title: Using functional signatures to identify repositioned drugs for breast, myelogenous leukemia and prostate cancer.
abstract: The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication. Although the genomic revolution appears to have had little impact on this problem, and might even have exacerbated it because of the flood of additional and usually ineffective leads, the emergence of high throughput resources promises the possibility of rapid, reliable and systematic identification of approved drugs for originally unintended uses. In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer, myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds. The method uses variable gene signatures to identify bioactive compounds that modulate a given disease. This is in contrast to previous methods that use small and fixed signatures. This strategy is based on the observation that diseases stem from failed/modified cellular functions, irrespective of the particular genes that contribute to the function, i.e., this strategy targets the functional signatures for a given cancer. This function-based strategy broadens the search space for the effective drugs with an impressive hit rate. Among the 79, 94 and 88 candidate drugs for breast cancer, myelogenous leukemia and prostate cancer, 32%, 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs, or drugs with suggestive literature evidences, with an FDR of 0.01. These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development, and has potential application for the personalized medicine.
authors.affiliation: Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
authors.forename: Daichi
authors.initials: D
authors.lastname: Shigemizu
authors.forename: Zhenjun
authors.initials: Z
authors.lastname: Hu
authors.forename: Jui-Hung
authors.initials: JH
authors.lastname: Hung
authors.forename: Chia-Ling
authors.initials: CL
authors.lastname: Huang
authors.forename: Yajie
authors.initials: Y
authors.lastname: Wang
authors.forename: Charles
authors.initials: C
authors.lastname: DeLisi
mesh_terms: D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D055808:Drug Discovery; D058492:Drug Repositioning; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007951:Leukemia, Myeloid / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D008297:Male; D011471:Prostatic Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction