RT Journal Article
SR Electronic
T1 Gene Expression Signature Predicting High-Grade Prostate Cancer Responses to Oxaliplatin
JF Molecular Pharmacology
JO Mol Pharmacol
FD American Society for Pharmacology and Experimental Therapeutics
SP 1205
OP 1216
DO 10.1124/mol.112.080333
VO 82
IS 6
A1 Stéphane Puyo
A1 Nadine Houédé
A1 Audrey Kauffmann
A1 Pierre Richaud
A1 Jacques Robert
A1 Philippe Pourquier
YR 2012
UL http://molpharm.aspetjournals.org/content/82/6/1205.abstract
AB Prostate cancer is one of the leading causes of cancer-related deaths among men. Several prognostic factors allow differentiation of low-grade tumors from high-grade tumors with high metastatic potential. High-grade tumors are currently treated with hormone therapy, to which taxanes are added when the tumors become resistant to castration. Clinical trials with other anticancer agents did not take into account the genetic backgrounds of the tumors, and most trials demonstrated low response rates. Here we used an in silico approach to screen for drug candidates that might be used as alternatives to taxanes, on the basis of a published expression signature involving 86 genes that could distinguish high-grade and low-grade tumors (Proc Natl Acad Sci USA 103:10991–10996, 2006). We explored the National Cancer Institute databases, which include data on the gene expression profiles of 60 human tumor cell lines and the in vitro sensitivities of the cell lines to anticancer drugs, and we identified several genes in the signature for which expression levels were correlated with chemosensitivity. As an example of the validation of this in silico approach, we identified a set of six genes for which expression levels could predict cell sensitivity to oxaliplatin but not cisplatin. This signature was validated in vitro through silencing of the genes in DU145, LNCaP, and C4-2B prostate cancer cells, which was accompanied by changes in oxaliplatin but not cisplatin cytotoxicity. These results demonstrate the relevance of our approach for the identification of both alternative treatments for high-grade prostate cancers and new biomarkers to predict clinical tumor responses.