The trans-DATA study: aims and design of a translational breast cancer prognostic marker identification study
SourceDiagnostic and Prognostic Research, 3, (2019), pp. 20
Article / Letter to editor
Display more detailsDisplay less details
Diagnostic and Prognostic Research
SubjectRadboudumc 17: Women's cancers RIHS: Radboud Institute for Health Sciences
Background: The effect of extended adjuvant aromatase inhibition in hormone-positive breast cancer after sequential tamoxifen, aromatase inhibitor treatment of 5 years was recently investigated by the DATA study. This study found no statistically significant effect of prolonged aromatase therapy. However, subgroup analysis showed post hoc statistically significant benefits in certain sub-populations. The trans-DATA study is a translational sub-study aiming to identify DNA methylation markers prognostic of patient outcome. Methods: Patients from the DATA study are included in the trans-DATA study. Primary breast tumour tissue will be collected, subtyped and used for DNA isolation. A genome-wide DNA methylation discovery assay will be performed on 60 patients that had a distant recurrence and 60 patients that did not have a distant recurrence using the Infinium Methylation EPIC Bead Chip platform. Differentially methylated regions of interest will be selected based on Akaike's Information Criterion, Gene Ontology Analysis and correlation between methylation and expression levels. Selected candidate genes will subsequently be validated in the remaining patients using qMSP. Discussion: The trans-DATA study uses a cohort derived from a clinical randomised trial. This study was designed to avoid common pitfalls in marker discovery studies such as selection bias, confounding and lack of reproducibility. In addition to the usual clinical risk factors, the results of this study may identify predictors of high recurrence risk in hormone receptor-positive breast cancer patients treated with sequential tamoxifen and aromatase inhibitor therapy.
This item appears in the following Collection(s)
- Academic publications 
- Electronic publications 
- Faculty of Medical Sciences 
- Open Access publications 
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.