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Clin Biochem Rev. 2006 May; 27(2)：99-116.
Clinical Proteomics: Present and Future Prospects
临床蛋白质组学：现状与前景
Abstract 摘要
Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.
蛋白质组学技术的进步为了解和治疗疾病的分子基础带来了巨大希望。过去十年的蛋白质组学研究，即对动态蛋白质表达、翻译后修饰、细胞和亚细胞蛋白质分布以及蛋白质与蛋白质相互作用的研究，最终确定了许多与疾病相关的生物标志物和潜在的新药靶点。虽然蛋白质组学仍然是发现研究的首选工具，但蛋白质组学技术的新创新为蛋白质组学分析成为临床实验室的标准实践提供了可能。事实上，蛋白质图谱可以作为强有力的诊断标志物，并能预测许多疾病（尤其是癌症）的治疗结果。要在临床中实现常规蛋白质组分析，还存在许多技术障碍；不过，方法的标准化和蛋白质组数据在公开数据库中的传播已开始克服这些障碍。目前，蛋白质组学最有前途的应用是筛选某些疾病的特定蛋白质生物标志物子集，而不是进行大规模的全面蛋白质分析。有了这些技术，为患者量身定制个性化治疗的时代即将到来。这篇综述总结了蛋白质组学的进展，这些进展推动我们进入了这个令人兴奋的临床蛋白质组学时代，并重点介绍了实现这一目标所需的未来工作。
Introduction 导言
The successful completion of the human genome project has led to a tremendous increase in our understanding of the molecular basis of diseases. However, a comprehensive understanding of the dynamic protein pathways involved in normal and disease states, and in response to medical treatment, is required if we are to effectively treat disease. The next major challenge toward this aim is to identify the constituents of the human proteome in order to understand the human genome. Of particular importance will be to decipher protein alterations between health and disease to enable the identification and prioritisation of pharmaceutically relevant targets. Indeed, from a therapeutics perspective, the majority of drug targets are proteins and not nucleic acids. Technologies available to date such as microarray that can identify large numbers of differentially expressed genes, fail to take into account the multiple protein products of these genes and their functional significance. Proteome analyses aim to not only identify changes in protein expression, but also post-translational modifications, protein-protein interactions, cellular and sub-cellular distribution, and temporal patterns of expression. The purpose of differential and functional proteomics is to obtain this information that will then lead to improved understanding of the cellular pathways and their inter-relationships in cells and living organisms. The power of proteomics as a tool for discovery of biological pathways and disease processes is now well established. Indeed, proteomics has already uncovered many potential new drug targets for varying diseases. The current era of proteomics is now beginning to investigate how this technology can serve the clinician for high-throughput diagnostic and prognostic applications. This report reviews the current status of clinical proteomics with a particular emphasis on cancer biology and treatment.
人类基因组计划的成功完成极大地提高了我们对疾病分子基础的认识。然而，要想有效地治疗疾病，我们还需要全面了解正常和疾病状态下的动态蛋白质通路，以及对药物治疗的反应。实现这一目标的下一个重大挑战是确定人类蛋白质组的组成成分，从而了解人类基因组。尤其重要的是，要破译健康与疾病之间的蛋白质变化，以便确定与药物相关的靶点并确定其优先次序。事实上，从治疗学的角度来看，大多数药物靶点都是蛋白质而不是核酸。微阵列等现有技术可以识别大量不同表达的基因，但未能考虑到这些基因的多种蛋白产物及其功能意义。蛋白质组分析不仅要确定蛋白质表达的变化，还要确定翻译后修饰、蛋白质与蛋白质之间的相互作用、细胞和亚细胞分布以及表达的时间模式。差异和功能蛋白质组学的目的是获取这些信息，从而加深对细胞和生物体内细胞通路及其相互关系的理解。蛋白质组学作为一种发现生物通路和疾病过程的工具，其威力现已得到公认。事实上，蛋白质组学已经发现了许多治疗各种疾病的潜在新药靶点。当前的蛋白质组学时代正开始研究这项技术如何为临床医生提供高通量诊断和预后应用服务。本报告回顾了临床蛋白质组学的现状，并特别强调了癌症生物学和治疗。
Power of Multiple Biomarkers of Disease
多种疾病生物标志物的威力
Proteomics was initially defined by Dr Marc Wilkins, at the time a PhD student of Macquarie University, as the “protein complement of a given genome” and thus refers to all proteins expressed by a cell or tissue. Since then, the term proteomics has come to encompass the systematic analysis of protein populations with a goal of concurrently identifying, quantifying, and analysing large numbers of proteins in a functional context. As such, the ultimate goal of most proteomic studies is to determine which proteins or groups of proteins are responsible for a specific function or phenotype. Proteomics thus has enormous potential in identifying proteins associated with different disease states. Traditional biomarker analysis has concentrated on identifying one marker of a particular disease. However there is now general agreement of the statistical argument that a panel of independent disease-related proteins considered in an aggregate should be less prone to the influence of genetic and environmental ‘noise’ than is the level of a single marker protein,1 and proteomics has the power to identify such panels of proteins in a high-throughput manner. For example, Rai et al. identified three potential biomarkers that could differentiate ovarian cancer from healthy individuals and compared their performance against the tumour marker, cancer antigen 125 (CA125).2 Each biomarker individually did not out-perform CA125, however the combination of two of the new biomarkers together with CA125 significantly improved their performance.2,3 Thus identification of new protein biomarkers should substantially improve our ability to diagnose and treat human disease.
蛋白质组学最初由马克-威尔金斯博士（当时还是麦考瑞大学的一名博士生）定义为 "特定基因组的蛋白质补体"，因此是指细胞或组织表达的所有蛋白质。从那时起，蛋白质组学一词开始涵盖对蛋白质群的系统分析，其目标是同时鉴定、量化和分析功能背景下的大量蛋白质。因此，大多数蛋白质组学研究的最终目标是确定哪些蛋白质或蛋白质群对特定功能或表型负责。因此，蛋白质组学在确定与不同疾病状态相关的蛋白质方面具有巨大的潜力。传统的生物标记物分析主要集中在确定一种特定疾病的标记物上。然而，现在人们普遍同意这样一种统计论点，即与单个标记蛋白质相比，一组独立的疾病相关蛋白质的总体考虑应不易受遗传和环境 "噪音 "的影响， 1 ，而蛋白质组学有能力以高通量的方式识别这样一组蛋白质。例如，Rai 等人发现了三种可以区分卵巢癌和健康人的潜在生物标记物，并将它们的表现与肿瘤标记物癌症抗原 125 (CA125) 进行了比较。 2 每种生物标记物单独使用的效果并不优于 CA125，但将其中两种新生物标记物与 CA125 结合使用，其效果就会明显改善。 2 3 因此，鉴定新的蛋白质生物标志物将大大提高我们诊断和治疗人类疾病的能力。
DNA Microarrays for Disease Profiling
用于疾病分析的 DNA 微阵列
Advancements in gene expression profiling are beginning to allow for correlations of clinical data with genome-wide expression.4 DNA microarrays are being used to uncover associations between gene expression and specific subtypes of disease. For example, a study of breast cancer found that gene expression data could be used to classify tumours into a basal epithelial-like group, an ErbB2 overexpressing group, and a normal breast group,5 and later studies showed significantly different outcomes for patients belonging to the various groups.6 Such studies have major importance when it comes to molecularly targeted treatments. The monoclonal antibody inhibitor of ErbB2, trastuzumab (HerceptinR) has been used successfully as monotherapy and in combination with chemotherapy in women with ErbB2 (HER-2) overexpressing metastatic breast cancer.7–10 However, response rates are generally less than 50%, indicating that patients either do not respond or have disease progress after an initial response. Identification by microarray of the other genes altered in the ErbB2 subtype are now highlighting other drug targets that can be used for combination therapy to improve these response rates.5,6 DNA microarrays can then provide sophisticated multiplex panels for various diseases. A large microarray study on breast cancer was able to distinguish between patients with the same stage of disease but different responses to treatment and overall outcome,11,12 and this has led to a nationwide clinical trial in the Netherlands in which gene expression profiles for 70 classifier genes are being collected on all breast cancer patients and used as an adjunct to classical clinical staging.
基因表达谱分析技术的进步开始使临床数据与全基因组表达相关联。 4 DNA 微阵列正被用于揭示基因表达与特定疾病亚型之间的关联。例如，一项关于乳腺癌的研究发现，基因表达数据可用于将肿瘤分为基底上皮样组、ErbB2 过表达组和正常乳腺组， 5 ，随后的研究显示，属于不同组别的患者的预后明显不同。 6 这些研究对分子靶向治疗具有重要意义。ErbB2 单克隆抗体抑制剂曲妥珠单抗（赫赛汀 R ）已被成功用于 ErbB2（HER-2）过表达转移性乳腺癌女性患者的单药治疗和联合化疗。 7 – 10 然而，应答率通常低于 50%，这表明患者要么没有应答，要么在初次应答后疾病进展。通过微阵列对 ErbB2 亚型中发生改变的其他基因进行鉴定，目前正凸显出可用于联合治疗的其他药物靶点，以提高这些反应率。 5 6 这样，DNA 微阵列就能为各种疾病提供复杂的多重检测面板。一项关于乳腺癌的大型微阵列研究能够区分疾病分期相同但对治疗的反应和总体预后不同的患者， 11 12 ，这促使荷兰开展了一项全国性临床试验，收集所有乳腺癌患者的 70 个分类基因的基因表达谱，并将其作为传统临床分期的辅助手段。
DNA profiling has also proved a powerful tool for classifying subtypes of leukaemia patients.13–18 Microarray profiling has been used to determine treatment-specific changes in acute lymphoblastic leukaemia (ALL) patients treated with methotrexate and mercaptopurine.19 Recently, Holleman et al. reported gene expression patterns in drug-resistant ALL.20 The in vitro sensitivity to vincristine, prednisolone, asparaginase and daunorubucin was determined for each patient, and the gene expression profiles correlated to drug sensitivity. Forty differentially expressed genes were identified in vincristine-resistant ALL, only one of which has been previously associated with drug resistance. Importantly, the gene expression signatures associated with resistance to the individual anticancer agents were also related to the patients’ responses to treatment, suggesting that the expression of genes associated with drug resistance is an independent predictor for outcome of treatment in ALL.20 This study has highlighted a number of genes that are potential targets for new therapies.
DNA 图谱分析也被证明是对白血病患者进行亚型分类的有力工具。 13 – 18 微阵列图谱分析已被用于确定接受甲氨蝶呤和巯嘌呤治疗的急性淋巴细胞白血病（ALL）患者的治疗特异性变化。 19 最近，Holleman 等人报告了耐药 ALL 的基因表达模式。 20 他们确定了每位患者对长春新碱、泼尼松龙、天冬酰胺酶和达诺布钦的体外敏感性，并将基因表达谱与药物敏感性相关联。在长春新碱耐药的 ALL 中发现了 40 个表达不同的基因，其中只有一个以前与耐药性相关。重要的是，与个别抗癌药物耐药性相关的基因表达特征还与患者的治疗反应有关，这表明与耐药性相关的基因表达是预测 ALL 治疗结果的独立指标。 20 这项研究强调了一些可能成为新疗法靶点的基因。
While gene expression profiling highlights the potential of individualised patient-tailored therapy, gene expression analysis does not correlate well with protein expression.21 In addition, mRNA analysis does not uncover any information on the post-translational modifications, activity, sub-cellular and tissue distribution, and interactions of proteins. Indeed, as most drug targets are proteins, a proteome profile is much more informative than simply gene expression. Traditionally, DNA microarrays have had limited use for the analysis of biological fluids, however, recent studies highlight the presence of circulating nucleic acids and their potential use as diagnostic tools.22,23 Indeed, for a biomarker to be useful in routine clinical tests its detection in samples that are easy to obtain, such as plasma or urine, is a major advantage.
虽然基因表达谱分析凸显了为患者量身定制个体化疗法的潜力，但基因表达分析与蛋白质表达并不十分相关。 21 此外，mRNA 分析无法揭示蛋白质的翻译后修饰、活性、亚细胞和组织分布以及相互作用等信息。事实上，由于大多数药物靶点都是蛋白质，因此蛋白质组图谱比单纯的基因表达更有参考价值。传统上，DNA 微阵列在生物液体分析中的应用有限，但最近的研究强调了循环核酸的存在及其作为诊断工具的潜在用途。 22 23 事实上，生物标志物要想在常规临床检测中发挥作用，在血浆或尿液等易于获取的样本中进行检测是一大优势。
Proteome Technologies 蛋白质组技术
A range of techniques are now available for the analytical separation and identification of proteins from complex mixtures (Figure 1). One and two dimensional gel electrophoresis, or high-performance liquid chromatography (HPLC) are the most common separation methods, while mass spectrometry (MS) is now the gold standard for protein identification.
目前有一系列技术可用于分析分离和鉴定复杂混合物中的蛋白质（图 1）。一维和二维凝胶电泳或高效液相色谱法（HPLC）是最常用的分离方法，而质谱法（MS）则是目前蛋白质鉴定的黄金标准。
A standard proteome approach. The most common approach to separate complex protein mixtures is by 2-dimensional gel electrophoresis (2D-GE), which separates proteins in a pH gradient according to isoelectric point in the first dimension, and in an acrylamide matrix according to molecular weight in the second dimension. Relative levels of expression are compared between gels of different samples using computer algorithms to determine differential protein changes. Proteins of interest are excised from the gel, trypsin digested, and subjected to mass spectrometry for identification and characterisation. An alternative approach is to pre-label protein mixtures and separate proteins, or more often peptides, by multidimensional liquid chromatography. Differences in peptide levels and protein identification are then performed by mass spectrometry.
标准蛋白质组方法。分离复杂蛋白质混合物最常用的方法是二维凝胶电泳（2D-GE），这种方法根据等电点在第一维的 pH 梯度中分离蛋白质，根据分子量在第二维的丙烯酰胺基质中分离蛋白质。利用计算机算法比较不同样本凝胶的相对表达水平，以确定蛋白质的不同变化。将感兴趣的蛋白质从凝胶中取出，胰蛋白酶消化，然后进行质谱鉴定和特征描述。另一种方法是预先标记蛋白质混合物，然后用多维液相色谱法分离蛋白质或肽。然后通过质谱法对肽的含量差异和蛋白质进行鉴定。
Two-Dimensional Gel Electrophoresis (2D-GE)
二维凝胶电泳（2D-GE）
Recent advancements in proteome technology now allow the protein complement of a given genome to be analysed. Since the introduction of 2D-GE in 197524 improvements such as immobilised pH gradients (IPGs), that separate proteins according to charge in the first dimension prior to separation of proteins according to size in the second dimension, have rendered the technique reproducible between laboratories.25 This has greatly facilitated higher throughput analyses, and high-resolution separation of proteins from cells or tissue extracts. Another important advancement is the utilisation of single pH unit IPGs for greater separation, and sub-cellular fractionation that not only increases sensitivity but also determines the localisation of proteins in a cell.26,27 Such information, e.g. translocation of proteins from one compartment to another under experimental conditions, is an important component of protein function, and this information is not attainable with genomic techniques. For differential proteome analysis, protein expression profiles are compared (for example between normal and disease tissue, sensitive and resistant cells, or control and drug-treated cells) using powerful computer algorithms that detect the expression levels of each protein spot. Whilst large scale protein profiling preceded genomic profiling, the power of proteomics was not fully realised for many years due to inherent limitations of traditional protein identification tools, most notably Edman sequencing, which is not amenable to high-throughput automation and requires large amounts of purified protein. Protein identification through MS based techniques now overcomes many of these limitations. Matrix-assisted ionisation-time of flight (MALDI-TOF) MS has fast-tracked the identification of proteins isolated by 2D-GE and other methods due to the exquisite speed and sensitivity of this tool.28 Combined with sequence database searching using the increasing publicly available nucleotide and protein databases, it is apparent why the identification and discovery component of proteomics is becoming the tool of choice in drug discovery.
蛋白质组技术的最新进展现在可以分析特定基因组的蛋白质补体。自 1975 年推出 2D-GE 24 以来，固定 pH 梯度（IPG）等技术不断改进，可在第一维度中根据电荷分离蛋白质，然后在第二维度中根据大小分离蛋白质，从而使该技术在不同实验室之间具有可重复性。 25 这极大地方便了更高通量的分析，以及从细胞或组织提取物中高分辨率地分离蛋白质。另一项重要进展是利用单 pH 单位 IPG 进行更大程度的分离和亚细胞分馏，这不仅提高了灵敏度，还能确定蛋白质在细胞中的定位。 26 27 这些信息，例如蛋白质在实验条件下从一个区室转移到另一个区室的情况，是蛋白质功能的重要组成部分，而基因组技术无法获得这些信息。在差异蛋白质组分析中，利用强大的计算机算法检测每个蛋白质点的表达水平，对蛋白质表达谱进行比较（例如正常组织与疾病组织、敏感细胞与耐药细胞或对照细胞与药物处理细胞之间的比较）。虽然大规模蛋白质分析早于基因组分析，但由于传统蛋白质鉴定工具的固有局限性，多年来蛋白质组学的威力并未得到充分发挥，其中最突出的是埃德曼测序法，这种方法无法实现高通量自动化，而且需要大量纯化蛋白质。现在，通过基于质谱的技术进行蛋白质鉴定克服了这些局限性。基质辅助电离-飞行时间（MALDI-TOF）质谱以其极快的速度和灵敏度，快速鉴定了通过 2D-GE 和其他方法分离的蛋白质。 28 结合使用越来越多的公开核苷酸和蛋白质数据库进行序列数据库搜索，蛋白质组学的鉴定和发现部分显然正在成为药物发现的首选工具。
Although 2D-GE is a powerful technique, one of its limitations is that 2D gels remain relatively low throughput and require large amounts of starting material (~50μg) with low sensitivity for detection of low abundance proteins such as cytokines and signalling molecules. In addition, certain basic proteins, and very high- or very low-molecular weight proteins are not separated well by 2D-GE. Techniques such as free-flow electrophoresis (FFE) have been developed to help resolve complex protein mixtures using a combination of FFE (liquid based IEF method) and 2D-GE.29 The use of narrow range, overlapping pH gradients in the first dimension also improves the number of proteins visualised on 2D gels.30 Until recently, a limitation in 2D-gel technology was the reproducibility, necessitating the use of multiple gels to obtain statistical validity. A major advance in this area has come from the introduction of Cy dye fluorophores for pre-labelling of protein samples. Two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) technology adds a quantitative component to conventional 2D-gel analyses, allowing for comparison of protein expression changes across multiple samples simultaneously without gel-to-gel variation, and hence with statistical confidence.31–33 2D-DIGE utilizes Cy dye fluorophores for protein labelling prior to fractionation. This facilitates multiplexing of protein samples, allowing for direct comparison of different samples within the one gel,32 and more importantly, enables the introduction of a standardised internal control (Figure 2).34 The power of 2D-DIGE has been demonstrated for a number of biological applications, including studies on cancer35–41 and analyses utilising mouse models.42–45
虽然二维凝胶电泳是一种功能强大的技术，但其局限性之一是二维凝胶的通量相对较低，需要大量的起始材料（约 50 微克），对细胞因子和信号分子等低丰度蛋白质的检测灵敏度较低。此外，2D-GE 还不能很好地分离某些碱性蛋白质以及分子量极高或极低的蛋白质。目前已开发出自由流动电泳（FFE）等技术，结合使用 FFE（液基 IEF 法）和 2D-GE 来帮助分辨复杂的蛋白质混合物。 29 在一维中使用窄范围、重叠的 pH 梯度也能提高二维凝胶上可视蛋白质的数量。 30 直到最近，二维凝胶技术的一个局限性是可重复性，因此必须使用多个凝胶才能获得统计有效性。该领域的一大进步是引入了用于蛋白质样本预标记的 Cy 染料荧光团。二维荧光差异凝胶电泳（2D-DIGE）技术为传统的二维凝胶分析增添了定量成分，可同时比较多个样本的蛋白质表达变化，而不会出现凝胶之间的差异，因此具有统计可信度。 31 – 33 2D-DIGE 利用 Cy 染料荧光团在分馏前对蛋白质进行标记。这有利于蛋白质样本的多重化，可直接比较同一凝胶中的不同样本， 32 ，更重要的是，可引入标准化的内部对照（图 2）。 34 2D-DIGE 的强大功能已在许多生物应用中得到证实，包括癌症研究 35 – 41 和利用小鼠模型进行的分析。 42 – 45
Two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) workflow. Protein samples to be compared are covalently labelled with either Cy3 or Cy5 fluorescent dyes. An internal control, to be run on every single gel in the experiment, is labelled with Cy2. All three samples are combined and separated on the one 2D-gel, thus eliminating gel-to-gel variation. The single gel is scanned at three different wavelengths to generate an image specific for each CyDyeTM fluore. The DeCyderTM software (Amersham Biosciences, GE Healthcare) normalises the test samples to the internal control, and then overlays the two test samples to identify changes in expression levels of individual protein spots. A 3-dimensional view of matched proteins is generated to ensure correct detection of protein spots. As all gels are run with the same internal standard, multiple gels from numerous experiments can all be compared with statistical confidence. Figure adapted from Amersham Biosciences (http://www1.amershambiosciences.com) and reference 42.
二维荧光差异凝胶电泳（2D-DIGE）工作流程。需要比较的蛋白质样本用 Cy3 或 Cy5 荧光染料共价标记。用 Cy2 标记的内部对照将在实验中的每个凝胶上运行。所有三个样品在一个二维凝胶上进行组合和分离，从而消除凝胶之间的差异。用三种不同的波长扫描单个凝胶，生成每个 CyDye TM fluore 的特定图像。DeCyder TM 软件（Amersham Biosciences、GE Healthcare）将测试样本与内部对照进行归一化处理，然后将两个测试样本重叠，以确定单个蛋白点表达水平的变化。生成匹配蛋白的三维视图，以确保正确检测蛋白点。由于所有凝胶都使用相同的内标，因此可以对多次实验中的多个凝胶进行比较，并具有统计学上的可信度。图改编自 Amersham Biosciences ( http://www1.amershambiosciences.com) 和参考文献 42 。
Mass-Spectrometry Based Proteomics
基于质谱的蛋白质组学
Whilst 2D-GE remains the most widely used tool for separating proteins, many new technologies have emerged over the past few years that complement and enhance the proteomic armoury. A mass spectrometer can be considered as a highly accurate weighing scale for extremely low mass particles. Importantly, mass spectrometers can be automated and can achieve sensitivity down to the femtomole level. Proteins are usually cleaved into smaller fragments (peptides) with an enzymatic protease (in most cases trypsin), and the peptide masses are detected by the mass spectrometer. Peptides can be combined with an acidic matrix and applied to a stainless steel plate (MALDI), or may be introduced through a needle in liquid form (electrospray, ES), and are ionised in the mass spectrometer. Different mass spectrometers detect the mass of peptides in various ways. In time of flight (TOF) instruments peptides fly down a flight tube and the time it takes for the peptide to reach a detector is proportional to the peptide mass. Other mass spectrometers utilise quadropoles, ion traps and Fourier transform ion cyclotron resonance (FTICR) to analyse the peptides.46 The resulting mass spectrum is converted to a list of peptide masses that is searched against the extensive genome databases, translated and trypsin digested in silico. Each protein will have a unique peptide mass fingerprint (PMF) based on its amino acid sequence, and hence the peptide masses determined by the mass spectrometer can identify the protein from the thousands of proteins in the database. An important advancement in mass spectrometry is the ability of the analyser to isolate an ion (or peptide) and subject it to further fragmentation into individual amino acids (tandem mass spectrometry, MS/MS). In this way the de novo sequence of peptides can be determined, which together with the peptide mass fingerprint can be used to positively identify the protein. Importantly, the development of specialised software algorithms that rapidly search MS data against known or predicted proteins within databases makes this process amenable to high throughput analysis.47 This is also an extremely powerful means to identify point mutations and post-translational modifications. Mass spectrometry can now be used to determine relative levels of expression without the need for prior gel separation. Isotope-coded affinity tags (ICATTM) is a high-throughput MS-based technique that facilitates direct qualitative and quantitative comparisons of complex protein mixtures.48 Samples to be compared (e.g. cancer versus normal cells) are each labelled with a heavy or light isotope, which couple to cysteine residues of the proteins, the samples are then mixed, proteins enzymatically digested, and peptides analysed by MS. Both the relative abundance of peptides from each sample, and protein identifications can be simultaneously obtained. Other labelling techniques are also available, such as O18-water labelling, global internal standard technology (GIST), and isotope tags for relative and absolute quantification (iTRAQTM), each of which have various pros and cons depending on the specific application.49 Stable isotope labelling with amino acids in cell culture (SILAC) is another quantification strategy for analysis of differential expression between two distinct cellular populations.50,51 Cells are cultured with media deficient in a natural amino acid but supplemented with a monoisotopically labelled amino acid (e.g. 12C and 13C, or 14N and 15N). Whilst a powerful technique for growing cells, only samples capable of undergoing protein synthesis in vitro are amenable.
虽然 2D-GE 仍是最广泛使用的蛋白质分离工具，但过去几年中出现了许多新技术，补充并增强了蛋白质组学的武器装备。质谱仪可以被看作是极低质量颗粒的高精度称重秤。重要的是，质谱仪可实现自动化，灵敏度可低至飞摩尔级。蛋白质通常用酶蛋白酶（多数情况下是胰蛋白酶）裂解成较小的片段（肽），质谱仪检测肽的质量。肽可以与酸性基质结合，然后涂抹在不锈钢板上（MALDI），也可以通过针头以液体形式导入（电喷雾，ES），然后在质谱仪中电离。不同的质谱仪以不同的方式检测肽的质量。在飞行时间（TOF）仪器中，肽沿着飞行管飞行，肽到达检测器的时间与肽的质量成正比。其他质谱仪利用四极杆、离子阱和傅立叶变换离子回旋共振（FTICR）来分析肽。 46 由此产生的质谱被转换成肽段质量列表，该列表可在广泛的基因组数据库中进行搜索、翻译和胰蛋白酶消化。每个蛋白质都有一个基于其氨基酸序列的独特肽质量指纹（PMF），因此质谱仪测定的肽质量可以从数据库中成千上万的蛋白质中识别出该蛋白质。质谱技术的一个重要进步是分析仪能够分离出离子（或肽），并将其进一步破碎成单个氨基酸（串联质谱，MS/MS）。这样就能确定肽的新序列，再加上肽的质量指纹，就能确定蛋白质。重要的是，由于开发了专门的软件算法，可根据数据库中已知或预测的蛋白质快速搜索 MS 数据，因此这一过程可用于高通量分析。 47 这也是鉴定点突变和翻译后修饰的一种极为强大的手段。质谱法现在可用于确定相对表达水平，而无需事先进行凝胶分离。同位素编码亲和标（ICAT TM ）是一种基于质谱的高通量技术，有助于对复杂的蛋白质混合物进行直接的定性和定量比较。 48 需要比较的样品（例如 然后将样本混合，对蛋白质进行酶解，并通过质谱分析肽。可以同时获得每个样品中肽的相对丰度和蛋白质的鉴定结果。还有其他标记技术可供选择，如 O 18 -水标记、全球内部标准技术（GIST）和用于相对和绝对定量的同位素标记（iTRAQ TM ），根据具体应用，这些技术各有利弊。 49 细胞培养中氨基酸稳定同位素标记（SILAC）是另一种用于分析两种不同细胞群之间差异表达的定量策略。 50 51 培养细胞的培养基缺乏天然氨基酸，但补充了单同位素标记的氨基酸（如 12 C 和 13 C，或 14 N 和 15 N）。虽然这是一种强大的细胞培养技术，但只有能够在体外进行蛋白质合成的样本才适用。
To reduce sample complexity, MS approaches are often coupled to multi-dimensional liquid chromatography (MDLC) prior to the mass spectrometer. Most MDLC approaches utilise a strong cation exchange followed by a reverse phase separation, and the chromatography columns can be physically attached on-line to the mass spectrometer. This separates complex protein samples into numerous fractions, and the matching peptides from the two samples will have the same chromatographic properties and thus will co-elute such that peptide abundance can still be compared. Such multidimensional protein identification technology (also referred to as MudPit) is an attractive approach for analysing complex samples in a large scale manner, and has been shown to be capable of identifying up to 1484 proteins from yeast in a single experiment.52
为了降低样品的复杂性，质谱方法通常会在使用质谱仪之前与多维液相色谱（MDLC）联用。大多数 MDLC 方法采用强阳离子交换，然后进行反相分离，色谱柱可在线连接到质谱仪上。这可将复杂的蛋白质样品分离成许多馏分，两个样品中匹配的肽具有相同的色谱特性，因此会共同沉淀，从而仍可比较肽的丰度。这种多维蛋白质鉴定技术（也称为 MudPit）是大规模分析复杂样品的一种极具吸引力的方法，已被证明能够在一次实验中从酵母中鉴定出多达 1484 种蛋白质。 52
Protein Chips 蛋白质薯片
As with genomics, chip technology is beginning to be applied in the proteomics field. As proteins are so heterogeneous, a simple “one-chip for all genes” is not currently achievable as no capture molecules capable of binding all possible proteins are available. However, a variety of protein and peptide arrays have been developed to analyse a specific protein or group of proteins.53 At present a major advantage of this technology over more traditional protein separation tools is that it allows for analysis of protein-protein, protein-DNA, or protein-RNA interactions, depending on the substrate cross-linked to the chip. Affinity-based MS techniques represent a further proteomic tool. Ciphergen Biosystems, Inc. have developed the surface-enhanced laser desorption-ionisation (SELDI) Protein ChipR, which involves the affinity capture of specific subgroups of proteins based on their biochemical and/or physical properties, coupled with automated MS analysis.54 This technique is particularly useful for proteins not amenable to 2D-GE, such as low abundant, or basic proteins. In addition, analysis of serum samples is greatly enhanced with this technique as the inherent “masking” of serum proteins by high abundant albumin species on 2D-gels is greatly reduced. Indeed, the SELDI platform has been successfully used to quantify relative levels of prostate-specific membrane antigen (PSMA) from serum, and in combination with prostate-specific antigen, could discriminate between benign prostatic hyperplasias and prostate cancer patients.55 This approach is very useful for detecting marker profiles of disease, however its use in discovery research is limited due to the inherent difficulties in determining the identity of the marker polypeptides. Perhaps the most widely heralded proteomics study to date is that of Lance Liotta and Emanuel Petricoin III who used SELDI to analyse the protein patterns of serum from ovarian cancer patients.56 The pattern profiles could detect all patients with ovarian cancer in a set of 50 samples, and falsely identified just three healthy patients from 66 control samples. Of great significance, the technique worked well on patients with early stage disease, offering the prospect of earlier diagnosis which would greatly enhance the chance of successful treatment outcome. This has led to the development of a commercial test, termed OvaCheck, for diagnosis of ovarian cancer, however the clinical development of the test is still ongoing due to controversy over the validity of some of the original data (see Pitfalls in current proteome technologies section of this review). Other researchers have developed similar MALDI-based tools for protein profiling, such as magnetic, reverse-phase beads for analytical capture followed by a MALDI-MS readout.57 This method is more sensitive than surface capture on chips because spherical particles have larger combined surface areas and therefore higher binding capacity than small-diameter spots.58 Villanueva et al. have used this method to identify peptide marker profiles of various cancer types.57
与基因组学一样，芯片技术也开始应用于蛋白质组学领域。由于蛋白质的异质性很强，目前还无法实现简单的 "一个芯片检测所有基因"，因为还没有能够结合所有可能蛋白质的捕获分子。不过，人们已经开发出各种蛋白质和肽阵列，用于分析特定的蛋白质或蛋白质组。 53 目前，与传统的蛋白质分离工具相比，该技术的一大优势是可以分析蛋白质-蛋白质、蛋白质-DNA 或蛋白质-RNA 之间的相互作用，具体取决于芯片上交联的底物。基于亲和的 MS 技术是另一种蛋白质组学工具。Ciphergen Biosystems 公司开发了表面增强激光解吸电离（SELDI）蛋白质芯片 R，根据蛋白质的生化和/或物理特性对特定亚群进行亲和捕获，并进行自动质谱分析。 54 这种技术对不适于 2D-GE 的蛋白质特别有用，例如低含量或碱性蛋白质。此外，由于二维凝胶上高含量白蛋白对血清蛋白的固有 "掩蔽 "作用大大降低，因此使用该技术可大大提高对血清样本的分析能力。事实上，SELDI 平台已成功用于量化血清中前列腺特异性膜抗原 (PSMA) 的相对水平，结合前列腺特异性抗原，可以区分良性前列腺增生和前列腺癌患者。 55 这种方法对于检测疾病的标志物特征非常有用，但由于确定标志物多肽的身份存在固有的困难，其在发现研究中的应用受到了限制。兰斯-利奥塔（Lance Liotta）和伊曼纽尔-佩特里科因三世（Emanuel Petricoin III）使用 SELDI 分析了卵巢癌患者血清中的蛋白质模式，这也许是迄今为止最广为人知的蛋白质组学研究。 56 他们利用 SELDI 分析了卵巢癌患者血清中的蛋白质模式，在 50 个样本中检测出了所有卵巢癌患者，而从 66 个对照样本中仅错误地识别出了 3 名健康患者。具有重要意义的是，这项技术对早期患者效果良好，为早期诊断提供了前景，从而大大提高了治疗成功的几率。这促使人们开发了一种用于诊断卵巢癌的商业检测方法，称为 OvaCheck，但由于对一些原始数据的有效性存在争议，该检测方法的临床开发仍在进行中（见本综述 "当前蛋白质组技术的陷阱 "部分）。 其他研究人员也开发了类似的基于 MALDI 的蛋白质分析工具，如用于分析捕获的反相磁珠，然后进行 MALDI-MS 读出。 57 这种方法比在芯片上进行表面捕获更为灵敏，因为球形颗粒具有更大的组合表面积，因此比小直径斑点具有更高的结合能力。 58 Villanueva 等人使用这种方法鉴定了各种癌症类型的肽标志物图谱。 57
A number of other protein microarray platforms are continually being developed.59–62 These include analytical arrays, for example forward phase arrays where a bait molecule (e.g. antibody) is immobilised to a solid surface and exposed to a test sample containing a mixture of proteins. Bound molecules are detected either by a secondary antibody or by direct labelling of the molecule. In a reverse phase array the sample is immobilised onto a solid surface and exposed to, for example, an antibody that is detected using a secondary antibody and signal amplification techniques.59,60 Reverse phase arrays can be designed for the detection and determination of relative levels of phosphoproteins that are important in cellular signalling pathways in cancer cells.60 In addition, functional microarray chips involving the immobilisation of purified peptides or native proteins onto suitable surfaces can also be used to study protein-protein interactions, DNA-protein interactions, or post-translational modifications and drug-target identification.59 One of the major contributions proteomics has made to the medical community is the identification of a multitude of potential drug targets. A major bottleneck in the transition of this knowledge from the bench to the bedside is the development of specific drugs to target these markers. Protein microarrays are currently being used for the high throughput screening of libraries to identify novel ligands or drugs that bind to specific bait molecules on the array.63,64
其他一些蛋白质微阵列平台也在不断开发中。 59 – 62 这些平台包括分析阵列，例如正相阵列。在正相阵列中，诱饵分子（如抗体）被固定在固体表面，并暴露在含有蛋白质混合物的测试样品中。通过第二抗体或直接标记分子来检测结合的分子。在反相阵列中，样品被固定在固体表面上，然后暴露在抗体上，利用第二抗体和信号放大技术对抗体进行检测。 59 60 反相阵列可用于检测和确定在癌细胞细胞信号通路中起重要作用的磷蛋白的相对水平。 60 此外，将纯化肽或原生蛋白固定在合适的表面上的功能微阵列芯片也可用于研究蛋白质与蛋白质之间的相互作用、DNA 与蛋白质之间的相互作用或翻译后修饰以及药物靶标鉴定。 59 蛋白质组学对医学界的主要贡献之一是确定了大量潜在的药物靶点。要将这些知识从实验室应用到临床，一个主要的瓶颈是开发针对这些标记物的特效药物。蛋白质微阵列目前正被用于高通量筛选库，以确定与阵列上特定诱饵分子结合的新型配体或药物。 63 64
Protein chips or microarrays probably have the best potential for analysing a set of known biomarkers or the activity of proteins in specific signalling pathways. Protein arrays have been employed to measure enzyme activity of secreted and membrane proteomes of cancer cell lines,65 and are now being used to measure kinase activity via specific detection of phosphoproteins. Many diseases, in particular cancer, are characterised by alterations in certain signalling pathways and identification of the aberrant pathway in a particular patient allows for therapy to be targeted to that specific pathway. For example, epithelial ovarian cancer is often characterised by activation of the epidermal growth factor receptor (EGFR) signalling pathway, and targeted therapies including monoclonal antibodies, such as cetuximab and small molecule inhibitors such as gefitinib are either in clinical use or under clinical trial for different stages of cancer.66,67 Similarly, the c-Kit and PDGFR inhibitor, imatinib, has shown remarkable success in chronic myeloid leukaemia and gastrointestinal stromal tumours, cancers that are maintained by activation of these receptor tyrosine kinases.68 While these targeted agents are proving successful in some cases, the heterogeneous nature of some cancers results in only a subset of patients responding. Drug resistance is also a major impediment to treatment success.69 Reverse phase array technology has been utilised to profile the active molecular pathways in breast cancer and primary and metastatic ovarian cancers.70–72 Epithelial cells were microdissected from frozen tumour sections and protein lysates printed on the arrays. The slides were then probed with 26 phosphospecific antibodies to proteins known to be involved in mitogenesis including growth factor receptors, signal transducing proteins, and nuclear transcription factors, to profile the phosphoproteomic signal pathway circuitry.72 Principal component analysis identified several phosphorylated proteins that represented most of the variation between primary and metastatic tissue expression patterns. Furthermore, partition analysis then found that most of the primary and metastatic tumours could be distinguished by expression of phosphorylated c-Kit alone.72 Thus these metastatic patients are likely to benefit from the c-Kit inhibitor, imatinib. As there was substantial heterogeneity in the pattern of activated signals in other pathways, treatment could be further enhanced by combination therapy with other specific kinase inhibitors, selectively applied based on each phosphoproteomic fingerprint. Thus the use of protein arrays can move us further towards the reality of patient-tailored individualised therapy. This type of analysis could also be applied to monitor the response of patients to chemotherapy. Molecularly targeted drugs have known binding proteins and are expected to induce certain signalling pathways. The early efficacy of such treatments can be monitored during therapy to determine if the treatment is having its desired molecular effect, and hence to infer the potential success of the treatment. If a patient’s tumour is not responding in the desired manner then treatment can be changed before further progression occurs. Such powerful analyses are not possible by gene arrays.
蛋白质芯片或微阵列在分析一组已知生物标记物或特定信号通路中的蛋白质活性方面可能最有潜力。蛋白质阵列已被用于测量癌细胞系分泌和膜蛋白质组的酶活性， 65 ，现在又被用于通过特异性检测磷酸蛋白来测量激酶活性。许多疾病，尤其是癌症，都以某些信号通路的改变为特征，识别出特定患者的异常通路，就能针对该特定通路进行治疗。例如，上皮性卵巢癌通常以表皮生长因子受体（EGFR）信号通路的激活为特征，包括西妥昔单抗（cetuximab）和吉非替尼（gefitinib）等小分子抑制剂在内的靶向疗法正在临床使用或临床试验中，用于不同阶段的癌症。 66 67 同样，c-Kit 和表皮生长因子受体抑制剂伊马替尼在慢性骨髓性白血病和胃肠道间质瘤方面也取得了显著的成功，这些癌症都是通过激活这些受体酪氨酸激酶而得以维持的。 68 虽然这些靶向药物在某些病例中证明是成功的，但一些癌症的异质性导致只有一部分患者有反应。耐药性也是治疗成功的一大障碍。 69 反相阵列技术被用来分析乳腺癌、原发性和转移性卵巢癌中的活跃分子通路。 70 – 72 从冷冻的肿瘤切片中对上皮细胞进行显微解剖，并将蛋白质裂解液印在阵列上。然后用 26 种磷酸特异性抗体对载玻片进行检测，这些抗体针对的是已知参与有丝分裂的蛋白质，包括生长因子受体、信号转导蛋白和核转录因子，从而描绘出磷酸蛋白组信号通路回路。 72 主成分分析确定了几种磷酸化蛋白，它们代表了原发组织和转移组织表达模式之间的大部分差异。此外，分区分析还发现，大多数原发性肿瘤和转移性肿瘤仅通过磷酸化 c-Kit 的表达就能区分开来。 72 因此，这些转移瘤患者可能会从 c-Kit 抑制剂伊马替尼中获益。由于其他通路的激活信号模式存在很大的异质性，因此可以根据每个磷酸化蛋白指纹图谱选择性地应用其他特异性激酶抑制剂进行联合治疗，从而进一步提高治疗效果。 因此，使用蛋白质阵列可以让我们进一步实现为患者量身定制的个性化治疗。这类分析还可用于监测病人对化疗的反应。分子靶向药物有已知的结合蛋白，预计会诱导某些信号通路。可以在治疗过程中监测这类治疗的早期疗效，以确定治疗是否产生了预期的分子效应，从而推断治疗的潜在成功率。如果病人的肿瘤没有产生预期的反应，就可以在病情进一步发展之前改变治疗方法。基因芯片无法进行如此强大的分析。
Protein microarrays still face a number of serious challenges.60 Firstly, the dynamic range of protein samples means that high abundant proteins can produce contaminating cross-reactivity, reducing the sensitivity of detection of low abundant proteins. Secondly, PCR-like direct amplification methods do not exist for proteins, and current signal amplification techniques such as biotin, peroxidases, alkaline phosphatases, fluorescent proteins, and immunoglobulins, can all cross-react with naturally occurring analytes hence increasing the background of the amplification reaction. The sensitivity of the chip is also hampered by the ability to obtain sufficient, and homogenous tissue samples. As most protein arrays utilise immunodetection, a vast number of specific antibodies must be available. Many commercial companies are beginning to accommodate this necessity, and a major initiative of the Human Proteome Organisation (HUPO) is the production and qualification of antibody libraries that will be made available to the scientific community.73,74 Of course each antibody will have unique affinity constants and may require specific conditions, thus multiplexing technology may reduce the sensitivity and/or specificity of individual antibodies due to a universal reaction.
