Genome-wide germline correlates of the epigenetic landscape of prostate cancer

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Abstract

Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor’s epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays.

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Fig. 1: Prostate cancer susceptibility loci associated with tumor methylation dysregulation.
Fig. 2: Germline variants associate with prognostic methylation levels.
Fig. 3: Landscape of cis-tumor meQTLs.
Fig. 4: Tumor meQTL associated with TCERG1L regulation.
Fig. 5: Tumor meQTL associated with AKT1 regulation.

Data availability

Methylation data are available in the Gene Expression Omnibus under accession GSE84043. Raw sequencing data are available in the European Genome-phenome Archive under accession EGAS00001000900. Processed variant calls are available through the ICGC Data Portal under the project PRAD-CA (https://dcc.icgc.org/projects/PRAD-CA). TCGA WGS/WES data are available from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/projects/TCGA-PRAD). Primary sample ChIP-Seq data were retrieved from the Gene Expression Omnibus under accession GSE120738. Cell line data sources are outlined in Supplementary Table 3. Detailed information on experimental design can be found in the Life Sciences Reporting Summary.

References

  1. 1.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

  2. 2.

    Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

  3. 3.

    Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17–37 (2013).

  4. 4.

    Tomasetti, C., Li, L. & Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330–1334 (2017).

  5. 5.

    Tomlinson, I. P. et al. A genome-wide association study identifies colorectal cancer susceptibility loci on chromosomes 10p14 and 8q23.3. Nat. Genet. 40, 623–630 (2008).

  6. 6.

    Peterson, G. M. et al. A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Nat. Genet. 42, 224–228 (2010).

  7. 7.

    Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

  8. 8.

    Knudson, A. G. Two genetic hits (more or less) to cancer. Nat. Rev. Cancer 1, 157–162 (2001).

  9. 9.

    Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumourigenesis. Cell 61, 759–767 (1990).

  10. 10.

    Nik-Zainal, S. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

  11. 11.

    Jones, P. A. & Baylin, S. B. The epigenomics of cancer. Cell 128, 683–692 (2007).

  12. 12.

    Reynolds, P. A. et al. Tumour suppressor p16INK4A regulates polycomb-mediated DNA hypermethylation in human mammary epithelial cells. J. Biol. Chem. 281, 24790–24802 (2006).

  13. 13.

    Suzuki, H. et al. Epigenetic inaction of SFRP genes allows constitutive WNT signaling in colorectal cancer. Nat. Genet. 36, 417–422 (2004).

  14. 14.

    Saghafinia, S. et al. Pan-cancer landscape of aberrant DNA methylation across human tumors. Cell Rep. 25, 1066–1080 (2018).

  15. 15.

    Whitington, T. et al. Gene regulatory mechanisms underpinning prostate cancer susceptibility. Nat. Genet. 48, 387–397 (2016).

  16. 16.

    Cowper-Sal-lari, R. et al. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat. Genet. 44, 1191–1198 (2012).

  17. 17.

    Heyn, H. et al. Linkage of DNA methylation quantitative trait loci to human cancer risk. Cell Rep. 24, 331–338 (2014).

  18. 18.

    Taylor, R. A. et al. Germline BRCA2 mutations drive prostate cancers with distinct evolutionary trajectories. Nat. Commun. 8, 13671 (2017).

  19. 19.

    Szulkin, R. et al. Genome-wide association study of prostate cancer-specific survival. Cancer Epidemiol. Biomarkers Prev. 24, 1796–1800 (2015).

  20. 20.

    Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017).

  21. 21.

    Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).

  22. 22.

    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).

  23. 23.

    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).

  24. 24.

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

  25. 25.

    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).

  26. 26.

    Fraser, M. et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature 541, 359–364 (2017).

  27. 27.

    Espiritu, S. G. et al. The evolutionary landscape of localized prostate cancers drives clinical aggression. Cell 173, 1003–1013 (2018).

  28. 28.

    Lin, D. W. et al. Genetic variants in the LEPR, CRY1, RNASEL, IL4, and ARVCF genes are prognostic markers of prostate cancer-specific mortality. Cancer Epidemiol. Biomarkers Prev. 20, 1928–1936 (2011).

  29. 29.

    Eeles, R. A. et al. Identification of seven new prostate cancer susceptibility loci through a genome-wide association study. Nat. Genet. 41, 1116–1121 (2009).

  30. 30.

    Eeles, R. A. et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat. Genet. 45, 385–391 (2013).

  31. 31.

    Lévesque, E. et al. Steroidogenic germline polymorphism predictors of prostate cancer progression in the estradiol pathway. Clin. Cancer Res. 20, 2971–2983 (2014).

  32. 32.

    Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928–936 (2018).

  33. 33.

    Matejcic, M. et al. Germline variation at 8q24 and prostate cancer risk in men of European ancestry. Nat. Commun. 9, 4616 (2018).

  34. 34.

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

  35. 35.

    Stelloo, S. et al. Integrative epigenetic taxonomy of primary prostate cancer. Nat. Commun. 9, 4900 (2018).

  36. 36.

    Jackson, W. C. et al. Intermediate endpoints after postprostatectomy radiotherapy: 5-year distant metastasis to predict overall survival. Eur. Urol. 74, 413–419 (2018).

  37. 37.

    Bhandari, V. et al. Molecular landmarks of tumor hypoxia across cancer types. Nat. Genet. 51, 308–318 (2019).

  38. 38.

    Sinha, A. et al. The proteogenomic landscape of curable prostate cancer. Cancer Cell 35, 414–427 (2019).

  39. 39.

    Kim, H. The retinoic acid synthesis gene ALDH1a2 is a candidate tumor supporessor in prostate cancer. Cancer Res. 65, 8118–8124 (2005).

  40. 40.

    Doose, G. et al. MINCR is a MYC-induced lncRNA able to modulate MYC’s transcriptional network in Burkitt lymphoma cells. Proc. Natl Acad. Sci. USA 112, E5261–E5280 (2015).

  41. 41.

    Wang, S. et al. Long non-coding RNA MINCR promotes gallbladder cancer progression through stimulating EZH2 expression. Cancer Lett. 380, 122–133 (2016).

  42. 42.

    GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  43. 43.

    Armenia, J. et al. The long tail of oncogenic drivers in prostate cancer. Nat. Genet. 50, 645–651 (2018).

  44. 44.

    Yi, J. M. et al. Genomic and epigenomic integration identifies a prognostic signature in colon cancer. Clin. Cancer Res. 17, 1535–1545 (2011).

  45. 45.

    Yi, J. M. et al. DNA methylation biomarker candidates for early detection of colon cancer. Tumour Biol. 33, 363–372 (2012).

  46. 46.

    Kron, K. J. et al. TMPRSS2–ERG fusion co-opts master transcription factors and activates NOTCH signaling in primary prostate cancer. Nat. Genet. 49, 1336–1345 (2017).

  47. 47.

    Zampieri, M. et al. ADP-ribose polymers localized on Ctcf–Parp1–Dnmt1 complex prevent methylation of Ctcf target sites. Biochem. J. 441, 645–652 (2012).

  48. 48.

    Lee, J. K. et al. N-Myc drives neuroendocrine prostate cancer initiated from human prostate epithelial cells. Cancer Cell 29, 536–547 (2016).

  49. 49.

    Kwon, E. M. et al. Genetic polymorphisms in inflammation pathway genes and prostate cancer risk. Cancer Epidemiol. Biomarkers Prev. 20, 923–933 (2011).

  50. 50.

    Karyadi, D. M. et al. Confirmation of genetic variants associated with lethal prostate cancer in a cohort of men from hereditary prostate cancer families. Int. J. Cancer 136, 2166–2171 (2015).

  51. 51.

    Liu, J. M. et al. Association between single nucleotide polymorphisms in AKT1 and the risk of prostate cancer in the Chinese Han population. Genet. Mol. Res. 16, gmr16019469 (2017).

  52. 52.

    Song, M. et al. AKT as a therapeutic target for cancer. Cancer Res. 79, 1019–1031 (2019).

  53. 53.

    Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).

  54. 54.

    Bernstein, B. E. et al. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

  55. 55.

    Shiah, Y.-J., Fraser, M., Bristow, R. G. & Boutros, P. C. Comparison of pre-processing methods for infinium HumanMethylation450 BeadChip array. Bioinformatics 33, 3151–3157 (2017).

  56. 56.

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

  57. 57.

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

  58. 58.

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

  59. 59.

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

  60. 60.

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

  61. 61.

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

  62. 62.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

  63. 63.

    Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

  64. 64.

    Gabriel, S. B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002).

  65. 65.

    Delaneau, O., Marchini, J. & Zagury, J.-F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2012).

  66. 66.

    Durbin, R. Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT). Bioinformatics 30, 1266–1272 (2014).

  67. 67.

    The Haplotype Reference Consortium A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

  68. 68.

    Yu, J. et al. An integrated network of androgen receptor, polycomb, and TMPRSS2ERG gene fusions in prostate cancer progression. Cancer Cell 17, 443–454 (2010).

