Article | Published:

Spatial genomic heterogeneity within localized, multifocal prostate cancer

Nature Genetics volume 47, pages 736745 (2015) | Download Citation


Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


Primary accessions

Gene Expression Omnibus


  1. 1.

    et al. NCCN clinical practice guidelines in oncology: prostate cancer. J. Natl. Compr. Canc. Netw. 8, 162–200 (2010).

  2. 2.

    et al. Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era. J. Clin. Oncol. 21, 2163–2172 (2003).

  3. 3.

    , , , & Validating the interval to biochemical failure for the identification of potentially lethal prostate cancer. J. Clin. Oncol. 30, 1857–1863 (2012).

  4. 4.

    , , & Multiple cancers in the prostate. Morphologic features of clinically recognized versus incidental tumors. Cancer 70, 2313–2318 (1992).

  5. 5.

    , & Optimal treatment of intermediate-risk prostate carcinoma with radiotherapy: clinical and translational issues. Cancer 104, 891–905 (2005).

  6. 6.

    et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).

  7. 7.

    et al. Genomic profiling reveals alternative genetic pathways of prostate tumorigenesis. Cancer Res. 67, 8504–8510 (2007).

  8. 8.

    et al. Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors. Hum. Mol. Genet. 13, 1303–1313 (2004).

  9. 9.

    et al. mRNA expression signature of Gleason grade predicts lethal prostate cancer. J. Clin. Oncol. 29, 2391–2396 (2011).

  10. 10.

    et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol. 15, 1521–1532 (2014).

  11. 11.

    et al. Prognostic value of blood mRNA expression signatures in castration-resistant prostate cancer: a prospective, two-stage study. Lancet Oncol. 13, 1114–1124 (2012).

  12. 12.

    et al. Epigenetic markers of prostate cancer in plasma circulating DNA. Hum. Mol. Genet. 21, 3619–3631 (2012).

  13. 13.

    , , , & Histological grade heterogeneity in multifocal prostate cancer. Biological and clinical implications. J. Pathol. 180, 295–299 (1996).

  14. 14.

    et al. Exome sequencing of prostate cancer supports the hypothesis of independent tumour origins. Eur. Urol. 63, 347–353 (2013).

  15. 15.

    et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).

  16. 16.

    et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).

  17. 17.

    et al. RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell Res. 22, 806–821 (2012).

  18. 18.

    et al. Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression. Nat. Biotechnol. 29, 742–749 (2011).

  19. 19.

    et al. Exome sequencing identifies a spectrum of mutation frequencies in advanced and lethal prostate cancers. Proc. Natl. Acad. Sci. USA 108, 17087–17092 (2011).

  20. 20.

    et al. Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell 23, 159–170 (2013).

  21. 21.

    et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013).

  22. 22.

    et al. Synergy of p53 and Rb deficiency in a conditional mouse model for metastatic prostate cancer. Cancer Res. 66, 7889–7898 (2006).

  23. 23.

    , , & Gene amplifications associated with the development of hormone-resistant prostate cancer. Clin. Cancer Res. 9, 5271–5281 (2003).

  24. 24.

    et al. The genetic landscape of high-risk neuroblastoma. Nat. Genet. 45, 279–284 (2013).

  25. 25.

    et al. Characterisation of retinoblastomas without RB1 mutations: genomic, gene expression, and clinical studies. Lancet Oncol. 14, 327–334 (2013).

  26. 26.

    , , & Negative autoregulation of c-Myc transcription. EMBO J. 9, 1113–1121 (1990).

  27. 27.

    et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

  28. 28.

    et al. Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling. J. Pathol. 231, 21–34 (2013).

  29. 29.

    et al. qpure: a tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles. PLoS ONE 7, e45835 (2012).

  30. 30.

    et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

  31. 31.

    et al. The genomic complexity of primary human prostate cancer. Nature 470, 214–220 (2011).

  32. 32.

    et al. The mitochondrial and autosomal mutation landscapes of prostate cancer. Eur. Urol. 63, 702–708 (2013).

  33. 33.

    et al. High frequency of mutations of the PIK3CA gene in human cancers. Science 304, 554 (2004).

  34. 34.

    et al. PIK3CA mutation H1047R is associated with response to PI3K/AKT/mTOR signaling pathway inhibitors in early-phase clinical trials. Cancer Res. 73, 276–284 (2013).

  35. 35.

    et al. Biomarkers of response to Akt inhibitor MK-2206 in breast cancer. Clin. Cancer Res. 18, 5816–5828 (2012).

  36. 36.

    et al. Screening for prostate cancer: systematic review and meta-analysis of randomised controlled trials. BMJ 341, c4543 (2010).

  37. 37.

    et al. Copy number alterations of c-MYC and PTEN are prognostic factors for relapse after prostate cancer radiotherapy. Cancer 118, 4053–4062 (2012).

  38. 38.

    et al. NKX3.1 haploinsufficiency is prognostic for prostate cancer relapse following surgery or image-guided radiotherapy. Clin. Cancer Res. 18, 308–316 (2012).

  39. 39.

    et al. Allelic loss of the loci containing the androgen synthesis gene, StAR, is prognostic for relapse in intermediate-risk prostate cancer. Prostate 72, 1295–1305 (2012).

  40. 40.

    et al. Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue. Nat. Genet. 47, 367–372 (2015).

  41. 41.

    et al. High-resolution array CGH identifies novel regions of genomic alteration in intermediate-risk prostate cancer. Prostate 69, 1091–1100 (2009).

  42. 42.

    et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

  43. 43.

    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).

  44. 44.

    et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).

  45. 45.

    et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

  46. 46.

    et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

  47. 47.

    Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).

  48. 48.

    et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011).

  49. 49.

    , & SeqWare Query Engine: storing and searching sequence data in the cloud. BMC Bioinformatics 11 (suppl. 12), S2 (2010).

  50. 50.

    et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  51. 51.

    et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  52. 52.

    et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  53. 53.

    NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 41, D8–D20 (2013).

  54. 54.

    Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011).

  55. 55.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  56. 56.

    et al. The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes. PLoS ONE 7, e50653 (2012).

  57. 57.

    et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

  58. 58.

    et al. Detecting false-positive signals in exome sequencing. Hum. Mutat. 33, 609–613 (2012).

  59. 59.

    et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011).

  60. 60.

    et al. nFuse: discovery of complex genomic rearrangements in cancer using high-throughput sequencing. Genome Res. 22, 2250–2261 (2012).

  61. 61.

    et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nat. Methods 8, 652–654 (2011).

  62. 62.

    & BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  63. 63.

    , , & Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8, 175–185 (1998).

  64. 64.

    & Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

  65. 65.

    & VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 35 (2011).

Download references


The authors thank all members of the Boutros and Bristow laboratories for helpful suggestions. This study was conducted with the support of Movember funds through Prostate Cancer Canada and with the additional support of the Ontario Institute for Cancer Research, funded by the government of Ontario. This study was conducted with the support of the Ontario Institute for Cancer Research to P.C.B. through funding provided by the government of Ontario. This work has been funded by a Doctoral Fellowship from the Canadian Institutes of Health Research (CIHR) to E.L. The authors gratefully thank the Princess Margaret Cancer Centre Foundation and the Radiation Medicine Program Academic Enrichment Fund for support (to R.G.B.). R.G.B. is a recipient of a Canadian Cancer Society Research Scientist Award. This work was supported by Prostate Cancer Canada and is proudly funded by the Movember Foundation, grant RS2014-01. P.C.B. was supported by a Terry Fox Research Institute New Investigator Award and a CIHR New Investigator Award. This project was supported by Genome Canada through a Large-Scale Applied Project contract to P.C.B., S.P.S. and R. Morin.

Author information

Author notes

    • Michael Fraser
    • , Nicholas J Harding
    • , Richard de Borja
    •  & Dominique Trudel

    These authors contributed equally to this work.


  1. Ontario Institute for Cancer Research, Toronto, Ontario, Canada.

    • Paul C Boutros
    • , Nicholas J Harding
    • , Richard de Borja
    • , Emilie Lalonde
    • , Pablo H Hennings-Yeomans
    • , Veronica Y Sabelnykova
    • , Amin Zia
    • , Natalie S Fox
    • , Julie Livingstone
    • , Yu-Jia Shiah
    • , Jianxin Wang
    • , Timothy A Beck
    • , Taryne Chong
    • , Michelle Sam
    • , Jeremy Johns
    • , Lee Timms
    • , Nicholas Buchner
    • , Ada Wong
    • , John D Watson
    • , Trent T Simmons
    • , Christine P'ng
    • , Francis Nguyen
    • , Xuemei Luo
    • , Kenneth C Chu
    • , Stephenie D Prokopec
    • , Andrew Brown
    • , Michelle A Chan-Seng-Yue
    • , Fouad Yousif
    • , Robert E Denroche
    • , Lauren C Chong
    • , Gregory M Chen
    • , Esther Jung
    • , Clement Fung
    • , Maud H W Starmans
    • , Hanbo Chen
    • , Shaylan K Govind
    • , James Hawley
    • , Alister D'Costa
    • , Daryl Waggott
    • , Lakshmi B Muthuswamy
    • , Lincoln D Stein
    • , Thomas J Hudson
    •  & John D McPherson
  2. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

    • Paul C Boutros
    • , Emilie Lalonde
    • , Natalie S Fox
    •  & Robert G Bristow
  3. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.

    • Paul C Boutros
    •  & Alice Meng
  4. Ontario Cancer Institute, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

    • Michael Fraser
    • , Gaetano Zafarana
    •  & Robert G Bristow
  5. Department of Pathology and Laboratory Medicine, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.

    • Dominique Trudel
    • , Cherry L Have
    •  & Theodorus van der Kwast
  6. School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.

    • Andrew McPherson
    • , Faraz Hach
    •  & Cenk Sahinalp
  7. Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

    • Jenna Sykes
    •  & Melania Pintilie
  8. Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.

    • Alan Dal Pra
    • , Alejandro Berlin
    •  & Robert G Bristow
  9. Department of Radiotherapy, Maastricht University, Maastricht, the Netherlands.

    • Philippe Lambin
  10. Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK.

    • Colin Cooper
    •  & Rosalind Eeles
  11. Department of Biological Sciences, University of East Anglia, Norwich, UK.

    • Colin Cooper
  12. School of Medicine, University of East Anglia, Norwich, UK.

    • Colin Cooper
  13. Royal Marsden National Health Service (NHS) Foundation Trust, London and Sutton, UK.

    • Rosalind Eeles
  14. Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK.

    • David Neal
  15. Department of Surgical Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.

    • David Neal
  16. Department of Pathology, Laval University, Quebec City, Quebec, Canada.

    • Bernard Tetu
  17. Division of Urology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

    • Neil Fleshner
  18. Department of Pathology, University of British Columbia, Vancouver, British Columbia, Canada.

    • Sohrab P Shah
  19. Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada.

    • Sohrab P Shah
  20. British Columbia Cancer Agency Research Centre, Vancouver, British Columbia, Canada.

    • Sohrab P Shah
  21. Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.

    • Colin C Collins
  22. Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada.

