Genome-wide plasma DNA methylation features of metastatic prostate cancer

Anjui Wu, Paolo Cremaschi, Daniel Wetterskog, Vincenza Conteduca, Gian Marco Franceschini, Dimitrios Kleftogiannis, Anuradha Jayaram, Shahneen Sandhu, Stephen Q Wong, Matteo Benelli, Samanta Salvi, Giorgia Gurioli, Andrew Feber, Mariana Buongermino Pereira, Anna Maria Wingate, Enrique Gonzalez-Billalebeitia, Ugo De Giorgi, Francesca Demichelis, Stefano Lise, Gerhardt Attard, Anjui Wu, Paolo Cremaschi, Daniel Wetterskog, Vincenza Conteduca, Gian Marco Franceschini, Dimitrios Kleftogiannis, Anuradha Jayaram, Shahneen Sandhu, Stephen Q Wong, Matteo Benelli, Samanta Salvi, Giorgia Gurioli, Andrew Feber, Mariana Buongermino Pereira, Anna Maria Wingate, Enrique Gonzalez-Billalebeitia, Ugo De Giorgi, Francesca Demichelis, Stefano Lise, Gerhardt Attard

Abstract

Tumor DNA circulates in the plasma of cancer patients admixed with DNA from noncancerous cells. The genomic landscape of plasma DNA has been characterized in metastatic castration-resistant prostate cancer (mCRPC) but the plasma methylome has not been extensively explored. Here, we performed next-generation sequencing (NGS) on plasma DNA with and without bisulfite treatment from mCRPC patients receiving either abiraterone or enzalutamide in the pre- or post-chemotherapy setting. Principal component analysis on the mCRPC plasma methylome indicated that the main contributor to methylation variance (principal component one, or PC1) was strongly correlated with genomically determined tumor fraction (r = -0.96; P < 10-8) and characterized by hypermethylation of targets of the polycomb repressor complex 2 components. Further deconvolution of the PC1 top-correlated segments revealed that these segments are comprised of methylation patterns specific to either prostate cancer or prostate normal epithelium. To extract information specific to an individual's cancer, we then focused on an orthogonal methylation signature, which revealed enrichment for androgen receptor binding sequences and hypomethylation of these segments associated with AR copy number gain. Individuals harboring this methylation pattern had a more aggressive clinical course. Plasma methylome analysis can accurately quantitate tumor fraction and identify distinct biologically relevant mCRPC phenotypes.

Trial registration: ClinicalTrials.gov NCT02288936.

Keywords: Cancer; Epigenetics; Genetics; Oncology; Prostate cancer.

Conflict of interest statement

Conflict of interest: AW, PC, DW, and GA have a patent application under consideration (GB1915469.9). The Institute of Cancer Research (ICR) developed abiraterone and therefore has a commercial interest in this agent. GA receives a reward from the ICR for his role as an inventor of abiraterone. GA has received honoraria, consulting fees, or travel support from Astellas, Medivation, and Janssen, and grant support from Janssen, AstraZeneca, and Arno. S Sandhu has received honoraria from Merck, Bristol Myers Squibb, Janssen, AstraZeneca, Merck Serono, Genentech, and Novartis, and grant support from Merck and Amgen. VC and UDG have received speaker honoraria or travel support from Bayer, Astellas, Janssen-Cilag, and Sanofi-Aventis. VC has received consulting fees from Bayer. EGB has received speaker honoraria or travel support from Astellas, Janssen-Cilag, and Sanofi-Aventis.

Figures

Figure 1. The mCRPC plasma methylome.
Figure 1. The mCRPC plasma methylome.
(A) Schematic overview of the workflow for integrating NGS of the plasma methylome and genome. (B) Genomically determined tumor fraction in baseline and progression samples from pre- and post-chemotherapy patients receiving abiraterone or enzalutamide. (C) Methylation ratio density (upper panel) and quantile-quantile plot (Q-Q plot, bottom panel) analysis based on the genomic annotation of methylation segments in promoter or other regions. Data from white blood cells (WBC) or plasma collected at baseline (BL) or progression (PD) from mCRPC patients or from healthy volunteers (HV) are presented separately. (D) Schematic workflow of methylation data analysis.
Figure 2. Tumor fraction is the major…
Figure 2. Tumor fraction is the major determinant of the plasma methylome.
(A) Bar chart shows the variance associated to each principal component (PC) on 19 baseline samples; the red dotted line indicates cumulative explained variance. (B) Correlation between PCs and tumor fraction. Size and the color of each circle show Pearson correlation and background shading denotes P value). (C) Correlation of genomically determined tumor fraction (y axis) and PC1 values (x axis) from high-coverage targeted methylation sequencing on 19 baseline samples, 16 progression plasma samples, and control samples (n = 4 healthy volunteer plasma samples, LNCaP prostate cancer cell line).
Figure 3. Methylation ratio across ct-MethSig can…
Figure 3. Methylation ratio across ct-MethSig can be a proxy for tumor fraction.
(A) Top 1000 segments (ct-MethSig) with the highest correlation coefficient between PC1 and methylation ratio. (B) ct-MethSig methylation ratio distribution by patient plasma sample split by negatively correlated and positively correlated segments. (C) Venn diagram showing the overlap of negatively correlated genes (dark blue) in ct-MethSig segments with targets of EED, SUZ12, and embryonic stem cells (ES) with H3K27ME3 marks. The number in white denotes the number of genes in the ct-MethSig negatively correlated group. (D) Circulating tumor fraction methylation signature comprises segments specific to either normal or malignant prostate epithelium. Left: Methylation ratios of ct-MethSig hypermethylated (n = 520) and hypomethylated (n = 480) groups from LNCaP (n = 4), healthy volunteers (n = 4), and normal prostate epithelium samples (PrEC). Right: The ct-MethSig hypermethylated and hypomethylated groups can be split into prostate cancer–specific segments and prostate epithelium–specific segments.
Figure 4. Methylation signatures that could allow…
Figure 4. Methylation signatures that could allow subgrouping of mCRPC.
(A) Top 1000 segments with the highest correlation coefficient between PC3 and methylation ratio. (B) Methylation ratio of top 1000 segments highly correlated with PC3 values derived from plasma, white blood cell, HSPC tumor, and CRPC tumor (CASCADE trial). (C) Comparison of intraindividual changes in the top-correlated segments defined by targeted methylation NGS on plasma DNA and changes in tumor fraction. The y axis denotes the difference (Δ) of mean methylation ratio of the top-correlated segments between baseline and progression samples and the x axis denotes the difference in tumor fraction. (D) Median methylation ratio of the top-correlated segments of different metastatic sites by patient from the CASCADE rapid warm autopsy program. (E) AR binding motif that is overrepresented in regions adjacent to the top correlated segments (top). The consensus AR binding motif is shown as a reference (bottom). (F) Methylation ratio of AR-MethSig segments of AR gain group (CRPC metastases n = 5, CRPC plasma n = 18) and nongain group (CRPC metastases n = 8, CRPC plasma n = 17; Mann-Whitney U test). (G) Overall survival analysis (start of ADT to death) for AR-MethSig low group versus AR-MethSig high group (Mantel-Cox log-rank test).

References

    1. Diaz LA, Bardelli A. Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol. 2014;32(6):579–586. doi: 10.1200/JCO.2012.45.2011.
    1. Newman AM, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20(5):548–554. doi: 10.1038/nm.3519.
    1. Dawson SJ, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med. 2013;368(13):1199–1209. doi: 10.1056/NEJMoa1213261.
    1. Chakravarthy A, et al. Pan-cancer deconvolution of tumour composition using DNA methylation. Nat Commun. 2018;9(1):3220. doi: 10.1038/s41467-018-05570-1.
    1. Benelli M, Romagnoli D, Demichelis F. Tumor purity quantification by clonal DNA methylation signatures. Bioinformatics. 2018;34(10):1642–1649. doi: 10.1093/bioinformatics/bty011.
    1. Altimari A, et al. Diagnostic role of circulating free plasma DNA detection in patients with localized prostate cancer. Am J Clin Pathol. 2008;129(5):756–762. doi: 10.1309/DBPX1MFNDDJBW1FL.
    1. Sun K, et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci USA. 2015;112(40):E5503–E5512. doi: 10.1073/pnas.1508736112.
    1. Guo S, Diep D, Plongthongkum N, Fung HL, Zhang K, Zhang K. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet. 2017;49(4):635–642. doi: 10.1038/ng.3805.
    1. Kang S, et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 2017;18(1):53. doi: 10.1186/s13059-017-1191-5.
    1. Li W, et al. CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res. 2018;46(15):e89. doi: 10.1093/nar/gky423.
    1. Shen SY, et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature. 2018;563(7732):579–583. doi: 10.1038/s41586-018-0703-0.
    1. Zemmour H, et al. Non-invasive detection of human cardiomyocyte death using methylation patterns of circulating DNA. Nat Commun. 2018;9(1):1443. doi: 10.1038/s41467-018-03961-y.
    1. Lehmann-Werman R, et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci USA. 2016;113(13):E1826–E1834. doi: 10.1073/pnas.1519286113.
    1. Moss J, et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun. 2018;9(1):5068. doi: 10.1038/s41467-018-07466-6.
    1. Landan G, et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat Genet. 2012;44(11):1207–1214. doi: 10.1038/ng.2442.
    1. Romanel A, et al. Plasma AR and abiraterone-resistant prostate cancer. Sci Transl Med. 2015;7(312):312re10. doi: 10.1126/scitranslmed.aac9511.
    1. Conteduca V, et al. Androgen receptor gene status in plasma DNA associates with worse outcome on enzalutamide or abiraterone for castration-resistant prostate cancer: a multi-institution correlative biomarker study. Ann Oncol. 2017;28(7):1508–1516. doi: 10.1093/annonc/mdx155.
    1. De Laere B, et al. TP53 outperforms other androgen receptor biomarkers to predict abiraterone or enzalutamide outcome in metastatic castration-resistant prostate cancer. Clin Cancer Res. 2019;25(6):1766–1773. doi: 10.1158/1078-0432.CCR-18-1943.
    1. Ulz P, et al. Whole-genome plasma sequencing reveals focal amplifications as a driving force in metastatic prostate cancer. Nat Commun. 2016;7:12008.
    1. Annala M, et al. Circulating tumor DNA genomics correlate with resistance to abiraterone and enzalutamide in prostate cancer. Cancer Discov. 2018;8(4):444–457. doi: 10.1158/-17-0937.
    1. Beltran H, et al. Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat Med. 2016;22(3):298–305. doi: 10.1038/nm.4045.
    1. Prandi D, et al. Unraveling the clonal hierarchy of somatic genomic aberrations. Genome Biol. 2014;15(8):439. doi: 10.1186/s13059-014-0439-6.
    1. Carreira S, et al. Tumor clone dynamics in lethal prostate cancer. Sci Transl Med. 2014;6(254):254ra125. doi: 10.1126/scitranslmed.3009448.
    1. Yu G, Wang LG, He QY. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 2015;31(14):2382–2383. doi: 10.1093/bioinformatics/btv145.
    1. Gama-Sosa MA, et al. The 5-methylcytosine content of DNA from human tumors. Nucleic Acids Res. 1983;11(19):6883–6894. doi: 10.1093/nar/11.19.6883.
    1. Feinberg AP, Vogelstein B. Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature. 1983;301(5895):89–92. doi: 10.1038/301089a0.
    1. Ehrlich M. DNA hypomethylation in cancer cells. Epigenomics. 2009;1(2):239–259. doi: 10.2217/epi.09.33.
    1. Massie CE, Mills IG, Lynch AG. The importance of DNA methylation in prostate cancer development. J Steroid Biochem Mol Biol. 2017;166:1–15. doi: 10.1016/j.jsbmb.2016.04.009.
    1. Fraser M, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541(7637):359–364. doi: 10.1038/nature20788.
    1. Lee TI, et al. Control of developmental regulators by Polycomb in human embryonic stem cells. Cell. 2006;125(2):301–313. doi: 10.1016/j.cell.2006.02.043.
    1. Yu J, et al. A polycomb repression signature in metastatic prostate cancer predicts cancer outcome. Cancer Res. 2007;67(22):10657–10663. doi: 10.1158/0008-5472.CAN-07-2498.
    1. Cao Q, et al. The central role of EED in the orchestration of polycomb group complexes. Nat Commun. 2014;5:3127.
    1. Højfeldt JW, et al. Accurate H3K27 methylation can be established de novo by SUZ12-directed PRC2. Nat Struct Mol Biol. 2018;25(3):225–232. doi: 10.1038/s41594-018-0036-6.
    1. Pidsley R, et al. Enduring epigenetic landmarks define the cancer microenvironment. Genome Res. 2018;28(5):625–638. doi: 10.1101/gr.229070.117.
    1. Zambelli F, Pesole G, Pavesi G. PscanChIP: Finding over-represented transcription factor-binding site motifs and their correlations in sequences from ChIP-Seq experiments. Nucleic Acids Res. 2013;41(W1):W535–W543. doi: 10.1093/nar/gkt448.
    1. Robinson D, et al. Integrative clinical genomics of advanced prostate cancer. Cell. 2015;162(2):454. doi: 10.1016/j.cell.2015.06.053.
    1. Millar DS, Ow KK, Paul CL, Russell PJ, Molloy PL, Clark SJ. Detailed methylation analysis of the glutathione S-transferase pi (GSTP1) gene in prostate cancer. Oncogene. 1999;18(6):1313–1324. doi: 10.1038/sj.onc.1202415.
    1. Capper D, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469–474. doi: 10.1038/nature26000.
    1. Quigley DA, et al. Genomic hallmarks and structural variation in metastatic prostate cancer. Cell. 2018;175(3):889. doi: 10.1016/j.cell.2018.10.019.
    1. Aryee MJ, et al. DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases. Sci Transl Med. 2013;5(169):169ra10.
    1. Feber A, et al. Using high-density DNA methylation arrays to profile copy number alterations. Genome Biol. 2014;15(2):R30. doi: 10.1186/gb-2014-15-2-r30.
    1. Brocks D, et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Rep. 2014;8(3):798–806. doi: 10.1016/j.celrep.2014.06.053.
    1. Mayrhofer M, et al. Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis. Genome Med. 2018;10(1):85. doi: 10.1186/s13073-018-0595-5.
    1. Alsop K, et al. A community-based model of rapid autopsy in end-stage cancer patients. Nat Biotechnol. 2016;34(10):1010–1014. doi: 10.1038/nbt.3674.
    1. Lê S, Josse J, Husson F. FactoMineR: An R package for multivariate analysis. J Stat Softw. 2008;25(1):18
    1. Josse J, Husson F. missMDA: A package for handling missing values in multivariate data analysis. J Stat Softw. 2016;70(1):31
    1. Adalsteinsson VA, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun. 2017;8(1):1324. doi: 10.1038/s41467-017-00965-y.

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