title: A personalized committee classification approach to improving prediction of breast cancer metastasis.
abstract: BACKGROUND
  Metastasis prediction is a well-known problem in breast cancer research. As breast cancer is a complex and heterogeneous disease with many molecular subtypes, predictive models trained for one cohort often perform poorly on other cohorts, and a combined model may be suboptimal for individual patients. Furthermore, attempting to develop subtype-specific models is hindered by the ambiguity and stereotypical definitions of subtypes.
  
  
  RESULTS
  Here, we propose a personalized approach by relaxing the definition of breast cancer subtypes. We assume that each patient belongs to a distinct subtype, defined implicitly by a set of patients with similar molecular characteristics, and construct a different predictive model for each patient, using as training data, only the patients defining the subtype. To increase robustness, we also develop a committee-based prediction method by pooling together multiple personalized models. Using both intra- and inter-dataset validations, we show that our approach can significantly improve the prediction accuracy of breast cancer metastasis compared with several popular approaches, especially on those hard-to-learn cases. Furthermore, we find that breast cancer patients belonging to different canonical subtypes tend to have different predictive models and gene signatures, suggesting that metastasis in different canonical subtypes are likely governed by different molecular mechanisms.
  
  
  METHODS
  Source code implemented in MATLAB and Java available at www.cs.utsa.edu/∼jruan/PCC/.
authors.affiliation: Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA, Department of Molecular Medicine and Cancer Therapy & Research Center, University of Texas Health Science Center, San Antonio, TX 78229, USA.
authors.forename: Md Jamiul
authors.initials: MJ
authors.lastname: Jahid
authors.affiliation: Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA, Department of Molecular Medicine and Cancer Therapy & Research Center, University of Texas Health Science Center, San Antonio, TX 78229, USADepartment of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA, Department of Molecular Medicine and Cancer Therapy & Research Center, University of Texas Health Science Center, San Antonio, TX 78229, USA.
authors.forename: Tim H
authors.initials: TH
authors.lastname: Huang
authors.affiliation: Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA, Department of Molecular Medicine and Cancer Therapy & Research Center, University of Texas Health Science Center, San Antonio, TX 78229, USADepartment of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA, Department of Molecular Medicine and Cancer Therapy & Research Center, University of Texas Health Science Center, San Antonio, TX 78229, USA.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ruan
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D016000:Cluster Analysis; D066264:Datasets as Topic; D006801:Humans; D009362:Neoplasm Metastasis