landscape of somatic mutations and clonal evolution in mantle cell lymphoma. Proc. Natl Acad. Sci. USA 110, 18250–18255 (2013).

    Article  CAS  Google Scholar 

  14. Zhang, J. et al. The genomic landscape of mantle cell lymphoma is related to the epigenetically determined chromatin state of normal B cells. Blood 123, 2988–2996 (2014).

    Article  CAS  Google Scholar 

  15. Kridel, R. et al. Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma. Blood 119, 1963–1971 (2012).

    Article  CAS  Google Scholar 

  16. Meissner, B. et al. The E3 ubiquitin ligase UBR5 is recurrently mutated in mantle cell lymphoma. Blood 121, 3161–3164 (2013).

    Article  CAS  Google Scholar 

  17. Rahal, R. et al. Pharmacological and genomic profiling identifies NF-κB-targeted treatment strategies for mantle cell lymphoma. Nat. Med. 20, 87–92 (2014).

    Article  CAS  Google Scholar 

  18. Greiner, T. C. et al. Mutation and genomic deletion status of ataxia telangiectasia mutated (ATM) and p53 confer specific gene expression profiles in mantle cell lymphoma. Proc. Natl Acad. Sci. USA 103, 2352–2357 (2006).

    Article  CAS  Google Scholar 

  19. Saba, N. S. et al. Pathogenic role of B-cell receptor signaling and canonical NF-κB activation in mantle cell lymphoma. Blood 128, 82–92 (2016).

    Article  CAS  Google Scholar 

  20. Wu, C. et al. Genetic heterogeneity in primary and relapsed mantle cell lymphomas: impact of recurrent CARD11 mutations. Oncotarget 7, 38180–38190 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. Zhao, X. et al. Unification of de novo and acquired ibrutinib resistance in mantle cell lymphoma. Nat. Commun. 8, 14920 (2017).

    Article  CAS  Google Scholar 

  22. Tahir, S. K. et al. Potential mechanisms of resistance to venetoclax and strategies to circumvent it. BMC Cancer 17, 399 (2017).

    Article  Google Scholar 

  23. van der Velden, V. H. et al. Analysis of minimal residual disease by Ig/TCR gene rearrangements: guidelines for interpretation of real-time quantitative PCR data. Leukemia 21, 604–611 (2007).

    Article  Google Scholar 

  24. Cheminant, M. et al. Minimal residual disease monitoring by 8-color flow cytometry in mantle cell lymphoma: an EU-MCL and LYSA study. Haematologica 101, 336–345 (2016).

    Article  CAS  Google Scholar 

  25. Ferrero, S., Dreyling, M. & European Mantle Cell Lymphoma Network. Minimal residual disease in mantle cell lymphoma: are we ready for a personalized treatment approach? Haematologica 102, 1133–1136 (2017).

    Article  CAS  Google Scholar 

  26. Eskelund, C. W. et al. TP53 mutations identify younger mantle cell lymphoma patients who do not benefit from intensive chemoimmunotherapy. Blood 130, 1903–1910 (2017).

    Article  CAS  Google Scholar 

  27. Halldorsdottir, A. M. et al. Impact of TP53 mutation and 17p deletion in mantle cell lymphoma. Leukemia 25, 1904–1908 (2011).

    Article  CAS  Google Scholar 

  28. Nordstrom, L. et al. SOX11 and TP53 add prognostic information to MIPI in a homogenously treated cohort of mantle cell lymphoma--a Nordic Lymphoma Group study. Br. J. Haematol. 166, 98–108 (2014).

    Article  Google Scholar 

  29. Delfau-Larue, M. H. et al. High-dose cytarabine does not overcome the adverse prognostic value of CDKN2A and TP53 deletions in mantle cell lymphoma. Blood 126, 604–611 (2015).

    Article  CAS  Google Scholar 

  30. Aukema, S. M. et al. Expression of TP53 is associated with outcome of MCL independent of MIPI and Ki-67 in trials of the European-MCL Network. Blood 131, 417–420 (2017).

    Article  Google Scholar 

  31. Woyach, J. A. et al. Resistance mechanisms for the Bruton’s tyrosine kinase inhibitor ibrutinib. N. Engl. J. Med. 370, 2286–2294 (2014).

    Article  Google Scholar 

  32. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  Google Scholar 

  33. Hartmann, E. M. et al. Pathway discovery in mantle cell lymphoma by integrated analysis of high-resolution gene expression and copy number profiling. Blood 116, 953–961 (2010).

    Article  CAS  Google Scholar 

  34. Salaverria, I. et al. Specific secondary genetic alterations in mantle cell lymphoma provide prognostic information independent of the gene expression-based proliferation signature. J. Clin. Oncol. 25, 1216–1222 (2007).

    Article  CAS  Google Scholar 

  35. Dreyling, M. H. et al. Alterations of the cyclin D1/p16-pRB pathway in mantle cell lymphoma. Cancer Res. 57, 4608–4614 (1997).

