Dataset Information

Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets.

ABSTRACT: BACKGROUND: Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer. METHODOLOGY/PRINCIPAL FINDINGS: Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66-5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20-1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). CONCLUSIONS/SIGNIFICANCE: The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

SUBMITTER: Yoshihara K

PROVIDER: S-EPMC2837379 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets.

Yoshihara Kosuke K Tajima Atsushi A Yahata Tetsuro T Kodama Shoji S Fujiwara Hiroyuki H Suzuki Mitsuaki M Onishi Yoshitaka Y Hatae Masayuki M Sueyoshi Kazunobu K Fujiwara Hisaya H Kudo Yoshiki Y Kotera Kohei K Masuzaki Hideaki H Tashiro Hironori H Katabuchi Hidetaka H Inoue Ituro I Tanaka Kenichi K

PloS one 20100312 3

<h4>Background</h4>Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer.<h4>Methodology/principal ...[more]

PMID: 20300634

Similar Datasets

Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

Project description:Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Gene expression analysis was performed on five serous borderline tumors with BRAF mutation and five serous borderline tumors without BRAF mutation randomly. RNA was extracted from microdissected tumor cells. Expression profiling was carried out with Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays

Dataset Information

Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets.

Publications

Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets