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Genomic hallmarks of localized, non-indolent prostate cancer

Abstract

Prostate tumours are highly variable in their response to therapies, but clinically available prognostic factors can explain only a fraction of this heterogeneity. Here we analysed 200 whole-genome sequences and 277 additional whole-exome sequences from localized, non-indolent prostate tumours with similar clinical risk profiles, and carried out RNA and methylation analyses in a subset. These tumours had a paucity of clinically actionable single nucleotide variants, unlike those seen in metastatic disease. Rather, a significant proportion of tumours harboured recurrent non-coding aberrations, large-scale genomic rearrangements, and alterations in which an inversion repressed transcription within its boundaries. Local hypermutation events were frequent, and correlated with specific genomic profiles. Numerous molecular aberrations were prognostic for disease recurrence, including several DNA methylation events, and a signature comprised of these aberrations outperformed well-described prognostic biomarkers. We suggest that intensified treatment of genomically aggressive localized prostate cancer may improve cure rates.

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Figure 1: Global mutational profile of localized non-indolent prostate cancer.
Figure 2: Coding somatic SNVs are rare in non-indolent, localized tumours.
Figure 3: Recurrent kataegis and chromothripsis in prostate cancer.
Figure 4: Multi-modal prediction of disease relapse.

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References

  1. Lozano, R. et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2095–2128 (2012)

    Article  Google Scholar 

  2. Klotz, L. et al. Long-term follow-up of a large active surveillance cohort of patients with prostate cancer. J. Clin. Oncol. 33, 272–277 (2015)

    Article  Google Scholar 

  3. 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  Google Scholar 

  4. Boutros, P. C. et al. Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat. Genet. 47, 736–745 (2015)

    Article  CAS  Google Scholar 

  5. 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  Google Scholar 

  6. 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  Google Scholar 

  7. 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  Google Scholar 

  8. Robinson, D. et al. Integrative clinical genomics of advanced prostate cancer. Cell 161, 1215–1228 (2015)

    Article  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. 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  Google Scholar 

  12. Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015)

  13. 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  Google Scholar 

  14. Ewing, A. D. et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat. Methods 12, 623–630 (2015)

    Article  CAS  Google Scholar 

  15. Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014)

    Article  ADS  CAS  Google Scholar 

  16. Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013)

    Article  CAS  Google Scholar 

  17. Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015)

    Article  CAS  Google Scholar 

  18. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013)

    Article  CAS  Google Scholar 

  19. Gandhi, M., Evdokimova, V. & Nikiforov, Y. E. Frequency of close positioning of chromosomal loci detected by FRET correlates with their participation in carcinogenic rearrangements in human cells. Genes Chromosom. Cancer 51, 1037–1044 (2012)

    Article  CAS  Google Scholar 

  20. Nikiforova, M. N. et al. Proximity of chromosomal loci that participate in radiation-induced rearrangements in human cells. Science 290, 138–141 (2000)

    Article  ADS  CAS  Google Scholar 

  21. Rickman, D. S. et al. Oncogene-mediated alterations in chromatin conformation. Proc. Natl Acad. Sci. USA 109, 9083–9088 (2012)

    Article  ADS  CAS  Google Scholar 

  22. Korbel, J. O. & Campbell, P. J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226–1236 (2013)

    Article  CAS  Google Scholar 

  23. Govind, S. K. et al. ShatterProof: operational detection and quantification of chromothripsis. BMC Bioinformatics 15, 78 (2014)

    Article  Google Scholar 

  24. 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  Google Scholar 

  25. Hieronymus, H. et al. Copy number alteration burden predicts prostate cancer relapse. Proc. Natl Acad. Sci. USA 111, 11139–11144 (2014)

    Article  ADS  CAS  Google Scholar 

  26. Mateo, J. et al. DNA-repair defects and olaparib in metastatic prostate cancer. N. Engl. J. Med. 373, 1697–1708 (2015)

    Article  CAS  Google Scholar 

  27. Schiewer, M. J. et al. Dual roles of PARP-1 promote cancer growth and progression. Cancer Discov . 2, 1134–1149 (2012)

    Article  CAS  Google Scholar 

  28. Feng, F. Y., de Bono, J. S., Rubin, M. A. & Knudsen, K. E. Chromatin to clinic: the molecular rationale for PARP1 inhibitor function. Mol. Cell 58, 925–934 (2015)

    Article  CAS  Google Scholar 

  29. Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015)

    Article  CAS  Google Scholar 

  30. Graff, J. N. & Beer, T. M. Should docetaxel be administered earlier in prostate cancer therapy? Expert Rev. Anticancer Ther. 15, 977–979 (2015)

    Article  CAS  Google Scholar 

  31. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010)

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  35. Taylor, B. J. et al. DNA deaminases induce break-associated mutation showers with implication of APOBEC3B and 3A in breast cancer kataegis. eLife 2, e00534 (2013)

    Article  Google Scholar 

  36. Song, S. et al. qpure: A tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles. PLoS One 7, e45835 (2012)

    Article  ADS  CAS  Google Scholar 

  37. Van Loo, P. et al. Analyzing cancer samples with SNP arrays. Methods Mol. Biol. 802, 57–72 (2012)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. 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  Google Scholar 

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

    Article  Google Scholar 

  41. Larson, D. E. et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28, 311–317 (2012)

    Article  CAS  Google Scholar 

  42. Cibulskis, K. et al. ContEst: estimating cross-contamination of human samples in next-generation sequencing data. Bioinformatics 27, 2601–2602 (2011)

    Article  CAS  Google Scholar 

  43. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res . 38, e164 (2010)

    Article  Google Scholar 

  44. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res . 29, 308–311 (2001)

    Article  CAS  Google Scholar 

  45. 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  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  48. Forbes, S. A. et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res . 43, D805–D811 (2015)

    Article  CAS  Google Scholar 

  49. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012)

    Article  CAS  Google Scholar 

  50. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012)

    Article  CAS  Google Scholar 

  51. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009)

    Article  CAS  Google Scholar 

  52. Pidsley, R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 293 (2013)

    Article  CAS  Google Scholar 

  53. Peters, T. J. et al. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin 8, 6 (2015)

    Article  Google Scholar 

  54. Chen, Y. A. et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8, 203–209 (2013)

    Article  CAS  Google Scholar 

  55. Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015)

    Article  ADS  CAS  Google Scholar 

  56. Brocks, D. et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Reports 8, 798–806 (2014)

    Article  CAS  Google Scholar 

  57. Massie, C. E. et al. The androgen receptor fuels prostate cancer by regulating central metabolism and biosynthesis. EMBO J . 30, 2719–2733 (2011)

    Article  CAS  Google Scholar 

  58. Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004)

    Article  CAS  Google Scholar 

  59. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)

    Article  ADS  CAS  Google Scholar 

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

    Article  Google Scholar 

  61. Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013)

    Article  ADS  Google Scholar 

Download references

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

Authors and Affiliations

Authors

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 to Robert G. Bristow or Paul C. Boutros.

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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.

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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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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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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