BACKGROUND: Multi-gene lists and single sample predictor models have been 
currently used to reduce the multidimensional complexity of breast cancers, and 
to identify intrinsic subtypes. The perceived inability of some models to deal 
with the challenges of processing high-dimensional data, however, limits the 
accurate characterisation of these subtypes. Towards the development of robust 
strategies, we designed an iterative approach to consistently discriminate 
intrinsic subtypes and improve class prediction in the METABRIC dataset.
FINDINGS: In this study, we employed the CM1 score to identify the most 
discriminative probes for each group, and an ensemble learning technique to 
assess the ability of these probes on assigning subtype labels using 24 
different classifiers. Our analysis is comprised of an iterative computation of 
these methods and statistical measures performed on a set of over 2000 samples. 
The refined labels assigned using this iterative approach revealed to be more 
consistent and in better agreement with clinicopathological markers and 
patients' overall survival than those originally provided by the PAM50 method.
CONCLUSIONS: The assignment of intrinsic subtypes has a significant impact in 
translational research for both understanding and managing breast cancer. The 
refined labelling, therefore, provides more accurate and reliable information by 
improving the source of fundamental science prior to clinical applications in 
medicine.