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摄动原诱导的细胞特征的中心，重点研究由小分子药物引起的摄动和使用多种生化和单细胞分析测量的细胞特征。结果将是一个庞大的、自我一致的、多样化的以网络为中心的药理反应特征集，为疾病过程、药物机制/选择性以及最终的患者对治疗的特异性反应提供独特的见解。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摄动原。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选择性测定大幅度扩大公开记录的抑制剂集合。大量人类肿瘤细胞和一些原代细胞对激酶抑制剂的反应，将使用多重生化分析（针对20-100种蛋白质）进行分析，包括基于珠状细胞的夹心免疫分析和反相裂解微阵列，以及单细胞分析（使用成像和流式细胞术），以检测细胞周期状态、衰老或凋亡的承诺、间充质与上皮表型以及原始（干细胞）状态的标记。数据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，这些软件工具可以操作基于新的XML/HDF5格式的语义类型数据数组。一个多方面的信息学项目将把这些细胞反应的表型和生化测量与其他人收集的丰富和不断增长的基因组数据联系起来。这些目标将通过追求六个相互联系的具体目标来实现。Aim 1将重点关注现有的激酶抑制剂（主要是临床级）和一组45个细胞系，这些细胞系已知表现出不同的药物反应，并且具有广泛的基因组数据。Aim 2将利用新的和现有的化学方法开发一个大型激酶抑制剂文库，并分析整个激酶组的生化特异性，从而扩大摄动原的集合。Aim 3将在一组1000个肿瘤细胞系的剂量反应分析中结合现有和新化合物，以确定代表性细胞系和异常值，在Aim 4中，将在单细胞水平上进行详细分析。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，这些工具将结合新兴的无sql和语义web概念。将开发适应性实验设计方法，将数据收集的重点放在剂量反应格局中特征信息最丰富的那些区域。最终产品将是一个大型的公开数据集，与表达谱和基因组数据完全不同，但高度互补，这些数据是当前高通量生物学研究的主要焦点，这些研究是关于摄流原诱导的细胞特征。

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.