Genomic hallmarks of localized, non-indolent prostate cancer (original) (raw)

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References

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Acknowledgements

We thank all members of the Boutros and Bristow labs for helpful suggestions, particularly C. M. Lalansingh for technical assistance with recurrent SNV analysis. The results described here are based in part upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. This study was conducted with the support of Movember through Prostate Cancer Canada and with the additional support of the Ontario Institute for Cancer Research, funded by the Government of Ontario, and of the Ontario Institute for Cancer Research to P.C.B. through funding from the Government of Ontario. We thank the Princess Margaret Cancer Centre Foundation and 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 to P.C.B.). P.C.B. was supported by a Terry Fox Research Institute New Investigator Award and a CIHR New Investigator Award. H.H.H. was supported by CIHR operating grant 142246 and CCSRI grant 703800. This project was supported by Genome Canada through a Large-Scale Applied Project contract to P.C.B., R. Morin and S. P. Shah. D.T. was part of the Terry Fox Foundation Strategic Health Research Training Program in Cancer Research at the Canadian Institute of Health Research and Ontario Institute for Cancer Research. E.L. was supported by a CIHR Fellowship. N.S.F. was supported by an NSERC Fellowship.

Author information

Author notes

1. Dominique Trudel & Alan Dal Pra
   Present address: †Present Addresses: Department of Pathology and Cancer Axis, Centre Hospitalier de l’Université de Montréal, Montréal, Canada (D.T.); Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 4, CH-3010 Bern, Switzerland. (A.D.P.).,
2. Michael Fraser, Veronica Y. Sabelnykova, Takafumi N. Yamaguchi, Lawrence E. Heisler, Julie Livingstone, Vincent Huang and Yu-Jia Shiah: These authors contributed equally to this work.

Authors and Affiliations

1. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
   Michael Fraser, Musaddeque Ahmed, Alice Meng, Junyan Zhang, Alexander Murison, Ken Kron, Mathieu Lupien, Housheng H. He & Robert G. Bristow
2. Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada
   Veronica Y. Sabelnykova, Takafumi N. Yamaguchi, Lawrence E. Heisler, Julie Livingstone, Vincent Huang, Yu-Jia Shiah, Fouad Yousif, Xihui Lin, Andre P. Masella, Natalie S. Fox, Michael Xie, Stephenie D. Prokopec, Emilie Lalonde, Xuemei Luo, Timothy A. Beck, Alister D’Costa, Robert E. Denroche, Haiying Kong, Shadrielle Melijah G. Espiritu, Nicholas J. Harding, Christine P’ng, Kathleen E. Houlahan, Kenneth C. Chu, Bryan Lo, Francis Nguyen, Constance H. Li, Ren X. Sun, Richard de Borja, Christopher I. Cooper, Julia F. Hopkins, Shaylan K. Govind, Clement Fung, Daryl Waggott, Jeffrey Green, Syed Haider, Michelle A. Chan-Seng-Yue, Esther Jung, Zhiyuan Wang & Paul C. Boutros
3. Department of Medical Biophysics, University of Toronto, Toronto, Canada
   Natalie S. Fox, Emilie Lalonde, Constance H. Li, Mathieu Lupien, Housheng H. He, John D. McPherson, Robert G. Bristow & Paul C. Boutros
4. Department of Radiation Oncology, University of Toronto, Toronto, Canada
   Alejandro Berlin, Melvin L. K. Chua, Alan Dal Pra & Robert G. Bristow
5. Department of Pathology and Laboratory Medicine, Toronto General Hospital/University Health Network, Toronto, Canada
   Dominique Trudel & Theodorus van der Kwast
6. Genome Technologies Program, Ontario Institute for Cancer Research, Toronto, Canada
   Ada Wong, Taryne Chong, Michelle Sam, Jeremy Johns, Lee Timms, Nicholas B. Buchner & John D. McPherson
7. Department of Pathology and Research Centre of CHU de Québec-Université Laval, Québec City, Canada
   Michèle Orain & Bernard Tetu
8. Division of Urology and Research Centre of CHU de Québec-Université Laval, Québec City, Canada
   Valérie Picard, Helène Hovington, Alain Bergeron, Louis Lacombe & Yves Fradet
9. Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
   Ren X. Sun & Paul C. Boutros
10. Department of Urologic Sciences, University of British Columbia, Vancouver, Canada
    Colin C. Collins
11. Vancouver Prostate Centre, Vancouver, Canada
    Colin C. Collins
12. School of Computing Science, Simon Fraser University, Burnaby, Canada
    Cenk Sahinalp
13. Division of Urology, Princess Margaret Cancer Centre/University Health Network, Toronto, Canada
    Neil E. Fleshner

Authors

1. Michael Fraser
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2. Veronica Y. Sabelnykova
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3. Takafumi N. Yamaguchi
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4. Lawrence E. Heisler
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5. Julie Livingstone
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6. Vincent Huang
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7. Yu-Jia Shiah
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8. Fouad Yousif
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9. Xihui Lin
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10. Andre P. Masella
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11. Natalie S. Fox
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12. Michael Xie
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13. Stephenie D. Prokopec
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14. Alejandro Berlin
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15. Emilie Lalonde
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16. Musaddeque Ahmed
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17. Dominique Trudel
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18. Xuemei Luo
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19. Timothy A. Beck
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20. Alice Meng
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21. Junyan Zhang
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22. Alister D’Costa
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23. Robert E. Denroche
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24. Haiying Kong
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25. Shadrielle Melijah G. Espiritu
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26. Melvin L. K. Chua
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27. Ada Wong
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28. Taryne Chong
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29. Michelle Sam
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30. Jeremy Johns
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31. Lee Timms
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32. Nicholas B. Buchner
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33. Michèle Orain
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34. Valérie Picard
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35. Helène Hovington
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36. Alexander Murison
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37. Ken Kron
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38. Nicholas J. Harding
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39. Christine P’ng
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40. Kathleen E. Houlahan
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41. Kenneth C. Chu
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42. Bryan Lo
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43. Francis Nguyen
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44. Constance H. Li
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45. Ren X. Sun
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46. Richard de Borja
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47. Christopher I. Cooper
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48. Julia F. Hopkins
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49. Shaylan K. Govind
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50. Clement Fung
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51. Daryl Waggott
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52. Jeffrey Green
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53. Syed Haider
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54. Michelle A. Chan-Seng-Yue
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55. Esther Jung
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56. Zhiyuan Wang
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57. Alain Bergeron
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58. Alan Dal Pra
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59. Louis Lacombe
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60. Colin C. Collins
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61. Cenk Sahinalp
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62. Mathieu Lupien
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63. Neil E. Fleshner
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64. Housheng H. He
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65. Yves Fradet
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66. Bernard Tetu
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67. Theodorus van der Kwast
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68. John D. McPherson
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69. Robert G. Bristow
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70. Paul C. Boutros
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Contributions

Sample preparation and data collection: M.F., A.B., A.M., J.Z., M.C., A.W., T.C., M.S., J.J., L.T., N.B.B., M.O., V.P., H.H., A.B., A.D.P., M.A. and K.K. Pathology analyses: D.T., B.T. and T.v.d.K. Statistical and bioinformatics analyses: V.Y.S., T.N.Y., L.E.H., J.L., V.H., Y.-J.S., F.Y., X.L., A.P.M., N.S.F., M.X., S.D.P., E.L., X.L., T.A.B., A.D., R.E.D., H.K., S.M.G.E., N.J.H., C.P., K.E.H., K.C.C., B.L., F.N., C.H.L., R.X.S., R.d.B., C.I.C., J.F.H., S.K.G., C.F., D.W., J.G., S.H., M.A.C.-S.-Y., E.J., Z.W., M.A., A.M., K.K. and H.H.H. Wrote the first draft of the manuscript: M.F., R.G.B. and P.C.B. Initiated the project: M.F., C.C.C., T.v.d.K., J.D.M., R.G.B. and P.C.B. Supervised research: T.A.B., L.L., C.C.C., C.S., N.E.F., Y.F., B.T., M.L., H.H.H., T.v.d.K., J.D.M., R.G.B. and P.C.B. Approved the manuscript: all authors.

Corresponding authors

Correspondence toRobert G. Bristow or Paul C. Boutros.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information

Nature thanks S. Chanock, C. Plass and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Study design.

The overall study cohort consisted of 137 patients who underwent radical prostatectomy (surgery) and 147 patients who underwent image-guided radiotherapy for localized prostate cancer (biopsy). For surgery patients, a fresh-frozen tissue specimen from the index lesion was obtained for macro-dissection. For radiotherapy patients, a fresh-frozen needle core ultrasound-guided biopsy to the index lesion was obtained for macro-dissection. All 284 tumour DNA specimens were analysed for CNA by OncoScan SNP arrays. Of these tumour DNA specimens, 130 were selected for further analysis by WGS (as was a matched normal DNA specimen from whole blood). For a subset of analyses, additional data (numbers as indicated) from publicly available whole-genome or whole-exome sequencing data sets were re-aligned and re-analysed and integrated to maximize statistical power.

Source data

Extended Data Figure 2 Comparison of molecular aberrations.

a, Pairwise comparison scatter plot of data type as indicated on the _x_- and _y_-axes. Spearman correlation and unadjusted P values are provided. b, Scatterplots and box plots of each mutation burden (CNA, CTX, INV, SNV counts and PGA) versus clinical variables (age, GS, T-category, PSA and ETS consensus fusion) is provided along with a model-derived P value, as described in Methods. Grey dots represent values for individual samples.

Source data

Extended Data Figure 3 Non-coding SNV profile.

We analysed 70 non-coding recurrent somatic SNVs: defined as at least 2% (4 of 200) of tumours having mutations in the same, non-coding position. a, The central heat map shows the 70 recurrent ncSNVs (rows) and the samples in which they are present (columns), with colour indicating their variant allele frequency (VAF). The top bar plot indicates the total number of ncSNVs mutated in each sample, while the right bar plot gives the total number of samples in which each ncSNV is mutated. b, Box plot showing VAF for recurrent ncSNVs. Each dot indicates the VAF of a recurrent ncSNV for a sample. The recurrent ncSNVs (rows) were sorted by median VAF. c, To determine whether ncSNVs were biased towards specific TFBSs, we tested whether experimentally derived TFBS locations from ENCODE were enriched for aberrations of different types using the binomial test. Heatmap of 58 TFBS cell lines for each sample coloured by the data type or combination of data types (SNV, CNV, and CTX flanked by 10 kbp) if it was aberrant in more samples than expected by chance (binomial test with FDR-adjusted P value). The samples are ordered by the number of significantly aberrant TFBSs (top barplot), the TFBS cell lines are ordered by fraction of samples with significantly mutated TFBSs by cell line (right barplot), covariates of pathological GS, pre-treatment PSA, T-category, and patient age at treatment are displayed at the bottom. d, Predicted chromatin effects of recurrent ncSNVs. The left heat map shows E-values, which measure the expected proportion of SNPs (found in the 1,000 Genomes Project) with a larger predicted effect for a chromatin feature, predicted by DeepSEA. The right heat map shows the overlaps between chromatin elements detected by LNCaP chromatin immunoprecipitation with sequencing (ChIP–seq) experiments and ncSNVs. The FDR adjusted P values (Q values) for the DeepSEA or ChiP–seq experiment features are shown above each plot. The ncSNV Q values for DeepSEA and ncSNV recurrence are shown on the right. Experimental conditions (cell line type, chromatin feature, and treatment) of the ChIP–seq data are represented by the covariates at the bottom. The heatmaps and barplots were sorted by Q values.

