Title: A novel, integrated, bioinformatics and pharmacoepidemiology approach to identifying and testing medications with potential breast cancer carcinogenicity, or anti-cancer properties
Funder: Cancer Research UK

Abstract: Background: Connectivity mapping is a novel bioinformatics technique which links gene expression signatures from a biological state, e.g. cancer, with a reference database of drug-induced gene expression profiles. Therefore, it allows medications to be screened to assess carcinogenicity\anti-cancer properties and has successfully identified medications which alter cancer risk. Connectivity mapping will be implemented to identify medications which alter breast cancer risk and progression and the candidate medications identified will be validated using pharmacoepidemiology methods in large population-based cohorts within the Clinical Practice Research Datalink (CPRD). The proposed study is the first example of validation of associations identified through connectivity mapping using pharmacoepidemiology. Aims: The aim is to use connectivity mapping to identify candidate medications which could alter breast cancer risk or progression and to evaluate the effects of these medications using pharmacoepidemiology by (i) comparing breast cancer risk in users of these medications compared with non-users, and (ii) comparing the rate of cancer-specific death in breast cancer patients who use these medications with non-users. Methods: Gene expression signatures for breast cancer risk and progression will be created from publically available gene expression data. Differential expression analysis will identify differentially expressed genes for breast cancer risk and progression. A connectivity mapping process will be implemented (using a reference database of over 6000 individual drug-induced expression profiles) producing a list of candidate medications with the potential to alter breast cancer risk or progression. Candidate medications will be selected for pharmacoepidemiological analysis in a linked database combining CPRD, English cancer registry and ONS mortality data. A breast cancer case-control study will investigate the association between medication usage and breast cancer risk adjusting for potential confounders, using conditional logistic regression. A cohort analysis of incident breast cancer patients will investigate the association between candidate medication usage after diagnosis (as a time varying covariate) and cancer progression (based upon cancer-specific death from ONS), adjusting for potential confounders, using Cox regression. How the results of this research will be used: This study has the potential to identify medications that reduce breast cancer risk or delay cancer progression and warrant further study in trials. It could also identify (unrecognized) medications which increase the risk of breast cancer development or progression; current licensing of such medications may need to be reconsidered. Furthermore, the study will demonstrate the value of combining bioinfomatic analysis with pharmacoepidemiology; a process which could be applied to other cancers.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CANCER AND NEOPLASMS