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Modulation of long noncoding RNAs by risk SNPs underlying genetic predispositions to prostate cancer

Nature Genetics volume 48, pages 1142–1150 (2016)Cite this article

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Abstract

Long noncoding RNAs (lncRNAs) represent an attractive class of candidates to mediate cancer risk. Through integrative analysis of the lncRNA transcriptome with genomic data and SNP data from prostate cancer genome-wide association studies (GWAS), we identified 45 candidate lncRNAs associated with risk to prostate cancer. We further evaluated the mechanism underlying the top hit, PCAT1, and found that a risk-associated variant at rs7463708 increases binding of ONECUT2, a novel androgen receptor (AR)-interacting transcription factor, at a distal enhancer that loops to the PCAT1 promoter, resulting in upregulation of PCAT1 upon prolonged androgen treatment. In addition, PCAT1 interacts with AR and LSD1 and is required for their recruitment to the enhancers of GNMT and DHCR24, two androgen late-response genes implicated in prostate cancer development and progression. PCAT1 promotes prostate cancer cell proliferation and tumor growth in vitro and in vivo. These findings suggest that modulating lncRNA expression is an important mechanism for risk-associated SNPs in promoting prostate transformation.

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Figure 1: Genomic distribution of prostate cancer risk-associated SNPs.
Figure 2: Identification of lncRNAs associated with prostate cancer risk.
Figure 3: A risk-SNP-containing enhancer loops to the PCAT1 promoter.
Figure 4: The prostate cancer risk SNP rs7463708 modulates ONECUT2 and AR binding at the PCAT1 enhancer.
Figure 5: PCAT1 interacts with AR and LSD1 to regulate GNMT and DHCR24 expression.
Figure 6: Graphical representation of the regulation and function of PCAT1 in prostate cancer.

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Acknowledgements

We thank M. Brown, X.S. Liu, D. Borges-Rivera and R.A. Young for discussion, as well as the Princess Margaret Genomic Centre for high-throughput sequencing support. The work was supported by the Princess Margaret Cancer Foundation (to H.H.H., M.L., T.J.P. and R.G.B.), the Canada Foundation for Innovation and the Ontario Research Fund (CFI32372 to H.H.H. and CFI32383 to T.J.P.), NSERC discovery grant (498706 to H.H.H.), the WICC Ontario 20th Anniversary Prostate Cancer Innovation Grant of the CCS (703800 to H.H.H.), and a CIHR transitional operating grant (142246 to H.H.H.) and Movember Rising Star awards from Prostate Cancer Canada (RS2014-01 to P.C.B., RS2014-04 to M.L. and RS2016-1022 to H.H.H.). H.H.H. holds an ICS-IG Maud Menten New Principal Investigator Prize (ICS-145381) and an OMIR Early Researcher Award. M.L. holds a young investigator award from the OICR and a new investigator salary award from the CIHR. R.G.B. is a recipient of a Canadian Cancer Society Research Scientist Award. P.C.B. is supported by a Terry Fox Research Institute New Investigator Award and a CIHR New Investigator Award. M.L.F. is supported by the Prostate Cancer Foundation (Challenge Award) and an NIH grant (R01CA193910). J.H. is a CIHR Graduate Student Fellowship recipient. S.D.B. is a CIHR Postdoctoral Fellowship recipient. M.J.W. is supported by grants 5RO1CA154903 and 5RO1HL103967 from the NIH. We acknowledge the ENCODE Consortium and the ENCODE production laboratories that generated the data sets provided by the ENCODE Data Coordination Center used in the manuscript. The results shown here are in whole or part based upon data generated by the TCGA Research Network.

