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Gene regulatory mechanisms underpinning prostate cancer susceptibility

Nature Genetics volume 48, pages 387–397 (2016)Cite this article

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Abstract

Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

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Figure 1: Identification of regulatory SNPs and eQTL genes at prostate cancer risk loci.
Figure 2: SNPs disrupt binding of AR-FOXA1 complexes at prostate cancer risk loci.
Figure 3: rs4919743 disrupts tumor-specific AR-FOXA1 binding and expression of KRT8.
Figure 4: LD proxy SNPs for prostate cancer risk alter diverse gene regulatory mechanisms.
Figure 5: aseQTL validation of regulatory SNPs and target genes.
Figure 6: Integrated visualization of eQTL and CRM annotation data.

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Acknowledgements

We thank the authors of all the ChIP-seq data used in this study; a full listing of the original publications and corresponding accession codes is provided in Supplementary Table 1. We acknowledge the ENCODE Consortium and the Stamatoyannopoulos laboratory for generating the DNase-seq data considered here. The RNA-seq data analyzed here were generated by the TCGA Research Network (see URLs). Computations were performed using resources provided by the Swedish National Infrastructure for Computing (SNIC), through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under project b2013051. We acknowledge the National Cancer Research Institute (National Institute of Health Research (NIHR)) collaborative study Prostate Cancer: Mechanisms of Progression and Treatment (ProMPT) collaborative (grant G0500966/75466) (see URLs) and the Addenbrooke's Hospital Human Research Tissue bank, NIHR Cambridge Biomedical Research Centre, which funded tissue collections in Cambridge. This work was funded by a Cancer Research UK program grant, C5047/A14835, awarded to D.E.N., Swedish Cancer Society grants CAN 2012/823 and 09-0677, a Linnaeus grant (contract 70867902), Swedish Research Council grant 2010/3674 and the Cancer Risk Prediction (CRisP) Center (see URLs). Academy of Finland (284618 and 279760) and Jane and Aatos Erkko Foundation grants were awarded to G.-H.W., and a Chinese Scholarship Council fellowship (201206300074) was awarded to P.G.

Author information

Author notes

  1. Thomas Whitington and Ping Gao: These authors contributed equally to this work.

  2. Johan Lindberg, Gong-Hong Wei and Fredrik Wiklund: These authors jointly supervised this work.

Authors and Affiliations

  1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Thomas Whitington, Daniel Klevebring, Robert Karlsson, Henrik Grönberg, Johan Lindberg & Fredrik Wiklund

  2. Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland

    Ping Gao, Wei Song, Yuehong Yang, Ilaria Svezia & Gong-Hong Wei

  3. Biocenter Oulu, University of Oulu, Oulu, Finland

    Ping Gao, Wei Song, Yuehong Yang, Ilaria Svezia & Gong-Hong Wei

  4. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

    Helen Ross-Adams, Alastair D Lamb, Silvia Halim & Mark J Dunning

  5. Department of Urology, Addenbrooke's Hospital, Cambridge, UK

    Alastair D Lamb

  6. Prostate Cancer Research Group, Centre for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo and Oslo University Hospital, Oslo, Norway

    Ian G Mills

  7. Department of Molecular Oncology, Institute of Cancer Research, Oslo University Hospital, Oslo, Norway

    Ian G Mills

  8. Prostate Cancer UK/Movember Centre of Excellence for Prostate Cancer Research, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK

    Ian G Mills

  9. Cancer Research UK Beatson Institute, Glasgow, UK

    Silvia Halim

  10. Department of Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden

    Lars Egevad

  11. Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden

    Lars Egevad

  12. Department of Pathology, Addenbrooke's Hospital, Cambridge, UK

    Anne Y Warren

  13. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK

    David E Neal

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  1. Thomas Whitington
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  2. Ping Gao
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Contributions

T.W., F.W., G.-H.W. and J.L. conceived the study. T.W. performed bioinformatic analyses following raw data processing and wrote the manuscript with assistance from F.W., G.-H.W., J.L. and P.G. P.G. performed most wet-lab experiments, with assistance from W.S., Y.Y. and I.S., under the supervision of G.-H.W. H.R.-A. and A.D.L. performed sample collection, annotation and curation for the Cambridge prostate tumor cohort, led by D.E.N. J.L. performed sample collection and curation for the Stockholm prostate tumor cohort. L.E. and A.Y.W. provided the pathological data for the Stockholm and Cambridge cohorts, respectively. M.J.D. and S.H. performed gene expression and genotype bioinformatic data processing, respectively, on the in-house prostate tumor data. D.K. performed bioinformatics data processing of RNA-seq data. R.K. assisted with bioinformatics analysis of genetics data. I.G.M. assisted with data interpretation and the coordination of in-house prostate tumor cohort data generation. D.E.N. and H.G. performed a clinical lead role. All authors critically revised the manuscript.

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Correspondence to Thomas Whitington.

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The authors declare no competing financial interests.

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Whitington, T., Gao, P., Song, W. et al. Gene regulatory mechanisms underpinning prostate cancer susceptibility. Nat Genet 48, 387–397 (2016). https://doi.org/10.1038/ng.3523

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