title: The molecular subtype classification is a determinant of sentinel node positivity in early breast carcinoma.
abstract: BACKGROUND
  Several authors have underscored a strong relation between the molecular subtypes and the axillary status of breast cancer patients. The aim of our work was to decipher the interaction between this classification and the probability of a positive sentinel node biopsy.
  
  
  METHODS
  Our dataset consisted of a total number of 2654 early-stage breast cancer patients. Patients treated at first by conservative breast surgery plus sentinel node biopsies were selected. A multivariate logistic regression model was trained and validated. Interaction covariate between ER and HER2 markers was a forced input of this model. The performance of the multivariate model in the training and the two validation sets was analyzed in terms of discrimination and calibration. Probability of axillary metastasis was detailed for each molecular subtype.
  
  
  RESULTS
  The interaction covariate between ER and HER2 status was a stronger predictor (p = 0.0031) of positive sentinel node biopsy than the ER status by itself (p = 0.016). A multivariate model to determine the probability of sentinel node positivity was defined with the following variables; tumour size, lympho-vascular invasion, molecular subtypes and age at diagnosis. This model showed similar results in terms of discrimination (AUC = 0.72/0.73/0.72) and calibration (HL p = 0.28/0.05/0.11) in the training and validation sets. The interaction between molecular subtypes, tumour size and sentinel nodes status was approximated.
  
  
  CONCLUSIONS
  We showed that biologically-driven analyses are able to build new models with higher performance in terms of breast cancer axillary status prediction. The molecular subtype classification strongly interacts with the axillary and distant metastasis process.
authors.affiliation: Department of Surgery, Institut Curie, Paris, France. Fabien.reyal@curie.net
authors.forename: Fabien
authors.initials: F
authors.lastname: Reyal
authors.forename: Roman
authors.initials: R
authors.lastname: Rouzier
authors.forename: Berenice
authors.initials: B
authors.lastname: Depont-Hazelzet
authors.forename: Marc A
authors.initials: MA
authors.lastname: Bollet
authors.forename: Jean-Yves
authors.initials: JY
authors.lastname: Pierga
authors.forename: Severine
authors.initials: S
authors.lastname: Alran
authors.forename: Remy J
authors.initials: RJ
authors.lastname: Salmon
authors.forename: Virginie
authors.initials: V
authors.lastname: Fourchotte
authors.forename: Anne
authors.initials: A
authors.lastname: Vincent-Salomon
authors.forename: Xavier
authors.initials: X
authors.lastname: Sastre-Garau
authors.forename: Martine
authors.initials: M
authors.lastname: Antoine
authors.forename: Serge
authors.initials: S
authors.lastname: Uzan
authors.forename: Brigitte
authors.initials: B
authors.lastname: Sigal-Zafrani
authors.forename: Yann
authors.initials: Y
authors.lastname: De Rycke
mesh_terms: D000368:Aged; D001943:Breast Neoplasms / Q000378:metabolism* / Q000473:pathology*; D002277:Carcinoma / Q000378:metabolism* / Q000473:pathology*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D015999:Multivariate Analysis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D021701:Sentinel Lymph Node Biopsy
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen