Interactive Informatics Resource for Research-driven Cancer Proteomics

描述(申请人提供)：2013年，预计将有超过160万新的癌症病例被诊断出来，预计将有超过58万人死于这种疾病。因此，在识别新的癌症诊断和预后生物标志物方面继续研究是必要的。虽然癌症被广泛认为是一种基因组疾病，但基于DNA的驱动因素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记物的应用仍然是一个引人注目的愿景。因此，NCI和其他研究中心在高通量全球蛋白质组学实验方面投入了大量资金，以挖掘癌症的新生物标记物。然而，这些标记中几乎没有什么取得成果。我们认为，从这些大型和复杂的蛋白质组学数据集中发现强大的蛋白质或途径生物标记物候选的主要挑战之一是由于没有考虑蛋白质组潜在复杂性(例如剪接变体、翻译后修饰)的幼稚数据分析方法。已经开发了最先进的统计算法，以改进质量评估、多肽和蛋白质量化以及通路建模的任务，这些算法旨在说明实验的设计；然而，由于这些方法处于原型阶段，通常需要统计编程知识，更大的社区对这些方法的使用受到了阻碍。此外，这些工具转向健壮软件的可能性很低，因为它们是在不支持从原型向软件过渡的现有赠款的背景下开发的。临床蛋白质组学领域要成功识别新的癌症发病机制，不仅需要与仪器相关的高质量数据，还需要对数据进行高质量的统计分析。该项目提出了一种新的信息学技术，其形式是一个强大、交互和跨平台的软件环境，使生物医学和生物科学家能够从质量评估和原始推断丰度(例如峰面积)的归一化角度对全球蛋白质组学数据进行深入分析，以确定蛋白质生物标志物和丰富的途径。该软件将采用单一编程语言(Java)设计，以确保通过基于向导的数据输入和高级数据报告实现跨平台轻松安装。Java还将支持高级图形用户界面的开发，用于数据呈现和具有现代外观和感觉的交互图形。这一办法将确保开发机构以外的科学家能够开发模块以纳入软件或数据集成扩展，而无需重新编译应用程序。该项目将开发的软件模块包括：Aim 1)多肽和蛋白质水平的质量评估和定量；Aim 2)通过探索性数据分析和机器学习发现蛋白质生物标记物；以及Aim 3)通过与NCI蛋白质相互作用数据库集成发现途径生物标记物。

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.