Hung Kenneth E, Kho Alvin T, Sarracino David, Richard Larissa Georgeon, Krastins Bryan, Forrester Sara, Haab Brian B, Kohane Isaac S, Kucherlapati Raju
Gastrointestinal Unit, Massachusetts General Hospital, 55 Fruit Street, Boston, 02114, USA.
J Proteome Res. 2006 Aug;5(8):1866-78. doi: 10.1021/pr060120r.
Early detection of cancer can greatly improve prognosis. Identification of proteins or peptides in the circulation, at different stages of cancer, would greatly enhance treatment decisions. Mass spectrometry (MS) is emerging as a powerful tool to identify proteins from complex mixtures such as plasma that may help identify novel sets of markers that may be associated with the presence of tumors. To examine this feature we have used a genetically modified mouse model, Apc(Min), which develops intestinal tumors with 100% penetrance. Utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified total plasma proteome (TPP) and plasma glycoproteome (PGP) profiles in tumor-bearing mice. Principal component analysis (PCA) and agglomerative hierarchial clustering analysis revealed that these protein profiles can be used to distinguish between tumor-bearing Apc(Min) and wild-type control mice. Leave-one-out cross-validation analysis established that global TPP and global PGP profiles can be used to correctly predict tumor-bearing animals in 17/19 (89%) and 19/19 (100%) of cases, respectively. Furthermore, leave-one-out cross-validation analysis confirmed that the significant differentially expressed proteins from both the TPP and the PGP were able to correctly predict tumor-bearing animals in 19/19 (100%) of cases. A subset of these proteins was independently validated by antibody microarrays using detection by two color rolling circle amplification (TC-RCA). Analysis of the significant differentially expressed proteins indicated that some might derive from the stroma or the host response. These studies suggest that mass spectrometry-based approaches to examine the plasma proteome may prove to be a valuable method for determining the presence of intestinal tumors.
癌症的早期检测可极大地改善预后。在癌症的不同阶段识别循环中的蛋白质或肽，将极大地优化治疗决策。质谱分析法（MS）正成为一种强大的工具，可从诸如血浆等复杂混合物中识别蛋白质，这可能有助于识别与肿瘤存在相关的新型标志物。为了研究这一特性，我们使用了一种基因改造的小鼠模型Apc(Min)，该模型会100%发生肠道肿瘤。利用液相色谱-串联质谱分析法（LC-MS/MS），我们确定了荷瘤小鼠的血浆总蛋白质组（TPP）和血浆糖蛋白组（PGP）图谱。主成分分析（PCA）和凝聚层次聚类分析表明，这些蛋白质图谱可用于区分荷瘤Apc(Min)小鼠和野生型对照小鼠。留一法交叉验证分析表明，总体TPP和总体PGP图谱分别可在17/19（89%）和19/19（100%）的病例中正确预测荷瘤动物。此外，留一法交叉验证分析证实，来自TPP和PGP的显著差异表达蛋白能够在19/19（100%）的病例中正确预测荷瘤动物。这些蛋白质中的一部分通过使用双色滚环扩增检测（TC-RCA）的抗体微阵列进行了独立验证。对显著差异表达蛋白的分析表明，其中一些可能来源于基质或宿主反应。这些研究表明，基于质谱分析法检测血浆蛋白质组可能是确定肠道肿瘤存在的一种有价值的方法。
