Visintin Irene, Feng Ziding, Longton Gary, Ward David C, Alvero Ayesha B, Lai Yinglei, Tenthorey Jeannette, Leiser Aliza, Flores-Saaib Ruben, Yu Herbert, Azori Masoud, Rutherford Thomas, Schwartz Peter E, Mor Gil
Department of Obstetrics and Gynecology, Yale University School of Medicine, New Haven, Connecticut 06520, USA.
Clin Cancer Res. 2008 Feb 15;14(4):1065-72. doi: 10.1158/1078-0432.CCR-07-1569. Epub 2008 Feb 7.
Early detection would significantly decrease the mortality rate of ovarian cancer. In this study, we characterize and validate the combination of six serum biomarkers that discriminate between disease-free and ovarian cancer patients with high efficiency.
We analyzed 362 healthy controls and 156 newly diagnosed ovarian cancer patients. Concentrations of leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, and CA-125 were determined using a multiplex, bead-based, immunoassay system. All six markers were evaluated in a training set (181 samples from the control group and 113 samples from OC patients) and a test set (181 sample control group and 43 ovarian cancer).
Multiplex and ELISA exhibited the same pattern of expression for all the biomarkers. None of the biomarkers by themselves were good enough to differentiate healthy versus cancer cells. However, the combination of the six markers provided a better differentiation than CA-125. Four models with <2% classification error in training sets all had significant improvement (sensitivity 84%-98% at specificity 95%) over CA-125 (sensitivity 72% at specificity 95%) in the test set. The chosen model correctly classified 221 out of 224 specimens in the test set, with a classification accuracy of 98.7%.
We describe the first blood biomarker test with a sensitivity of 95.3% and a specificity of 99.4% for the detection of ovarian cancer. Six markers provided a significant improvement over CA-125 alone for ovarian cancer detection. Validation was performed with a blinded cohort. This novel multiplex platform has the potential for efficient screening in patients who are at high risk for ovarian cancer.
早期检测可显著降低卵巢癌的死亡率。在本研究中，我们对六种血清生物标志物的组合进行了表征和验证，该组合能高效地区分无病患者和卵巢癌患者。
我们分析了362名健康对照者和156名新诊断的卵巢癌患者。使用基于微珠的多重免疫分析系统测定瘦素、催乳素、骨桥蛋白、胰岛素样生长因子II、巨噬细胞抑制因子和CA - 125的浓度。所有六种标志物在一个训练集（181个来自对照组的样本和113个来自卵巢癌患者的样本）和一个测试集（181个样本对照组和43例卵巢癌患者）中进行评估。
多重免疫分析和酶联免疫吸附测定对所有生物标志物呈现相同的表达模式。单独的生物标志物均不足以区分健康细胞与癌细胞。然而，六种标志物的组合比CA - 125具有更好的区分能力。训练集中分类错误率<2%的四个模型在测试集中均比CA - 125（特异性为95%时，敏感性为72%）有显著改善（特异性为95%时，敏感性为84% - 98%）。所选模型在测试集中正确分类了224个样本中的221个，分类准确率为98.7%。
我们描述了首个用于检测卵巢癌的血液生物标志物检测方法，其敏感性为95.3%，特异性为99.4%。六种标志物在卵巢癌检测方面比单独使用CA - 125有显著改善。使用盲法队列进行了验证。这种新型的多重检测平台有潜力对卵巢癌高危患者进行高效筛查。