Author information

Author notes

  1. John R Prensner

    Present address: Present address: Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.,

  2. Haiyang Guo and Musaddeque Ahmed: These authors contributed equally to this work.

Authors and Affiliations

  1. Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada

    Haiyang Guo, Musaddeque Ahmed, Yi Liang, Junjie Hua, Fraser Soares, Jens Langstein, Yuchen Li, Christine Poon, Swneke D Bailey, Michael Fraser, Trevor J Pugh, Robert G Bristow, Mathieu Lupien & Housheng Hansen He

  2. Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Fan Zhang, SiDe Li, Yifei Sun & Martin J Walsh

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

    Cindy Q Yao, Dorota H Sendorek, Mathieu Lupien & Paul C Boutros

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

    Junjie Hua, Trevor J Pugh, Robert G Bristow, Mathieu Lupien, Paul C Boutros & Housheng Hansen He

  5. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Jens Langstein

  6. Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, New Hampshire, USA

    Kinjal Desai

  7. Department of Medical Oncology, Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Teng Fei, Mark Pomerantz & Matthew L Freedman

  8. Medical College of Xiamen University, Xiamen, China

    Qiyuan Li

  9. Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA

    John R Prensner

  10. Department of Radiation Oncology, University of California at San Francisco, San Francisco, California, USA

    Felix Y Feng

  11. Department of Urology, University of California at San Francisco, San Francisco, California, USA

    Felix Y Feng

  12. Department of Medicine, University of California at San Francisco, San Francisco, California, USA

    Felix Y Feng

  13. Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, California, USA

    Felix Y Feng

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

    Paul C Boutros

  15. Eli and Edythe L. Broad Institute, Cambridge, Massachusetts, USA

    Matthew L Freedman

  16. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Martin J Walsh

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  1. Haiyang Guo
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  2. Musaddeque Ahmed
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  3. Fan Zhang
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  5. SiDe Li
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  19. John R Prensner
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  20. Trevor J Pugh
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  28. Housheng Hansen He
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Contributions

H.G., M.A. and H.H.H. designed the studies and wrote the manuscript. H.G., Y. Liang, J.H. and J.L. performed the experiments with help from S.L., Y.S., C.P., T.F., K.D., J.R.P., F.S. and Y. Li. M.A., H.G. and H.H.H. conducted the data analysis with help from F.Z., C.Q.Y., D.H.S., P.C.B., R.G.B., M.L.F., S.D.B., Q.L., T.J.P., M.P., F.Y.F., M.L.F. and M.J.W. M.L., M.L.F., F.Y.F., J.R.P., M.P., M.J.W. and P.C.B. revised the manuscript.

Corresponding author

Correspondence to Housheng Hansen He.

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Competing interests

The University of Michigan holds a patent, on which J.R.P. is a co-inventor, for Noncoding RNA and Uses Thereof, which has been licensed to Wafergen, Inc., and GenomeDx Biosciences.

Integrated supplementary information

Supplementary Figure 1 Genomic distribution of prostate cancer risk SNPs.

(a) Distribution of the number of transcription factor cistromes across all DHSs in LNCaP cells with at least one risk SNP. (b) Matrix of overlap between DHSs and the cistrome of each transcription factor tested. (c) Enrichment of the prostate cancer associated variant set (AVS) across regions with different epigenetic modifications in prostate cancer LNCaP cells. The box plots denote the enrichment of null distributions, i.e., 100 matching AVSs generated from the pool of tag SNPs present on the GWAS arrays (Illumina Human OmniExpress). The diamonds represent enrichment of prostate cancer AVSs in respective genomic regions. The first cutoff bar (in gray) denotes the P-value cutoff of 0.05 and the second bar denotes the cutoff for Bonferroni correction. The diamonds in red represent significant enrichment over the null distribution. (d) Venn diagram for risk SNPs derived from GWAS studies and significantly associated SNPs from several fine-mapping studies in the European population1–3. Over 80% of the associated SNPs derived from the fine-mapping data are present in the risk SNP set derived from GWAS. (e,f) Enrichment of all significantly associated SNPs from the fine-mapping studies across different genomic regions (e) or epigenetic modifications (f). The null distribution is constructed to maintain a structure similar to that of associated SNPs in each of 123 loci that have been fine-mapped. Statistics were calculated using VSE. (g) Distribution between DHSs and non-DHSs of the index SNPs from 123 loci that were fine-mapped. DHSs were detected in LNCaP cells. P value was calculated using Pearson’s χ2 test with Yates’ continuity correction.

Supplementary Figure 2 Expression analyses of protein-coding and lncRNA genes in TCGA samples.

(a) Number of genes from GENCODE database v19. Expression was calculated in TCGA prostate cancer benign and tumor samples using the R Bioconductor package EdgeR. (b) Expression levels of lncRNAs are typically lower than those of protein-coding genes. (c) Lower expression is typical in all samples, and more abundant genes have comparatively higher expression. (d) Distribution of abundance of lncRNAs and protein-coding genes in TCGA prostate cancer samples. The y axis is limited to a maximum of 1,000 genes. (e) Number of lncRNAs above the threshold for different statistics. The statistics were calculated using all lncRNAs that were expressed in at least 50% of the clinical samples. CPM, count per million; lncRNA, long noncoding RNA; PCG, protein-coding gene.

Supplementary Figure 3 Global interactions between risk-SNP-containing DHSs and lncRNA promoters as predicted by intercellular functional correlation analysis.

Distances between DHSs and lncRNA promoters are restricted to ≤500 kb. Each interaction has a Pearson’s correlation coefficient of 0.7 or greater. All lncRNAs have expression levels of over –2.49 log CPM, the median expression level for lncRNAs, in at least 50% of all prostate adenocarcinoma (PRAD) samples in TCGA. rs7012442 is located at the edge of the DHS and is excluded from the follow-up functional validation.

Supplementary Figure 4 Candidate susceptibility lncRNAs in the 8q24.21 gene desert region.

Protein-coding genes are marked in red, and lncRNAs are marked in blue. Chromatin interactions between lncRNA promoters and risk-SNP-containing DHSs predicted by intercellular functional correlation analysis are represented by Bezier curves in green. lncRNA cis-eQTLs are represented by Bezier curves in purple. SNPs located in the PCAT1 promoter and enhancer DHSs are labeled in green, and the related tag SNPs are labeled in black. Risk SNPs and risk loci are represented by red bars and orange boxes, respectively.

Supplementary Figure 5 Suppression of PCAT1 expression with two siRNAs decreased LNCaP, VCaP and 22RV1 cell proliferation.

P value was calculated by one-way ANOVA: *P < 0.05, **P < 0.01.

Supplementary Figure 6 The rs7463708 locus overlaps with the PCAT1 enhancer region.

(a) Survival analysis by rs7463708 showed that the risk allele (blue) is associated with worse BCR rates. “1” (red) indicates patients homozygous for the non-risk alleles of rs7463708 and rs72725879 (the SNP with the strongest LD with rs7463708), and “0” (blue) indicates the remaining patients. A Cox proportional hazards model was fit to compare BCR rates between the two patient groups (HR = 0.52, 95% confidence interval = 0.26–1.06; P = 0.071, Wald test). (b) AR, HOXB13 and FOXA1 bind to the DHS containing rs7463708, rs1456315 and rs72725879. (c) Sanger sequencing results show that rs7463708 is the only heterozygous SNP in this DHS.

Supplementary Figure 7 Physical association between the PCAT1 promoter and the rs7463708 locus.