Source data

Extended Data Figure 4 Genome rearrangements overview.

a, Global overview of somatic structural variants in 180 localized GS 3 + 3, 3 + 4 and 4 + 3 prostate cancers. The central heat map shows per-sample inter-chromosomal translocations (CTXs), inversions and deletions for 1-Mbp bins across the genome (columns) and for each patient (rows). The striking TMPRSS2:ERG peak on chromosome 21 is by far the most frequent aberration, but additional recurrent inversion breakpoints were identified on chromosomes 3 and 10, and CTX breakpoints on chromosome 6. b, Number of CTXs joining each chromosome pair and their occurrences relative to random chance. Dot size represents the number of translocations enriched (number greater than expected) while background colour indicates their significance as calculated using a one-tailed permutation test (1 million replicates) with FDR correction. c, Mean shortest distance between a CTX and the corresponding nearest HiC point in each chromosome pair. Dot size represents the difference between the mean observed CTX–HiC distances and their expected distances, while the background indicates significance as calculated using a one-tailed permutation test (1 million replicates) corrected using the FDR method. Orange dots indicate distances greater than expected by chance alone (top right), while blue dots show distances smaller than expected by chance alone (bottom left).

Source data

Extended Data Figure 5 Effects of inversion on mRNA abundance and PTEN.

a, For each gene in the inversion window (chr10:89–90 Mbp), mRNA abundance levels were re-normalized and centred by the median across all patients. Box plot (top) demonstrates the renormalized mRNA abundance levels (_y_-axis) of patients with no inversion (n = 70, orange) and with inversions (n = 3, green) for each gene. A linear model was used to calculate the P values between the two patient groups. Bar plot (bottom) shows unadjusted P values with genes ordered by chromosome location. b, Spearman’s ρ was used to identify the top ten genes most correlated with PTEN mRNA abundances. The per sample mean mRNA abundances of the ten genes was used to represent the overall effects of various types of PTEN inactivation. PTEN inactivation as a result of CNV loss led to a significantly lower abundance of PTEN-associated proteins when compared to copy number-neutral PTEN (Mann–Whitney U test, P = 2.0 × 10−4) whereas PTEN inversions yielded further reduced abundances (Mann–Whitney U test, P = 0.016). c, For each gene in the inversion window (chr3:129–130 Mbp), mRNA abundance levels were re-normalized and centred by the median across all patients. Box plot (top) shows the renormalized mRNA abundance levels (_y_-axis) of patients with no inversion (n = 65, orange) or with inversions (n = 8, green) for each gene. A linear model was used to calculate P values between the two patient groups. Bar plot (bottom) shows the P values with genes ordered by chromosome location.

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Extended Data Figure 6 Hypermutation associations.

a, Box plot of ShatterProof scores grouped by T-category. Each grey dot represents a single sample. P value is from a one-way ANOVA. b) To assess the association between genome stability (measured as PGA) and the presence of one or more chromothriptic events in a tumour, we compared the mean PGA between tumours with a chromothriptic event (4.28% ± 5.04%) and those without one (7.79% ± 5.3%). This difference of 3.52% was statistically significant (P = 1.10 × 10−3; two-sided _t_-test). c, To assess the association between genome stability (measured as PGA) and the presence of one or more kataegic events in a tumour, we compared the mean PGA between tumours with a kataegic event (6.87% ± 5.62%) and those without one (4.34% ± 5.13%). This difference of 2.53% was statistically significant (P = 7.52 × 10−3; two-sided _t_-test). d, Scatter plot of ShatterProof scores against per cent infiltrating immune cells as measured by a pathologist. e, Scatter plot of ShatterProof scores against estimated immune score calculated by the ESTIMATE software. For both these plots, Spearman’s ρ is given, along with its P value. f, Scatterplot showing the correlation between pathologist and ESTIMATE predictions for 22 samples.

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Extended Data Figure 7 Chromothripsis associations and mutational burden.

a, Scatter plots of mRNA abundance against ShatterProof scores for four genes found to be associated with chromothripsis. Spearman’s ρ and P values are shown. Box plots of mRNA abundance against copy number status (DEL, deletion; NEU, copy number neutral). P values are from a two-sided _t_-test. b, Scatterplots of mutation burden (SNV, INV, CNA, CTX counts) and qpure cellularity values against ShatterProof score. Spearman’s ρ and corresponding P values are shown.

