Pharmaco Response Signatures and Disease Mechanism

描述（由申请人提供）：该提案将在哈佛医学院和合作机构建立一个大规模生产扰动诱导细胞特征的中心，重点关注小分子药物引起的扰动和使用各种生物化学和单细胞测定法测量的细胞特征。其结果将是一个大的，自我一致的和多样化的网络为中心的药理学反应签名集，提供独特的洞察疾病过程，药物机制/选择性和最终的患者特异性反应的治疗。该中心最初的重点将是小分子激酶抑制剂，具有高翻译潜力的多功能微扰剂。我们将使用已知的抑制剂，并通过新的药物化学和使用全激酶组选择性测定来显着扩大公开记录的抑制剂的收集。将使用多重生物化学测定法测定大量人类肿瘤细胞和一些原代细胞对激酶抑制剂的反应（20-100种蛋白质），包括基于珠的夹心免疫测定和反相裂解物微阵列，以及单细胞测定（使用成像和流式细胞术）用于细胞周期状态，衰老或凋亡的承诺，间充质对上皮表型和原始（干细胞）状态的标志物。将使用一系列新颖的、可互操作的软件工具来收集、集成和分发数据，这些软件工具基于新的XML/HDF 5格式来操作语义类型化的数据阵列。一个多方面的信息学计划将把这些细胞反应的表型和生化指标与其他人收集的丰富和不断增长的基因组数据联系起来。这些目标将通过实现六个相互关联的具体目标来实现。目标1将集中在现有的-主要是临床级-激酶抑制剂和一组45个细胞系，已知显示不同的药物反应，并为广泛的基因组数据是可用的。目标2将通过使用新的和现有的化学方法开发一个大型激酶抑制剂库并分析整个激酶组的生物化学特异性来扩大扰动物的集合。目标3将在一组>1000个肿瘤细胞系的剂量-反应分析中组合联合收割机现有的和新的化合物，以鉴定代表性细胞系和离群值，在目标4中，将在单细胞水平进行详细分析。目标5-6将开发和部署收集、系统化和分发各种数据类型所需的信息处理系统。这将涉及一套新的可互操作的软件工具，其中包括新兴的无SQL和语义网概念。将开发自适应实验设计方法，以将数据收集集中在特征信息量最大的剂量反应领域。最终产品将是一个大型的公开可用的数据集，完全不同，但高度互补，表达谱和基因组数据，是目前高通量生物学研究的主要重点扰动诱导的细胞签名。

DESCRIPTION (provided by applicant): This proposal will create a center for Large Scale Production of Perturbagen-lnduced Cellular Signatures at Harvard Medical School and collaborating institutions, with a focus on perturbations provoked by small molecule drugs and cellular signatures measured using diverse biochemical and single-cell assays. The result will be a large, self-consistent and diverse set of network-centric Pharmacological Response Signatures that provide unique insight into disease processes, drug mechanism/selectivity and ultimately patient-specific responses to therapy. The initial focus of the Center will be small molecule kinase inhibitors, versatile perturbagens with high translational potential. We will use known inhibitors and also expand dramatically the publicly documented collection of inhibitors through new medicinal chemistry and use of kinome-wide selectivity assays. The responses of a large collection of human tumor cells and some primary cells to kinase inhibitors, will be assayed using multiplex biochemical assays (for 20-100 proteins) involving bead-based sandwich immunoassays and reverse-phase lysate microarrays, and single-cell assays (using imaging and flow cytometry) for cell cycle state, commitment to senescence or apoptosis, mesenchymal vs. epithelial phenotype and markers of primitive (stem-cell) status. Data will be collected, integrated and distributed using a series of novel, interoperable software tools that manipulate semantically-typed data arrays based on a new XML/HDF5 format. A multi-faceted informatics program will link these phenotypic and biochemical measures of cellular response to a rich and growing set of genomic data being collected by others. These goals will be met through pursuit of six linked specific aims. Aim 1 will focus on existing - largely clinical grade - kinase inhibitors and a set of 45 cell lines that are known to display diverse drug responses and for which extensive genomic data are available. Aim 2 will enlarge the set of perturbagens by developing a large library of kinase inhibitors using new and existing chemistry and profiling biochemical specificity across the kinome. Aim 3 will combine existing and novel compounds in a dose-response analysis across a set of >1000 tumor cell lines to identify representative cell lines and outliers which, in Aim 4, will subjected to detailed analysis at a single-cell level. Aims 5-6 will develop and deploy the information processing systems needed to collect, systematize and distribute diverse data types. This will involve a novel set of interoperable software tools that incorporate emerging no-SQL and semantic web concepts. Methods for adaptive experimental design will be developed to focus data collection on those areas of the dose response landscape where signatures are most informative. The final product will be a large publicly available data set radically different from, but highly complementary to, the expression profiles and genome data that are the primary focus of current high-throughput biological studies on perturbagen-induced cellular signatures.