Pharmaco Response Signatures and Disease Mechanism

药物反应特征和疾病机制

• 批准号: 8545951
• 负责人: Timothy J Mitchison
• 金额: $ 22.5万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-27 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8545951
• 关键词: Algorithms; Apoptosis; Area; Behavior; Biochemical; Biochemistry; Biological; Biological Assay; Cell Cycle; Cell Line; Cell Survival; Cell physiology; Cells; Chemistry; Clinical; Collection; Communities; Complex; Data; Data Analyses; Data Collection; Data Provenance; Data Quality; Data Set; Disease; Dose; Drug resistance; Epithelial; Experimental Designs; Extensible Markup Language; Flow Cytometry; Genome; Genomics; Genotype; Goals; Growth Factor; Human; Image Cytometry; Immunoassay; Informatics; Institution; Investigational Drugs; Kinetics; Knowledge; Libraries; Life; Ligands; Link; Logic; Malignant Neoplasms; Maps; Measures; Mesenchymal; Methodology; Methods; Modeling; Molecular Profiling; Normal Cell; Output; Pathway interactions; Patients; Pharmaceutical Chemistry; Pharmaceutical Preparations; Phase; Phenotype; Phosphotransferases; Physiology; Process; Production; Proteins; Reporting; Resistance; Resolution; Running; Sampling; Series; Signal Pathway; Signal Transduction; Signaling Protein; Software Tools; Specificity; Stem cells; Structure; System; Techniques; Therapeutic; Time; Tumor Cell Line; base; cancer cell; cellular imaging; computer based Semantic Analysis; cytokine; drug mechanism; flexibility; heuristics; information processing; inhibitor/antagonist; insight; interest; kinase inhibitor; large scale production; large-scale database; malignant breast neoplasm; mathematical model; medical schools; meetings; movie; neoplastic cell; novel; open source; programs; public health relevance; response; senescence; small molecule; small molecule libraries; tool

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.

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干扰素诱导的细胞特征的中心，重点关注小分子药物引起的干扰和使用不同的生化和单一方法测量的细胞特征。 -细胞测定。结果将是一个庞大的、自洽的、多样化的以网络为中心的药理学反应特征，为疾病过程、药物机制/选择性以及最终患者特定的治疗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，这是一种具有高转化潜力的多功能扰动剂。我们将使用已知的抑制剂，并通过新的药物化学和全激酶组选择性测定法大幅扩展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，将使用多重生化测定（针对 20-100 种蛋白质）进行测定，包括基于珠子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）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。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，这些工具基于新的 XML/HDF5 格式操纵语义类型的数据数组。多方面的信息学计划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且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关联的具体目标来实现。目标 1 将重点关注现有的（主要是临床级的）激酶抑制剂和一组 45 种细胞系，这些细胞系已知显示出不同的药物反应，并且可以获得大量的基因组数据。目标 2 将通过使用新的和现有的化学方法开发大型激酶抑制剂库并分析整个激酶组的生化特异性来扩大干扰源的范围。目标 3 将结合现有的和新型化合物，对一组 > 1000 个肿瘤细胞系进行剂量反应分析，以确定代表性细胞系和异常值，在目标 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、系统化和分发不同数据类型所需的信息处理系统。这将涉及一套新颖的可互操作软件工具，其中包含新兴的非 SQL 和语义 Web 概念。将开发适应性实验设计方法，将数据收集集中在剂量反应景观中特征信息最丰富的区域。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，而表达谱和基因组数据是当前针对扰动原诱导的细胞特征的高通量生物学研究的主要焦点。