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Novel Methylation Markers in a Prostate Cancer Cohort are Associated with Disease Development and Relapse

Lach, R. P.; Pita, S.; Leung, W.-K.; Babbage, A.; Merson, S.; Hawkins, S.; Luxton, H.; Kay, J.; Whitaker, H. C.; Woodcock, D. J.; Haberland, V.; Kote-Jarai, Z.; Milne-Clark, T.; O'Neill, K.; Brendler-Spaeth, T.; Cheung, M.; Ko, M.; CRUK ICGC Prostate Cancer Group, ; Dev, H.; Butler, A.; Lambert, A.; Hamdy, F. C.; Verrill, C.; Field, S.; Bova, G. S.; Foster, C.; Neal, D. E.; Wedge, D. C.; Gnanapragasam, V. J.; Warren, A. Y.; Eeles, R. A.; Cooper, C. S.; Brewer, D. S.; Massie, C. E.; Lynch, A. G.

2026-08-27 cancer biology
10.64898/2026.08.26.747370 bioRxiv
Show abstract

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

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