title: Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation.
abstract: BACKGROUND
  In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs).
  
  
  METHODS
  In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs.
  
  
  RESULTS
  The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study - although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours - were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models.
  
  
  CONCLUSIONS
  We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology.
authors.affiliation: Department of Electrical Engineering ESAT-SCD-Sista, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be
authors.forename: Olivier
authors.initials: O
authors.lastname: Gevaert
authors.forename: Frank
authors.initials: F
authors.lastname: De Smet
authors.forename: Toon
authors.initials: T
authors.lastname: Van Gorp
authors.forename: Nathalie
authors.initials: N
authors.lastname: Pochet
authors.forename: Kristof
authors.initials: K
authors.lastname: Engelen
authors.forename: Frederic
authors.initials: F
authors.lastname: Amant
authors.forename: Bart
authors.initials: B
authors.lastname: De Moor
authors.forename: Dirk
authors.initials: D
authors.lastname: Timmerman
authors.forename: Ignace
authors.initials: I
authors.lastname: Vergote
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use; D018450:Disease Progression; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008875:Middle Aged; D008954:Models, Biological; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D017671:Platinum Compounds / Q000627:therapeutic use; D025341:Principal Component Analysis; D011379:Prognosis
chemical_list: D000970:Antineoplastic Agents; D017671:Platinum Compounds