    CAS  PubMed  Google Scholar 

  36. Kadoch, C. & Crabtree, G. R. Mammalian SWI/SNF chromatin remodeling complexes and cancer: mechanistic insights gained from human genomics. Sci. Adv. 1, e1500447 (2015).

    Article  Google Scholar 

  37. Chueh, A. C. et al. ATF3 repression of BCL-XL determines apoptotic sensitivity to HDAC inhibitors across tumor types. Clin. Cancer Res. 23, 5573–5584 (2017).

    Article  CAS  Google Scholar 

  38. Yeh, P. et al. Circulating tumour DNA reflects treatment response and clonal evolution in chronic lymphocytic leukaemia. Nat. Commun. 8, 14756 (2017).

    Article  CAS  Google Scholar 

  39. Herling, C. D. et al. Clonal dynamics towards the development of venetoclax resistance in chronic lymphocytic leukemia. Nat. Commun. 9, 727 (2018).

    Article  Google Scholar 

  40. Dawson, M. A. The cancer epigenome: concepts, challenges, and therapeutic opportunities. Science 355, 1147–1152 (2017).

    Article  CAS  Google Scholar 

  41. Dawson, S. J. et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013).

    Article  CAS  Google Scholar 

  42. Needleman, S. B. & Wunsch, C. D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443–453 (1970).

    Article  CAS  Google Scholar 

  43. 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).

    Article  CAS  Google Scholar 

  44. Doig, K. D. et al. PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories. Genome Med. 9, 38 (2017).

    Article  Google Scholar 

  45. Wong, S. Q. et al. Assessing the clinical value of targeted massively parallel sequencing in a longitudinal, prospective population-based study of cancer patients. Br. J. Cancer 112, 1411–1420 (2015).

    Article  CAS  Google Scholar 

  46. Wong, S. Q. et al. UV-associated mutations underlie the etiology of MCV-negative Merkel cell carcinomas. Cancer Res. 75, 5228–5234 (2015).

    Article  CAS  Google Scholar 

  47. Scheinin, I. et al. DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res. 24, 2022–2032 (2014).

    Article  CAS  Google Scholar 

  48. Fong, C. Y. et al. BET inhibitor resistance emerges from leukaemia stem cells. Nature 525, 538–542 (2015).

    Article  CAS  Google Scholar 

  49. Anderson, M. A. et al. The BCL2 selective inhibitor venetoclax induces rapid onset apoptosis of CLL cells in patients via a TP53-independent mechanism. Blood 127, 3215–3224 (2016).

    Article  CAS  Google Scholar 

  50. Leverson, J. D. et al. Exploiting selective BCL-2 family inhibitors to dissect cell survival dependencies and define improved strategies for cancer therapy. Sci. Transl. Med. 7, 279ra240 (2015).

    Article  Google Scholar 

  51. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  Google Scholar 

  52. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  Google Scholar 

  53. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  Google Scholar 

  54. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  55. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  56. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  Google Scholar 

  57. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  Google Scholar 

  58. Heinze, G. & Schemper, M. A solution to the problem of monotone likelihood in Cox regression. Biometrics 57, 114–119 (2001).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The clinical study was funded by Abbvie and Janssen. The work included in this manuscript was funded by the Leukemia and Lymphoma Society (grant no. 0862-15 (S.-J.D., M.A.D., C.S.T., J.F.S.) and SCOR grant no. 11283-17 (A.W.R., D.C.S.H.)). We thank K. Doig, G. Arnau, T. Semple, T. Holloway and J. Carmody for advice and assistance with genomic sequencing, G. Lessene for providing A-1331852 and the following funders for fellowship, scholarship and grant support: CSL Centenary fellowship (S.-J.D.), Leukaemia Foundation Australia senior fellowship and Howard Hughes Medical Institute international research scholarship 55008729 (M.A.D.), NHMRC postgraduate scholarship 1114242 (R.A.) and HSANZ new investigator fellowship (R.A.), NHMRC fellowships (1089072 (C.E.T.), 1090236 (D.H.D.G.) and 1079560 (A.W.R.), NHMRC grant no. 1104549 (S.-J.D., M.A.D., C.S.T., J.F.S.) and programs 1113577, 1016701 (A.W.R., D.C.S.H.) and Leukemia and Lymphoma Society Independent Research Institutes Infrastructure Support Scheme grant no. 9000220 (R.T.), Snowdome Foundation (M.A.A.), Maddie Riewoldt’s Vision 064728 (Y.-C.C.), Victorian State Government Operational Infrastructure Support grant and Cancer Council Victoria grant (nos. 1146518, 1102104) and the Peter MacCallum Cancer Foundation. Mass cytometry was performed in part at the Materials Characterization and Fabrication Platform at the University of Melbourne and the Victorian Node of the Australian National Fabrication Facility, with support from the Victorian Comprehensive Cancer Centre.

Author information

Author notes

  1. These authors contributed equally: Rishu Agarwal, Yih-Chih Chan.

Authors and Affiliations

  1. Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Rishu Agarwal, Yih-Chih Chan, Tane Hunter, Dane Vassiliadis, Paul Yeh, Stephen Q. Wong, Sarah Ftouni, Enid Y. N. Lam, Omer Gilan, Charles C. Bell, Kathy Knezevic, Piers Blombery, Jason Li, Mark A. Dawson & Sarah-Jane Dawson

  2. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia

    Rishu Agarwal, Yih-Chih Chan, Tane Hunter, Dane Vassiliadis, Paul Yeh, Enid Y. N. Lam, Omer Gilan, Charles C. Bell, Jason Li, John F. Seymour, Mark A. Dawson & Sarah-Jane Dawson

  3. Department of Haematology, Royal Melbourne Hospital & Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Constantine S. Tam, Paul Yeh, Mary Ann Anderson, Piers Blombery, John F. Seymour, Andrew W. Roberts & Mark A. Dawson

  4. Department of Haematology, St. Vincent’s Hospital, Melbourne, Victoria, Australia

    Constantine S. Tam

  5. Department of Medicine, St. Vincent’s Hospital, Melbourne, Victoria, Australia

    Constantine S. Tam & Meaghan Wall

  6. Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

    Charis E. Teh, Rachel Thijssen, Mary Ann Anderson, David C. S. Huang, Daniel H. D. Gray & Andrew W. Roberts

  7. Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia

    Charis E. Teh, Rachel Thijssen, Mary Ann Anderson, David C. S. Huang, Daniel H. D. Gray & Andrew W. Roberts

  8. University Hospital of Schleswig-Holstein, Kiel, Germany

    Christiane Pott

  9. Victorian Cancer Cytogenetics Service, Melbourne, Victoria, Australia

    Kathleen Rayeroux, Adrian Zordan & Meaghan Wall

  10. St. Vincent’s Institute of Medical Research, Melbourne, Victoria, Australia

    Meaghan Wall

  11. Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, Australia

    Andrew W. Roberts, Mark A. Dawson & Sarah-Jane Dawson

Authors
  1. Rishu Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yih-Chih Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Constantine S. Tam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tane Hunter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dane Vassiliadis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Charis E. Teh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rachel Thijssen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Paul Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stephen Q. Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sarah Ftouni
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Enid Y. N. Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mary Ann Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Christiane Pott
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Omer Gilan
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Charles C. Bell
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Kathy Knezevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Piers Blombery
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Kathleen Rayeroux
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Adrian Zordan
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jason Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. David C. S. Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Meaghan Wall
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. John F. Seymour
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Daniel H. D. Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Andrew W. Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Mark A. Dawson
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Sarah-Jane Dawson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.A. and Y.-C.C. performed the majority of the experiments, helped develop the overall concept behind the analysis, and helped write the manuscript. T.H., D.V., C.E.T., R.T., P.Y., S.Q.W., S.F., E.Y.N.L., M.A.A., C.P., O.G., C.C.B., K.K., P.B., K.R., A.Z., J.L., M.W. and D.H.D.G. contributed to data analysis. D.C.S.H. provided critical reagents. C.S.T., J.F.S. and A.W.R initiated and conducted the AIM clinical trial, provided patient data, facilitated biospecimen collection and contributed to the interpretation of the research findings. S.-J.D. and M.A.D. developed the overall concept behind the study, supervised the experiments, analyzed the data and wrote the manuscript.

Corresponding authors

Correspondence to Mark A. Dawson or Sarah-Jane Dawson.

Ethics declarations

Competing interests

C.E.T., R.T., M.A.A., D.C.S.H., D.H.D.G. and A.W.R. are employees of Walter and Eliza Hall Institute of Medical Research which receives milestone and royalty payments related to venetoclax. C.S.T., J.F.S. and A.W.R. received funding from Janssen and AbbVie to conduct the AIM clinical trial. The remaining authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Tables 4, 6 and 8–11

Reporting Summary

Supplementary Table 1

Clinicopathological and molecular characteristics of MCL cases on AIM study

Supplementary Table 2

Clinical samples used for tumor and plasma whole-exome sequencing (WES), targeted sequencing (TS) and low-coverage whole-genome sequencing (LC-WGS)

Supplementary Table 3

All variants identified from whole-exome sequencing

Supplementary Table 5

Differential expressed genes of RNA-seq data comparing SMARCA4 knockdown in Z-138 cells to control

Supplementary Table 7

List of primers used for targeted amplicon sequencing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, R., Chan, YC., Tam, C.S. et al. Dynamic molecular monitoring reveals that SWI–SNF mutations mediate resistance to ibrutinib plus venetoclax in mantle cell lymphoma. Nat Med 25, 119–129 (2019). https://doi.org/10.1038/s41591-018-0243-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-018-0243-z

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