    • Colin C Collins


  1. Search for Paul C Boutros in:

  2. Search for Michael Fraser in:

  3. Search for Nicholas J Harding in:

  4. Search for Richard de Borja in:

  5. Search for Dominique Trudel in:

  6. Search for Emilie Lalonde in:

  7. Search for Alice Meng in:

  8. Search for Pablo H Hennings-Yeomans in:

  9. Search for Andrew McPherson in:

  10. Search for Veronica Y Sabelnykova in:

  11. Search for Amin Zia in:

  12. Search for Natalie S Fox in:

  13. Search for Julie Livingstone in:

  14. Search for Yu-Jia Shiah in:

  15. Search for Jianxin Wang in:

  16. Search for Timothy A Beck in:

  17. Search for Cherry L Have in:

  18. Search for Taryne Chong in:

  19. Search for Michelle Sam in:

  20. Search for Jeremy Johns in:

  21. Search for Lee Timms in:

  22. Search for Nicholas Buchner in:

  23. Search for Ada Wong in:

  24. Search for John D Watson in:

  25. Search for Trent T Simmons in:

  26. Search for Christine P'ng in:

  27. Search for Gaetano Zafarana in:

  28. Search for Francis Nguyen in:

  29. Search for Xuemei Luo in:

  30. Search for Kenneth C Chu in:

  31. Search for Stephenie D Prokopec in:

  32. Search for Jenna Sykes in:

  33. Search for Alan Dal Pra in:

  34. Search for Alejandro Berlin in:

  35. Search for Andrew Brown in:

  36. Search for Michelle A Chan-Seng-Yue in:

  37. Search for Fouad Yousif in:

  38. Search for Robert E Denroche in:

  39. Search for Lauren C Chong in:

  40. Search for Gregory M Chen in:

  41. Search for Esther Jung in:

  42. Search for Clement Fung in:

  43. Search for Maud H W Starmans in:

  44. Search for Hanbo Chen in:

  45. Search for Shaylan K Govind in:

  46. Search for James Hawley in:

  47. Search for Alister D'Costa in:

  48. Search for Melania Pintilie in:

  49. Search for Daryl Waggott in:

  50. Search for Faraz Hach in:

  51. Search for Philippe Lambin in:

  52. Search for Lakshmi B Muthuswamy in:

  53. Search for Colin Cooper in:

  54. Search for Rosalind Eeles in:

  55. Search for David Neal in:

  56. Search for Bernard Tetu in:

  57. Search for Cenk Sahinalp in:

  58. Search for Lincoln D Stein in:

  59. Search for Neil Fleshner in:

  60. Search for Sohrab P Shah in:

  61. Search for Colin C Collins in:

  62. Search for Thomas J Hudson in:

  63. Search for John D McPherson in:

  64. Search for Theodorus van der Kwast in:

  65. Search for Robert G Bristow in:


Sample preparation and molecular biology: M.F., A. Meng, T.C., M.S., C.L.H., J.J., L.T., N.B., A.W., J.D.W., T.T.S., G.Z., A.D.P., A. Berlin, S.D.P. and A. Brown. Pathology analyses: D.T., B.T. and T.v.d.K. Statistics and bioinformatics: P.C.B., N.J.H., R.d.B., E.L., P.H.H.-Y., A. McPherson, V.Y.S., A.Z., N.S.F., J.L., Y.-J.S., J.W., T.A.B., T.T.S., C.P., F.N., X.L., K.C.C., J.S., M.A.C.-S.-Y., F.Y., R.E.D., L.C.C., G.M.C., E.J., M.H.W.S., H.C., S.K.G., J.H., A.D., M.P., C.F., F.H. and D.W. Initiation of the project: P.C.B., M.F., C.C., T.J.H., J.D.M., T.v.d.K., R.E., D.N. and R.G.B. Supervision of research: P.C.B., M.F., T.A.B., P.L., L.B.M., B.T., C.C.C., L.D.S., N.F., S.P.S., C.S., T.J.H., L.B.M., T.v.d.K. and R.G.B. Writing of the first draft of the manuscript: P.C.B. Writing and editing the revised manuscript: M.F., P.C.B. and R.G.B. All authors approved the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Paul C Boutros or Robert G Bristow.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–24.

Excel files

  1. 1.

    Supplementary Table 1

    GeneWise CNA profiles for all patients. For each sample that received OncoScan SNP array interrogation of copy number aberrations (n = 75), this table gives for each gene whether it is amplified (1), deleted (–1) or unchanged (0). Additionally, each gene is annotated with the Ensembl gene and transcript IDs, the chromosome, the starting and ending base pairs, and the gene symbols from both HUGO and HGNC.

  2. 2.

    Supplementary Table 2

    Regions of recurrent CNAs. GISTIC analysis of copy number array data identified regions of recurrent copy number alteration (rows). The columns give the name for each region, its chromosomal location (both arm and precise coordinates and probes involved) and statistical support (q values and amplitude estimates). For each patient, a coding of 0 (no event) versus 1/2 (event) is given.

  3. 3.

    Supplementary Table 3

    GISTIC genes. Genes identified in recurrent GISTIC peaks are listed, along with their individual locations, Cytobands, q values and gene symbols are given.

  4. 4.

    Supplementary Table 4

    Validation of MYCL1 and MYC amplification. We performed quantitative PCR using probes directed to the putatively amplified regions of either MYCL1 or MYC, using a probe directed against RPPH1 (RNase P, component H) as a control gene. Overall validation rates are shown.

  5. 5.

    Supplementary Table 5

    Summary of flanking qPCR. We performed qPCR analysis using the indicated probes, which flank the MYCL1 locus (which encompasses the probe shown in yellow) over a region of ~2 Mb. NCI-H510A non–small cell lung cancer cells were used as a positive control for MYCL1 amplification, as these cells contain a ~2.9-Mb amplification of chromosome 1p, including the entire region covered by these probes. PC3 prostate cancer cells were used as a negative control.

  6. 6.

    Supplementary Table 6

    Genomic instability associated with MYC family gain. For each MYC family member, we assessed the mean, median and standard deviation of PGA and the total number of CNAs detected.

  7. 7.

    Supplementary Table 7

    Differential CNAs associated with MYCL1 amplification. For each gene, we compared its frequency of CNA in MYCL1-amplified tumors and in MYC-amplified tumors. This table shows gene ID (both Ensembl gene and transcript) along with gene symbols and genomic location. It lists the frequency of occurrence in MYCL1-amplified tumors, the frequency of occurrence in MYC-amplified tumors, the P value from a proportion test and the multiple testing–adjusted q value.

  8. 8.

    Supplementary Table 8

    MYCL1-associated transcriptome dysregulation. Comparison of tumors harboring MYCL1 amplifications (n = 8) and those without (n = 16) identified 294 genes showing differential abundance (q < 0.05, Bayesian-moderated t test; Online Methods). A list of gene symbols for these genes is given.

  9. 9.

    Supplementary Table 9

    Patient annotation. Key clinical information about each patient, including age at time of treatment, diagnostic Gleason score, clinical T category, biochemical recurrence status and ERG fusion status.

  10. 10.

    Supplementary Table 10

    Tumor cellularity analysis. For each tumor sample subjected to whole-genome sequencing, tumor cellularity was assessed both by a urological pathologist (CellularityPath) and the Qpure algorithm executed on SNP microarray data (CellularityQpure).

  11. 11.

    Supplementary Table 11

    Sequencing statistics. Overview of whole-genome sequencing. For each tumor and region, the collapsed coverage values for blood (replicated for each region) and tumor are given, along with the input material type for the tumor sequencing and the numbers of SNVs (of various functional categories), CNAs and genomic rearrangements. The number of somatic events in FFPE samples is elevated, likely owing to artifacts of the FFPE procedure.

  12. 12.

    Supplementary Table 12

    All genomic rearrangements. All detected genomic rearrangements, along with their chromosomal positions and a categorization of the rearrangement type, genes involved and the score output from the deStruct algorithm.

  13. 13.

    Supplementary Table 13

    Functional SNVs. All detected functional somatic SNVs, along with their genomic locations, base change and status in each sequenced tumor region.

  14. 14.

    Supplementary Table 14

    WGA effects. Comparison of samples with and without WGA amplification based on the identity of SNPs detected by the OncoScan microarray platform.

  15. 15.

    Supplementary Table 15

    Pathway analysis of MYCL1-associated mRNA differences. The GOEAST tool was used to assess functional enrichment among genes showing different mRNA abundance in MYCL1-amplified and MYCL1-neutral tumors.

  16. 16.

    Supplementary Table 16

    Effects of WGA on SNP array performance. Comparison of concordance of SNP calls between matched WGA and non-WGA specimens on the OncoScan array platform.

About this article

Publication history





Further reading

Newsletter Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing