Spatial genomic heterogeneity within localized, multifocal prostate cancer (original) (raw)

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1. Mohler, J. et al. NCCN clinical practice guidelines in oncology: prostate cancer. J. Natl. Compr. Canc. Netw. 8, 162–200 (2010).
   Article CAS PubMed Google Scholar
2. D'Amico, A.V. 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).
   Article PubMed Google Scholar
3. Buyyounouski, M.K., Pickles, T., Kestin, L.L., Allison, R. & Williams, S.G. Validating the interval to biochemical failure for the identification of potentially lethal prostate cancer. J. Clin. Oncol. 30, 1857–1863 (2012).
   Article PubMed Google Scholar
4. Villers, A., McNeal, J.E., Freiha, F.S. & Stamey, T.A. Multiple cancers in the prostate. Morphologic features of clinically recognized versus incidental tumors. Cancer 70, 2313–2318 (1992).
   Article CAS PubMed Google Scholar
5. Nichol, A.M., Warde, P. & Bristow, R.G. Optimal treatment of intermediate-risk prostate carcinoma with radiotherapy: clinical and translational issues. Cancer 104, 891–905 (2005).
   Article PubMed Google Scholar
6. Taylor, B.S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).
   Article CAS PubMed PubMed Central Google Scholar
7. Lapointe, J. et al. Genomic profiling reveals alternative genetic pathways of prostate tumorigenesis. Cancer Res. 67, 8504–8510 (2007).
   Article CAS PubMed Google Scholar
8. Paris, P.L. et al. Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors. Hum. Mol. Genet. 13, 1303–1313 (2004).
   Article CAS PubMed Google Scholar
9. Penney, K.L. et al. mRNA expression signature of Gleason grade predicts lethal prostate cancer. J. Clin. Oncol. 29, 2391–2396 (2011).
   Article CAS PubMed PubMed Central Google Scholar
10. Lalonde, E. 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).
    Article PubMed Google Scholar
11. Olmos, D. 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).
    Article CAS PubMed PubMed Central Google Scholar
12. Cortese, R. et al. Epigenetic markers of prostate cancer in plasma circulating DNA. Hum. Mol. Genet. 21, 3619–3631 (2012).
    Article CAS PubMed Google Scholar
13. Ruijter, E.T., van de Kaa, C.A., Schalken, J.A., Debruyne, F.M. & Ruiter, D.J. Histological grade heterogeneity in multifocal prostate cancer. Biological and clinical implications. J. Pathol. 180, 295–299 (1996).
    Article CAS PubMed Google Scholar
14. Lindberg, J. et al. Exome sequencing of prostate cancer supports the hypothesis of independent tumour origins. Eur. Urol. 63, 347–353 (2013).
    Article CAS PubMed Google Scholar
15. Grasso, C.S. et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).
    Article CAS PubMed PubMed Central Google Scholar
16. Barbieri, C.E. et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).
    Article CAS PubMed PubMed Central Google Scholar
17. Ren, S. 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).
    Article CAS PubMed PubMed Central Google Scholar
18. Prensner, J.R. 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).
    Article CAS PubMed PubMed Central Google Scholar
19. Kumar, A. 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).
    Article CAS PubMed PubMed Central Google Scholar
20. Weischenfeldt, J. et al. Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell 23, 159–170 (2013).
    Article CAS PubMed Google Scholar
21. Baca, S.C. et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013).
    Article CAS PubMed PubMed Central Google Scholar
22. Zhou, Z. et al. Synergy of p53 and Rb deficiency in a conditional mouse model for metastatic prostate cancer. Cancer Res. 66, 7889–7898 (2006).
    Article CAS PubMed Google Scholar
23. Edwards, J., Krishna, N.S., Witton, C.J. & Bartlett, J.M. Gene amplifications associated with the development of hormone-resistant prostate cancer. Clin. Cancer Res. 9, 5271–5281 (2003).
    CAS PubMed Google Scholar
24. Pugh, T.J. et al. The genetic landscape of high-risk neuroblastoma. Nat. Genet. 45, 279–284 (2013).
    Article CAS PubMed PubMed Central Google Scholar
25. Rushlow, D.E. et al. Characterisation of retinoblastomas without RB1 mutations: genomic, gene expression, and clinical studies. Lancet Oncol. 14, 327–334 (2013).
    Article CAS PubMed Google Scholar
26. Penn, L.J., Brooks, M.W., Laufer, E.M. & Land, H. Negative autoregulation of c-Myc transcription. EMBO J. 9, 1113–1121 (1990).
    Article CAS PubMed PubMed Central Google Scholar
27. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
    Article CAS PubMed PubMed Central Google Scholar
28. Bashashati, A. et al. Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling. J. Pathol. 231, 21–34 (2013).
    Article CAS PubMed PubMed Central Google Scholar
29. Song, S. et al. qpure: a tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles. PLoS ONE 7, e45835 (2012).
    Article CAS PubMed PubMed Central Google Scholar
30. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).
