Format

Send to

Choose Destination
J Natl Cancer Inst. 2014 Apr 3;106(5). pii: dju048. doi: 10.1093/jnci/dju048.

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.

Author information

1
Affiliations of authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (MR, ACC, LT, FM, CH, GP); Department of Biostatistics, Harvard School of Public Health, Boston, MA (MR, ACC, LT, FM, CH, GP); Center for Cancer Research (WW, S-hK, MB) and Department of Pathology (EO), Massachusetts General Hospital, Boston, MA; City University of New York School of Public Health, Hunter College, New York, NY (LW); Sung-hoon Kim, Yonsei University College of Medicine, Seoul, Korea (S-HK).
2
Affiliations of authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (MR, ACC, LT, FM, CH, GP); Department of Biostatistics, Harvard School of Public Health, Boston, MA (MR, ACC, LT, FM, CH, GP); Center for Cancer Research (WW, S-hK, MB) and Department of Pathology (EO), Massachusetts General Hospital, Boston, MA; City University of New York School of Public Health, Hunter College, New York, NY (LW); Sung-hoon Kim, Yonsei University College of Medicine, Seoul, Korea (S-HK). mbirrer@partners.org.

Abstract

BACKGROUND:

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

METHODS:

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

RESULTS:

The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

CONCLUSIONS:

Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.

PMID:
24700803
PMCID:
PMC4580556
DOI:
10.1093/jnci/dju048
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center