title: Breast cancer prognosis by combinatorial analysis of gene expression data.
abstract: BACKGROUND
  The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors.
  
  
  METHODS
  Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines.
  
  
  RESULTS
  LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics.
  
  
  CONCLUSIONS
  The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized explanation of the reasons for that prognosis for each patient). Moreover, the LAD model provides valuable insights into the roles of individual and combinatorial biomarkers, allows the discovery of new classes of patients, and generates a vast library of biomedical research hypotheses.
authors.affiliation: RUTCOR (Rutgers University Center for Operations Research), Piscataway, New Jersey, USA. galexe@ias.edu
authors.forename: Gabriela
authors.initials: G
authors.lastname: Alexe
authors.forename: Sorin
authors.initials: S
authors.lastname: Alexe
authors.forename: David E
authors.initials: DE
authors.lastname: Axelrod
authors.forename: Tibérius O
authors.initials: TO
authors.lastname: Bonates
authors.forename: Irina I
authors.initials: II
authors.lastname: Lozina
authors.forename: Michael
authors.initials: M
authors.lastname: Reiss
authors.forename: Peter L
authors.initials: PL
authors.lastname: Hammer
mesh_terms: D001943:Breast Neoplasms / Q000453:epidemiology* / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005819:Genetic Markers; D006801:Humans; D008957:Models, Genetic; D011379:Prognosis; D013223:Statistics as Topic
chemical_list: D005819:Genetic Markers