Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation.

BMC Cancer
Olivier GevaertIgnace Vergote

Abstract

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). 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. 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 publishe...Continue Reading

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Oct 24, 2013·Systems Biology in Reproductive Medicine·Batoul Y AbdallahHenry H Q Heng
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Apr 29, 2021·Aging·Srinivasulu Yerukala Sathipati, Shinn-Ying Ho

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Datasets Mentioned

BETA
E-MEXP-995
E-MEXP-979

Methods Mentioned

BETA
biopsies
PCA

Software Mentioned

ArrayExpress
SVMlab
MATLAB
LS
SAS

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