Dataset Information

A seven-gene prognostic model for platinum-treated ovarian carcinomas.

ABSTRACT:

Background

Prognosis of ovarian carcinoma is poor, heterogeneous, and not accurately predicted by histoclinical features. We analysed gene expression profiles of ovarian carcinomas to identify a multigene expression model associated with survival after platinum-based therapy.

Methods

Data from 401 ovarian carcinoma samples were analysed. The learning set included 35 cases profiled using whole-genome DNA chips. The validation set included 366 cases from five independent public data sets.

Results

Whole-genome unsupervised analysis could not distinguish poor from good prognosis samples. By supervised analysis, we built a seven-gene optimal prognostic model (OPM) out of 94 genes identified as associated with progression-free survival. Using the OPM, we could classify patients in two groups with different overall survival (OS) not only in the learning set, but also in the validation set. Five-year OS was 57 and 27% for the predicted 'Favourable' and 'Unfavourable' classes, respectively. In multivariate analysis, the OPM outperformed the individual current prognostic factors, both in the learning and the validation sets, and added independent prognostic information.

Conclusion

We defined a seven-gene model associated with outcome in 401 ovarian carcinomas. Prospective studies are warranted to confirm its prognostic value, and explore its potential ability for better tailoring systemic therapies in advanced-stage tumours.

SUBMITTER: Sabatier R

PROVIDER: S-EPMC3142802 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionIntegrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic parameters into the conventional FIGO staging system for stratifying patients with epithelial ovarian carcinomas and evaluating their survival.Material and methodsCancer-specific survival data for epithelial ovarian carcinomas were extracted from the Surveillance, Epidemiology, and End Results (SEER) program. Two datasets were constructed based upon the year of diagnosis. Dataset 1 (39 514 cases) was limited to primary tumor (T), regional lymph nodes (N) and distant metastasis (M). Dataset 2 (25 291 cases) included additional parameters of age at diagnosis (A) and histologic type and grade (H). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to generate prognostic groups with depiction in dendrograms. C-indices provided dendrogram cutoffs and comparisons of prediction accuracy.ResultsDataset 1 was stratified into nine epithelial ovarian carcinoma prognostic groups, contrasting with 10 groups from FIGO methodology. The EACCD grouping had a slightly higher accuracy in survival prediction than FIGO staging (C-index = 0.7391 vs 0.7371, increase in C-index = 0.0020, 95% confidence interval [CI] 0.0012-0.0027, p = 1.8 × 10-7 ). Nevertheless, there remained a strong inter-system association between EACCD and FIGO (rank correlation = 0.9480, p = 6.1 × 10-15 ). Analysis of Dataset 2 demonstrated that A and H could be smoothly integrated with the T, N and M criteria. Survival data were stratified into nine prognostic groups with an even higher prediction accuracy (C-index = 0.7605) than when using only T, N and M.ConclusionsEACCD was successfully applied to integrate A and H with T, N and M for stratification and survival prediction of epithelial ovarian carcinoma patients. Additional factors could be advantageously incorporated to test the prognostic impact of emerging diagnostic or therapeutic advances.