Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Journal name:
Nature Medicine
Volume:
23,
Pages:
703–713
Year published:
DOI:
doi:10.1038/nm.4333
Received
Accepted
Published online
Corrected online

Abstract

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

At a glance

Figures

  1. Overview of the MSK-IMPACT clinical workflow.
    Figure 1: Overview of the MSK-IMPACT clinical workflow.

    Patients provide informed consent for paired tumor–normal sequence analysis, and a blood sample is collected as a source of normal DNA. DNA is extracted from tumor and blood samples using automated protocols, and sequence libraries are prepared and captured using hybridization probes targeting all coding exons of 410 genes and select introns of recurrently rearranged genes. Following sequencing, paired reads are analyzed through a custom bioinformatics pipeline that detects multiple classes of genomic rearrangements. Results are loaded into a genomic variants database developed in house, MPath, where they are manually reviewed for quality and accuracy. Genomic alterations are reported in the electronic medical record, transmitted to an institutional database (Darwin) that facilitates automated clinical trial matching and automatically uploaded to the cBioPortal for data mining and interpretation.

  2. Overview of the MSK-IMPACT cohort.
    Figure 2: Overview of the MSK-IMPACT cohort.

    (a) Distribution of tumor types among cases successfully sequenced from 10,336 patients. Cases represented 62 principal tumor types encapsulating 361 detailed tumor types. (b) Frequency of gene alterations in TCGA and MSK-IMPACT cohorts. Genes that were significantly mutated in TCGA studies are displayed, and genes in which mutations showed a significant difference in frequency between the two cohorts are labeled. The color of each symbol corresponds to the cancer type in TCGA in which the gene was significantly enriched for mutations. (c) Recurrent somatic alterations across common tumor types. Genes with a cohort-level alteration frequency of ≥5% or a tumor type–specific alteration frequency of ≥30% are displayed. Bars indicate the percentage of cases within each tumor type harboring different classes of genomic alterations.

  3. The spectrum of TERT promoter mutations in cancer.
    Figure 3: The spectrum of TERT promoter mutations in cancer.

    (a) Location of all TERT promoter mutations relative to the transcription start site (+1 bp). Observed nucleotide changes leading to presumptive ETS transcription factor binding sites are shown in red for the three most common mutational hotspots (−124 bp, −146 bp, −138 bp). The inset shows the distribution of cancer types harboring mutations at each individual hotspot. (b) Bar plots depicting the percentage of cases in each common principal tumor type (left) and melanoma subtype (right) harboring a TERT promoter mutation. (c) Kaplan–Meier survival curves for the most prominent detailed tumor types belonging to the principal tumor types with the highest prevalence of TERT promoter mutations. Survival was measured starting from the date of the procedure during which the sequenced specimen was obtained. Cases where specimens were obtained more than 12 months before MSK-IMPACT sequencing were excluded from this analysis. The log-rank test was used for statistical analysis. WT, wild type.

  4. Spectrum of kinase fusions identified by MSK-IMPACT.
    Figure 4: Spectrum of kinase fusions identified by MSK-IMPACT.

    (a) Kinase genes recurrently rearranged to form putative gene fusions that include the kinase domain, displayed across principal tumor types. (b) List of fusions containing the BRAF kinase domain. *, novel fusion partner; , complex fusion resolved using an orthogonal RNA–seq-based assay. Each red line indicates a fusion point between BRAF and its upstream partner. (c) In-frame intragenic deletions observed in BRAF, encompassing 5–9 exons upstream of the kinase domain. Orange bars indicate the location of the labeled protein domains.

  5. Mutational signatures derived from MSK-IMPACT targeted sequencing data.
    Figure 5: Mutational signatures derived from MSK-IMPACT targeted sequencing data.

    (a) Violin plots show the distribution of the somatic tumor mutation burden (TMB), defined as the number of nonsynonymous coding mutations per megabase, for common principal tumor types. The width of each plot indicates the frequency of samples with a given TMB. The red line indicates the threshold for samples with a high mutation burden (13.8 mutations/Mb). (b) The distribution of observed mutation rates across all tumors sequenced was used to identify a threshold of 13.8 mutations/Mb (red line), indicative of high mutation burden. (c) Dominant mutation signatures identified in cases with a high mutation burden. The percentage of cases harboring a dominant mutation signature is shown for each principal tumor type. POLE, POLE-associated hypermutation; MMR, mismatch-repair deficiency; TMZ, temozolomide. (d) Individual tumors harboring dominant mutation signatures. Bar charts display the total number of coding mutations (gray) and the fraction of mutations explained by the major signatures (colored). Tracks below the bar charts indicate (i) POLE mutation status, (ii) MMR pathway mutation status, (iii) MSIsensor score, (iv) indel-to-SNV (single-nucleotide variant) ratio, (v) reported smoking status and (vi) cancer type. (e) Tumor type distribution for samples with a high mutation burden, dominant MMR signature and inferred MSI. (f) A 55-year-old patient with castration- and enzalutamide-resistant prostate cancer with an MMR signature (19 mutations, including 6 frameshift indels) and no clear underlying somatic or germline MMR pathway lesion. A pathogenic germline MUTYH variant was detected, which may contribute to the MSI phenotype. Upon initiation of treatment on an anti-PD-L1 immunotherapy regimen, rapid tumor regression was observed. Line charts show relative tumor size based on Response Evaluation Criteria in Solid Tumors (RECIST) criteria and serum prostate-specific antigen (PSA) levels. MRI images show the decreasing tumor size at indicated time points. Scale bars, 10 cm.

  6. Clinical actionability of somatic alterations revealed by MSK-IMPACT.
    Figure 6: Clinical actionability of somatic alterations revealed by MSK-IMPACT.

    (a) Alterations were annotated based on their clinical actionability according to OncoKB, and samples were assigned to the level of the most actionable alteration. Briefly, levels of evidence varied according to whether mutations are FDA-recognized biomarkers (level 1), predict response to standard-of-care therapies (level 2) or predict response to investigational agents in clinical trials (level 3). Levels 2 and 3 were subdivided according to whether the evidence existed for the pertinent tumor type (2A, 3A) or a different tumor type (2B, 3B). The distribution of the highest level of actionability across all patients is displayed. (b) Distribution of levels of actionability across tumor types. GNET, gastrointestinal neuroendocrine tumor. Colors are defined as in a. (c) Number of patients enrolled on genomically matched clinical trials on the basis of different gene alterations.

Change history

Corrected online 14 June 2017
In the version of this article initially published online, the top value in the y axis of the Kaplan–Meier plots in Figure 3c was incorrectly denoted as 0.1. The correct value is 1. The error has been corrected in the HTML and PDF versions of the article.

References

  1. Garraway, L.A. Genomics-driven oncology: framework for an emerging paradigm. J. Clin. Oncol. 31, 18061814 (2013).
  2. Varghese, A.M. & Berger, M.F. Advancing clinical oncology through genome biology and technology. Genome Biol. 15, 427 (2014).
  3. Lindeman, N.I. et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: guideline from the College of American Pathologists, International Association for the Study of Lung Cancer, and Association for Molecular Pathology. J. Mol. Diagn. 15, 415453 (2013).
  4. Chapman, P.B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 25072516 (2011).
  5. Hyman, D.M. et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373, 726736 (2015).
  6. Singh, R.R. et al. Clinical validation of a next-generation sequencing screen for mutational hotspots in 46 cancer-related genes. J. Mol. Diagn. 15, 607622 (2013).
  7. Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl. Med. 3, 111ra121 (2011).
  8. Frampton, G.M. et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat. Biotechnol. 31, 10231031 (2013).
  9. Beltran, H. et al. Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol. 1, 466474 (2015).
  10. Sholl, L.M. et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. JCI Insight 1, e87062 (2016).
  11. Cheng, D.T. et al. Memorial Sloan Kettering–integrated mutation profiling of actionable cancer targets (MSK-IMPACT): a hybridization capture–based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251264 (2015).
  12. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401404 (2012).
  13. Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 11271133 (2013).
  14. Simen, B.B. et al. Validation of a next-generation-sequencing cancer panel for use in the clinical laboratory. Arch. Pathol. Lab. Med. 139, 508517 (2015).
  15. Forbes, S.A. et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 45, D777D783 (2017).
  16. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333339 (2013).
  17. Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell 159, 676690 (2014).
  18. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202209 (2014).
  19. Davis, C.F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319330 (2014).
  20. Powell, E., Piwnica-Worms, D. & Piwnica-Worms, H. Contribution of p53 to metastasis. Cancer Discov. 4, 405414 (2014).
  21. Watson, P.A., Arora, V.K. & Sawyers, C.L. Emerging mechanisms of resistance to androgen receptor inhibitors in prostate cancer. Nat. Rev. Cancer 15, 701711 (2015).
  22. Toy, W. et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat. Genet. 45, 14391445 (2013).
  23. Robinson, D.R. et al. Activating ESR1 mutations in hormone-resistant metastatic breast cancer. Nat. Genet. 45, 14461451 (2013).
  24. Chang, M.T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155163 (2016).
  25. Baca, S.C. et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666677 (2013).
  26. Horn, S. et al. TERT promoter mutations in familial and sporadic melanoma. Science 339, 959961 (2013).
  27. Huang, F.W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957959 (2013).
  28. Killela, P.J. et al. TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc. Natl. Acad. Sci. USA 110, 60216026 (2013).
  29. Gao, K. et al. TERT promoter mutations and long telomere length predict poor survival and radiotherapy resistance in gliomas. Oncotarget 7, 87128725 (2016).
  30. Melo, M. et al. TERT promoter mutations are a major indicator of poor outcome in differentiated thyroid carcinomas. J. Clin. Endocrinol. Metab. 99, E754E765 (2014).
  31. Piscuoglio, S. et al. Massively parallel sequencing of phyllodes tumours of the breast reveals actionable mutations, and TERT promoter hotspot mutations and TERT gene amplification as likely drivers of progression. J. Pathol. 238, 508518 (2016).
  32. Stransky, N., Cerami, E., Schalm, S., Kim, J.L. & Lengauer, C. The landscape of kinase fusions in cancer. Nat. Commun. 5, 4846 (2014).
  33. Ross, J.S. et al. The distribution of BRAF gene fusions in solid tumors and response to targeted therapy. Int. J. Cancer 138, 881890 (2016).
  34. Menzies, A.M. et al. Clinical activity of the MEK inhibitor trametinib in metastatic melanoma containing BRAF kinase fusion. Pigment Cell Melanoma Res. 28, 607610 (2015).
  35. Poulikakos, P.I. et al. RAF inhibitor resistance is mediated by dimerization of aberrantly spliced BRAFV600E. Nature 480, 387390 (2011).
  36. Yao, Z. et al. BRAF mutants evade ERK-dependent feedback by different mechanisms that determine their sensitivity to pharmacologic inhibition. Cancer Cell 28, 370383 (2015).
  37. Alexandrov, L.B. et al. Signatures of mutational processes in human cancer. Nature 500, 415421 (2013).
  38. Niu, B. et al. MSIsensor: microsatellite instability detection using paired tumor–normal sequence data. Bioinformatics 30, 10151016 (2014).
  39. Le, D.T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 25092520 (2015).
  40. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. J. Clin. Oncol. Precision Oncol. http://dx.doi.org/10.1200/PO.17.00011 (2017).
  41. Meric-Bernstam, F. et al. Feasibility of large-acale genomic testing to facilitate enrollment onto genomically matched clinical trials. J. Clin. Oncol. 33, 27532762 (2015).
  42. Ross, J.S. et al. Comprehensive genomic profiling of carcinoma of unknown primary site: new routes to targeted therapies. JAMA Oncol. 1, 4049 (2015).
  43. Zhu, Z. et al. Inhibition of KRAS-driven tumorigenicity by interruption of an autocrine cytokine circuit. Cancer Discov. 4, 452465 (2014).
  44. Manchado, E. et al. A combinatorial strategy for treating KRAS-mutant lung cancer. Nature 534, 647651 (2016).
  45. Eubank, M.H. et al. Automated eligibility screening and monitoring for genotype-driven precision oncology trials. J. Am. Med. Inform. Assoc. 23, 777781 (2016).
  46. Schwaederle, M. et al. On the road to precision cancer medicine: analysis of genomic biomarker actionability in 439 patients. Mol. Cancer Ther. 14, 14881494 (2015).
  47. Stockley, T.L. et al. Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial. Genome Med. 8, 109 (2016).
  48. Jones, S. et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci. Transl. Med. 7, 283ra53 (2015).
  49. Garofalo, A. et al. The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine. Genome Med. 8, 79 (2016).
  50. Schrader, K.A. et al. Germline variants in targeted tumor sequencing using matched normal DNA. JAMA Oncol. 2, 104111 (2016).
  51. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 17541760 (2009).
  52. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 12971303 (2010).
  53. Mose, L.E., Wilkerson, M.D., Hayes, D.N., Perou, C.M. & Parker, J.S. ABRA: improved coding indel detection via assembly-based realignment. Bioinformatics 30, 28132815 (2014).
  54. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213219 (2013).
  55. Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 28652871 (2009).
  56. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333i339 (2012).
  57. Thorvaldsdóttir, H., Robinson, J.T. & Mesirov, J.P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178192 (2013).
  58. Yoshihara, K. et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene 34, 48454854 (2015).
  59. Manning, G., Whyte, D.B., Martinez, R., Hunter, T. & Sudarsanam, S. The protein kinase complement of the human genome. Science 298, 19121934 (2002).
  60. Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32, D493D496 (2004).
  61. Zheng, Z. et al. Anchored multiplex PCR for targeted next-generation sequencing. Nat. Med. 20, 14791484 (2014).
  62. Jordan, E.J. et al. Prospective comprehensive molecular characterization of lung adenocarcinomas for efficient patient matching to approved and emerging therapies. Cancer Discov. http://dx.doi.org/10.1158/2159-8290.CD-16-1337 (2017).