  69. 69.

    Wang, D. et al. Reprogramming transcription by distinct classes of enhancers functionally defined by eRNA. Nature 474, 390–394 (2011).

  70. 70.

    Tan, P. Y. et al. Integration of regulatory networks by NKX3-1 promotes androgen-dependent prostate cancer survival. Mol. Cell. Biol. 32, 399–414 (2012).

  71. 71.

    Hazelett, D. J. et al. Comprehensive functional annotation of 77 prostate cancer risk loci. PLoS Genet. 10, e1004102 (2014).

  72. 72.

    Jin, H. J. et al. Cooperativity and equilibrium with FOXA1 define the androgen receptor transcriptional program. Nat. Commun. 5, 3972 (2014).

  73. 73.

    Xu, K. et al. EZH2 oncogenic activity in castration-resistant prostate cancer cells is Polycomb-independent. Science 338, 1465–1469 (2012).

  74. 74.

    Zhang, X. et al. Integrative functional genomics identifies an enhancer looping to the SOX9 gene disrupted by the 17q24.3 prostate cancer risk locus. Genome Res. 22, 1437–1446 (2012).

  75. 75.

    Chen, Y. et al. ETS factors reprogram the androgen receptor cistrome and prime prostate tumorigenesis in response to PTEN loss. Nat. Med. 19, 1023–1029 (2013).

  76. 76.

    ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  77. 77.

    Liang, Y. et al. LSDI-mediated epigenetic reprogramming drives CENPE expression and prostate cancer progression. Cancer Res. 77, 5479–5490 (2017).

  78. 78.

    Sutinen, P. et al. SUMOylation modulates the transcriptional activity of androgen receptor in a target gene and pathway selective manner. Nucleic Acids Res. 42, 8310–8319 (2014).

  79. 79.

    Taberlay, P. C. et al. Reconfiguration of nucleosome-depleted regions at distal regulatory elements accompanies DNA methylation of enhancers and insulators in cancer. Genome Res. 24, 1421–1432 (2014).

  80. 80.

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

  81. 81.

    Mehrmohamadi, M. et al. Integrative modelling of tumour DNA methylation quantifies the contribution of metabolism. Nat. Commun. 7, 13666 (2016).

  82. 82.

    Van de Geijn, B. et al. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 1061–1063 (2015).

  83. 83.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  84. 84.

    Li, G. et al. ChIA-PET2: a versatile and flexible pipeline for ChIA-PET data analysis. Nucleic Acids Res. 45, e4 (2016).

  85. 85.

    Reimand, J. et al. g:Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 44, W83–W89 (2016).

  86. 86.

    Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

  87. 87.

    P’ng, C. et al. BPG: seamless, automated and interactive visualization of scientific data. BMC Bioinformatics 20, 42 (2019).

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Acknowledgements

The authors thank all members of the Boutros laboratory, as well as K. Kron and A. Meng, for helpful suggestions and support. The results described here are based in part on 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. 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 the 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.; grant RS2014-02 to M.L.; and grant RS-2016-01 to H.H.H.). 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. K.E.H. was supported by a CIHR Vanier Fellowship. R.S.M. acknowledges funding from the Prostate Cancer Research Program Impact Award from the US Department of Defense (W81XWH-17-1-0675), as well as the Individual Investigator Research Award from CPRIT (RP190454). M.L.F. acknowledges funding from NIH (5R01CA193910), the Challenge Award from the Prostate Cancer Foundation, and the H.L. Snyder Medical Foundation. B.P. acknowledges funding from the National Human Genome Research Institute (R01HG009120). This work was supported by the NIH/NCI under award number P30CA016042, and by an operating grant from the National Cancer Institute Early Detection Research Network (1U01CA214194-01) to P.C.B. and T.K.

Author information

A.Shetty, M.F., M.S., L.T., J.J., A.W., M.O., V.P., H.H. and A.Sinha prepared the samples. B.T. and T.v.d.K. performed the pathology analyses. K.E.H., Y.-J.S. and M.A. performed the statistical and bioinformatics analyses. A.G., J.Y., S.G.R., C.Q.Y., V.H., L.E.H., Y.-J.S., J.L., T.N.Y., S.M.G.E., A.R., A.F., A.M., C.B. and E.O’C. processed the data. K.E.H. wrote the first draft of the manuscript. K.E.H., R.G.B. and P.C.B. initiated the project. M.L.K.C., M.M.P., J.D.M., M.L., T.K., B.P., M.L.F., R.S.M., H.H.H., R.G.B. and P.C.B. supervised the research. Y.F., B.T., A.B. and L.L. generated tools and reagents. All authors approved the manuscript.

