Laurent Jacob, Anke Witteveen, Inès Beumer, Leonie Delahaye, Diederik Wehkamp, Jeroen van den Akker, Mireille Snel, Bob Chan, Arno Floore, Niels Bakx, Guido Brink, Coralie Poncet, Jan Bogaerts, Mauro Delorenzi, Martine Piccart, Emiel Rutgers, Fatima Cardoso, Terence Speed, Laura van ’t Veer, and Annuska Glas
Gene expression data obtained in large studies hold great promises for discovering disease signatures or subtypes through data analysis. It is also prone to technical variation, whose removal is essential to avoid spurious discoveries. Because this variation is not always known and can be confounded with biological signals, its removal is a challenging task. Here we provide a step-wise procedure and comprehensive analysis of the MINDACT microarray dataset. The MINDACT trial enrolled 6693 breast cancer patients and prospectively validated the gene expression signature MammaPrint for outcome prediction. The study also yielded a full-transcriptome microarray for each tumor. We show for the first time in such a large dataset how technical variation can be removed while retaining expected biological signals. Because of its unprecedented size, we hope the resulting adjusted dataset will be an invaluable tool to discover or test gene expression signatures and to advance our understanding of breast cancer.