Proteomic-based integrated subject-specific networks in cancer

项目总结/摘要 本提案的总体目标是开发通用分析工具， 通过序列分析来表征患者特异性途径特征的框架 癌症特异性（全球）和患者特异性（局部）网络的估计。我们 制定创新和灵活的知识导向的量化框架， 整合多种信息源：定性和非结构化知识 数据库，数据驱动的从头因果结构以及上游多， 在基因组、表观基因组、转录组和 蛋白质组水平我们的方法的动机和应用到新的， 未发表的，基于反相蛋白质阵列的蛋白质组学图谱， 来自癌症基因组图谱的32种癌症类型的患者肿瘤样本 （TCGA）以及来自MD安德森细胞系项目的细胞系样品 （MCLP）在19种肿瘤谱系中的表达。这使我们能够全面 描述肿瘤网络生物学的共性和差异 血统，以提供深入了解潜在的生物学机制， 发现可靠的无监督和监督预测模型， 临床和药物敏感性结果-帮助翻译和精确 药

Project Summary/Abstract The overall objective of this proposal is to develop general analytic frameworks to characterize patient-specific pathway signatures by sequential estimations of cancer-specific (global) and patient-specific (local) networks. We develop innovative and flexible knowledge-guided quantitative frameworks that integrate multiple sources of information: qualitative and unstructured knowledge databases, data-driven de novo causal structures as well as upstream multi- platform molecular profiling data at the genomic, epigenomic, transcriptomic and proteomic levels. Our methods are motivated by and applied to novel, unpublished, reverse-phase protein array-based proteomic profiles generated on patient tumor samples across 32 cancer types from The Cancer Genome Atlas (TCGA) as well as cell line samples from the MD Anderson Cell lines Project (MCLP) across 19 tumor lineages. This allows us to comprehensively characterize commonalities and differences in network biology across tumor lineages to provide insight into the underlying biological mechanisms, and discovery of reliable unsupervised and supervised prediction models for relevant clinical and drug sensitivity outcomes -- to aid translational and precision medicine.