Correspondence to Robert G. Bristow or Paul C. Boutros.

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Peer review information Kate Gao was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Data analysis and quality controls.

a, Identity-by-state clustering showed no evidence of population stratification. The heat map shows the identity-by-state values for all pairwise comparisons. The first covariate along the right shows the cluster provided by plink (version 1.9). The second covariate indicates whether the sample was previously published or novel to the present study. b, Validation dataset workflow. SNP6 array and WES calls were tested for >80% concordance, and merged and additional genotypes were imputed using the Sanger Impute Server. meQTLs were validated in the imputed cohort using the same Spearman’s correlation test. c, Estimates of per-SNP imputation accuracy by comparison of imputation calls from SNP6 alone with WES genotypes. d, The accuracy per sample was consistently above 0.8, with a median per-sample accuracy of 0.849. e, The accuracy per chromosome was consistent with no chromosomal bias, with the exception of chromosome X. f, Definition of BCR following radical prostatectomy and image-guided radiotherapy (IGRT). g, Number of probes with 0–10 SNPs within 50 bp of each methylation probe. Multiple SNPs within this region could effect hybridization of the probe. h, Null distribution of probes with more than three SNPs within 50 bp of the probe. The distribution was generated by randomly sampling 12,650 probes 106 times. Source data

Extended Data Fig. 2 Characterizing risk meQTLs.

ae, Three out of five of the meQTLs reported by Heyn et al.17 validated in this cohort (P < 0.01; Spearman’s correlation). Box plots represents median values with 0.25 and 0.75 quantiles. Whiskers represent 1.5× the IQR range. Blue points represent DNA methylation. Numbers of samples with each genotype are given in parentheses. f, Distribution of distances between loci and probes, with respect to each locus. gl, Overlap of risk SNP meQTLs and regulatory regions in LNCaP (g), PC3 (h), RWPE-1 (i), 22Rv1 (j) and VCaP cell lines (k) and primary samples (l). In gk, bar plots show the numbers of tumor meQTLs that overlap each target/treatment pair. Background shading indicates FDRs < 0.05 based on permutation analysis (n = 105 permutations). Each red × reflects the number of overlapping SNPs expected by chance alone. In l, the bar plot shows the number of patients with either androgen receptor (n = 88 patients), H3K27ac (n = 92), H3K27me3 (n = 56) or H3K4me3 (n = 76) peaks overlapping each risk meQTL. Source data

Extended Data Fig. 3 Characterizing meQTLs targeting prognostic methylation sites.

a, Methylation β values for the 58 selected prognostic methylation probes (rows). Each column represents an individual, and clinical cohorts of the individuals are presented along the bottom. The covariates on the right indicate whether or not the probe was identified as a tumor meQTL, and the CpG class of the probe. The forest plot on the far right depicts HR and 95% CI values considering BCR as endpoint, as determined by the CoxPH model. cGS, clinical Gleason score; cT, clinical T category. b, Three probes, located within an open sea region on chromosome 10 within c10orf26, were highly correlated (Spearman’s correlation). c, Methylation of all of these probes (y axis) was associated with the same six SNPs (x axis). Black indicates that the SNP was significantly associated with methylation of the probe (P < 5 × 10−8). Source data

Extended Data Fig. 4 Characterizing tumor meQTLs.

a, A subset of tumor meQTLs had opposite effects in tumoral and reference tissue (n = 234). b, A subset of tumor meQTLs had overlapped DMRs between tumoral and reference tissue. cg, Tumor meQTLs were enriched at active regulatory elements in RWPE-1 (c), PC3 (d), 22Rv1 (e) and VCaP cell lines (f) and primary samples (g). cf show bar plots of the numbers of tumor meQTLs that overlap each target. Gray shading indicates significant enrichment (FDR < 0.05; n = 105 permutations). Each red × represents the expected number of overlapping SNPs by chance. In g, the box plot shows per-sample FDRs quantifying the enrichment of tumor meQTLs overlapping each target (105 permutations). The numbers above indicate the percentages of samples with significant enrichment (FDR < 0.05). h, Tumor meQTLs overlap allelic imbalance loci in FOXA1, H3K27ac, H3K4me3, HOXB13 and H3K4me2 ChIP-Seq. Black indicates that the tag SNP overlaps the target, while gray indicates that the SNP in strong linkage disequilibrium with the tag SNP overlaps the target. The covariate indicates whether allelic imbalance was identified in tumor, reference, or tumor versus reference analysis. i, Tumor meQTLs overlap with RAD21 and RNA Pol-II chromatin loops. Black indicates that the SNP overlaps with the RAD21 or RNA Pol-II ChIA-PET peak or intrachromosomal loops from paired-end tags (PETs). The covariate indicates the cell line. j, Long-range gene targets were identified for 17 tumor meQTLs. Dot sizes and colors show the magnitude and direction of Spearman’s ρ, respectively. Background shading indicates the FDR. k, Six of the ten genes in tumor meQTL–eQTLs were differentially abundant in tumor versus reference tissue (FDR < 0.05). Dot sizes and colors indicate the log2[fold change] magnitude and sign, respectively. Background shading indicates the FDR. l,m, rs2456274 was associated with mRNA (l) and protein (m) abundance of VPS53 (Spearman’s correlation). Box plots represent median values with 0.25 and 0.75 quantiles. Whiskers represent 1.5× the IQR range. Purple and red points represent mRNA and protein abundance, respectively. Numbers of samples with each genotype are givecoule effectn in parentheses. Source data

Extended Data Fig. 5 Characterizing TCERG1L tumor meQTLs.

a, Haplotype strongly associated with 5′ and 3′ methylation of TCERG1L. Dot sizes and colors represent the magnitude and directionality of Spearman’s ρ, respectively. Background shading reflects the P value. Pairwise D′ values are shown to the right (solid red means that D′ = 1). b, Manhattan plot showing P values after adjusting for tumor cellularity. c, Methylation of 5′ and 3′ probes of TCERG1L showed opposite effects on BCR. P values were determined by log-rank test. d, Methylation of 5′ and 3′ probes was negatively correlated (Spearman’s correlation). e, Methylation of the 5′ probe is negatively correlated with mRNA abundance of TCERG1L (Spearman’s correlation), while methylation of the 3′ probe is positively correlated. f, meQTLs are stronger in tumoral tissue than reference tissue. The box plot represents the bootstrapped distribution of Spearman’s ρ in tumoral tissue (nsampled = 47; npermutations = 106), and shows median values with 0.25 and 0.75 quantiles. Whiskers represent 1.5× the IQR. The red dot represents Spearman’s ρ in the reference tissue (the P value is the proportion of iterations where |ρtumor| < |ρreference|). g, The TCERG1L eQTL is seen in reference tissue (Spearman’s correlation). Purple points represent mRNA abundance. Numbers of samples with each genotype are given in parentheses. h, TCERG1L methylation (cg03943081) is the strongest prognostic measure compared with genotype and mRNA abundance of TCERG1L (CoxPH model). i, Methylation is significantly associated with Gleason score in the validation cohort (Mann–Whitney U-test; effect size = fold change). j, The tag SNP (rs4074033) overlaps H3K27ac histone modifications. The heat map shows the ChIP-Seq peak signal intensity for each patient (y axis) against the spanning region of the SNP (x axis). The covariate along the top indicates ±100 bp around the SNP (black). The covariates on the right indicate methylation β values and genotypes of the patients. k, The H3K27ac ChIP-Seq peak signal is negatively correlated with methylation (cg03943081; Spearman’s correlation). l, Integrative Genomics Viewer screen shot of CTCF ChIP-Seq peaks across eight cell lines, showing only the cell lines that are heterozygous or homozygous for the alternative allele show CTCF binding. Source data

Extended Data Fig. 6 Characterizing AKT1 tumor meQTLs.

a, Manhattan plot showing P values after adjusting for tumor cellularity. b, meQTL validates in the TCGA cohort (Spearman’s correlation). Box plots represent median values with 0.25 and 0.75 quantiles. Whiskers represent 1.5× the IQR range. Blue points represent methylation values. c, The meQTL is stronger in tumoral than reference tissue. The box plot represents the bootstrapped distribution of Spearman’s ρ in tumoral tissue (nsampled = 47; npermutations = 106). The red dot represents Spearman’s ρ in reference tissue (P values represent the proportion of iterations where |ρtumor| < |ρreference|). d, The meQTL overlaps H3K27ac modification. The heat map shows the H3K27ac ChIP-Seq signal as previously outlined in Extended Data Fig. 5. e, Scatterplot showing negative correlation between the H3K27ac peak score and methylation of cg18664856 (Spearman’s correlation). f, Methylation at cg18664856 is negatively correlated with mRNA abundance of AKT1. g, Association between the rs2494734 genotype and mRNA abundance of AKT1 replicated in TCGA (Spearman’s correlation). Purple points represent mRNA abundance. h, The association is weaker in reference tissue, as quantified using Spearman’s correlation. Source data

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