Cohort Profile: the Predictors of Breast Cancer Recurrence (ProBe CaRE) Premenopausal Breast Cancer Cohort Study in Denmark

Abstract

PurposeThe Predictors of Breast Cancer Recurrence (ProBe CaRe) study was established to evaluate modification of tamoxifen (TAM) effectiveness in premenopausal women through reduced activity of TAM-metabolising enzymes. It comprehensively evaluates the effects of pharmacogenetic variants, use of concomitant medications and biomarkers involved in oestrogen metabolism on breast cancer recurrence risk.ParticipantsThe ProBe CaRe study was established using resources from the Danish Breast Cancer Group (DBCG), including 5959 premenopausal women diagnosed with stage I–III primary breast cancer between 2002 and 2010 in Denmark. Eligible participants were divided into two groups based on oestrogen receptor alpha (ERα) expression and receipt of TAM therapy, 4600 are classified as ERα+/TAM+ and 1359 are classified as ERα−/TAM−. The ProBe CaRe study is a population-based cohort study nested in a nearly complete source population, clinical, tumour and demographic data were abstracted from DBCG registry data. Linkage to Danish registries allows for abstraction of information regarding comorbid conditions, comedication use and mortality. Formalin-fixed paraffin-embedded tissue samples have been prepared for DNA extraction and immunohistochemical assay.Findings to dateTo mitigate incorrect classification of patients into specific categories, we conducted a validation substudy. We compared data acquired from registry and from medical record review to calculate positive predictive values (PPVs) and negative predictive values. We observed PPVs near 100% for tumour size, lymph node involvement, receptor status, surgery type, receipt of radiotherapy, receipt of chemotherapy and TAM treatment. We found that the PPVs were 96% (95% CI 83% to 100%) for change in endocrine therapy and 61% (95% CI 42% to 77%) for menopausal transition.Future plansThe ProBeCaRe cohort study is well positioned to comprehensively examine pharmacogenetic variants. We will use a Bayesian pathway analysis to evaluate the complete TAM metabolic path to allow for gene–gene interactions, incorporating information of other important patient characteristics.