An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping

Abstract

BackgroundGene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer).ResultsWe obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs.ConclusionsA few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1989-x) contains supplementary material, which is available to authorized users.

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“…We initially identified 67 medications for inclusion in the analysis [17], but excluded 57 of these (Appendix 2) as they were rarely prescribed within primary care (n=28, 42%), already used in cancer treatment (n=21, 31%), typically given in topical or nasal spray formulations which are unlikely to have a systemic effect (n=5, 7%), widely available over-the-counter (n=2, 3%), or already known to influence breast cancer risk (n=1, 1%). Of the ten remaining substances, six were identified as potentially increasing (meloxicam, azithromycin, rizatriptan, citalopram, rosiglitazone, verapamil) and four as potentially reducing (bendroflumethiazide, sertraline, fluvastatin, budesonide) the risk of breast cancer.…”

In vitro and clinical data analysis of Osteopontin as a prognostic indicator in colorectal cancer

“…Any drugs with a P ‐value smaller than the set threshold are considered statistically significant (those above the blue line in Figure ). These criteria control the expected number of falsely significant drugs at 1, as previously described and justified for multiple testing issues . Given the number of significant drugs as 95 in this study, the overall false discovery rate of our results is approximately 1% (1/95).…”

A combined connectivity mapping and pharmacoepidemiology approach to identify existing medications with breast cancer causing or preventing properties

“…Our application of the connectivity mapping is described briefly in Appendix 1, and more fully elsewhere. 17 Further details of this process are available upon request. The primary outcome of this was the "connectivity score", 14 which is similar to a z-score from a standard normal distribution, and can be interpreted as the strength of agreement between the gene signature of the disease and medication.…”

“…Our application of the connectivity mapping is described briefly in Appendix 1, and more fully elsewhere . Further details of this process are available upon request.…”

“…We initially identified 67 medications for inclusion in the analysis but excluded 57 of these (Appendix 2) as they were rarely prescribed within primary care (n = 28, 42%), already used in cancer treatment (n = 21, 31%), typically given in topical or nasal spray formulations that are unlikely to have a systemic effect (n = 5, 7%), widely available over‐the‐counter (n = 2, 3%), or already known to influence breast cancer risk (n = 1, 1%). Of the 10 remaining substances, 6 were identified as potentially increasing (meloxicam, azithromycin, rizatriptan, citalopram, rosiglitazone, and verapamil) and 4 as potentially reducing (bendroflumethiazide, sertraline, fluvastatin, and budesonide) the risk of breast cancer.…”