Carlsson Anders, Wingren Christer, Ingvarsson Johan, Ellmark Peter, Baldertorp Bo, Fernö Mårten, Olsson Håkan, Borrebaeck Carl A K
Department of Immunotechnology, Lund University, BMC D13, SE-221 84 Lund, Sweden.
Eur J Cancer. 2008 Feb;44(3):472-80. doi: 10.1016/j.ejca.2007.11.025. Epub 2008 Jan 2.
The driving force behind oncoproteomics is to identify biomarker signatures associated with a particular malignancy. Here, we have for the first time used large-scale recombinant scFv antibody microarrays in an attempt to classify metastatic breast cancer versus healthy controls, based on differential protein expression profiling of whole serum samples. Using this multiplexed and miniaturised assay set-up providing pM range sensitivities, breast cancer could be classified with a specificity and sensitivity of 85% based on 129 serum analytes. However, by adopting a condensed 11 analyte biomarker signature, composed of nine non-redundant serum proteins, we were able to distinguish cancer versus healthy serum proteomes with a 95% sensitivity and specificity, respectively. When a subgroup of patients, not receiving anti-inflammatory drugs, was analysed, a novel eight analyte biomarker signature with a further improved predictive power was indicated. In a longer perspective, antibody microarray analysis could provide a tool for the development of improved diagnostics and intensified biomarker discovery for breast cancer patients.
肿瘤蛋白质组学背后的驱动力是识别与特定恶性肿瘤相关的生物标志物特征。在此，我们首次使用大规模重组单链抗体片段（scFv）抗体微阵列，试图基于全血清样本的差异蛋白质表达谱，对转移性乳腺癌与健康对照进行分类。使用这种提供皮摩尔（pM）级灵敏度的多重且小型化的检测设置，基于129种血清分析物，乳腺癌的分类特异性和灵敏度可达85%。然而，通过采用由9种非冗余血清蛋白组成的精简的11种分析物生物标志物特征，我们能够分别以95%的灵敏度和特异性区分癌症与健康血清蛋白质组。当分析未接受抗炎药物治疗的患者亚组时，发现了一种具有进一步提高的预测能力的新型8种分析物生物标志物特征。从更长远的角度来看，抗体微阵列分析可为乳腺癌患者开发改进的诊断方法和加强生物标志物发现提供一种工具。
