title: Use of a Combined Gene Expression Profile in Implementing a Drug Sensitivity Predictive Model for Breast Cancer.
abstract: OBJECTIVE
  Chemotherapy targets all rapidly growing cells, not only cancer cells, and thus is often associated with unpleasant side effects. Therefore, examination of the chemosensitivity based on genotypes is needed in order to reduce the side effects.
  
  
  METHODS
  Various computational approaches have been proposed for predicting chemosensitivity based on gene expression profiles. A linear regression model can be used to predict the response of cancer cells to chemotherapeutic drugs, based on genomic features of the cells, and appropriate sample size for this method depends on the number of predictors. We used principal component analysis and identified a combined gene expression profile to reduce the number of predictors.
  
  
  RESULTS
  The coefficients of determinanation (R2) of prediction models with combined gene expression and several independent gene expressions were similar. Corresponding F values, which represent model significances were improved by use of a combined gene expression profile, indicating that the use of a combined gene expression profile is helpful in predicting drug sensitivity. Even better, a prediction model can be used even with small samples because of the reduced number of predictors.
  
  
  CONCLUSIONS
  Combined gene expression analysis is expected to contribute to more personalized management of breast cancer cases by enabling more effective targeting of existing therapies. This procedure for identifying a cell-type-specific gene expression profile can be extended to other chemotherapeutic treatments and many other heterogeneous cancer types.
authors.affiliation: Department of Pathology, Yanbian University Medical College, Yanji City, China.
authors.forename: Xianglan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Oral and Maxillofacial Surgery, College of Dentistry, Yonsei University, Seoul, Korea.
authors.forename: In-Ho
authors.initials: IH
authors.lastname: Cha
authors.affiliation: BK21 PLUS Project, Yonsei University College of Dentistry, Yonsei University, Seoul, Korea.
authors.forename: Ki-Yeol
authors.initials: KY
authors.lastname: Kim
mesh_terms: D000704:Analysis of Variance; D000970:Antineoplastic Agents / Q000494:pharmacology* / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics*; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D019008:Drug Resistance, Neoplasm / Q000187:drug effects*; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects*; D006801:Humans; D015233:Models, Statistical; D010597:Pharmacogenetics / Q000379:methods; D059467:Transcriptome
chemical_list: D000970:Antineoplastic Agents
keywords: Combined predictor; Drug sensitivity; Gene expression