Defining aggressive prostate cancer using a 12-gene model.

Published

Journal Article

The critical clinical question in prostate cancer research is: How do we develop means of distinguishing aggressive disease from indolent disease? Using a combination of proteomic and expression array data, we identified a set of 36 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Another five prostate cancer biomarkers were included using linear discriminant analysis, we determined that the optimal model used to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, transcriptional levels of the 12 genes encoding for these proteins predicted prostate-specific antigen failure in 79 men following surgery for clinically localized prostate cancer (P = .0015). This study demonstrates that cross-platform models can lead to predictive models with the possible advantage of being more robust through this selection process.

Full Text

Cited Authors

  • Bismar, TA; Demichelis, F; Riva, A; Kim, R; Varambally, S; He, L; Kutok, J; Aster, JC; Tang, J; Kuefer, R; Hofer, MD; Febbo, PG; Chinnaiyan, AM; Rubin, MA

Published Date

  • January 2006

Published In

Volume / Issue

  • 8 / 1

Start / End Page

  • 59 - 68

PubMed ID

  • 16533427

Pubmed Central ID

  • 16533427

Electronic International Standard Serial Number (EISSN)

  • 1476-5586

Digital Object Identifier (DOI)

  • 10.1593/neo.05664

Language

  • eng

Conference Location

  • United States