Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping - PubMed (original) (raw)

Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping

Darragh G McArt et al. PLoS One. 2011.

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. setscore-vs-setsize plot of significant connections to the cervical cancer signature.

Green- significant connections; Blue- significant negative connection setscores with high PS and large setszie; Red- significant positive connection setscores with high PS and large setsize.

Figure 2

Figure 2. Significant connections to the letrozole treatment signature in breast cancer.

Green- significant connections; Blue- significant positive connection setscores with high PS and large setsize; Red- significant negative connection setscores with high PS and large setsize.

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References

    1. Smalley JL, Gant TW, Zhang SD. Application of connectivity mapping in predictive toxicology based on gene-expression similarity. Toxicology. 2010;268:143–146. - PubMed
    1. Lamb J. The connectivity map: a new tool for biomedical research. Nat Rev Cancer. 2007;7:54–60. - PubMed
    1. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, et al. The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–1935. - PubMed
    1. Gullans SR. Connecting the dots using gene-expression profiles. N Engl J Med. 2006;355:2042–2044. - PubMed
    1. Zhang SD, Gant T. A simple and robust method for connecting small-molecule drugs using gene-expression signatures. BMC Bioinformatics. 2008;9:258. - PMC - PubMed

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