Risk Prediction for Late-Stage Ovarian Cancer by Meta-analysis of 1525 Patient Samples
Risk Prediction for Late-Stage Ovarian Cancer by Meta-analysis of 1525 Patient Samples
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DOI：
10.1093/jnci/dju048
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发表时间：
2014-05-14
影响因子：
10.3
通讯作者：
Birrer, Michael J.
中科院分区：
文献类型：
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作者：
Riester, Markus;Wei, Wei;Birrer, Michael J.
背景卵巢癌在美国每年导致超过15000人死亡。患者的生存是相当异质性，准确的预后工具将有助于这些patients.Methods的临床管理，我们开发和验证了两个基因表达的签名，第一个预测生存在晚期，浆液性卵巢癌和第二个预测减积状态。我们整合了13个公开的数据集，共计1525名受试者。我们使用复合协变量方法的荟萃分析变异来训练预测模型，通过“留一个”程序测试模型，并在其他独立数据集中验证模型。通过免疫组织化学和定量逆转录聚合酶链反应（qRT-PCR）分别在179例和78例患者的两个独立队列中验证了减积特征中的选定基因。所有统计学检验均为双侧。结果生存签名将患者分为高风险组和低风险组（风险比= 2.19; 95%置信区间[CI] = 1.84至2.61），在统计学上显著优于TCGA签名（P = 0.04）。通过qRT-PCR验证了POR 4、CXCL 14、FAP、NUAK 1、PTCH 1和TGFBR 2（P <0.05），通过免疫组织化学验证了POR 4、CXCL 14和磷酸化Smad 2/3（P <0.001）作为减积状态的独立预测因子。这三种蛋白的免疫组化强度的总和提供了一个工具，正确分类92.8%的样本中的高风险和低风险组次优减积（曲线下面积= 0.89; 95%CI = 0.84至0.93）。结论我们的生存签名提供了最准确和验证的早期和晚期高级别，浆液性卵巢癌的预后模型。减瘤特征准确预测了细胞减灭术的结果，可能允许对患者进行原发性与继发性细胞减灭术的分层。
Background Ovarian cancer causes more than 15 000 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.