Download references

Author information

  1. Present addresses: Illumina, Inc., San Francisco, California, USA (D.T.C.) and Baylor College of Medicine, Houston, Texas, USA (R. Chandramohan).

    • Donavan T Cheng &
    • Raghu Chandramohan
  2. These authors contributed equally to this work.

    • Ahmet Zehir &
    • Ryma Benayed

Affiliations

  1. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Ahmet Zehir,
    • Ryma Benayed,
    • Ronak H Shah,
    • Aijazuddin Syed,
    • Sumit Middha,
    • Hyunjae R Kim,
    • Preethi Srinivasan,
    • Meera Hameed,
    • Snjezana Dogan,
    • Dara S Ross,
    • Jaclyn F Hechtman,
    • Deborah F DeLair,
    • JinJuan Yao,
    • Diana L Mandelker,
    • Donavan T Cheng,
    • Raghu Chandramohan,
    • Abhinita S Mohanty,
    • Ryan N Ptashkin,
    • Gowtham Jayakumaran,
    • Meera Prasad,
    • Mustafa H Syed,
    • Anoop Balakrishnan Rema,
    • Zhen Y Liu,
    • Khedoudja Nafa,
    • Laetitia Borsu,
    • Justyna Sadowska,
    • Jacklyn Casanova,
    • Ruben Bacares,
    • Iwona J Kiecka,
    • Anna Razumova,
    • Julie B Son,
    • Lisa Stewart,
    • Tessara Baldi,
    • Kerry A Mullaney,
    • Hikmat Al-Ahmadie,
    • Efsevia Vakiani,
    • Niedzica Camacho,
    • Helen H Won,
    • David S Klimstra,
    • Maria E Arcila,
    • Marc Ladanyi &
    • Michael F Berger
  2. Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Jianjiong Gao,
    • Debyani Chakravarty,
    • Benjamin E Gross,
    • Ritika Kundra,
    • Zachary J Heins,
    • Hsiao-Wei Chen,
    • Sarah Phillips,
    • Hongxin Zhang,
    • Jiaojiao Wang,
    • Angelica Ochoa,
    • Barry S Taylor,
    • Nikolaus Schultz,
    • David B Solit &
    • Michael F Berger
  3. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Sean M Devlin,
    • Adam A Abeshouse,
    • Alexander V Penson,
    • Philip Jonsson,
    • Matthew T Chang,
    • Barry S Taylor &
    • Nikolaus Schultz
  4. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Matthew D Hellmann,
    • Alison M Schram,
    • Wassim Abida,
    • Andrea Cercek,
    • Darren R Feldman,
    • Mrinal M Gounder,
    • James J Harding,
    • Gopa Iyer,
    • Yelena Y Janjigian,
    • Emmet J Jordan,
    • Ciara M Kelly,
    • Maeve A Lowery,
    • Nitya Raj,
    • Pedram Razavi,
    • Alexander N Shoushtari,
    • Tara E Soumerai,
    • Anna M Varghese,
    • Rona Yaeger,
    • Gregory J Riely,
    • Leonard B Saltz,
    • Howard I Scher,
    • Paul J Sabbatini,
    • Mark E Robson,
    • Jose Baselga,
    • David M Hyman &
    • David B Solit
  5. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • David A Barron
  6. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Alexander V Penson,
    • Philip Jonsson,
    • Matthew T Chang,
    • Barry S Taylor,
    • Jose Baselga,
    • Nikolaus Schultz,
    • David B Solit,
    • Marc Ladanyi &
    • Michael F Berger
  7. Information Systems, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Jonathan Wills,
    • Michael Eubank,
    • Stacy B Thomas &
    • Stuart M Gardos
  8. Clinical Research Administration, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Dalicia N Reales,
    • Jesse Galle,
    • Robert Durany,
    • Roy Cambria,
    • Jonathan Coleman &
    • Bernard Bochner
  9. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • A Ari Hakimi &
    • Luc G T Morris
  10. Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Antonio M Omuro
  11. Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Neerav Shukla

Contributions

A.Z., R. Benayed and M.F.B. wrote the manuscript. R. Benayed, J.S., J. Casanova, R. Bacares, I.J.K., A.R., J.B.S., L.S., T.B. and K.A.M. generated the genomic data. A.Z., R. Benayed, R.H.S., S.M., H.R.K., P.S., S.M.D., M.H., S.D., D.S.R., J.F.H., D.F.D., J.Y., D.L.M., D.T.C., R. Chandramohan, A.S.M., R.N.P., G.J., K.N., L.B., P.J., N.C., M.T.C., H.H.W., B.S.T., N.S., D.M.H., M.E.A., D.B.S., M.L. and M.F.B. reviewed and analyzed the genomic data. M.D.H., D.A.B., A.M.S., H.A.-A., E.V., J.W., M.E., S.B.T., S.M.G., D.N.R., J. Galle, R.D., R. Cambria, W.A., A.C., D.R.F., M.M.G., A.A.H., J.J.H., G.I., Y.Y.J., E.J.J., C.M.K., M.A.L., L.G.T.M., A.M.O., N.R., P.R., A.N.S., N.S., T.E.S., A.M.V., R.Y., D.M.H. and D.B.S. provided clinical data. A.Z., A.S., J. Gao, D.C., D.T.C., M.P., M.H.S., A.B.R., Z.Y.L., A.A.A., A.V.P., B.E.G., R.K., Z.J.H., H.-W.C., S.P., H.Z., J.W., A.O., B.S.T. and N.S. created bioinformatics tools and systems to support data analysis, annotation and dissemination. J. Coleman, B.B., G.J.R., L.B.S., H.I.S., P.J.S., D.S.K., J.B. and D.B.S. provided support for the MSK-IMPACT sequencing initiative. M.E.R., D.M.H. and D.B.S. developed the institutional molecular profiling protocol. All authors reviewed the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (1,816 KB)

    Supplementary Figures 1–14

Excel files

  1. Supplementary Tables (1,946 KB)

    Supplementary Tables 1–7

Additional data