title: Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset.
abstract: 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.
  
  
  RESULTS
  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.
authors.affiliation: Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia ; School of Environmental and Life Science, The University of Newcastle, University Drive, Callaghan, 2308 Australia.
authors.forename: Heloisa H
authors.initials: HH
authors.lastname: Milioli
authors.affiliation: Centro de Informática, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, Recife, 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, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia ; School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 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, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia ; School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 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, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia ; School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 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, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia ; School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 Australia.
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
keywords: Breast cancer; CM1 score; Classifiers; Data mining; Ensemble learning; Feature selection; Intrinsic subtypes; METABRIC; Predictor models; Subtype prediction