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

Nature Medicine volume 25, pages 1615–1626 (2019)Cite this article

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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.

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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

Authors and Affiliations

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

    Kathleen E. Houlahan, Yu-Jia Shiah, Cindy Q. Yao, Vincent Huang, Michael Fraser, Lawrence E. Heisler, Julie Livingstone, Takafumi N. Yamaguchi, Alexandre Rouette, Adrien Foucal, Shadrielle Melijah G. Espiritu, Michelle Sam, Lee Timms, Jeremy Johns, Ada Wong, Mathieu Lupien, John D. McPherson & Paul C. Boutros

  2. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Kathleen E. Houlahan, Ankit Sinha, Mathieu Lupien, Thomas Kislinger, Housheng H. He, Robert G. Bristow & Paul C. Boutros

  3. Vector Institute, Toronto, Ontario, Canada

    Kathleen E. Houlahan & Paul C. Boutros

  4. Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    Alexander Gusev & Anamay Shetty

  5. Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Alexander Gusev

  6. Department of Pathology, UT Southwestern Medical Center, Dallas, TX, USA

    Jiapei Yuan, Susmita G. Ramanand & Ram S. Mani

  7. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Musaddeque Ahmed, Ankit Sinha, Alex Murison, Mathieu Lupien, Thomas Kislinger, Melvin L. K. Chua, Theodorus van der Kwast, Housheng H. He & Robert G. Bristow

  8. University of Cambridge, Cambridge, UK

    Anamay Shetty

  9. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Connor Bell, Edward O’Connor, Mark M. Pomerantz & Matthew L. Freedman

  10. Department of Pathology, Centre de recheche du CHU de Québec-Université Laval, Québec City, Québec, Canada

    Michèle Orain & Bernard Têtu

  11. Division of Urology, Centre de recheche du CHU de Québec-Université Laval, Québec City, Québec, Canada

    Valérie Picard, Hélène Hovington, Alain Bergeron, Louis Lacombe & Yves Fradet

  12. Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc

  13. Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc

  14. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc & Paul C. Boutros

  15. Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore

    Melvin L. K. Chua

  16. Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada

    Theodorus van der Kwast

  17. The Eli and Edythe L. Broad Institute, Cambridge, MA, USA

    Matthew L. Freedman

  18. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

    Matthew L. Freedman

  19. Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada

    Robert G. Bristow

  20. Division of Cancer Sciences, Faculty of Biology, Health and Medicine, University of Manchester, Manchester, UK

    Robert G. Bristow

  21. The Christie NHS Foundation Trust, Manchester, UK

    Robert G. Bristow

  22. Cancer Research UK Manchester Institute, Manchester, UK

    Robert G. Bristow

  23. Manchester Cancer Research Centre, Manchester, UK

    Robert G. Bristow

  24. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada

    Paul C. Boutros

  25. Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  26. Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  27. Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

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  1. Kathleen E. Houlahan
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Contributions

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.

Corresponding authors

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

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Competing interests

The authors declare no competing interests.

Additional information

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

a–e, 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. g–l, 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 g–k, 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. c–g, 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). c–f 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.

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Supplementary information

Supplementary Information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–9

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Houlahan, K.E., Shiah, YJ., Gusev, A. et al. Genome-wide germline correlates of the epigenetic landscape of prostate cancer. Nat Med 25, 1615–1626 (2019). https://doi.org/10.1038/s41591-019-0579-z

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