Amonkar Suraj D, Bertenshaw Greg P, Chen Tzong-Hao, Bergstrom Katharine J, Zhao Jinghua, Seshaiah Partha, Yip Ping, Mansfield Brian C
Correlogic Systems, Inc., Rockville, Maryland, United States of America.
PLoS One. 2009;4(2):e4599. doi: 10.1371/journal.pone.0004599. Epub 2009 Feb 25.
Most women with a clinical presentation consistent with ovarian cancer have benign conditions. Therefore methods to distinguish women with ovarian cancer from those with benign conditions would be beneficial. We describe the development and preliminary evaluation of a serum-based multivariate assay for ovarian cancer. This hypothesis-driven study examined whether an informative pattern could be detected in stage I disease that persists through later stages.
METHODOLOGY/PRINCIPAL FINDINGS: Sera, collected under uniform protocols from multiple institutions, representing 176 cases and 187 controls from women presenting for surgery were examined using high-throughput, multiplexed immunoassays. All stages and common subtypes of epithelial ovarian cancer, and the most common benign ovarian conditions were represented. A panel of 104 antigens, 44 autoimmune and 56 infectious disease markers were assayed and informative combinations identified. Using a training set of 91 stage I data sets, representing 61 individual samples, and an equivalent number of controls, an 11-analyte profile, composed of CA-125, CA 19-9, EGF-R, C-reactive protein, myoglobin, apolipoprotein A1, apolipoprotein CIII, MIP-1alpha, IL-6, IL-18 and tenascin C was identified and appears informative for all stages and common subtypes of ovarian cancer. Using a testing set of 245 samples, approximately twice the size of the model building set, the classifier had 91.3% sensitivity and 88.5% specificity. While these preliminary results are promising, further refinement and extensive validation of the classifier in a clinical trial is necessary to determine if the test has clinical value.
CONCLUSIONS/SIGNIFICANCE: We describe a blood-based assay using 11 analytes that can distinguish women with ovarian cancer from those with benign conditions. Preliminary evaluation of the classifier suggests it has the potential to offer approximately 90% sensitivity and 90% specificity. While promising, the performance needs to be assessed in a blinded clinical validation study.
大多数临床表现符合卵巢癌的女性实际患有良性疾病。因此，能够区分卵巢癌女性患者与良性疾病患者的方法将很有帮助。我们描述了一种基于血清的卵巢癌多变量检测方法的开发及初步评估。这项基于假设的研究探讨了在I期疾病中检测到的信息模式是否会在后续阶段持续存在。
方法/主要发现：按照统一方案从多个机构收集血清，这些血清来自接受手术的女性，共176例病例和187例对照，采用高通量多重免疫测定法进行检测。涵盖了上皮性卵巢癌的所有阶段和常见亚型以及最常见的良性卵巢疾病。检测了一组104种抗原、44种自身免疫和56种传染病标志物，并确定了有信息价值的组合。使用包含91个I期数据集（代表61个个体样本）及等量对照的训练集，确定了由CA - 125、CA 19 - 9、表皮生长因子受体（EGF - R）、C反应蛋白、肌红蛋白、载脂蛋白A1、载脂蛋白CIII、巨噬细胞炎性蛋白 - 1α（MIP - 1α）、白细胞介素 - 6（IL - 6）、白细胞介素 - 18（IL - 18）和腱生蛋白C组成的11种分析物谱，该谱对卵巢癌的所有阶段和常见亚型似乎都有信息价值。使用约为模型构建集两倍大小的245个样本的测试集，该分类器的灵敏度为91.3%，特异性为88.5%。虽然这些初步结果很有前景，但为确定该检测是否具有临床价值，有必要在临床试验中对分类器进行进一步优化和广泛验证。
结论/意义：我们描述了一种基于血液的检测方法，使用11种分析物可区分卵巢癌女性患者与良性疾病患者。对该分类器的初步评估表明它有可能提供约90%的灵敏度和90%的特异性。虽然前景乐观，但仍需在盲法临床验证研究中评估其性能。
