title: Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses.
abstract: BACKGROUND
  DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets.
  
  
  RESULTS
  Our method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test.
  
  
  CONCLUSIONS
  Prediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.
authors: AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr
authors: René
authors: R
authors: Natowicz
authors: Roberto
authors: R
authors: Incitti
authors: Euler Guimarães
authors: EG
authors: Horta
authors: Benoît
authors: B
authors: Charles
authors: Philippe
authors: P
authors: Guinot
authors: Kai
authors: K
authors: Yan
authors: Charles
authors: C
authors: Coutant
authors: Fabrice
authors: F
authors: Andre
authors: Lajos
authors: L
authors: Pusztai
authors: Roman
authors: R
authors: Rouzier
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use*; D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy* / Q000235:genetics*; D015342:DNA Probes / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011300:Preoperative Care / Q000379:methods; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor; D015342:DNA Probes; D009363:Neoplasm Proteins