Comparative Meta-analysis of Prognostic Gene Signatures for Late-Stage Ovarian Cancer
Comparative Meta-analysis of Prognostic Gene Signatures for Late-Stage Ovarian Cancer
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DOI：
10.1093/jnci/dju049
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发表时间：
2014-05-14
影响因子：
10.3
通讯作者：
Parmigiani, Giovanni
中科院分区：
文献类型：
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作者：
Waldron, Levi;Haibe-Kains, Benjamin;Parmigiani, Giovanni
背景 卵巢癌是美国女性癌症死亡的第五大常见原因。已经提出了许多患者预后的基因特征，但是不同的数据和方法使得这些特征难以以有临床意义的方式进行比较或使用。我们试图通过使用公共数据的系统验证来确定成功发表的预后基因特征。方法系统评价确定了 14 个晚期卵巢癌的预后模型。对于每一个，我们评估了它的 1) 原始研究中描述的重新实现，2) 独立数据中总体生存预后的性能，以及 3) 与随机基因特征相比的性能。我们通过在 10 个已发表的数据集（包括 1251 名主要是高级别、晚期浆液性卵巢癌患者）中的验证对模型进行比较和排名。所有统计显着性检验均为双向。结果 12 个已发表的模型的 C 指数置信区间为 95%，不包括零值 0.5；八个优于 97.5% 的签名，包括相同数量的随机选择的基因并在相同的数据上进行训练。四个排名靠前的模型的总体验证 C 指数为 0.56 至 0.60，并且与免疫反应途径的表达具有共同的反相关性。大多数模型在新数据集中的准确性低于其出版物中提出的验证集的准确性。结论该分析为少数预后模型提供了明确的支持，但也证实了这些模型需要改进才能具有临床价值。这项工作解决了卵巢癌文献中的突出争议，并为基因特征的荟萃分析评估提供了可重复的框架。
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 anti-correlation 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.