Interactive Informatics Resource for Research-driven Cancer Proteomics

描述（由申请人提供）：2013年，预计将有超过160万的新癌症病例被诊断出来，预计将有超过58万人死于癌症。因此，继续研究新的癌症诊断和预后生物标志物是必要的。尽管癌症被广泛认为是一种基因组疾病，但基于dna的驱动程序的指令是在蛋白质及其生物学功能水平上执行的，潜在的蛋白质水平生物标志物的应用仍然是一个令人信服的愿景。因此，NCI和其他研究中心在高通量全球蛋白质组学实验中进行了大量投资，以挖掘新的癌症生物标志物。然而，这些标志几乎没有实现。我们认为，从这些庞大而复杂的蛋白质组学数据集中发现强大的蛋白质或通路生物标志物候选物的主要挑战之一是由于幼稚的数据分析方法没有考虑到蛋白质组的潜在复杂性（例如，剪接变异体，翻译后修饰）。已经开发了最先进的统计算法，以改进质量评估，肽和蛋白质定量以及旨在解释实验设计的途径建模任务；然而，由于这些方法还处于原型阶段，并且通常需要统计编程知识，因此阻碍了大型社区使用这些方法。此外，这些工具转移到健壮软件的可能性很低，因为它们是在现有授权的背景下开发的，不支持从原型到软件的转换。对于临床蛋白质组学领域来说，要成功识别癌症的新机制病因，不仅需要高质量的仪器数据，还需要高质量的数据统计分析。该项目提出了一种新的信息学技术，以一种强大的、交互式的、跨平台的软件环境的形式，使生物医学和生物科学家能够对全球蛋白质组学数据进行深入的分析，从质量评估和原始推断丰度（例如，峰面积）的规范化到蛋白质生物标记物和富集途径的鉴定。该软件将以单一编程语言（Java）设计，以确保易于跨平台安装，具有基于向导的数据输入和高级数据报告。Java还将支持开发高级图形用户界面，用于数据表示和具有现代外观的交互式图形。这种方法将确保开发机构之外的科学家可以开发包含在软件中的模块或数据集成的扩展，而不需要重新编译应用程序。该项目将开发的软件模块包括：Aim 1)肽和蛋白质水平质量评估和定量；Aim 2)通过探索性数据分析和机器学习发现蛋白质生物标志物；Aim 3)通过与NCI蛋白质相互作用数据库集成发现通路生物标志物。

DESCRIPTION (provided by applicant): In 2013 over 1.6 million new cases of cancer are expected to be diagnosed and over 580,000 people are expected to die of the disease. Thus, continued research in the identification of new diagnostic and prognostic biomarkers of cancer is necessary. Although cancer is widely recognized as a genomic disease, the directives of the DNA-based drivers are executed at the level of proteins and their biological functions, and the application of potential protein level biomarkers remains a compelling vision. Thus, a large investment has been made by NCI and other research centers in high-throughput global proteomics experiments to mine for novel biomarkers of cancer. However, few of these markers have come to fruition. We believe that one of the major challenges to the discovery of robust protein- or pathway-biomarker candidates from these large and complex proteomics datasets is due to naive data analysis approaches that do not take into account the underlying complexity of the proteome (e.g., splice variants, post- translational modifications). State-of-the-art statistical algorithms to improve the tasks of quality assessment, peptide and protein quantification, and pathway modeling that are designed to account for the design of the experiment have been developed; however access to these methodologies by the larger community is hindered since they are in the prototype stage and typically require knowledge of statistical programming. Furthermore, the likelihood of these tools moving to robust software is low since they are developed within the context of existing grants that do not support the transition from prototype to software. For the field of clinical proteomics to successfully identif new mechanistic etiologies of cancer requires not only high quality data with respect to the instrument, but also high quality statistical analysis of the data. This project proposes new informatics technology in the form of a robust, interactive and cross- platform software environment that will enable biomedical and biological scientists to perform in-depth analyses of global proteomics data from the point of quality assessment and normalization of raw inferred abundances (e.g., peak area) to the identification of protein biomarkers and enriched pathways. The software will be designed in a single programming language (Java) to assure easy installation across platforms with wizard-based data entry and advanced data reporting. Java will also support the development of advanced graphical user interfaces for data presentation and interactive graphics with a modern look and feel. This approach will ensure that scientists outside of the development institution can develop modules to include in the software or extensions for data integration without challenges of re-compiling the application. The software modules to be developed under this project are Aim 1) peptide and protein level quality assessment and quantification, Aim 2) protein biomarker discovery via exploratory data analysis and machine learning, and Aim 3) pathway biomarker discovery through integration with the NCI Protein Interaction Database.