The goal of this study was to determine whether patterns of expression profiles 
of p73 isoforms and of p53 mutational status are useful combinatorial biomarkers 
for predicting outcome in a gynecological cancer cohort. This is the first such 
study using matched tumor/normal tissue pairs from each patient. The median 
follow-up was over two years. The expression of all 5 N-terminal isoforms 
(TAp73, DeltaNp73, DeltaN'p73, Ex2p73 and Ex2/3p73) was measured by real-time 
RT-PCR and p53 status was analyzed by immunohistochemistry. TAp73, DeltaNp73 and 
DeltaN'p73 were significantly upregulated in tumors. Surprisingly, their range 
of overexpression was age-dependent, with the highest differences delta 
(tumor-normal) in the youngest age group. Correction of this age effect was 
important in further survival correlations. We used all 6 variables (five p73 
isoform levels plus p53 status) as input into a principal component analysis 
with Varimax rotation (VrPCA) to filter out noise from non-disease related 
individual variability of p73 levels. Rationally selected and individually 
weighted principal components from each patient were then used to train a 
support vector machine (SVM) algorithm to predict clinical outcome. This SVM 
algorithm was able to predict correct outcome in 30 of the 35 patients. We use 
here a mathematical tool for pattern recognition that has been commonly used in 
e.g. microarray data mining and apply it for the first time in a prognostic 
model. We find that PCA/SVM is able to test a clinical hypothesis with robust 
statistics and show that p73 expression profiles and p53 status are useful 
prognostic biomarkers that differentiate patients with good vs. poor prognosis 
with gynecological cancers.