title: An epigenetic signature in peripheral blood predicts active ovarian cancer.
abstract: BACKGROUND
  Recent studies have shown that DNA methylation (DNAm) markers in peripheral blood may hold promise as diagnostic or early detection/risk markers for epithelial cancers. However, to date no study has evaluated the diagnostic and predictive potential of such markers in a large case control cohort and on a genome-wide basis.
  
  
  RESULTS
  By performing genome-wide DNAm profiling of a large ovarian cancer case control cohort, we here demonstrate that active ovarian cancer has a significant impact on the DNAm pattern in peripheral blood. Specifically, by measuring the methylation levels of over 27,000 CpGs in blood cells from 148 healthy individuals and 113 age-matched pre-treatment ovarian cancer cases, we derive a DNAm signature that can predict the presence of active ovarian cancer in blind test sets with an AUC of 0.8 (95% CI (0.74-0.87)). We further validate our findings in another independent set of 122 post-treatment cases (AUC = 0.76 (0.72-0.81)). In addition, we provide evidence for a significant number of candidate risk or early detection markers for ovarian cancer. Furthermore, by comparing the pattern of methylation with gene expression data from major blood cell types, we here demonstrate that age and cancer elicit common changes in the composition of peripheral blood, with a myeloid skewing that increases with age and which is further aggravated in the presence of ovarian cancer. Finally, we show that most cancer and age associated methylation variability is found at CpGs located outside of CpG islands.
  
  
  CONCLUSIONS
  Our results underscore the potential of DNAm profiling in peripheral blood as a tool for detection or risk-prediction of epithelial cancers, and warrants further in-depth and higher CpG coverage studies to further elucidate this role.
authors.affiliation: Medical Genomics Group, University College London Cancer Institute, University College London, London, United Kingdom. a.teschendorff@ucl.ac.uk
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Teschendorff
authors.forename: Usha
authors.initials: U
authors.lastname: Menon
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Gentry-Maharaj
authors.forename: Susan J
authors.initials: SJ
authors.lastname: Ramus
authors.forename: Simon A
authors.initials: SA
authors.lastname: Gayther
authors.forename: Sophia
authors.initials: S
authors.lastname: Apostolidou
authors.forename: Allison
authors.initials: A
authors.lastname: Jones
authors.forename: Matthias
authors.initials: M
authors.lastname: Lechner
authors.forename: Stephan
authors.initials: S
authors.lastname: Beck
authors.forename: Ian J
authors.initials: IJ
authors.lastname: Jacobs
authors.forename: Martin
authors.initials: M
authors.lastname: Widschwendter
mesh_terms: D000375:Aging / Q000235:genetics / Q000473:pathology; D016000:Cluster Analysis; D018899:CpG Islands / Q000235:genetics; D019175:DNA Methylation / Q000235:genetics; D044127:Epigenesis, Genetic; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis / Q000235:genetics*