Human prostate cancers display numerous DNA methylation changes compared to normal tissue samples. However, definitive identification of features related to the cells’ malignant status has been compromised by the predominance of cells with luminal features in prostate cancers.
We generated genome-wide DNA methylation profiles of cell subpopulations with basal or luminal features isolated from matched prostate cancer and normal tissue samples.
Many frequent DNA methylation changes previously attributed to prostate cancers are here identified as differences between luminal and basal cells in both normal and cancer samples. We also identified changes unique to each of the two cancer subpopulations. Those specific to cancer luminal cells were associated with regulation of metabolic processes, cell proliferation and epithelial development. Within the prostate cancer TCGA dataset, these changes were able to distinguish not only cancers from normal samples, but also organ-confined cancers from those with extraprostatic extensions. Using changes present in both basal and luminal cancer cells, we derived a new 17-CpG prostate cancer signature with high predictive power in the TCGA dataset.
This study demonstrates the importance of comparing phenotypically matched prostate cell populations from normal and cancer tissues to unmask biologically and clinically relevant DNA methylation changes.
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Ethics approval and consent to participate:The study was conducted in accordance with the Declaration of Helsinki. All participating patients allowed the use of their medical records for clinical research purposes (if alive at the time of data collection). As an observational study, no approval ethics approval was required.
Consent for publication:this manuscript does not contain any individual person’s data.
Note: This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License)
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Ananthanarayanan, V., Deaton, R. J., Yang, X. J., Pins, M. R. & Gann, P. H. Alteration of proliferation and apoptotic markers in normal and premalignant tissue associated with prostate cancer. Bmc. Cancer 6, 73 (2006).
De Marzo, A. M., Meeker, A. K., Epstein, J. I. & Coffey, D. S. Prostate stem cell compartments: expression of the cell cycle inhibitor p27Kip1 in normal, hyperplastic, and neoplastic cells. Am. J. Pathol. 153, 911–919 (1998).
Polson, E. et al. Monoallelic expression of TMPRSS2/ERG in prostate cancer stem cells. Nat. Commun. 4, 1623 (2013).
Frame, F. M. et al. HDAC inhibitor confers radiosensitivity to prostate stem-like cells. Br. J. Cancer 109, 3023–3033 (2013).
Birnie, R. et al. Gene expression profiling of human prostate cancer stem cells reveals a pro-inflammatory phenotype and the importance of extracellular matrix interactions. Genome Biol. 9, R83 (2008).
Collins, A. T., Berry, P. A., Hyde, C., Stower, M. J. & Maitland, N. J. Prospective identification of tumorigenic prostate cancer stem cells. Cancer Res. 65, 10946–10951 (2005).
Massie, C. E., Mills, I. G. & Lynch, A. G. The importance of DNA methylation in prostate cancer development. J. Steroid Biochem. Mol. Biol. 166, 1–15 (2017).
Goering, W., Kloth, M. & Schulz, W. A. DNA methylation changes in prostate cancer. Methods Mol. Biol. 863, 47–66 (2012).
Aryee, M. J. et al. DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases. Sci. Transl. Med. 5, 169ra10 (2013).
Gifford, C. A. et al. Transcriptional and epigenetic dynamics during specification of human embryonic. Stem Cells 153, 1149–1163 (2013).
Farlik, M. et al. DNA methylation dynamics of human hematopoietic stem cell differentiation. Cell. Stem. Cell. 19, 808–822 (2016).
Roadmap Epigenomics Consortium, Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Pellacani, D. et al. Analysis of normal human mammary epigenomes reveals cell-specific active enhancer states and associated transcription factor networks. Cell Rep. 17, 2060–2074 (2016).
Goldstein, A. S., Huang, J., Guo, C., Garraway, I. P. & Witte, O. N. Identification of a cell of origin for human prostate cancer. Science 329, 568–571 (2010).
Zhang, D. et al. Stem cell and neurogenic gene-expression profiles link prostate basal cells to aggressive prostate cancer. Nat. Commun. 7, 10798 (2016).
Drost, J. et al. Organoid culture systems for prostate epithelial and cancer tissue. Nat. Protoc. 11, 347–358 (2016).
Karthaus, W. R. et al. Identification of multipotent luminal progenitor cells in human prostate organoid cultures. Cell 159, 163–175 (2014).
Pellacani, D. et al. DNA hypermethylation in prostate cancer is a consequence of aberrant epithelial differentiation and hyperproliferation. Cell Death Differ. 21, 761–773 (2014).
Frame, F. M., Pellacani, D., Collins, A. T. & Maitland, N. J. Harvesting human prostate tissue material and culturing primary prostate epithelial cells. Methods Mol. Biol. 1443, 181–201 (2016).
Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinforma. 10, 232 (2009).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Akalin, A. et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).
Wang, H.-Q., Tuominen, L. K. & Tsai, C.-J. SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures. Bioinformatics 27, 225–231 (2011).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Pohl, A. & Beato, M. bwtool: a tool for bigWig files. Bioinformatics 30, 1618–1619 (2014).
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T. & Lowe, C. B. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).
Yu, Y. P. et al. Whole-genome methylation sequencing reveals distinct impact of differential methylations on gene transcription in prostate cancer. Am. J. Pathol. 183, 1960–1970 (2013).
Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).
Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).
Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).
Zhao, S. et al. Epigenome-wide tumor DNA methylation profiling identifies novel prognostic biomarkers of metastatic-lethal progression in men diagnosed with clinically localized prostate cancer. Clin. Cancer Res. Am. Assoc. Cancer Res. 23, 311–319 (2017).
Geybels, M. S. et al. Epigenomic profiling of DNA methylation in paired prostate cancer versus adjacent benign tissue. Prostate 75, 1941–1950 (2015).
Geybels, M. S. et al. Epigenetic signature of Gleason score and prostate cancer recurrence after radical prostatectomy. Clin. Epigenetics. BioMed. Cent. 8, 97 (2016).
Mundbjerg, K. et al. Identifying aggressive prostate cancer foci using a DNA methylation classifier. Genome Biol. BioMed. Cent. 18, 3 (2017).
Tang, Y. et al. Promoter DNA methylation analysis reveals a combined diagnosis of CpG-based biomarker for prostate cancer. Oncotarget. Impact J. 8, 58199–58209 (2017).
Strand, S. H., Ørntoft, T. F. & Sørensen, K. D. Prognostic DNA methylation markers for prostate cancer. Int J. Mol. Sci. Multidiscip. Digit. Publ. Inst. 15, 16544–16576 (2014).
Chen, R. Z., Pettersson, U., Beard, C., Jackson-Grusby, L. & Jaenisch, R. DNA hypomethylation leads to elevated mutation rates. Nature 395, 89–93 (1998).
Eden, A., Gaudet, F., Waghmare, A. & Jaenisch, R. Chromosomal instability and tumors promoted by DNA hypomethylation. Sci. Am. Assoc. Adv. Sci. 300, 455–455 (2003).
Babaian, A. & Mager, D. L. Endogenous retroviral promoter exaptation in human cancer. Mob. Dna. BioMed. Cent. 7, 24 (2016).
Ellinger, J. et al. Global levels of histone modifications predict prostate cancer recurrence. Prostate 70, 61–69 (2010).
Angulo, J. C. et al. Development of castration resistant prostate cancer can be predicted by a DNA hypermethylation profile. J. Urol. 195, 619–626 (2016).
McDonald, O. G. et al. Epigenomic reprogramming during pancreatic cancer progression links anabolic glucose metabolism to distant metastasis. Nat. Genet. 49, 367–376 (2017).
The authors thank the urology surgeons and patients from Castle Hill Hospital for kind donations of clinical prostate samples. We thank Artem Babaian, Rod Docking, Dr. Kieran O Neill, Hye-Jung E. Chun, Dr. Misha Bilenky, Dr. Alireza Heravi-Moussavi and Dr. Martin Hirst for the useful discussions regarding the bioinformatic analyses conducted in this study.
D.P. and N.J.M. designed the project. M.S.S. and V.M.M. procured the tissue samples. D.P. processed and sorted the tissue samples, and performed all other experiments. D.P., F.M.F. and A.T.C. developed the tissue processing and sorting protocol. D.P. and A.P.D. conducted all bioinformatic analyses. D.P., C.J.E. and N.J.M. wrote the manuscript. All authors contributed to the interpretation of the results and read and approved the manuscript.