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Extended Data Figure 8 Characteristics of mRNA genes and methylation probes in chromothripsis region.

a, Histogram of percentiles from mRNA genes (2,197 unique genes) located in a chromothriptic region. Upper left corner indicates Pearson’s correlation between each bin and the frequency of genes that reside in that bin. b, Histogram as in a for the 43,985 unique methylation probes located in chromothriptic regions. c, Box plot of genes that are in chromothriptic regions against genes not in chromothriptic regions and which are deleted in at least one patient. Only non-chromothriptic patients are included, making this analysis conservative. P values were generated by a two-sided Wilcoxon rank-sum test.

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Extended Data Figure 9 mRNA–methylation associations in tumours with focal genomic events.

a, Density plot of Spearman correlations between the 10,000 most variable methylation probes and the 10,000 most variable mRNA transcripts in tumours with chromothriptic events, with kataegic events, and with neither focal abnormality. b, Density plot as in a for the 14,778 methylation probes in promoter regions and corresponding mRNA transcripts. c, Scatter plot of methylation (β-values for cg07227024 on chr2q) and mRNA abundance for OR2AK2 (on chr1q), which have the highest difference in correlations between chromothriptic (R = −0.90, P = 9.42 × 10−6) and non-chromothriptic (R = 0.52, P = 2.0 × 10−4) tumours. Dotted lines represent the regression line for each group. d, Enrichment pathway network plot of genes differentially correlated between chromothriptic and stable samples in promoter regions (|δ| > 0.8). Each node represents a gene set, which is defined as a set of genes that underlies a functional profile by g:Profiler. Node size corresponds to the number of genes within the gene set. The colour of the node represents the significance of the enriched gene set (hypergeometric test) ranging from FDR-adjusted P = 1.99 × 10−3 to P = 0.05 (red to pink). Gene sets are connected by a grey line if they share common genes and the thickness of the line corresponds to the size of the overlap. Gene sets with similar functions are grouped together by a purple dotted circle.

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Extended Data Figure 10 Methylation survival validation and multi-modal signature survival.

Top, Kaplan–Meier plots of the six prognostic methylation probes in the validation data set (100 prostate tumours). Statistical analysis done using Cox proportional hazards modelling and P values generated by the Wald test, except for a where the log-rank test was performed owing to failure of the proportional-hazards assumption. a, _TCERG1L_-3′. b, SOX14. c, TUBA3C. d, _TCERG1L_-5′. e, MIR129-2. f, ACTL6B. g, A Kaplan–Meier plot for a multi-modal biomarker predicting biochemical recurrence, tested via cross-validation. This curve shows prediction of 18-month biochemical relapse-free survival. h, A Kaplan–Meier plot of the same biomarker, showing full biochemical relapse-free survival to the maximum follow-up time. In both plots, P values were generated using the Wald test.

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Supplementary information

Supplementary Information

This file contains a Supplementary Discussion, Legends for Supplementary Tables 1-18 (see separate zipped file), Supplementary Figures 1-10, Legends for Supplementary Data 1-3 (see separate zipped file) and Supplementary References. (PDF 4365 kb)

Supplementary Tables

This file contains Supplementary Tables 1-18 (see pages 3-5 of the Supplementary Information file for details). (ZIP 8162 kb)

Supplementary Data

This file contains Supplementary Data 1-3 (see page 18 of the Supplementary Information file for details). (ZIP 61447 kb)

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Fraser, M., Sabelnykova, V., Yamaguchi, T. et al. Genomic hallmarks of localized, non-indolent prostate cancer.Nature 541, 359–364 (2017). https://doi.org/10.1038/nature20788

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• Received: 09 December 2015
• Accepted: 14 November 2016
• Published: 09 January 2017
• Issue Date: 19 January 2017
• DOI: https://doi.org/10.1038/nature20788