Statistical Methodology for the Design and Analysis of Protein Mass Spectrometry Studies

目前癌症研究的关键目标是确定可用于（a）诊断和分期疾病，（B）监测对治疗的反应，（c）预测哪些患者将对治疗反应和（d）预测患者结果的分子信息。这将使癌症的早期检测和分期得到改善。此外，它将使治疗能够针对有一定机会受益的个人，并继续在那些表现出值得改善的人中进行。在血液中循环的蛋白质是一个可能的信息来源。使用非常少量的血液，目前的技术能够产生一个？蛋白质组特征它提供了样本中存在的数千种不同蛋白质的浓度信息。然后，目的是比较来自不同类型患者的样本，例如癌症与非癌症或应答者与非应答者，以确定是否有任何蛋白质在其浓度上不同。如果这些蛋白质被鉴定出来，它们可以用于帮助诊断癌症或为未来的患者提供适当的治疗。数据的统计分析是任何研究过程的一个重要组成部分，它使科学假设的结论能够从与不确定性测量相关的数据中得出。在蛋白质组学领域，有效和高效的统计设计和分析对于确保科学研究提供可靠和令人信服的结论至关重要。这是一个新的和迅速发展的科学领域，可能会对病人护理产生直接影响，有一个研究，探索与这些研究相关的统计方法的巨大需求。对于统计学家来说，这是一个极具挑战性的领域，并且需要根据其对蛋白质组数据的具体应用来评估已经可用的方法。这项研究的结果将只与该领域的专家直接相关，但最终这将提高人们的信心，即在患者组之间检测到的蛋白质组特征差异是真正的生物学差异，使该技术能够在未来造福于患者护理。

Current key goals in cancer research are to identify molecular information that can be used to (a) diagnose and stage disease, (b) monitor response to treatment, (c) predict which patients will respond to therapy and (d) predict patient outcome. This will enable the potential early detection and improved staging of cancer. In addition it will enable treatments to be targeted at individuals who have some chance of benefiting and continued in those showing a worthwhile level of improvement. The proteins that are circulating in the blood are a possible source of information. Using a very small amount of blood, current technology is able to generate a ?proteomic signature? for a patient, which gives information on the concentration of thousands of different proteins that are present in the sample. The aim is then to compare samples from different types of patients, for example cancer versus non-cancer or responder versus non-responder, to determine if any of proteins differ in their concentration. If such proteins are identified they could be used to aid the diagnosis of cancer or inform appropriate treatment for future patients. Statistical analysis of data is an important part of any research process, enabling conclusions regarding scientific hypotheses to be drawn from the data in association with a measure of uncertainty. In the field of proteomics, valid and efficient statistical design and analysis are crucial to ensure that this scientific research provides robust and convincing conclusions. This is a new and rapidly developing scientific field that could have a direct impact on patient care and there is a great need for research to explore the statistical methodology associated with such studies. This is a hugely challenging area for statisticians and the methods that are already available needs to be assessed in terms of their specific application to proteomic data. The findings of this research will only be directly relevant to specialists in this field but ultimately this will result in increased confidence that differences detected in proteomic signatures between groups of patients are true biological differences, enabling the technology to benefit patient care in the future.