title: Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm.
abstract: OBJECTIVE
  Selection of treatment options with the highest likelihood of successful outcome for individual breast cancer patients is based to a large degree on accurate classification into subgroups with poor and good prognosis reflecting a different probability of disease recurrence and survival after therapy. Here we propose a breast cancer classification algorithm taking into account three main prognostic features determined at the time of diagnosis: estrogen receptor (ER) status; lymph node (LN) status; and gene expression signatures associated with distinct therapy outcome.
  
  
  METHODS
  Using microarray expression profiling and quantitative reverse transcription-PCR analyses, we compared expression profiles of the 70-gene breast cancer survival signature in established breast cancer cell lines and primary breast carcinomas from cancer patients. We classified 295 breast cancer patients using 14-, 13-, 6-, and 4-gene survival predictor signatures into subgroups having statistically distinct probability of therapy failure (P < 0.0001). We evaluated the prognostic power of breast cancer survival predictor signatures alone and in combination with ER and LN status using Kaplan-Meier analysis.
  
  
  RESULTS
  The breast cancer survival predictor algorithm allowed highly accurate classification into subgroups with dramatically distinct 5- and 10-year survival after therapy of a large cohort of 295 breast cancer patients with either ER+ or ER- tumors as well as LN+ or LN- disease (P < 0.0001, log-rank test).
  
  
  CONCLUSIONS
  Our data imply that quantitative laboratory tests measuring expression profiles of a limited set of identified small gene clusters may be useful in stratification of breast cancer patients at the time of diagnosis into subgroups with statistically distinct probability of positive outcome after therapy and assisting in selection of optimal treatment strategies. The estimated increase in survival due to the optimization of treatment protocols may reach many thousands of breast cancer survivors every year at the 10-year follow-up check point.
authors.affiliation: Sidney Kimmel Cancer Center, San Diego, California 92121, USA. gglinsky@skcc.org
authors.forename: Gennadi V
authors.initials: GV
authors.lastname: Glinsky
authors.forename: Takuya
authors.initials: T
authors.lastname: Higashiyama
authors.forename: Anna B
authors.initials: AB
authors.lastname: Glinskii
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics* / Q000473:pathology; D045744:Cell Line, Tumor; D006801:Humans; D008207:Lymphatic Metastasis; D005810:Multigene Family; D009362:Neoplasm Metastasis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011379:Prognosis; D012333:RNA, Messenger / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D020133:Reverse Transcriptase Polymerase Chain Reaction; D013997:Time Factors
chemical_list: D012333:RNA, Messenger; D011960:Receptors, Estrogen