Zheng Yingye, Katsaros Dionyssios, Shan Shannon J C, de la Longrais Irene Rigault, Porpiglia Mauro, Scorilas Andreas, Kim Nam W, Wolfert Robert L, Simon Iris, Li Lin, Feng Ziding, Diamandis Eleftherios P
The Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Clin Cancer Res. 2007 Dec 1;13(23):6984-92. doi: 10.1158/1078-0432.CCR-07-1409.
Our goal was to examine a panel of 11 biochemical variables, measured in cytosolic extracts of ovarian tissues (normal, benign, and malignant) by quantitative ELISAs for their ability to diagnose, prognose, and predict response to chemotherapy of ovarian cancer patients.
Eleven proteins were measured (9 kallikreins, B7-H4, and CA125) in cytosolic extracts of 259 ovarian tumor tissues, 50 tissues from benign conditions, 35 normal tissues, and 44 tissues from nonovarian tumors that metastasized to the ovary. Odds ratios and hazard ratios and their 95% confidence interval were calculated. Time-dependent receiver operating characteristic curves for censored survival data were used to evaluate the performance of the biomarkers. Resampling was used to validate the performance.
Most biomarkers effectively separated cancer from noncancer groups. A composite marker provided an area under the curve of 0.97 (95% confidence interval, 0.95-0.99) for discriminating normal and cancer groups. Univariately, hK5 and hK6 were positively associated with progression. After adjusting for clinical variables in multivariate analysis, both hK10 and hK11 significantly predicted time to progression. Increasing levels of hK13 were associated with chemotherapy response, and the predictive power of hK13 to chemotherapy response was improved by a panel of five biomarkers.
The evidence shows that a group of kallikreins and multiparametric combinations with other biomarkers and clinical variables can significantly assist with ovarian cancer classification, prognosis, and response to platinum-based chemotherapy. In particular, we developed a multiparametric strategy for predicting ovarian cancer response to chemotherapy, comprising several biomarkers and clinical features.
我们的目标是检测一组11种生化变量,通过定量酶联免疫吸附测定法在卵巢组织(正常、良性和恶性)的胞质提取物中进行测量,以评估其对卵巢癌患者进行诊断、预后评估及预测化疗反应的能力。
在259例卵巢肿瘤组织、50例良性病变组织、35例正常组织以及44例转移至卵巢的非卵巢肿瘤组织的胞质提取物中测量11种蛋白质(9种激肽释放酶、B7-H4和CA125)。计算优势比和风险比及其95%置信区间。使用生存数据删失时的时间依赖性受试者工作特征曲线来评估生物标志物的性能。采用重采样来验证性能。
大多数生物标志物能有效区分癌症组和非癌症组。一种复合标志物在区分正常组和癌症组时曲线下面积为0.97(95%置信区间,0.95 - 0.99)。单因素分析中,hK5和hK6与进展呈正相关。在多因素分析中对临床变量进行校正后,hK10和hK11均显著预测进展时间。hK13水平升高与化疗反应相关,并且一组五种生物标志物可提高hK13对化疗反应的预测能力。
证据表明,一组激肽释放酶以及与其他生物标志物和临床变量的多参数组合可显著辅助卵巢癌的分类、预后评估及对铂类化疗的反应。特别是,我们开发了一种预测卵巢癌化疗反应的多参数策略,该策略包含多种生物标志物和临床特征。