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Research Articles: Therapeutics, Targets, and Development

Prediction of drug combination chemosensitivity in human bladder cancer

Departments of ¹ Molecular Physiology and ² Public Health Sciences and ³ Mellon Prostate Cancer Institute, University of Virginia, Charlottesville, Virginia; ⁴ Department of Statistics, Korea University, Seoul, Korea; and ⁵ Genomics Institute of the Novartis Research Foundation, San Diego, California

Requests for reprints: Dan Theodorescu, Department of Molecular Physiology and Biological Physics, University of Virginia Health Sciences Center, Box 422, Charlottesville, VA 22908. Phone: 434-924-0042; Fax: 434-982-3652. E-mail: dt9d{at}virginia.edu

Abstract

The choice of therapy for metastatic cancer is largely empirical because of a lack of chemosensitivity prediction for available combination chemotherapeutic regimens. Here, we identify molecular models of bladder carcinoma chemosensitivity based on gene expression for three widely used chemotherapeutic agents: cisplatin, paclitaxel, and gemcitabine. We measured the growth inhibition elicited by these three agents in a series of 40 human urothelial cancer cell lines and correlated the GI₅₀ (50% of growth inhibition) values with quantitative measures of global gene expression to derive models of chemosensitivity using a misclassification-penalized posterior approach. The misclassification-penalized posterior–derived models predicted the growth response of human bladder cancer cell lines to each of the three agents with sensitivities of between 0.93 and 0.96. We then developed an in silico approach to predict the cellular growth responses for each of these agents in the clinically relevant two-agent combinations. These predictions were prospectively evaluated on a series of 15 randomly chosen bladder carcinoma cell lines. Overall, 80% of the predicted combinations were correct (P = 0.0002). Together, our results suggest that chemosensitivity to drug combinations can be predicted based on molecular models and provide the framework for evaluation of such models in patients undergoing combination chemotherapy for cancer. If validated in vivo, such predictive models have the potential to guide therapeutic choice at the level of an individual's tumor. [Mol Cancer Ther 2007;6(2):578–86]

Grant support: NIH grant CA075115.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

⁶ http://dtp.nci.nih.gov

⁷ www.bioconductor.org

Received 8/15/06; revised 10/28/06; accepted 12/18/06.

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