Pierobon Mariaelena, Wulfkuhle Julie, Liotta Lance A, Petricoin Iii Emanuel F
Center for Applied Proteomics and Molecular Medicine, George Mason University, 20110, Manassas, VA, USA.
Cancer Treat Res. 2019;178:171-187. doi: 10.1007/978-3-030-16391-4_6.
Genomic analysis of tumor specimens has revealed that cancer is fundamentally a proteomic disease at the functional level: driven by genomically defined derangements, but selected for in the proteins that are encoded and the aberrant activation of signaling and biochemical networks. This activation is measured by posttranslational modifications such as phosphorylation and other modifications that modulate cellular signaling, and these events cannot be effectively measured by genomic analysis alone. Moreover, these signaling networks by and large represent the targets for many FDA-approved and experimental molecularly targeted therapeutics. Consequently, it is important that we consider new classification schemas for oncology based not on tumor site of origin or histology under the microscope but on the functional protein signaling architecture. There are numerous proteomic technologies that could be discussed from a purely technological standpoint, but this chapter will concentrate on an overview of the main proteomic technologies available for conducting protein pathway activation analysis of clinical specimens such as multiplex immunoassays, phospho-specific flow cytometry, reverse phase protein microarrays, quantitative immunohistochemistry, and mass spectrometry. This chapter will focus on the application of these technologies to cancer-based clinical studies evaluating prognostic/predictive markers or for stratifying patients to personalized treatments.
肿瘤标本的基因组分析表明，癌症在功能层面上从根本上说是一种蛋白质组疾病：由基因组定义的紊乱驱动，但在编码的蛋白质以及信号和生化网络的异常激活中进行选择。这种激活通过翻译后修饰（如磷酸化和其他调节细胞信号传导的修饰）来衡量，而这些事件仅靠基因组分析无法有效测量。此外，这些信号网络大体上代表了许多FDA批准的和实验性分子靶向疗法的靶点。因此，重要的是我们要考虑基于功能蛋白信号架构而非肿瘤起源部位或显微镜下组织学的肿瘤学新分类模式。从纯粹的技术角度来看，可以讨论众多蛋白质组技术，但本章将集中概述可用于对临床标本进行蛋白质途径激活分析的主要蛋白质组技术，如多重免疫测定、磷酸化特异性流式细胞术、反相蛋白质微阵列、定量免疫组织化学和质谱。本章将重点关注这些技术在基于癌症的临床研究中的应用，这些研究旨在评估预后/预测标志物或对患者进行分层以实现个性化治疗。
