title: Absolute assignment of breast cancer intrinsic molecular subtype.
abstract: BACKGROUND
  Massively parallel gene expression profiling has provided a more objective, molecular-level characterization of breast cancer subtypes. Several bioinformatics tools are available to infer patient subtype from a gene expression profile including the well-studied PAM50. The specific algorithmic methods used in these tools require access to a broad patient dataset. The choice of subtype for an individual is determined relative to all other patients across the panel, making subtypes heavily dependent on the composition of the dataset. Our aim was to develop a bioinformatics approach assigning absolute breast cancer subtypes, independent of dataset composition.
  
  
  METHODS
  Using a dataset of 4924 breast cancer patients, we defined a new bioinformatics approach: Absolute Intrinsic Molecular Subtyping (AIMS) that assigns subtype from a gene expression profile for an individual sample without the need for a large, diverse, and normalized dataset. We evaluated the agreement of AIMS with PAM50 and compared subtype assignment and prognostic value of the subtypes. We assessed AIMS' robustness using a benchmark set of tests including subtype reproducibility between technologies, gene removal, and normal gene expression contamination, and compared it with PAM50. All statistical tests, except where noted, were two-sided.
  
  
  RESULTS
  AIMS vastly agreed with PAM50, with 76% and 77% agreement for cross validation and the test set, respectively, and the prognostic capacity of the intrinsic subtypes was preserved. AIMS is fully stable, and its absolute nature enables its use on a wide range of datasets and technologies, including RNA-seq.
  
  
  CONCLUSIONS
  The instability of a breast cancer subtyping scheme like PAM50 could have important consequences in clinical management of patients. AIMS is a fully stable and robust subtyping scheme that recapitulates PAM50.
authors.affiliation: Centre for Bioinformatics, McGill University, Montreal, Quebec, H3G 0B1, Canada (ERP, MTH); The Rosalind and Morris Goodman Cancer Research Centre, McGill University, Montreal, Quebec, H3A 1A3, Canada (ERP, MTH); Department of Biochemistry, McGill University, Montreal, Quebec, H3G 1Y6, Canada (ERP, MTH).
authors.forename: Eric R
authors.initials: ER
authors.lastname: Paquet
authors.affiliation: Centre for Bioinformatics, McGill University, Montreal, Quebec, H3G 0B1, Canada (ERP, MTH); The Rosalind and Morris Goodman Cancer Research Centre, McGill University, Montreal, Quebec, H3A 1A3, Canada (ERP, MTH); Department of Biochemistry, McGill University, Montreal, Quebec, H3G 1Y6, Canada (ERP, MTH). michael.t.hallett@mcgill.ca.
authors.forename: Michael T
authors.initials: MT
authors.lastname: Hallett
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000737:chemistry*; D066264:Datasets as Topic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008875:Middle Aged; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis; D011960:Receptors, Estrogen / Q000032:analysis; D011980:Receptors, Progesterone / Q000032:analysis
chemical_list: D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; C508053:ERBB2 protein, human