Table 1.
Clinical characteristics of the TCGA data.
Figure 1.
Correlation of TCGA clinical outcome measures.
(A) PFS and PFI are strongly correlated and do not need to be predicted separately. (B) PFS and OS are not well correlated, so we derived separate predictive signatures for each (data only for un-censored patients).
Table 2.
Results from individual data types and the integrated versions.
Table 3.
Results for the mRNA prognostic signature applied to external datasets.
Figure 2.
Integration Procedure and CoxPath Methodology.
Integration combines multiple data types for the multivariate Cox Proportional hazards model.
Figure 3.
Quality of outcome prediction for survival time (A, B) and discrete risk categories (C, D).
(A) Prediction of time-to-event (PFS; un-censored data); (B) prediction of time-to-event (OS; un-censored data); (C) statistically significant stratification into low-, intermediate- and high-risk patients using the prediction method for TCGA test data based on c-score (Integrated PFS signature); and (D) stratification for the TCGA test data based on t-score (Integrated PFS signature).
Figure 4.
Canonical pathway analysis of 156 genes from the integrated PFS gene signature.
IPA [12] identified 23 statistically significant canonical pathways (p<0.1 and ≥3 genes).
Figure 5.
Overrepresented GO categories for genes in the integrated PFS signature.
Six biological processes categories and two molecular function categories were indentified by Bingo [13] containing (3<n<100) genes in the signature, a corrected p-value of <0.1.
Figure 6.
Network derived from the integrated PFS signature using IPA.
The top four networks identified were merged using IPA analysis. The features most discriminative between short and long-recurrence times are shown on larger scale. The nearest neighbor interactions of these nodes are highlighted in different colors. Nodes are colored based on the mRNA expression profile of different genes (green: down-regulated in short recurrence patients (PFS<6mo) compared to long recurrence (PFS>40mo), and red: up-regulated).
Figure 7.
Netbox modules identified using the integrated PFS signature.
Different modules are spatially separated for visualization. The genes present in our signature are shaped as octagons (mRNA features), diamonds (methylation features) and rectangles (copy number feature). The linker nodes are represented as small circles. Nodes are colored based on the mRNA expression profile of different genes (green: down-regulated in short recurrence patients (PFS<6mo) compared to long recurrence (PFS>40mo), and red: up-regulated).
Figure 8.
Features from the integrated PFS signature ranked based on their stratification performance.
Top ranked features (categorized based on their values from the respective data type as low [bottom 15%], intermediate and high [top 15%]) could potentially act as biomarkers and therapeutic targets.