Deeb Sally J, Tyanova Stefka, Hummel Michael, Schmidt-Supprian Marc, Cox Juergen, Mann Matthias
From the ‡Proteomics and Signal Transduction Group and.
From the ‡Proteomics and Signal Transduction Group and §Computational Systems Biochemistry, Max Planck Institute of Biochemistry, D-82152 Martinsried, Germany.
Mol Cell Proteomics. 2015 Nov;14(11):2947-60. doi: 10.1074/mcp.M115.050245. Epub 2015 Aug 26.
Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded tissues of patients with closely related subtypes of diffuse large B-cell lymphoma. We combined a super-SILAC approach with label-free quantification (hybrid LFQ) to address situations where the protein is absent in the super-SILAC standard but present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9,000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of diffuse large B-cell lymphoma patients according to their cell of origin using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb, S. J., D'Souza, R. C., Cox, J., Schmidt-Supprian, M., and Mann, M. (2012) Mol. Cell. Proteomics 11, 77-89). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistently with known trends between the subtypes. We used machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8, and TBC1D4) is predicted to classify patients with low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology.
肿瘤在分子水平上的特征分析增进了我们对癌症病因和进展的了解。对其信号通路进行蛋白质组学分析有望在功能层面加深我们对癌症异常的理解，但这需要准确且可靠的工具。在此，我们开发了一种先进的定量质谱流程，用于对弥漫性大B细胞淋巴瘤密切相关亚型患者的福尔马林固定石蜡包埋组织进行特征分析。我们将超级稳定同位素标记氨基酸细胞培养技术（super-SILAC）方法与无标记定量（混合LFQ）相结合，以解决蛋白质在超级SILAC标准中不存在但在患者样本中存在的情况。在四极杆轨道阱上进行的鸟枪法蛋白质组分析对20名患者的近9000种肿瘤蛋白进行了定量。我们方法的定量准确性使得弥漫性大B细胞淋巴瘤患者能够根据其起源细胞进行分类，这既利用了他们的整体蛋白质表达模式，也利用了先前从患者来源的细胞系中获得的由55种蛋白质组成的特征谱（迪布，S. J.，德索萨，R. C.，考克斯，J.，施密特 - 苏普里安，M.，和曼，M.（2012年）《分子与细胞蛋白质组学》11卷，77 - 89页）。各个驱动分类的蛋白质的表达水平以及诸如细胞外基质蛋白等类别在各亚型之间的表现与已知趋势一致。我们使用机器学习（支持向量机）来提取具有最高分类能力的候选蛋白质。预计由四种蛋白质（PALD1、MME、TNFAIP8和TBC1D4）组成的一组蛋白质能够以低错误率对患者进行分类。支持向量分析中排名靠前的蛋白质揭示了各亚型之间核心信号分子的差异表达，阐明了它们病理生物学的一些方面。
