Integromics with Application to Prostate Cancer Progression

描述（由申请人提供）：本申请解决了广泛的挑战是“（06）使能技术”和特定的挑战主题“06-CA-106数据集成和可视化方法和工具”。前列腺癌是西方世界老年男性中的一种高度流行的疾病，其起始、不受调节的生长、侵袭和转移的特征在于多个复杂事件。收集基因组，蛋白质组和代谢组表达数据提供了破译区分疾病进行性和非进行性形式的分子网络的可能性;此外，它们提供了对侵袭性前列腺癌生物学的深入了解，并有助于识别生物标志物，从而有助于选择患者进行治疗。使用前列腺癌作为一个目标应用程序，我们建议开发统计和生物信息学方法，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）。这些将由计算和可视化工具补充，以实现综合管道的鲁棒性。 具体而言，一个框架，用于进行推理的丰富途径的识别，从 基于图论思想和混合线性统计模型开发了集成组学数据。 此外，还讨论了相关的参数估计，假设检验和算法问题。还讨论了基因组/蛋白质组/代谢组生物标志物的鉴定技术。最后，以改变途径的形式进行分析的科学发现将通过相关细胞系和动物模型的测试进行验证。从拟议的研究中获得的结果将提供一套计算方法，用于整合不同的组学数据，并确定前列腺癌进展中改变的途径。总体而言，这种整合的结果将使得能够使用分子标记物（基因组学、蛋白质组学和代谢组学）的组合作为癌症进展的内源性传感器。此外，该提议开发的用于整合数据的方法将通过构建用于合并使用下一代测序、比较基因组杂交、DNA甲基化等生成的其他类型的高通量组学数据的平台来推进生物信息学领域。

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.