In the Lab: Dr. Andrey V. Bortsov
This is based on the article, “μ-Opioid Receptor Gene A118G Polymorphism Predicts Survival in Patients with Breast Cancer” published in this month’s Anesthesiology, and covered on Page2Anesthesiology two days ago.
Increasing evidence suggests that opioids promote tumor growth. Clinical data regarding the influence of exogenous opioids on cancer outcomes are limited because of the imperative to treat cancer pain. In this setting, we felt that an observational study examining the association between a functional genetic polymorphism in the µ-opioid receptor gene and cancer survival might be useful because if opioid pathways are involved in tumor growth, then genetic variants that influence the function of the µ-opioid receptor should be associated with breast cancer survival.
First we sought to find a prospective cancer cohort study. Fortunately, the University of North Carolina is the home of many large-scale prospective observational studies, including The Carolina Breast Cancer Study. This is a population-based study designed to identify causes of breast cancer among Caucasian and African-American women who are residents of a 24-county area of central and eastern North Carolina.
We approached Dr. Millikan, the principal investigator of the Carolina Breast Cancer Study, regarding this collaboration and he kindly agreed. Genotyping was performed on patient blood samples using the TaqMan® platform (Applied Biosystems Inc., Foster City, CA) at the A118G SNP (rs1799971, located within the first exon) and five other informative SNPs within other parts of the µ-opioid receptor gene OPRM1. Work was performed in the UNC Mammalian Genotyping Core by Jason Kuo and Amanda Beaty.
Genetic results were delivered to the TRYUMPH Research Program in the UNC Department of Anesthesiology.
Associations between opioid polymorphisms and breast cancer survival were performed by Dr Andrey Bortsov. Dr. Bortsov used HapMap software to identify linkage disequilibrium plots, and SAS software to perform Cox proportional hazards regression analysis and Kaplan-Meier plots by genetic polymorphisms and cancer survival.