(a) Sanger sequencing of the PCAT1 promoter–enhancer 3C amplicon of the anchor (PCAT1 TSS) and rs7463708 locus. (b) The uniform DHS signals near PCAT1 in 17 cell lines obtained from ENCODE. The DHSs significantly gained (P < 0.05) in untreated (Veh) or androgen-treated LNCaP cells are denoted by asterisks. The DHS that contains rs7463708 (orange bar) was gained in both treated and untreated LNCaP cells and is the only site correlated with DHS signal in the PCAT1 promoter region. (c) The uniform DHS signal profile of 17 cell lines in the DHS region containing rs7463708. The significance of enrichment for DHS signal is calculated using one-sample t tests. (d) Disruption of the rs7463708 locus by CRISPR/Cas9 decreased interaction between the PCAT1 promoter and the rs7463708 locus. The relative 3C enrichment in the control sample (CRISPR_Scramble) was normalized to 1. (e) Disruption of the rs7463708 locus by CRISPR/Cas9 decreased PCAT1 expression. The relative expression level of PCAT1 in the control sample was normalized to 1. P value was calculated by Student’s t test: *P < 0.05, **P < 0.01.

Supplementary Figure 8 CTCF looping determined by ChIA-PET surrounding PCAT1.

The correlation bar in the bottom panel denotes the correlation between the PCAT1 promoter and the rs7463708 enhancer in LNCaP cells. Putative TADs were estimated in human embryonic stem cells4,5. CTCF looping was determined using ChIA-PET in hESC, MCF-7 and K562 cells. The CTCF signal is strong in identical regions in LNCaP and PrEC prostate cancer cells. CTCF peaks in LNCaP and PrEC cells (green bars) overlap with interacting CTCF peaks in hESC, MCF-7 and K562 cells. hESC, human embryonic stem cells; PRAD, prostate adenocarcinoma.

Supplementary Figure 9 Characterization of the PCAT1 distal enhancer in the rs7463708 locus.

(a) ONECUT2 has the highest expression in the ONECUT family and is upregulated in prostate tumors in comparison to benign prostate tissues. Data used here are prostate cancer RNA-seq data from TCGA. (b) AR ChIP-seq analysis in LNCaP cells shows that AR binding in the rs7463708 locus is induced by DHT stimulation. (c) AR preferentially binds to the T risk allele of rs7463708, as determined by allele-specific ChIP–qPCR. P value was calculated by Student’s t test: *P < 0.05, **P < 0.01.

Supplementary Figure 10 Positive and negative controls of RIP experiments.

Left, RIP–qPCR of R34 by hnRNPL pulldown. R34 was found to interact with hnRNPL by a RIP-seq screening analysis (unpublished data) and is used as a positive control here. Right, RIP–qPCR of PCAT1 by YY1 pulldown. Antibody to YY1 was used as a negative-control antibody for RIP.

Supplementary Figure 11 Identification of AR late-response genes regulated by PCAT1.

(a) qPCR validation of androgen late-response genes (with AR and LSD1 binding within ±10 kb of the TSS). (b) Expression of androgen late-response genes after PCAT1 knockdown. (c) Expression of androgen late-response genes with PCAT1 overexpression. (d) Outlier analysis shows that GNMT is upregulated in prostate cancer tumors as compared to normal prostate tissues. Inset tables quantify expression for ‘positive’ and ‘negative’ samples defined using a cutoff at the 75th percentile (red dashed line). Statistical significance was determined using χ2 tests. (e–j) Regulatory regions of androgen late-response genes for ChIP–qPCR. The highlighted AR and LSD1 peak regions were used to design primers for ChIP–qPCR. (k) AR and LSD1 occupancy at enhancers of androgen late-response genes after PCAT1 knockdown. (l) PCAT1 ChIRP enriches for human PCAT1 RNA. (m) PCAT1 ChIRP–qPCR in LNCaP cells. Enhancers of DHCR24 and GNMT are regions that interact with PCAT1. The KLK2 enhancer served as a negative control. P value was calculated by Student’s t test in l and one-way ANOVA in m: *P < 0.05, **P < 0.01.

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Guo, H., Ahmed, M., Zhang, F. et al. Modulation of long noncoding RNAs by risk SNPs underlying genetic predispositions to prostate cancer. Nat Genet 48, 1142–1150 (2016). https://doi.org/10.1038/ng.3637

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