Integromics with Application to Prostate Cancer Progression

描述(申请人提供)：本申请涉及广泛的挑战“(06)使能技术”和具体的挑战主题“06-CA-106数据集成和可视化方法和工具”。前列腺癌是西方老年男性中的一种高发疾病，其发病、生长、侵袭和转移具有多个复杂事件的特点。收集基因组、蛋白质组和代谢组表达数据提供了破译区分进展型和非进展型疾病的分子网络的可能性；此外，它们还提供了对侵袭性前列腺癌生物学的洞察，并帮助确定生物标记物，这反过来将有助于选择接受治疗的患者。以前列腺癌为目标应用，我们建议开发统计和生物信息学方法来整合和分析这三个匹配的数据源(基因组、蛋白质组和代谢组表达谱)。这些将得到计算和可视化工具的补充，以实现综合管道中的稳健性。 具体地说，一种用于进行关于从 综合OMICS数据是基于图论思想和混合线性统计模型开发的。 此外，还讨论了相关的感兴趣参数估计、假设检验和算法问题。还讨论了识别基因组/蛋白质组/代谢组生物标志物的技术。最后，通过在相关细胞系和动物模型中进行测试，以改变途径的形式进行分析的科学发现将得到验证。从拟议的研究中获得的结果将提供一套计算方法，用于整合不同的Omics数据，并识别前列腺癌进展过程中的改变路径。总体而言，这种整合的结果将使分子标记(基因组学、蛋白质组学和代谢组学)的组合能够用作癌症进展的内源性传感器。此外，该方案开发的综合数据方法将通过建立平台，融合使用下一代测序、比较基因组杂交、DNA甲基化等产生的其他类型的高通量Omics数据，从而推动生物信息学领域的发展。

DESCRIPTION (provided by applicant): This application addresses broad Challenge Are "(06) Enabling Technologies" and Specific Challenge Topic "06-CA-106 Data integration and visualization methods and tools". Prostate cancer is a highly prevalent disease in older men of the Western world, whose initiation, unregulated growth, invasion and metastasis is characterized by multiple complex events. Gathering genomic, proteomic and metabolomic expression data offers the possibility of deciphering the molecular networks that distinguish progressive from non-progressive forms of the disease; in addition, they provide insight into the biology of aggressive prostate cancer and help in identifying biomarkers that in turn will aid in the selection of patients for treatment. Using prostate cancer as a target application, we propose developing statistical and bioinformatics methodology for the integration and analysis of these three matched data sources (genomic, proteomic and metabolomic expression profiles). These will be complemented by computational and visualization tools to enable robustness in the integrative pipeline. Specifically, a framework for carrying out inference about the identification of enriched pathways from integrated Omics data is developed based on graph theoretic ideas and mixed linear statistical models. Further, the associated estimation of parameters of interest, hypothesis testing and algorithmic issues, are also addressed. Techniques for the identification of genomic/proteomic/metabolomic biomarkers are also discussed. Finally, the scientific findings from the analysis in the form of altered pathways will be validated through testing in associated cell lines and animal models. The results obtained from the proposed studies will provide a suite of computational approaches for integrating diverse Omics data and identify altered pathways in prostate cancer progression. Overall, the results of such integration will enable the use of a combination of molecular markers (genomics, proteomics and metabolomics) as endogenous sensors for cancer progression. Additionally, the methods developed by this proposal for integrative data will advance the field of bioinformatics by building the platform for amalgamation of other types of high throughput Omics data generated using next generation sequencing, comparative genomic hybridization, DNA methylation etc.