Defining aggressive prostate cancer using a 12-gene model.
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.
Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Tarek A
TA
Bismar
Francesca
F
Demichelis
Alberto
A
Riva
Robert
R
Kim
Sooryanarayana
S
Varambally
Le
L
He
Jeff
J
Kutok
Jonathan C
JC
Aster
Jeffery
J
Tang
Rainer
R
Kuefer
Matthias D
MD
Hofer
Phillip G
PG
Febbo
Arul M
AM
Chinnaiyan
Mark A
MA
Rubin
D014408:Biomarkers, Tumor; D016000:Cluster Analysis; D015331:Cohort Studies; D015972:Gene Expression Regulation, Neoplastic; D020022:Genetic Predisposition to Disease; D006801:Humans; D007150:Immunohistochemistry; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000601:surgery; D040901:Proteomics / Q000379:methods; D013997:Time Factors
D014408:Biomarkers, Tumor