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J Natl Cancer Inst. 2014 Apr 3;106(5). pii: dju049. doi: 10.1093/jnci/dju049.

Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer.

Author information

1
Affiliations of authors: City University of New York School of Public Health, Hunter College, New York, NY (LW); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (LW, AC, MR, JD, XVW, ST, TR, BG, GP); Department of Biostatistics, Harvard School of Public Health, Boston, MA (LW, AC, CH, GP); Center for Cancer Research, Massachusetts General Hospital, Boston, MA (MB); Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada (BH); Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada (BH);Institute for Medical Information Sciences, Biometry, and Epidemiology, LMU Munich, Munich, Germany (CB).
2
Affiliations of authors: City University of New York School of Public Health, Hunter College, New York, NY (LW); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA (LW, AC, MR, JD, XVW, ST, TR, BG, GP); Department of Biostatistics, Harvard School of Public Health, Boston, MA (LW, AC, CH, GP); Center for Cancer Research, Massachusetts General Hospital, Boston, MA (MB); Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada (BH); Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada (BH);Institute for Medical Information Sciences, Biometry, and Epidemiology, LMU Munich, Munich, Germany (CB). gp@jimmy.harvard.edu.

Abstract

BACKGROUND:

Ovarian cancer is the fifth most common cause of cancer deaths in women in the United States. Numerous gene signatures of patient prognosis have been proposed, but diverse data and methods make these difficult to compare or use in a clinically meaningful way. We sought to identify successful published prognostic gene signatures through systematic validation using public data.

METHODS:

A systematic review identified 14 prognostic models for late-stage ovarian cancer. For each, we evaluated its 1) reimplementation as described by the original study, 2) performance for prognosis of overall survival in independent data, and 3) performance compared with random gene signatures. We compared and ranked models by validation in 10 published datasets comprising 1251 primarily high-grade, late-stage serous ovarian cancer patients. All tests of statistical significance were two-sided.

RESULTS:

Twelve published models had 95% confidence intervals of the C-index that did not include the null value of 0.5; eight outperformed 97.5% of signatures including the same number of randomly selected genes and trained on the same data. The four top-ranked models achieved overall validation C-indices of 0.56 to 0.60 and shared anticorrelation with expression of immune response pathways. Most models demonstrated lower accuracy in new datasets than in validation sets presented in their publication.

CONCLUSIONS:

This analysis provides definitive support for a handful of prognostic models but also confirms that these require improvement to be of clinical value. This work addresses outstanding controversies in the ovarian cancer literature and provides a reproducible framework for meta-analytic evaluation of gene signatures.

PMID:
24700801
PMCID:
PMC4580554
DOI:
10.1093/jnci/dju049
[Indexed for MEDLINE]
Free PMC Article

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