Evaluation of New Drugs for Treatment of Prostate Cancer Patients Using Gene Signatures and the Connectivity Map Database

Abstract

Taking advantage of the Connectivity Map database that contains gene signatures for 1309 drugs we tested the hypothesis that drugs that elicit gene signatures most opposed to metastatic prostate cancer will be effective anti-cancer drugs. Our data provide strong evidence that gene expression profiles can be utilized to predict which drugs may be effective in killing hormone-refractory PCa cells. We have identified a metastatic PCa-specific gene signature by identifying a set of genes commonly deregulated in at least 4 published datasets of metastatic PCa. We have demonstrated that the Connectivity Map database can be exploited to identify potential anti-PCa drugs that can be tested in cell culture and eventually in animal models for preclinical validation. Since the drugs that we selected are FDA approved, clinical trials could be rapidly initiated if the animal experiments are successful. 5 out of 11 drugs, selected as high scorers in the Connectivity Map analysis of metastatic PCa, are indeed potent inducers of apoptosis in PCa cell lines, and 3 of them demonstrate enhanced efficacy in killing of hormone-refractory PCa cells indicating that this set of structurally unrelated drugs indeed elicits anti-PCa activity. These drugs will be tested in PCa mouse models for anti-tumor activity.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2012
Accession Number
ADA612317

Entities

People

  • Towia A Libermann

Organizations

  • Beth Israel Deaconess Medical Center

Tags

DTIC Thesaurus Topics

  • Anticonvulsants
  • Apoptosis
  • Biomedical Research
  • Cell Line
  • Cell Physiological Processes
  • Cells
  • Clinical Trials
  • Culture Techniques
  • Databases
  • Diseases And Disorders
  • Gene Expression
  • Neoplasms
  • Prostate
  • Prostate Cancer
  • Small Molecules
  • Tissues
  • Validation

Fields of Study

  • Biology

Readers

  • Neural Network Machine Learning.
  • Oncology (Cancer Research).
  • Prostate Cancer Biology.