    CAS PubMed PubMed Central Google Scholar
31. Berger, M.F. et al. The genomic complexity of primary human prostate cancer. Nature 470, 214–220 (2011).
    Article CAS PubMed PubMed Central Google Scholar
32. Lindberg, J. et al. The mitochondrial and autosomal mutation landscapes of prostate cancer. Eur. Urol. 63, 702–708 (2013).
    Article CAS PubMed Google Scholar
33. Samuels, Y. et al. High frequency of mutations of the PIK3CA gene in human cancers. Science 304, 554 (2004).
    Article CAS PubMed Google Scholar
34. Janku, F. 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).
    Article CAS PubMed Google Scholar
35. Sangai, T. et al. Biomarkers of response to Akt inhibitor MK-2206 in breast cancer. Clin. Cancer Res. 18, 5816–5828 (2012).
    Article CAS PubMed PubMed Central Google Scholar
36. Djulbegovic, M. et al. Screening for prostate cancer: systematic review and meta-analysis of randomised controlled trials. BMJ 341, c4543 (2010).
    Article PubMed PubMed Central Google Scholar
37. Zafarana, G. et al. Copy number alterations of c-MYC and PTEN are prognostic factors for relapse after prostate cancer radiotherapy. Cancer 118, 4053–4062 (2012).
    Article CAS PubMed Google Scholar
38. Locke, J.A. et al. NKX3.1 haploinsufficiency is prognostic for prostate cancer relapse following surgery or image-guided radiotherapy. Clin. Cancer Res. 18, 308–316 (2012).
    Article CAS PubMed Google Scholar
39. Locke, J.A. 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).
    Article CAS PubMed Google Scholar
40. Cooper, C.S. 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).
    Article CAS PubMed PubMed Central Google Scholar
41. Ishkanian, A.S. et al. High-resolution array CGH identifies novel regions of genomic alteration in intermediate-risk prostate cancer. Prostate 69, 1091–1100 (2009).
    Article CAS PubMed Google Scholar
42. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
    Article CAS PubMed PubMed Central Google Scholar
43. 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 PubMed PubMed Central CAS Google Scholar
44. Dai, M. et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).
    Article PubMed PubMed Central CAS Google Scholar
45. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).
    Article PubMed PubMed Central Google Scholar
46. Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).
    Article PubMed PubMed Central CAS Google Scholar
47. Smyth, G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).
    Article PubMed Google Scholar
48. Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011).
    Article PubMed PubMed Central Google Scholar
49. O'Connor, B.D., Merriman, B. & Nelson, S.F. SeqWare Query Engine: storing and searching sequence data in the cloud. BMC Bioinformatics 11 (suppl. 12), S2 (2010).
    Article PubMed PubMed Central Google Scholar
50. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
    Article CAS PubMed PubMed Central Google Scholar
51. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    Article CAS PubMed PubMed Central Google Scholar
52. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
    Article CAS PubMed PubMed Central Google Scholar
53. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 41, D8–D20 (2013).
54. Li, H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011).
    Article PubMed PubMed Central CAS Google Scholar
55. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
    Article PubMed PubMed Central CAS Google Scholar
56. Ouedraogo, M. et al. The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes. PLoS ONE 7, e50653 (2012).
    Article CAS PubMed PubMed Central Google Scholar
57. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).
    Article CAS PubMed PubMed Central Google Scholar
58. Fuentes Fajardo, K.V. et al. Detecting false-positive signals in exome sequencing. Hum. Mutat. 33, 609–613 (2012).
    Article CAS PubMed Google Scholar
59. Forbes, S.A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011).
    Article CAS PubMed Google Scholar
60. McPherson, A. et al. nFuse: discovery of complex genomic rearrangements in cancer using high-throughput sequencing. Genome Res. 22, 2250–2261 (2012).
    Article CAS PubMed PubMed Central Google Scholar
61. Wang, J. et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nat. Methods 8, 652–654 (2011).
    Article CAS PubMed PubMed Central Google Scholar
62. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
    CAS PubMed PubMed Central Google Scholar
63. Ewing, B., Hillier, L., Wendl, M.C. & Green, P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8, 175–185 (1998).
    Article CAS PubMed Google Scholar
64. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).
    Article PubMed PubMed Central CAS Google Scholar
65. Chen, H. & Boutros, P.C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 35 (2011).
    Article PubMed PubMed Central Google Scholar

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Acknowledgements

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.

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Author notes

1. Michael Fraser, Nicholas J Harding, Richard de Borja and Dominique Trudel: These authors contributed equally to this work.

Authors and Affiliations

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

Authors

1. Paul C Boutros
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2. Michael Fraser
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3. Nicholas J Harding
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4. Richard de Borja
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5. Dominique Trudel
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6. Emilie Lalonde
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7. Alice Meng
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8. Pablo H Hennings-Yeomans
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9. Andrew McPherson
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10. Veronica Y Sabelnykova
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11. Amin Zia
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12. Natalie S Fox
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13. Julie Livingstone
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14. Yu-Jia Shiah
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15. Jianxin Wang
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16. Timothy A Beck
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17. Cherry L Have
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18. Taryne Chong
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19. Michelle Sam
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20. Jeremy Johns
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21. Lee Timms
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22. Nicholas Buchner
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23. Ada Wong
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24. John D Watson
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25. Trent T Simmons
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26. Christine P'ng
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27. Gaetano Zafarana
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28. Francis Nguyen
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29. Xuemei Luo
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30. Kenneth C Chu
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31. Stephenie D Prokopec
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32. Jenna Sykes
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33. Alan Dal Pra
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34. Alejandro Berlin
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35. Andrew Brown
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36. Michelle A Chan-Seng-Yue
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37. Fouad Yousif
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38. Robert E Denroche
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39. Lauren C Chong
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40. Gregory M Chen
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41. Esther Jung
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42. Clement Fung
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43. Maud H W Starmans
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44. Hanbo Chen
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45. Shaylan K Govind
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46. James Hawley
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47. Alister D'Costa
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48. Melania Pintilie
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49. Daryl Waggott
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50. Faraz Hach
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51. Philippe Lambin
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52. Lakshmi B Muthuswamy
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53. Colin Cooper
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54. Rosalind Eeles
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55. David Neal
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56. Bernard Tetu
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57. Cenk Sahinalp
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58. Lincoln D Stein
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59. Neil Fleshner
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60. Sohrab P Shah
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61. Colin C Collins
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62. Thomas J Hudson
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63. John D McPherson
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64. Theodorus van der Kwast
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65. Robert G Bristow
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Contributions

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.

Corresponding authors

Correspondence toPaul C Boutros or Robert G Bristow.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–24. (PDF 3665 kb)

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. (XLS 26652 kb)

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. (XLS 80 kb)

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. (XLS 709 kb)

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. (XLS 24 kb)

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. (XLS 22 kb)

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. (XLS 19 kb)

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. (XLS 1686 kb)

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. (XLS 37 kb)

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. (XLS 37 kb)

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). (XLS 26 kb)

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. (XLS 25 kb)

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. (XLS 275 kb)

Supplementary Table 13

Functional SNVs. All detected functional somatic SNVs, along with their genomic locations, base change and status in each sequenced tumor region. (XLS 398 kb)

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. (XLS 17 kb)

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. (XLS 80 kb)

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. (XLS 30 kb)

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Boutros, P., Fraser, M., Harding, N. et al. Spatial genomic heterogeneity within localized, multifocal prostate cancer.Nat Genet 47, 736–745 (2015). https://doi.org/10.1038/ng.3315

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• Received: 22 December 2014
• Accepted: 01 May 2015
• Published: 25 May 2015
• Issue Date: July 2015
• DOI: https://doi.org/10.1038/ng.3315