title: Using the epigenetic field defect to detect prostate cancer in biopsy negative patients.
abstract: OBJECTIVE
  We determined whether a novel combination of field defect DNA methylation markers could predict the presence of prostate cancer using histologically normal transrectal ultrasound guided biopsy cores.
  
  
  METHODS
  Methylation was assessed using quantitative Pyrosequencing® in a training set consisting of 65 nontumor and tumor associated prostate tissues from University of Wisconsin. A multiplex model was generated using multivariate logistic regression and externally validated in blinded fashion in a set of 47 nontumor and tumor associated biopsy specimens from University of Washington.
  
  
  RESULTS
  We observed robust methylation differences in all genes at all CpGs assayed (p <0.0001). Regression models incorporating individual genes (EVX1, CAV1 and FGF1) and a gene combination (EVX1 and FGF1) discriminated nontumor from tumor associated tissues in the original training set (AUC 0.796-0.898, p <0.001). On external validation uniplex models incorporating EVX1, CAV1 or FGF1 discriminated tumor from nontumor associated biopsy negative specimens (AUC 0.702, 0.696 and 0.658, respectively, p <0.05). A multiplex model (EVX1 and FGF1) identified patients with prostate cancer (AUC 0.774, p = 0.001) and had a negative predictive value of 0.909. Comparison between 2 separate cores in patients in this validation set revealed similar methylation defects, indicating detection of a widespread field defect.
  
  
  CONCLUSIONS
  A widespread epigenetic field defect can be used to detect prostate cancer in patients with histologically negative biopsies. To our knowledge this assay is unique, in that it detects alterations in nontumor cells. With further validation this marker combination (EVX1 and FGF1) has the potential to decrease the need for repeat prostate biopsies, a procedure associated with cost and complications.
authors.affiliation: University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Truong
authors.forename: Bing
authors.initials: B
authors.lastname: Yang
authors.forename: Andrew
authors.initials: A
authors.lastname: Livermore
authors.forename: Jennifer
authors.initials: J
authors.lastname: Wagner
authors.forename: Puspha
authors.initials: P
authors.lastname: Weeratunga
authors.forename: Wei
authors.initials: W
authors.lastname: Huang
authors.forename: Rajiv
authors.initials: R
authors.lastname: Dhir
authors.forename: Joel
authors.initials: J
authors.lastname: Nelson
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Lin
authors.forename: David F
authors.initials: DF
authors.lastname: Jarrard
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001707:Biopsy, Needle; D019175:DNA Methylation; D057890:Epigenomics / Q000379:methods*; D016220:Fibroblast Growth Factor 1 / Q000235:genetics* / Q000378:metabolism; D015972:Gene Expression Regulation, Neoplastic; D005819:Genetic Markers; D018398:Homeodomain Proteins / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D007150:Immunohistochemistry; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000473:pathology*; D012016:Reference Values; D012044:Regression Analysis; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric; D046509:Tissue Culture Techniques
chemical_list: D005819:Genetic Markers; D018398:Homeodomain Proteins; D016220:Fibroblast Growth Factor 1; C074437:EVX1 protein, human