TY - JOUR
T1 - Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer
AU - Waldron, Levi
AU - Haibe-Kains, Benjamin
AU - Culhane, Aedín C.
AU - Riester, Markus
AU - Ding, Jie
AU - Wang, Xin Victoria
AU - Ahmadifar, Mahnaz
AU - Tyekucheva, Svitlana
AU - Bernau, Christoph
AU - Risch, Thomas
AU - Ganzfried, Benjamin Frederick
AU - Huttenhower, Curtis
AU - Birrer, Michael
AU - Parmigiani, Giovanni
PY - 2014/5/14
Y1 - 2014/5/14
N2 - 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.
AB - 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.
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U2 - 10.1093/jnci/dju049
DO - 10.1093/jnci/dju049
M3 - Article
AN - SCOPUS:84902460506
SN - 0027-8874
VL - 106
JO - Journal of the National Cancer Institute
JF - Journal of the National Cancer Institute
IS - 5
M1 - dju049
ER -