Informatics and Integration

该提案将创建一个大规模生产扰动诱导细胞签名的中心 在哈佛医学院和合作机构，重点是小扰动引起的 分子药物和使用不同的生物化学和单细胞测定测量的细胞签名。的 结果将是一个大的，自我一致的和多样化的网络中心的药理学反应 特征提供了对疾病过程、药物机制/选择性以及最终 患者对治疗的反应。该中心最初的重点将是小分子激酶抑制剂， 具有高平移势的多功能微扰源。我们将使用已知的抑制剂， 通过新的药物化学和使用公开记录的抑制剂， 全激酶组选择性测定。大量人类肿瘤细胞和一些原发性肿瘤细胞的反应 将使用多重生物化学测定法（对于20-100种蛋白质）进行测定， 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、衰老或凋亡的发生、间充质与 上皮表型和原始（干细胞）状态的标志物。数据将被收集、整合， 使用一系列新颖的、可互操作的软件工具来操作语义类型化的数据， 基于新的XML/HDF 5格式的数组。一个多方面的信息学程序将把这些表型 和生化措施的细胞反应，以丰富和不断增长的一套基因组数据正在收集， 他人 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将侧重于现有的-主要是 临床级激酶抑制剂和一组已知显示不同药物反应的45种细胞系 可获得大量基因组数据的土地。目标2将通过以下方式扩大扰动根集： 使用新的和现有的化学和生物化学分析开发激酶抑制剂的大型文库 特异性。目标3将以剂量-反应的方式将联合收割机现有的和新的化合物结合起来 在一组>1000个肿瘤细胞系中进行分析，以鉴定代表性细胞系和离群值， 4，将在单细胞水平上进行详细分析。目标5-6将开发和部署信息 处理系统需要收集、系统化和分发不同的数据类型。这将涉及一本小说 一组可互操作的软件工具，包含了新兴的非SQL和语义Web概念。方法 将开发适应性实验设计，以将数据收集集中在剂量反应的这些领域， 签名信息量最大的地方。最终的产品将是一个大型的公开的 数据集与表达谱和基因组完全不同，但高度互补 这些数据是目前对微扰诱导的细胞的高通量生物学研究的主要焦点， 签名.

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 land 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 doseresponse landscape where signatures are most informative. The final product will be a large publiclyavailable 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-lnduced cellular signatures.