title: Modeling precision treatment of breast cancer.
abstract: BACKGROUND
  First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.
  
  
  RESULTS
  We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.
  
  
  CONCLUSIONS
  These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.
authors.forename: Anneleen
authors.initials: A
authors.lastname: Daemen
authors.forename: Obi L
authors.initials: OL
authors.lastname: Griffith
authors.forename: Laura M
authors.initials: LM
authors.lastname: Heiser
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Wang
authors.forename: Oana M
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authors.lastname: Enache
authors.forename: Zachary
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authors.lastname: Sanborn
authors.forename: Francois
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authors.lastname: Pepin
authors.forename: Steffen
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authors.lastname: Durinck
authors.forename: James E
authors.initials: JE
authors.lastname: Korkola
authors.forename: Malachi
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authors.lastname: Griffith
authors.forename: Joe S
authors.initials: JS
authors.lastname: Hur
authors.forename: Nam
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authors.lastname: Huh
authors.forename: Jongsuk
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authors.lastname: Chung
authors.forename: Leslie
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authors.lastname: Cope
authors.forename: Mary Jo
authors.initials: MJ
authors.lastname: Fackler
authors.forename: Christopher
authors.initials: C
authors.lastname: Umbricht
authors.forename: Saraswati
authors.initials: S
authors.lastname: Sukumar
authors.forename: Pankaj
authors.initials: P
authors.lastname: Seth
authors.forename: Vikas P
authors.initials: VP
authors.lastname: Sukhatme
authors.forename: Lakshmi R
authors.initials: LR
authors.lastname: Jakkula
authors.forename: Yiling
authors.initials: Y
authors.lastname: Lu
authors.forename: Gordon B
authors.initials: GB
authors.lastname: Mills
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Cho
authors.forename: Eric A
authors.initials: EA
authors.lastname: Collisson
authors.forename: Laura J
authors.initials: LJ
authors.lastname: van't Veer
authors.forename: Paul T
authors.initials: PT
authors.lastname: Spellman
authors.forename: Joe W
authors.initials: JW
authors.lastname: Gray
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000378:metabolism*; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics; D006801:Humans; D008954:Models, Biological; D011379:Prognosis; D040901:Proteomics; D012326:RNA Splicing; D015203:Reproducibility of Results; D015398:Signal Transduction; D060388:Support Vector Machine; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents