The Development of an Angiogenic Protein “Signature” in Ovarian Cancer Ascites as a Tool for Biologic and Prognostic Profiling - Fig 5

<p><b>Progression-free (A) and Overall (B) survival.</b> Progression-free (A) and Overall (B) survival of 69 patients with advanced ovarian cancer according to ascites angiogenesis-related protein profile.</p

Performances of four different classification methods, combined with 4-fold and Leave-One-Out cross validation.

<p>Performances of four different classification methods, combined with 4-fold and Leave-One-Out cross validation.</p

Angiogenic profile of patient with ovarian cancer.

<p>Demonstration of the angiogenic profile determined in the ascites of a patient with ovarian cancer using the Proteome Profiler Angiogenesis Array kit. Each spot corresponds to an angiogenic factor shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156403#pone.0156403.t001" target="_blank">Table 1</a>.</p

Feature selection cross validation scores.

<p>Feature selection cross validation scores, plotted against the number of selected factors. The optimal subset, maximizing the cross validation score, comprised 25 angiogenic factors (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156403#pone.0156403.t003" target="_blank">Table 3</a>).</p

HeatMap of expression levels of the 25 angiogenic factors.

<p>Relative expression levels of the 25 angiogenic factors, which resulted in the maximum 4-fold cross-validation score (A) and the subset of 5 factors with the highest contribution to the signature (B). Expression values are displayed according to the colour scale, in which red represents above median expression and green represents below median expression. Given the complexity of the expression profiles, the two patient classes are not easily separated by clustering analysis, which justifies the utilization of more sensitive classification methodologies, like SVM.</p

The 55 angiogenesis-related factors used to distinguish between platinum resistant and platinum sensitive patients.

<p>The 55 angiogenesis-related factors used to distinguish between platinum resistant and platinum sensitive patients.</p

Classification algorithms.

<p>ROC curves, showing the performances of four different classification algorithms, applied to the reduced subset of four factors: a) Support Vector Machines b) LDA c) Naïve Bayes d) Random Forests. The SVM classifier optimally separated the positive and negative samples, with a mean AUC of 0.85. The other algorithms showed lower performances but still were able to classify the samples above the randomness cut-off of 0.50 AUC, and thus further confirmed the discriminative potential of the 25 factors.</p