title: The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.
abstract: 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.
  
  
  RESULTS
  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.
authors.affiliation: Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia; School of Environmental and Life Science, The University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Heloisa Helena
authors.initials: HH
authors.lastname: Milioli
authors.affiliation: Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia; Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil.
authors.forename: Renato
authors.initials: R
authors.lastname: Vimieiro
authors.affiliation: Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia; School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Carlos
authors.initials: C
authors.lastname: Riveros
authors.affiliation: Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia; School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Inna
authors.initials: I
authors.lastname: Tishchenko
authors.affiliation: Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia; School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Regina
authors.initials: R
authors.lastname: Berretta
authors.affiliation: Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia; School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
mesh_terms: D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000401:mortality; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D066264:Datasets as Topic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics / Q000379:methods; D006801:Humans; D011379:Prognosis; D015203:Reproducibility of Results; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor