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Quantitative GSTP1 methylation and the detection of prostate adenocarcinoma in sextant biopsies

  • Susan V. Harden
  • , Harriette Sanderson
  • , Steven N. Goodman
  • , Alan A.W. Partin
  • , Patrick C. Walsh
  • , Jonathan I. Epstein
  • , David Sidransky

Research output: Contribution to journalReview ArticleResearchpeer-review

Abstract

Hypermethylation of the 5′ promoter region of the glutathione S-transferase π gene (GSTP1) occurs at a very high frequency in prostate adenocarcinoma. We compared the results of blinded histologic review of sextant biopsy samples from 72 excised prostates with those obtained using a quantitative methylation-specific polymerase chain reaction assay (QMSP) for GSTP1. Formal surgical pathologic review of the resected prostates was used to determine the number of patients with (n = 61) and without (n = 11) prostate cancer. Histology alone detected prostate carcinoma with 64% sensitivity (95% confidence interval [CI] = 51% to 76%) and 100% specificity (95% CI = 72% to 100%), whereas the combination of histology and GSTP1 QMSP at an assay threshold greater than 10 detected prostate carcinoma with 75% sensitivity (95% CI = 63% to 86%) and 100% specificity (95% CI = 72% to 100%), an 11% improvement (95 % CI = 5% to 22%) in sensitivity over histology alone. The combination of histology and GSTP1 QMSP at an assay threshold greater than 5 detected prostate adenocarcinoma with 79% sensitivity (95% CI = 68% to 89%), a 15% improvement (95% CI = 7% to 26%) over histology alone. Thus, GSTP1 QMSP improved the sensitivity of histologic review of random needle biopsies for prostate cancer diagnosis. Further studies should determine whether detection of GSTP1 hypermethylation in a biopsy sample with normal histology indicates the need for an early repeat biopsy at the same site.

Original languageEnglish
Pages (from-to)1634-1637
Number of pages4
JournalJournal of the National Cancer Institute
Volume95
Issue number21
DOIs
Publication statusPublished - 5 Nov 2003
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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