Using pharmacoepidemiology to test medications associated with altered risk of breast cancer progression identified from connectivity mapping

Date of ISAC Approval: 
06/06/2016
Lay Summary: 
Connectivity mapping is a new technique that allows medications to be screened to assess their ability to cause cancer or have anti-cancer properties that are unrelated to the established therapeutic effects of these drugs. Connectivity mapping was conducted using publically available gene expression datasets. This connectivity map process has been implemented and has identified prescription medications (used for non-cancer purposes) that potentially alter risk of breast cancer progression. The aim of this study is to determine the association between use of the identified medications (from connectivity mapping) after breast cancer diagnosis and risk of breast cancer-specific mortality. The study could identify (previously unrecognized) medications which increase the risk of breast cancer progression; current licensing and use of such medications may need to be reconsidered. This study also has the potential to identify licensed medications that reduce risk of breast cancer progression and warrant further study in clinical trials.
Technical Summary: 
Background: Connectivity mapping is a novel bioinformatics technique linking gene expression data with expression events induced by medications. It therefore allows medications to be screened to assess carcinogenicity and anti-cancer properties. This connectivity map process has been implemented, using publically available gene expression datasets, to identify prescription medications (used for non-cancer purposes) that may alter risk of breast cancer progression. Aims: To determine the association between use of the identified medications (from connectivity mapping) and breast-cancer specific mortality. Methods: A cohort study will be conducted. Breast cancer patients will be identified from cancer registries. Candidate medication use will be determined from prescription records and cancer-specific mortality from ONS mortality records. Cox regression models (with medication use as a time-varying covariate) will be used to calculate hazard ratios (HRs), and 95% confidence intervals (95%CIs), for the association between candidate medication use after diagnosis and cancer-specific mortality after adjustment for potential confounders. Potential: The study could identify (previously unrecognized) medications which increase the risk of breast cancer progression; current licensing of such medications may need to be reconsidered. This study also has the potential to identify licensed medications that reduce risk of breast cancer progression and warrant further study in clinical trials.
Health Outcomes to be Measured: 
To determine if use of the identified medications after breast cancer diagnosis is associated with increased, or reduced, breast cancer-specific mortality
Collaborators: 

Dr Chris Cardwell - Chief Investigator - Queens University Belfast
Dr Chris Cardwell - Corresponding Applicant - Queens University Belfast
Fabio Liberante - Collaborator - Queens University Belfast
Gayathri Thillaiyampalam - Collaborator - Queens University Belfast
Ken Mills - Collaborator - Queens University Belfast
Liam Murray - Collaborator - Queens University Belfast
Shu-Dong Zhang - Collaborator - Queens University Belfast

Linkages: 
Cancer Registration Data;ONS;Patient IMD;Practice IMD (Standard)