蛋白质微阵列仍然面临着许多严峻的挑战。 60 首先，蛋白质样本的动态范围意味着高含量蛋白质会产生污染性交叉反应，从而降低低含量蛋白质的检测灵敏度。其次，蛋白质不存在类似 PCR 的直接扩增方法，目前的信号扩增技术，如生物素、过氧化物酶、碱性磷酸酶、荧光蛋白和免疫球蛋白，都会与天然存在的分析物发生交叉反应，从而增加扩增反应的背景。此外，芯片的灵敏度还受制于获取足够的同质组织样本的能力。由于大多数蛋白质阵列都采用免疫检测，因此必须有大量的特异性抗体。许多商业公司已经开始满足这一需求，人类蛋白质组组织（HUPO）的一项重要计划就是生产和鉴定抗体库，并将其提供给科学界。 73 74 当然，每种抗体都有独特的亲和力常数，可能需要特定的条件，因此多重技术可能会因反应的普遍性而降低单个抗体的灵敏度和/或特异性。
Tissue Microarrays 组织芯片
Pathological assessment of tissues has been the linchpin of cancer diagnosis for the past century. With improvements in arraying technology, traditional immunohistochemical detection of protein expression in tissue sections can now be adapted to a high-throughput array format. Using immunohistochemistry on tissue microarrays, Jacquemier et al. monitored the expression of 26 selected proteins in over 1,600 cancer samples from 552 consecutive patients with early breast cancer.75 Hierarchical clustering identified relevant clusters of co-expressed proteins and clusters of tumours. The method identified a set of 21 proteins whose combined expression significantly correlated with metastasis- free survival in a learning set of 368 patients and in a validation set of 184 patients. Importantly, in a multivariate analysis the 21-protein set was the strongest independent predictor of clinical outcome, showing that protein expression profiling may be a clinically useful approach to assess breast cancer heterogeneity and prognosis.75 That this study utilised proteins of known or putative importance in breast cancer also supports the proposal that current clinical proteomics tests are best applied to a targeted subset of the proteome, rather than entire protein profiling of samples.
在过去的一个世纪里，组织病理评估一直是癌症诊断的关键。随着阵列技术的改进，传统的组织切片蛋白质表达免疫组化检测现在也可以采用高通量阵列格式。Jacquemier 等人利用组织芯片上的免疫组化技术，监测了 552 名连续早期乳腺癌患者的 1600 多个癌症样本中 26 种选定蛋白质的表达。 75 分层聚类确定了相关的共表达蛋白质群和肿瘤群。在由 368 名患者组成的学习组和由 184 名患者组成的验证组中，该方法识别出了一组 21 种蛋白质，它们的综合表达与无转移生存期有显著相关性。重要的是，在多变量分析中，这21种蛋白质组是临床结果的最强独立预测因子，这表明蛋白质表达谱分析可能是评估乳腺癌异质性和预后的一种临床有用的方法。 75 这项研究利用了已知或推测在乳腺癌中具有重要作用的蛋白质，这也支持了这样一种观点，即目前的临床蛋白质组学检测最好应用于蛋白质组的目标子集，而不是对样本进行整体蛋白质分析。
Imaging Mass Spectrometry
成像质谱仪
An emerging technique for discovery of protein signatures involves the identification of biomarkers by MS directly in tissue biopsies.76 Direct imaging of protein expression in normal and disease tissues has been achieved by in situ MS analysis of tissue sections. Frozen tissue is sliced and sections are applied on a MALDI plate and analysed at regular intervals. The mass spectra obtained at each interval are compared between samples, yielding a spatial distribution of individual masses across the section. Such analyses have uncovered differences in protein expression between normal and tumour tissues that may have specificity for different tumour types.77 Traditionally this technology had required substantial manual data analysis and thus was not suitable for routine clinical analysis. Recently however Schwartz et al. analysed over 100 glioma patients in a reasonably high-throughput manner.78 Application of direct tissue MALDI-MS to human brain tumours identified protein patterns that distinguished primary gliomas from normal brain tissue and one grade of gliomas from another, with high sensitivity and specificity.78 Importantly, the protein patterns described served as an independent indicator of patient survival, suggesting that this new molecular approach can provide clinically relevant information. In situ MS analysis has also been utilised on samples captured by laser capture microdissection.79 Further advancements in the data processing and analytical assessment of imaging MS is beginning to validate the utilisation of this technique in clinical practice.80
发现蛋白质特征的一种新兴技术是通过质谱直接在组织活检中鉴定生物标记物。 76 通过对组织切片进行原位质谱分析，可以对正常组织和疾病组织中的蛋白质表达进行直接成像。将冷冻组织切片，然后将切片置于 MALDI 板上，每隔一定时间进行分析。将每个间隔期获得的质谱在不同样本之间进行比较，得出整个切片中各个质量的空间分布。这种分析发现了正常组织和肿瘤组织之间蛋白质表达的差异，可能对不同类型的肿瘤具有特异性。 77 传统上，这种技术需要大量的人工数据分析，因此不适合常规临床分析。不过，最近 Schwartz 等人以相当高通量的方式分析了 100 多名胶质瘤患者。 78 将直接组织 MALDI-MS 应用于人类脑肿瘤，发现了能区分原发性胶质瘤与正常脑组织以及一种级别胶质瘤与另一种级别胶质瘤的蛋白质模式，具有很高的灵敏度和特异性。 78 重要的是，所描述的蛋白质模式是患者存活率的独立指标，这表明这种新的分子方法可以提供与临床相关的信息。原位质谱分析也被用于激光捕获显微切割采集的样本。 79 成像 MS 的数据处理和分析评估方面的进一步发展开始验证这种技术在临床实践中的应用。 80
Proteomics in Cancer Research
癌症研究中的蛋白质组学
By combining the myriad of proteome tools available to the researcher, entire proteomes can now begin to be unravelled in order to better understand the molecular basis of disease and to identify novel biomarker sets and potential drug targets. The power of this approach has been most successfully used to date in the field of cancer research.73,74 Celis and co-workers have utilised 2D-GE and MS analysis to identify differential protein expression between healthy and diseased tissue including normal urothelium vs squamous cell carcinomas (SCCs), which has defined some of the steps involved in the squamous differentiation of the bladder transitional epithelium.81 This analysis has culminated in a comprehensive 2D-gel database of bladder cancer that is publicly available.82 Similar proteome analyses have been used to identify markers of urothelial papillomas, renal cell carcinomas, breast cancer, lung cancer, ovarian cancer, and leukaemia, to name just a few (reviewed in references 81,83–85). Heterogeneity of tumour tissue, where mixtures of normal and cancerous cells co-exist, may present problems for proteomic analysis. One approach to overcome this is to use laser capture microdissection where specific cell types or tumour regions can be isolated. This technique was used in a recent study to identify proteins that distinguish between low malignant potential ovarian tumours and the invasive form.86 Since metastatic disease is often more difficult to effectively treat with chemotherapy, these new markers may prove effective targets for new drug design or predicting therapeutic response.87 Zhou et al. recently combined microdissection with the powerful technique of fluorescent 2D-DIGE to study squamous cell carcinoma of the oesophagus (ESCC), a major subtype of oesophageal carcinoma that is one of the most aggressive cancers with a dismal prognosis.88 The poor outcome for ESCC is attributed not only to the aggressive nature of the disease, but because the molecular mechanism of its progression is largely unknown, and due to the lack of adequate biomarkers for early detection and prediction of clinical behaviour. This study identified 28 proteins differentially expressed in ESCC patient cancer cells compared to adjacent normal epithelial cells.88 These proteins shed new light on the underlying mechanism of tumourigenesis in this aggressive cancer, as well as providing candidate biomarkers for earlier detection.
通过结合研究人员可用的大量蛋白质组工具，现在可以开始揭示整个蛋白质组，以便更好地了解疾病的分子基础，并确定新的生物标志物集和潜在的药物靶点。迄今为止，这种方法在癌症研究领域的应用最为成功。 73 74 Celis 及其合作者利用二维-凝胶电泳（2D-GE）和质谱（MS）分析，确定了健康组织和病变组织（包括正常尿路上皮细胞和鳞状细胞癌（SCC））之间的蛋白质表达差异，从而确定了膀胱过渡上皮细胞鳞状分化的一些步骤。 81 这项分析的最终成果是建立了一个全面的膀胱癌二维凝胶数据库，该数据库已向公众开放。 82 类似的蛋白质组分析还被用于确定尿路乳头状瘤、肾细胞癌、乳腺癌、肺癌、卵巢癌和白血病等的标记物（参考文献 81 83 – 85 ）。肿瘤组织的异质性，即正常细胞和癌细胞的混合共存，可能会给蛋白质组分析带来问题。克服这一问题的一种方法是使用激光捕获显微切割技术，可以分离出特定的细胞类型或肿瘤区域。在最近的一项研究中，这项技术被用来鉴定区分低恶性潜能卵巢肿瘤和侵袭性卵巢肿瘤的蛋白质。 86 由于转移性疾病通常更难通过化疗得到有效治疗，这些新标记物可能被证明是设计新药或预测治疗反应的有效靶点。 87 Zhou 等人最近将显微切割与强大的荧光 2D-DIGE 技术相结合，研究了食道鳞状细胞癌（ESCC），这是食道癌的一个主要亚型，是侵袭性最强、预后最差的癌症之一。 88 ESCC预后不佳的原因不仅在于该病的侵袭性，还在于其进展的分子机制在很大程度上是未知的，以及缺乏足够的生物标志物用于早期检测和预测临床表现。这项研究发现，与邻近的正常上皮细胞相比，有 28 种蛋白质在 ESCC 患者的癌细胞中有不同程度的表达。 88 这些蛋白质揭示了这种侵袭性癌症肿瘤发生的潜在机制，并为早期检测提供了候选生物标志物。
High-throughput MS analysis of human plasma/serum proteomes is emerging as a powerful technique for identifying distinct protein profiles in cancer patients. Proteomic patterning of serum was recently developed for the early detection of ovarian cancer.56 While at present the validity of such data is still under investigation and the specific proteins that give rise to the altered SELDI-TOF spectra are yet to be defined, it does demonstrate the utility of examining serum from patients to detect disease states. SELDI has been used to successfully discriminate serum peaks capable of distinguishing between normal, benign prostate hyperplasia, and prostate cancer patients,89 between normal and early and late stage breast cancer,56 and for prediction of chemoradiosensitivity of oesophageal cancer.90 The relative ease by which serum can be obtained from patients combined with rapid analysis is sure to see MS-based proteomics used more readily for cancer detection and progression in the future.86 In addition, LC-MS/MS MudPIT approaches have been used to identify proteins from a variety of sources,91–93 post-translational modifications,94 and quantitative expression comparisons.48,52 Global gene expression as a marker for disease classification is currently a very popular research approach. Interestingly, the use of proteomics to classify disease subtypes preceded the advent of gene arrays. In the 1980s Hanash and colleagues utilised 2D-GE to identify lineage-related protein differences in lymphoblasts from children with acute lymphoblastic leukaemia (ALL).95 Twelve polypeptides were detected that could distinguish between the major subgroups of ALL, including a new marker for common ALL and markers for cells of B and T lineages. Protein identification at the time was extremely difficult, however the new marker of ALL was later sequenced and identified as heat-shock protein 27 (HSP27).96 Additionally, a phosphorylated form of HSP27 was also identified as a marker that distinguished infant ALL from ALL in older children.97 Interestingly, 2D-GE analysis has demonstrated a correlation between increased HSP27 expression and shorter survival times for B-cell chronic lymphocytic leukaemia patients,98 suggesting that patients’ HSP27 levels may predict response to conventional chemotherapy in this disease. Research from the same laboratory has undertaken a comprehensive cell surface proteome analysis of cancer cells using 2D-GE and MS.99 Distinct patterns of expression of cell surface proteins were detected that were commonly shared amongst the different cells and distinct markers that were unique to certain cancer cell subtypes.99,100 This work represents the power of proteomic approaches for both classification of disease subtypes, identification of the origins of cancer and cancer cell surface markers.
