title: Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping.
abstract: Connectivity mapping is a recently developed technique for discovering the underlying connections between different biological states based on gene-expression similarities. The sscMap method has been shown to provide enhanced sensitivity in mapping meaningful connections leading to testable biological hypotheses and in identifying drug candidates with particular pharmacological and/or toxicological properties. Challenges remain, however, as to how to prioritise the large number of discovered connections in an unbiased manner such that the success rate of any following-up investigation can be maximised. We introduce a new concept, gene-signature perturbation, which aims to test whether an identified connection is stable enough against systematic minor changes (perturbation) to the gene-signature. We applied the perturbation method to three independent datasets obtained from the GEO database: acute myeloid leukemia (AML), cervical cancer, and breast cancer treated with letrozole. We demonstrate that the perturbation approach helps to identify meaningful biological connections which suggest the most relevant candidate drugs. In the case of AML, we found that the prevalent compounds were retinoic acids and PPAR activators. For cervical cancer, our results suggested that potential drugs are likely to involve the EGFR pathway; and with the breast cancer dataset, we identified candidates that are involved in prostaglandin inhibition. Thus the gene-signature perturbation approach added real values to the whole connectivity mapping process, allowing for increased specificity in the identification of possible therapeutic candidates.
authors.affiliation: Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom.
authors.forename: Darragh G
authors.initials: DG
authors.lastname: McArt
authors.forename: Shu-Dong
authors.initials: SD
authors.lastname: Zhang
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics; D049490:Systems Biology / Q000379:methods; D002583:Uterine Cervical Neoplasms