Zhong Li, Hidalgo Giovanna E, Stromberg Arnold J, Khattar Nada H, Jett James R, Hirschowitz Edward A
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Kentucky, Chandler Medical Center, K528 Kentucky Clinic, 740 S. Limestone, Lexington, Kentucky 40536, USA.
Am J Respir Crit Care Med. 2005 Nov 15;172(10):1308-14. doi: 10.1164/rccm.200505-830OC. Epub 2005 Aug 18.
Phenotypic and genotypic heterogeneity of lung cancer likely precludes the identification of a single predictive marker and suggests the importance of identifying and measuring multiple markers.
We describe the use of a fluorescent protein microarray to identify and measure multiple non-small cell lung cancer-associated antibodies and show how simultaneous measurements can be combined into a single diagnostic assay.
T7 phage cDNA libraries of non-small cell lung cancer were first biopanned with plasma samples from normal subjects and patients with non-small cell lung cancer to enrich the component of tumor-associated proteins, and then applied to microarray slides. Two hundred twelve immunogenic phage-expressed proteins were identified from roughly 4,000 clones, using high-throughput screening with patient plasmas and assayed with 40 cancer and 41 normal plasma samples. Twenty patient and 21 normal plasma samples were randomly chosen and used for statistical determination of the predictive value of each putative marker. Statistical analysis identified antibody reactivity to seven unique phage-expressed proteins that were significantly different (p < 0.01) between patient and normal groups. The remaining 20 patient and 20 normal plasma samples were used as an independent test of the predictive ability of the selected markers.
Measurements of the 5 most predictive phage proteins were combined in a logistic regression model that achieved 90% sensitivity and 95% specificity in prediction of patient samples, whereas leave-one-out statistical analysis achieved 88.9% diagnostic accuracy among all 81 samples.
Our data indicate that antibody profiling is a promising approach that could achieve high diagnostic accuracy for non-small cell lung cancer.
肺癌的表型和基因型异质性可能妨碍单一预测标志物的识别，并表明识别和测量多种标志物的重要性。
我们描述了使用荧光蛋白微阵列来识别和测量多种非小细胞肺癌相关抗体，并展示了如何将同时测量结果整合到单一诊断检测中。
首先用正常受试者和非小细胞肺癌患者的血浆样本对非小细胞肺癌的T7噬菌体cDNA文库进行生物淘选，以富集肿瘤相关蛋白成分，然后将其应用于微阵列玻片。通过对患者血浆进行高通量筛选，从大约4000个克隆中鉴定出212种具有免疫原性的噬菌体表达蛋白，并对40份癌症血浆样本和41份正常血浆样本进行检测。随机选择20份患者血浆样本和21份正常血浆样本，用于统计确定每个假定标志物的预测价值。统计分析确定了对7种独特的噬菌体表达蛋白的抗体反应性，患者组和正常组之间存在显著差异（p<0.01）。其余20份患者血浆样本和20份正常血浆样本用作所选标志物预测能力的独立测试。
将5种最具预测性的噬菌体蛋白的测量结果纳入逻辑回归模型，该模型在预测患者样本时灵敏度达到90%，特异性达到95%，而留一法统计分析在所有81个样本中诊断准确率达到88.9%。
我们的数据表明，抗体谱分析是一种有前景的方法，可实现对非小细胞肺癌的高诊断准确率。