对人体血浆/血清蛋白质组进行高通量质谱分析，正在成为鉴定癌症患者不同蛋白质特征的一项强大技术。最近开发的血清蛋白质组图谱可用于卵巢癌的早期检测。 56 虽然目前这些数据的有效性仍在研究中，导致 SELDI-TOF 光谱改变的特定蛋白质也有待确定，但它确实证明了检查患者血清以检测疾病状态的实用性。SELDI 已被成功用于区分血清峰值，能够区分正常、良性前列腺增生和前列腺癌患者， 89 ，区分正常和早期及晚期乳腺癌， 56 ，以及预测食道癌的化学放射敏感性。 90 从病人身上获取血清相对容易，再加上分析速度快，因此基于 MS 的蛋白质组学将来肯定会更多地用于癌症的检测和进展。 86 此外，LC-MS/MS MudPIT 方法已被用于鉴定各种来源的蛋白质、 91 – 93 翻译后修饰、 94 和定量表达比较。 48 52 将全球基因表达作为疾病分类的标记是目前非常流行的研究方法。有趣的是，利用蛋白质组学对疾病亚型进行分类要早于基因芯片的出现。20 世纪 80 年代，Hanash 及其同事利用 2D-GE 技术鉴定了急性淋巴细胞白血病（ALL）患儿淋巴母细胞中与系谱相关的蛋白质差异。 95 他们检测到 12 种多肽，可以区分急性淋巴细胞白血病的主要亚群，包括普通急性淋巴细胞白血病的新标记物以及 B 和 T 系细胞的标记物。当时蛋白质的鉴定非常困难，但后来对 ALL 的新标记物进行了测序，并确定其为热休克蛋白 27（HSP27）。 96 此外，HSP27 的磷酸化形式也被确定为区分婴儿 ALL 和年长儿童 ALL 的标志物。 97 有趣的是，2D-GE分析表明，HSP27表达的增加与B细胞慢性淋巴细胞白血病患者生存时间的缩短之间存在相关性， 98 ，这表明患者的HSP27水平可以预测该疾病对常规化疗的反应。同一实验室的研究利用二维-凝胶电泳和质谱对癌细胞进行了全面的细胞表面蛋白质组分析。 99 结果发现，不同细胞的细胞表面蛋白表达模式各不相同，而某些癌细胞亚型的细胞表面蛋白则具有独特的标记。 99 100 这项工作体现了蛋白质组学方法在疾病亚型分类、癌症起源鉴定和癌细胞表面标志物方面的强大功能。
Traditionally the combination and timing of cancer treatment has been empirical, based primarily on clinical presentation and histological features of the tumour, rather than on molecular mechanisms. While this approach has proven effective in the treatment of certain human cancers, drug resistance remains a major block in the successful treatment of this disease. Mechanisms mediating resistance, or indeed, drug sensitivity are still not well understood. Proteomic technologies have been extensively utilised for characterisation of normal and transformed cells and tissues, and the extension of this approach for the analysis of drug resistance is emerging as an exciting field.85 The Developmental Therapeutics Program of the US National Cancer Institute (NCI) has an extensive profile of 60 different cancer cell lines that are representative of different tissue origins and have been screened against over 60,000 chemical compounds. A 2D-GE database of the 60 NCI cell lines has been developed correlating changes in protein expression with drug response.101 Sinha and colleagues have performed 2D-GE analyses on numerous drug-resistant cell lines that have culminated in the identification of a number of resistance-associated protein changes.102 Further analyses of different chemoresistant cell lines continues to identify novel proteins associated with resistance, and interestingly, the 14-3-3 family of phosphoproteins are emerging as proteins that appear to be involved in resistance to many types of agents. Overexpression of 14-3-3γ and 14-3-3σ was shown to be associated with chemoresistance in malignant melanoma and pancreatic adenocarcinoma respectively.103,104 In a recent study from our laboratory on antimicrotubule resistance in leukaemia, 14-3-3τ and 14-3-3ɛ were also differentially expressed.105 We have also utilised 2D-GE and MS analysis to identify modifications to the drug target, tubulin, in antimicrotubule resistant leukaemia cells.106,107 While most of these studies have concentrated on in vitro selected cell lines, we have recently extended these analyses to a clinically relevant mouse model of drug resistant leukaemia.45 2D-DIGE was used to analyse the protein expression in ALL xenografts for which their intrinsic sensitivity to vincristine (VCR), a major component of combination chemotherapy for ALL, was known. To better understand mechanisms mediating acquired clinical drug resistance, 2D-DIGE was also utilised to examine xenografts with in vivo-derived VCR resistance. Of the 19 proteins displaying altered expression, 11 are associated with the actin cytoskeleton. A number of other proteins are associated with microtubules, showing that similar cytoskeletal proteins are altered in in vivo ALL models as were found in the in vitro cell lines.45,105 It is not only important to show that similar mechanisms are involved in in vivo animal models, but also in clinical samples obtained from patients, and we have since shown that for at least one gene, the change identified in the experimental models is reflected in non-xenografted primary samples obtained from ALL patients. γ-Actin, a major cytoskeletal protein, was down-regulated in both intrinsic and acquired drug-resistant leukaemia xenografts, and γ-actin expression was shown to be significantly lower in leukaemia cells obtained from ALL patients at relapse compared to diagnosis (Verrills et al. unpublished data). These studies provide the first evidence for a role of the actin cytoskeleton in in vivo antimicrotubule drug resistance, and highlight the power of 2D-DIGE for the discovery of drug resistance markers in relapsed leukaemia.
传统上，癌症治疗的组合和时机都是经验性的，主要基于肿瘤的临床表现和组织学特征，而不是分子机制。虽然这种方法已被证明对治疗某些人类癌症有效，但耐药性仍然是成功治疗这种疾病的主要障碍。人们对产生耐药性或药物敏感性的机制仍不甚了解。蛋白质组技术已被广泛用于描述正常和转化细胞及组织的特征，将这种方法扩展到耐药性分析正成为一个令人兴奋的领域。 85 美国国家癌症研究所（NCI）的 "发展治疗计划 "拥有 60 种不同癌症细胞系的大量资料，这些细胞系代表了不同的组织来源，并针对 60,000 多种化合物进行了筛选。目前已开发出 60 个 NCI 细胞系的 2D-GE 数据库，将蛋白质表达的变化与药物反应联系起来。 101 Sinha 及其同事对许多耐药细胞系进行了 2D-GE 分析，最终确定了一些与耐药性相关的蛋白质变化。 102 对不同化疗耐药细胞系的进一步分析继续发现与耐药性相关的新蛋白，有趣的是，14-3-3 磷酸化蛋白家族正在成为似乎与多种药物耐药性有关的蛋白。研究表明，14-3-3γ 和 14-3-3σ 的过表达分别与恶性黑色素瘤和胰腺癌的化疗耐药性有关。 103 104 在我们实验室最近一项关于白血病抗微管抗性的研究中，14-3-3τ 和 14-3-3ɛ 也有不同程度的表达。 105 我们还利用 2D-GE 和 MS 分析来确定抗微管抗性白血病细胞中药物靶标微管蛋白的修饰情况。 106 107 虽然这些研究大多集中在体外筛选的细胞系上，但我们最近将这些分析扩展到了与临床相关的抗药性白血病小鼠模型上。 45 我们利用二维-DIGE分析了ALL异种移植物中的蛋白质表达，这些异种移植物对长春新碱（VCR）（ALL联合化疗的主要成分）的内在敏感性是已知的。为了更好地了解获得性临床耐药性的机制，研究人员还利用二维-DIGE检测了体内产生VCR耐药性的异种移植物。在表达发生改变的19种蛋白质中，有11种与肌动蛋白细胞骨架有关。 其他一些蛋白质与微管有关，这表明体内 ALL 模型中的细胞骨架蛋白质发生了与体外细胞系中类似的改变。 45 105 我们已经证明，至少有一个基因在实验模型中发现的变化反映在从 ALL 患者获得的非异种移植原始样本中。γ-肌动蛋白是一种主要的细胞骨架蛋白，在固有和获得性耐药白血病异种移植物中都出现了下调，而且从ALL患者复发时获得的白血病细胞中，γ-肌动蛋白的表达也明显低于诊断时的水平（Verrills等人，未发表数据）。这些研究首次证明了肌动蛋白细胞骨架在体内抗微管药物耐药性中的作用，并凸显了二维 DIGE 在发现复发白血病耐药性标记物方面的强大功能。
One caveat to these powerful proteomic approaches is that they can identify a large number of candidate proteins that require validation as therapeutic targets. This process would be greatly facilitated if differential proteomics can be combined with specific functional proteomics profiling. Such an approach would allow one to pinpoint proteins involved in both drug response and drug resistance, and in turn, identification of such targets would allow them to be used for the development of specific inhibitors. By identifying proteins involved in the response of leukaemia cells to antimicrotubule agents, together with protein expression changes in leukaemia cells resistant to antimicrotubule agents, we were able to restrict the number of potential targets to ten proteins by identifying which are altered in both drug response and drug resistance.105 These proteins highlight known and novel pathways involved in the mechanism of action of microtubule-targeted anticancer drugs, and are potential targets for improved therapy of drug resistant cancer. This was a valuable approach to study resistance mechanisms that could be used in the investigation of a broad range of anti-cancer agents, and can lead to the identification of novel markers of relapsed cancer.
这些功能强大的蛋白质组学方法需要注意的一点是，它们可以鉴定出大量需要验证为治疗靶点的候选蛋白质。如果能将差异蛋白质组学与特定的功能蛋白质组学分析相结合，将大大促进这一过程。这种方法可以精确定位参与药物反应和耐药性的蛋白质，反过来，确定这些靶点可以用于开发特定的抑制剂。通过鉴定白血病细胞对抗原微管药物的反应蛋白，以及对抗原微管药物产生耐药性的白血病细胞中的蛋白表达变化，我们将潜在靶点的数量限制在十个蛋白，确定了哪些蛋白在药物反应和耐药性中都会发生变化。 105 这些蛋白质突出了微管靶向抗癌药物作用机制中的已知和新型途径，是改善耐药性癌症治疗的潜在靶点。这是研究抗药性机制的一种有价值的方法，可用于研究多种抗癌药物，并能确定复发癌症的新型标记物。
Proteomics is a powerful tool for analysis of post-translational modifications (PTMs), not possible by analysis at the gene level. Phosphorylation is a dynamic PTM that regulates the function of many proteins, and is intimately involved in cellular signalling pathways. Using a proteomic approach, Nishio et al. identified marked differences in the phosphorylation status of specific nuclear proteins between drug sensitive, and cis-diamminedichloroplatinum (II)-resistant cell lines.108 Interestingly, using more traditional techniques, no difference in protein kinase C activity or total protein phosphatase activity, nor total cellular phosphorylation was detected, in this case highlighting the power of proteomics over traditional approaches. In addition, dynamic changes in phosphorylation in signal transduction pathways can now be profiled using protein microarrays and has been applied to the study of breast and ovarian cancer.70–72 Post-translational modification of drug targets can also affect the efficacy of treatment. The extent of glutamylation for tubulin proteins has been determined by tandem MS.109 The level of tubulin glutamylation modulates the binding of microtubule-associated proteins,110,111 and thus may affect the stability of microtubules and hence the action of antimicrotubule drugs. Tyrosination-detyrosination is another PTM of tubulin, and Western blotting analysis has shown that tyrosinated tubulin is increased in paclitaxel-resistant breast cancer cells.112 MS has also been used to study polyglycylation of tubulin.113 These powerful proteomic approaches could be used to analyse antimicrotubule resistant cells, and may lead to the identification of novel changes in tubulin isotype expression and PTMs associated with the resistance phenotype.
蛋白质组学是分析翻译后修饰（PTMs）的强大工具，而基因水平的分析则无法实现这一点。磷酸化是一种动态的 PTM，可调节许多蛋白质的功能，并与细胞信号通路密切相关。Nishio 等人利用蛋白质组学方法发现，对药物敏感的细胞系和对顺式二氯二氨铂 (II) 耐药的细胞系之间，特定核蛋白的磷酸化状态存在明显差异。 108 有趣的是，使用更传统的技术，并没有检测到蛋白激酶 C 活性或总蛋白磷酸酶活性的差异，也没有检测到细胞总磷酸化的差异。此外，信号转导通路中磷酸化的动态变化现在也可以通过蛋白质微阵列进行分析，并已应用于乳腺癌和卵巢癌的研究。 70 – 72 药物靶点的翻译后修饰也会影响治疗效果。通过串联质谱测定了微管蛋白的谷氨酰化程度。 109 微管蛋白谷氨酰化的程度会调节微管相关蛋白（ 110 111 ）的结合，从而可能影响微管的稳定性，进而影响抗微管药物的作用。酪氨酸化-二酪氨酸化是微管蛋白的另一种 PTM，Western 印迹分析表明，紫杉醇抗性乳腺癌细胞中酪氨酸化微管蛋白增加。 112 MS 也被用于研究微管蛋白的多乙酰化。 113 这些功能强大的蛋白质组学方法可用于分析抗微管蛋白耐药细胞，并有可能发现与耐药表型相关的微管蛋白异型表达和PTM的新变化。
Autoantibodies may be useful biomarkers of some diseases, in particular cancer.114–117 Le Naour et al. utilised 2D-GE of cultured cells and tumour and non-tumour tissues, followed by immunoblotting with patient serum, to identify a distinct repertoire of autoantibodies associated with hepatocellular carcinoma that may have utility in early diagnosis among high risk subjects.118 Importantly, using a proteomics approach to identify autoantibodies allows for detection of immune responses to post-translationally modified proteins. For example, a study on lung cancer identified autoantibodies to a glycosylated form of annexin I and II in 60% of patients with lung adenocarcinoma and from 33% of patients with squamous cell lung carcinoma, but from no normal controls.119
自身抗体可能是某些疾病，特别是癌症的有用生物标志物。 114 – 117 Le Naour 等人利用培养细胞以及肿瘤和非肿瘤组织的二维-革兰氏染色法，然后用病人血清进行免疫印迹，找出了与肝细胞癌相关的独特的自身抗体，这些自身抗体可能对高危人群的早期诊断有用。 118 重要的是，利用蛋白质组学方法鉴定自身抗体可以检测翻译后修饰蛋白质的免疫反应。例如，一项关于肺癌的研究在 60% 的肺腺癌患者和 33% 的鳞状细胞肺癌患者中发现了附件蛋白 I 和 II 糖基化形式的自身抗体，但没有发现正常对照组。 119
Proteomics for Other Diseases
其他疾病的蛋白质组学
While the power of proteomics has been used extensively in cancer research, the same approaches can also be applied to the myriad of other human diseases. Infectious diseases remain a leading cause of death worldwide. The sequencing of many pathogenic genomes now allows for substantial proteomic analyses of such pathogens. For example, a comparative proteomic study of the malaria parasite, Plasmodium falciparum, has led to the identification of new potential drug and vaccine targets.120,121 Pseudomonas aeruginosa is an opportunistic pathogen for humans, causing infections in cystic fibrosis and burns patients, as well as other immuno-compromised individuals.122 Nouwens et al. identified numerous potential drug targets by performing comprehensive 2D-gel based proteomic analysis of the membrane fractions of P. aeruginosa clinical isolates.123,124 Drug resistance is also a major problem for the treatment of infectious diseases, and identification of mechanisms of resistance and biomarkers specific to the resistant strains are required if treatments are to be effective. Tuberculosis remains one of the world’s most serious infectious diseases, claiming millions of lives every year.125 2D-GE and MS were used to identify proteins secreted by common clinical isolates of Mycobacterium tuberculosis. Two of these proteins have shown potential as serodiagnostic antigens.126 Clearly proteins that are secreted into the serum of infected patients are strong candidates for simple kit-based serum screening tests. Various other genomics and proteomics studies have also uncovered new vaccine candidates currently in clinical trials for tuberculosis.127 Severe acute respiratory syndrome (SARS) is a major priority area for current research. Proteomic analysis of sera from SARS patients has identified a potential protein marker, truncated α1-antitrypsin, which was consistently increased in SARS patients compared to healthy controls, that could be used as a specific diagnostic or potential drug target.128 Similar studies using SELDI technology have identified other potential biomarkers for the early diagnosis of SARS patients.129–131 One potential caveat of these studies is that the control cases were either healthy persons or patients with non-SARS infections, with the degree of similarity of symptoms between the SARS and control groups not considered. For effective diagnosis of infectious disease, differentiation is most often required to differentiate disease carrying patients from those patients presenting with similar symptoms, rather than healthy individuals. Indeed, it was recently shown that biomarkers identified by comparing SARS versus normal patients may not be clinically useful.132 In a recent study Pang et al. took this into account and identified a number of serum biomarkers present in SARS patients compared to non-SARS patients presenting with similar symptoms, that were correlated with clinical and/or biological variables.132 This powerful study suggests that serum proteomic fingerprints could be used to identify SARS cases early during onset, and could enable the correct treatment to be administered to each patient group by differentiating similarly presenting SARS and non-SARS patients.
虽然蛋白质组学的威力已被广泛应用于癌症研究，但同样的方法也可应用于人类的其他各种疾病。传染病仍然是全球死亡的主要原因。通过对许多病原体基因组进行测序，现在可以对这些病原体进行大量的蛋白质组学分析。例如，对疟疾寄生虫恶性疟原虫的比较蛋白质组学研究发现了新的潜在药物和疫苗靶点。 120 121 铜绿假单胞菌是人类的机会性病原体，可导致囊性纤维化和烧伤患者以及其他免疫力低下的人感染。 122 Nouwens 等人通过对铜绿假单胞菌临床分离株的膜分馏物进行全面的二维凝胶蛋白质组分析，发现了许多潜在的药物靶点。 123 124 耐药性也是治疗传染病的一个主要问题，要想使治疗有效，就必须确定耐药机制和耐药菌株的特异性生物标志物。结核病仍然是世界上最严重的传染病之一，每年夺去数百万人的生命。 125 二维-凝胶电泳（2D-GE）和质谱（MS）被用来鉴定常见临床分离的结核分枝杆菌分泌的蛋白质。其中两种蛋白质已显示出作为血清诊断抗原的潜力。 126 显然，分泌到受感染病人血清中的蛋白质非常适合用于基于试剂盒的简单血清筛选测试。其他各种基因组学和蛋白质组学研究也发现了目前正在进行临床试验的结核病候选新疫苗。 127 严重急性呼吸系统综合症（SARS）是当前研究的一个主要优先领域。通过对 SARS 患者血清进行蛋白质组学分析，发现了一种潜在的蛋白质标记物--截短的α1-抗胰蛋白酶，与健康对照组相比，SARS 患者体内的α1-抗胰蛋白酶持续升高，可用作特异性诊断或潜在的药物靶点。 128 利用 SELDI 技术进行的类似研究还发现了其他潜在的生物标记物，可用于早期诊断 SARS 患者。 129 – 131 这些研究的一个潜在注意事项是，对照病例要么是健康人，要么是非 SARS 感染者，没有考虑到 SARS 组和对照组之间症状的相似程度。为了有效诊断传染病，通常需要区分带病病人和症状相似的病人，而不是健康人。 事实上，最近的研究表明，通过比较 SARS 患者和正常患者所发现的生物标志物可能对临床并无用处。 132 在最近的一项研究中，Pang 等人考虑到了这一点，与症状相似的非 SARS 患者相比，他们发现了一些 SARS 患者的血清生物标志物，这些标志物与临床和/或生物变量相关。 132 这项有力的研究表明，血清蛋白质组指纹图谱可用于在非典发病初期识别非典病例，并可通过区分症状相似的非典患者和非非典患者，为每组患者提供正确的治疗。
Protein microarrays have been used in the clinical analysis of proteins that induce an antibody response in autoimmune disorders.133 Microarrays, generated by attaching hundreds of proteins and peptides to the surface of derivatised glass slides, were incubated with patient serum and fluorescent labels were used to detect autoantibody binding to specific proteins known to be associated with autoimmune diseases, such as rheumatoid arthritis and systemic lupus erythematosus.133 Many years of research have culminated in the identification of numerous biomarkers associated with cardiovascular disease. As with most diseases, the use of a single biomarker has limited applicability in cardiovascular disease, and a recent review compiled a list of 177 potential biomarkers for the different forms of this disease.1 Such a list is a powerful start to developing multiplexed panels for candidate-based proteome analysis. Proteomics is also being applied to diseases such as asthma,134–136 Alzheimers disease,137 dermatological disorders,138 rheumatoid arthritis139 and cystic fibrosis140 to name just a few.
