title: Breast cancer subtype specific classifiers of response to neoadjuvant chemotherapy do not outperform classifiers trained on all subtypes.
abstract: BACKGROUND
  Despite continuous efforts, not a single predictor of breast cancer chemotherapy resistance has made it into the clinic yet. However, it has become clear in recent years that breast cancer is a collection of molecularly distinct diseases. With ever increasing amounts of breast cancer data becoming available, we set out to study if gene expression based predictors of chemotherapy resistance that are specific for breast cancer subtypes can improve upon the performance of generic predictors.
  
  
  METHODS
  We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously. Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394). We evaluated predictors based on either mRNA gene expression or clinical features.
  
  
  RESULTS
  For HER2+, ER- breast cancer, subtype specific predictor based on clinical features outperformed the generic, non-specific predictor. This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype. In all other scenarios the generic predictors outperformed the subtype specific predictors or showed equal performance.
  
  
  CONCLUSIONS
  Since it depends on the specific context which type of predictor - subtype specific or generic- performed better, it is highly recommended to evaluate both specific and generic predictors when attempting to predict treatment response in breast cancer.
authors.affiliation: Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands ; Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Jorma J
authors.initials: JJ
authors.lastname: de Ronde
authors.affiliation: Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Marc Jan
authors.initials: MJ
authors.lastname: Bonder
authors.affiliation: Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands ; Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Esther H
authors.initials: EH
authors.lastname: Lips
authors.affiliation: Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Sjoerd
authors.initials: S
authors.lastname: Rodenhuis
authors.affiliation: Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands ; Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.
authors.forename: Lodewyk F A
authors.initials: LF
authors.lastname: Wessels
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000145:classification* / Q000188:drug therapy*; D017024:Chemotherapy, Adjuvant / Q000379:methods*; D047628:Estrogen Receptor alpha / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016015:Logistic Models; D020360:Neoadjuvant Therapy / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D012333:RNA, Messenger / Q000378:metabolism; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D060388:Support Vector Machine; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents; D047628:Estrogen Receptor alpha; D012333:RNA, Messenger; D018719:Erb-b2 Receptor Tyrosine Kinases; C506487:ESR1 protein, human; C508053:ERBB2 protein, human