BACKGROUND: The prediction of breast cancer intrinsic subtypes has been 
introduced as a valuable strategy to determine patient diagnosis and prognosis, 
and therapy response. The PAM50 method, based on the expression levels of 50 
genes, uses a single sample predictor model to assign subtype labels to samples. 
Intrinsic errors reported within this assay demonstrate the challenge of 
identifying and understanding the breast cancer groups. In this study, we aim 
to: a) identify novel biomarkers for subtype individuation by exploring the 
competence of a newly proposed method named CM1 score, and b) apply an ensemble 
learning, as opposed to the use of a single classifier, for sample subtype 
assignment. The overarching objective is to improve class prediction.
METHODS AND FINDINGS: The microarray transcriptome data sets used in this study 
are: the METABRIC breast cancer data recorded for over 2000 patients, and the 
public integrated source from ROCK database with 1570 samples. We first computed 
the CM1 score to identify the probes with highly discriminative patterns of 
expression across samples of each intrinsic subtype. We further assessed the 
ability of 42 selected probes on assigning correct subtype labels using 24 
different classifiers from the Weka software suite. For comparison, the same 
method was applied on the list of 50 genes from the PAM50 method.
CONCLUSIONS: The CM1 score portrayed 30 novel biomarkers for predicting breast 
cancer subtypes, with the confirmation of the role of 12 well-established genes. 
Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers 
are more consistent and homogeneous than the original PAM50 labels. The new 
subtypes show accurate distributions of current clinical markers ER, PR and 
HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the 
paradoxical attribution of the original labels reinforces the limitations of 
employing a single sample classifiers to predict breast cancer intrinsic 
subtypes.