Proteomics for Prediction of Clinical Outcome
Cancer is an extremely complex disease, with a multitude of molecular aberrations resulting in huge variability in clinical behaviour. As such, traditional analysis of one, or even a few biological parameters, has proved to be insufficient for accurate prediction of disease outcome. Advancements in gene expression profiling are beginning to allow for correlations of clinical data with genome-wide expression.4 Similar correlations were recently demonstrated using proteomic profiles.98 Importantly, by identifying functional components, i.e. the proteins, as precise prognostic markers, novel drug targets and chemotherapy strategies specifically designed for individual patients is a foreseeable outcome in the near future. Correlating the protein expression profiles by 2D-GE with clinical staging of 24 B-cell chronic lymphocytic leukaemia patients (B-CLL), allowed for the identification of 20 proteins with characteristic expression in the three patient distributions studied. Among those, HSP27 was over-expressed in patients with shorter survival times. Down-regulation of thioredoxin peroxidase 2 and protein disulfide isomerase also correlated to decreased survival times.98 Identification of these proteins is of particular prognostic value in B-CLL patients, but with further analysis may also be useful targets for improved therapy. Similar studies in other cancer types are now emerging. Prediction of outcome is also possible via other proteomics methods. As described above, a powerful application of proteomics is the identification of protein activity in specific pathways known to be involved in a disease process, or in response to treatment, and phosphorylation-specific arrays allow the simultaneous detection of pathway activation. Another approach is the use of tissue microarrays which require much less sample than 2D-gels, and as described above can be extremely useful for analysing known sets of protein markers.75 Access to sufficient clinical material can sometimes preclude large-scale proteomic analyses of clinical samples. For the discovery phase of proteomics projects the use of animal models can be a powerful means to overcome this. Moreover, using such models, investigations into in vivo drug response and drug resistance can be conducted in controlled experimental conditions.45
Many researchers are also investigating the use of blood or urine proteomes as these samples are much more readily available. Indeed, serum profiling has been shown to predict response to the cyclooxygenase-2 inhibitor, celecoxib, for cancer prevention and treatment.141 Celecoxib was shown to be efficacious in cancer prevention of patients with the inherited autosomal dominant condition, familial adenomatous polyposis (FAP), however there was a large heterogeneity in patients’ responses.142 Using SELDI TOF MS serum proteomic profiles from patients on this FAP/celecoxib clinical trial, Xiao et al. identified expression changes of several markers that were modulated after treatment with celecoxib, and identified one marker as a strong discriminator between response and non-response.141 SELDI-TOF MS serum profiling has also been used for prediction of liver fibrosis and cirrhosis in chronic hepatitis B infection,143 and, as described above, has been used to predict treatment outcome in patients with SARS.132
Proteomics can also be a powerful tool for identification of disease causing genes. In a seminal study using 2D-GE and affinity chromatography of autopsy brain tissue from late-infantile neuronal ceroid lipofuscinosis (LINCL) patients, a fatal neurodegenerative disease whose defective gene had remained elusive, Sleat et al. identified a protein absent in LINCL patients compared to normals as a pepstatin-insensitive lysosomal peptidase, termed CLN2.144 Importantly, sequence analysis identified mutations in this gene in LINCL patients.144 Future analysis of the myriad of proteomic markers identified in other diseases is certain to reveal further disease causing genes.
Pitfalls of Current Proteomics Technologies
While proteomics has proven extremely useful for discovery based research, its routine use in the clinic is currently hampered by a number of factors. The international Human Proteome Organisation (HUPO) (http://www.hupo.org) was launched in 2001 to start to overcome some of these pitfalls. HUPO’s mission is (i) to consolidate national and regional proteome organisations into a worldwide organisation (ii) to spread proteomics technologies and disseminate knowledge pertaining to the human proteome and model organisms by engaging in scientific and educational activities and (iii) to assist in the coordination of public proteome initiatives aimed at characterising specific tissue and cell proteomes.73 An extremely important aspect of HUPO is to provide standardisation of techniques particularly once proteome analyses become routine use in the clinical setting. HUPO’s Proteomics Standards Initiative is developing standards for data generation and presentation, including standardised formats for databases of all proteomics measurements.
Sample collection procedures must be carefully considered in any clinical laboratory. Specimen manipulation, be it sample collection, pipetting, and diluting, contributes to pre-analytical variables.145 Using MALDI-TOF MS, Marshall et al. demonstrated that changes in serum protein profiles can be generated simply by the amount of time between sample draw and analysis.146 Another source of variation to be considered is biological inter- and intra-patient variability.147 As with any biochemical analysis, protein expression between people resulting from factors such as age, gender, or race contributes to between-subject variation. Within patient variation can also occur based on time of day, fasting state, or age. Testing protein changes due to such variation requires good experimental design, a solid understanding of the limitations of proteomic technologies, and the proper use of statistics.145
In biomarker research, samples are usually collected from multiple sites and randomly divided into discovery (training) sets and validation (testing) sets. Differences in sample collection, handling or storage, and profiling techniques, may influence the protein profile obtained from a given sample.148,149 To obtain meaningful results a sufficiently large number of collection sites and sample populations must be employed to best represent the target population of interest.145 As mentioned previously, the use of SELDI to diagnose ovarian cancer at the earliest stages of the disease was heralded as a major advancement in cancer research.56 However, since the publication of this paper, several reports have emerged that question the validity of the data.150,151 The problems were only brought to light because the original authors made their data publicly available on the internet. The subsequent studies reanalysed the data and found numerous differences in the protein patterns that discriminated between the cancer patients and healthy controls, however they were concerned that the differences looked more like experimental artefacts rather than real biological differences.150,151 The original group have since refined its methods in a new paper152 and make the point that their 2002 Lancet paper56 still shows the feasibility of the approach, and the results prove that there are indeed low molecular weight molecules that can discriminate between the disease states.153 In addition, other groups have examined their data and support the original conclusions.154 This experience is testimony to the fact that relevant data must be made publicly available and must be able to be validated in independent laboratories. As this is one of the major initiatives of HUPO, more public scrutiny is sure to improve the validity of proteomics tests. OvaCheck, the commercial test based on the SELDI patterns described earlier is still being validated,56 however the company says it has validated the test and ensure it will work for women under many different testing conditions. If the company releases its primary data leading to this conclusion such that other researchers can examine the data, and allows researchers to send blinded test samples to determine if the test gives accurate results, the scientific community is then likely to regain confidence in the test.
Plasma, or its close cognate serum, is the primary biochemically useful clinical specimen. However plasma contains the largest and deepest human proteome making it the most difficult sample to work with in proteomics. The enormous depth of the plasma proteome comes from the huge dynamic range of protein concentrations within it. Approximately half of the total protein content in plasma comes from albumin (~55 mg/mL), and a further 10 proteins together make up 90% of the total. On the other hand, low abundance proteins such as cytokines are normally present at 1–5 pg/mL. Indeed, there may be even more proteins at lower concentrations that we have not yet discovered. Thus there are at least 10 orders of magnitude difference between the highest and lowest abundant proteins in plasma. Currently any one proteomics technique can only analyse proteins within 3–4 orders of magnitude, and mostly at the higher concentration end of the spectrum. The removal of high abundant proteins from plasma or serum is thus a prerequisite for conducting more detailed proteomic studies of low abundant proteins, and has been the subject of intense recent investigations (reviewed in reference 145). The most powerful method to date is the simultaneous removal of the 12 most abundant blood proteins by immunological means,155 and reasonably high-throughput columns are now commercially available to achieve this. However, it is also important to note that many potentially important biomarkers may also be lost in this process due to non-specific binding or the co-removal of peptides/proteins intrinsically bound to the high abundant carrier proteins. In particular albumin removal has been shown to result in significant loss of cytokines.156
Another precluding factor for the widespread use of proteomics in the clinical laboratory is the cost. Most proteomics technologies use complex instrumentation, critical computing power, and expensive consumables. At this stage, expertise of operators is still required, particularly for high-throughput analysis of MS data. It has been suggested that this technology could be most reliable and cost-effective if it is offered through large clinical 4 laboratories that are experienced in MS technology.157 While the cost of analyses at this stage may still preclude routine testing, prices are sure to reduce once bulk samples are being analysed. Furthermore, the technology is still a major tool in the discovery armoury, and identification of specific protein markers for disease are better developed into a cheaper and more user-friendly test, such as a protein or tissue microarray, or the ‘tried and tested’ ELISA. Indeed, the technology of ELISAs has improved in recent years and multiplexed assays to detect a combination of proteins are now possible.158
Future Perspectives
Medical research is poised to benefit greatly from the increasing use of proteomics, with the potential to develop better diagnostic and prognostic tests, to identify potential new therapeutic targets, and to head towards individualised patient therapy (Figure 3). Although proteomics has proved its promise for biomarker discovery, further work is still required to enhance the performance and reproducibility of established proteomics tools before they can be routinely used in the clinical laboratory. Issues regarding pre-analytical variables, analytical variability and biological variation must be tackled. The efforts of many researchers, together with the HUPO consortium, are now starting to address many of these issues, such that the future remains extremely bright for the widespread use of clinical proteomics. As current standard proteomics technologies require skilled operators and expensive equipment, their use in routine clinical laboratories is not yet feasible. Thus it is likely that the major input from standard proteomics studies will continue to be the identification of a particular set of biological markers for a specific disease, and more traditional biochemical and immunological tests will then be developed based on these discoveries. However the emerging multiplexing technology of protein chips and arrays should further enhance the throughput of such analytical tests in the near future.
The promise of proteomics. With current technologies, proteomics has the power to greatly enhance our understanding of the molecular basis of disease and to identify novel drug targets. Recent advancements allow for identification of protein biomarkers that will be used for individualising patient care in the near future.
Acknowledgements
Nicole M. Verrills is supported by a National Health and Medical Research Council (NHMRC) Peter Doherty (Biomedical) Fellowship, and grants from the Hunter Medical Research Institute (HMRI).