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Bioinformatics Advance Access originally published online on October 8, 2007
Bioinformatics 2008 24(3):389-395; doi:10.1093/bioinformatics/btm447
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Successful anti-cancer drug targets able to pass FDA review demonstrate the identifiable signature distinct from the signatures of random genes and initially proposed targets

Anatoly L. Mayburd 1,*, Inna Golovchikova 2 and James L. Mulshine 3,*

1CPA Global, LLC, 1725 Duke Street, Alexnadria, VA 22314, 2IGMZ consulting, 456 Washington Avenue, Belleville, NJ 07109 and 3Rush University Medical Center, Chicago, IL 60612, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: New efforts to guide and prioritize the selection of cancer drug targets are urgently needed, as is evident by the slow development of novel anti-cancer agents and the narrow therapeutic index of existing drugs. Given these limitations, the current study was conducted to explore the classification features defining the therapeutic success that can result from targeting a particular gene.

Results: Classification was based on extracting features specific to known successful anti-cancer targets and combining them in a linear classifier, resulting in calculation of an enrichment score for each gene. Extended description, the search tool used in this study, enriched existing drug target candidates by up to 10-fold at an ~50% recall rate, covering ~24 000 genes or ~80% of genome. More importantly, the target category with high attrition rate was classified from target category with low attrition rate, allowing to refine the drug development portfolios. Biological relevance of the parameters comprising the enrichment score was explored. Enrichment in cancer-specific effects was independently demonstrated by literature analysis. Imposing these enrichment scores on existing structural, pathway and phenotype-based procedures for prospective target selection may enhance the efficiency and accuracy of target identification and accelerate drug design.

Availability: The software used in this work is available upon request.

Contact: amayburd{at}cpaglobal.com, James_L_Mulshine{at}rush.edu

Supplementary information: Supplementary data are available at www.mayburd.com; http://www.rush.edu/rumc/page-1120170920643.html

Associate Editor: Jonathan Wren

Received on April 12, 2007; revised on July 30, 2007; accepted on August 25, 2007

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