Title | Defining aggressive prostate cancer using a 12-gene model. |
Publication Type | Journal Article |
Year of Publication | 2006 |
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 |
Journal | Neoplasia |
Volume | 8 |
Issue | 1 |
Pagination | 59-68 |
Date Published | 2006 Jan |
ISSN | 1476-5586 |
Keywords | Cluster Analysis, Cohort Studies, Gene Expression Regulation, Neoplastic, Genetic Predisposition to Disease, Humans, Immunohistochemistry, Male, Oligonucleotide Array Sequence Analysis, Prostatic Neoplasms, Proteomics, Time Factors, Tumor Markers, Biological |
Abstract | 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. |
DOI | 10.1593/neo.05664 |
Alternate Journal | Neoplasia |
PubMed ID | 16533427 |
PubMed Central ID | PMC1584291 |
Grant List | 5P30 CA46592 / CA / NCI NIH HHS / United States CA97063 / CA / NCI NIH HHS / United States P50CA69568 / CA / NCI NIH HHS / United States P50CA90381 / CA / NCI NIH HHS / United States R01AG21404 / AG / NIA NIH HHS / United States U01 CA111275-01 / CA / NCI NIH HHS / United States |