Li Huiyan, Bergeron Sébastien, Annis Matthew G, Siegel Peter M, Juncker David
From the ‡Biomedical Engineering Department, §McGill University and Genome Quebec Innovation Centre.
‖Rosalind and Morris Goodman Cancer Research Centre, and.
Mol Cell Proteomics. 2015 Apr;14(4):1024-37. doi: 10.1074/mcp.M114.046516. Epub 2015 Feb 13.
Proteins in serum or plasma hold great potential for use in disease diagnosis and monitoring. However, the correlation between tumor burden and protein biomarker concentration has not been established. Here, using an antibody colocalization microarray, the protein concentration in serum was measured and compared with the size of mammary xenograft tumors in 11 individual mice from the time of injection; seven blood samples were collected from each tumor-bearing mouse as well as control mice on a weekly basis. The profiles of 38 proteins detected in sera from these animals were analyzed by clustering, and we identified 10 proteins with the greatest relative increase in serum concentration that correlated with growth of the primary mammary tumor. To evaluate the diagnosis of cancer based on these proteins using either an absolute threshold (i.e. a concentration cutoff) or self-referenced differential threshold based on the increase in concentration before cell injection, receiver operating characteristic curves were produced for 10 proteins with increased concentration, and the area under curve was calculated for each time point based on a single protein or on a panel of proteins, in each case showing a rapid increase of the area under curve. Next, the sensitivity and specificity of individual and optimal protein panels were calculated, showing high accuracy as early as week 2. These results provide a foundation for studies of tumor growth through measuring serial changes of protein concentration in animal models.
血清或血浆中的蛋白质在疾病诊断和监测方面具有巨大的应用潜力。然而，肿瘤负荷与蛋白质生物标志物浓度之间的相关性尚未确立。在此，我们使用抗体共定位微阵列，从注射时起对11只小鼠的血清蛋白质浓度进行了测量，并将其与乳腺异种移植瘤的大小进行了比较；每周从每只荷瘤小鼠以及对照小鼠采集7份血样。通过聚类分析了这些动物血清中检测到的38种蛋白质的图谱，我们鉴定出10种血清浓度相对增加最大且与原发性乳腺肿瘤生长相关的蛋白质。为了基于这些蛋白质，使用绝对阈值（即浓度临界值）或基于细胞注射前浓度增加的自我参照差异阈值来评估癌症诊断，我们针对10种浓度增加的蛋白质绘制了受试者工作特征曲线，并基于单一蛋白质或一组蛋白质计算了每个时间点的曲线下面积，每种情况下曲线下面积均迅速增加。接下来，计算了单个蛋白质和最佳蛋白质组的敏感性和特异性，结果表明早在第2周就具有很高的准确性。这些结果为通过测量动物模型中蛋白质浓度的系列变化来研究肿瘤生长奠定了基础。
