Pharmaco Response Signatures and Disease Mechanism

药物反应特征和疾病机制

• 批准号: 9754857
• 负责人: PETER Karl SORGER
• 金额: $ 214.52万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-10 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9754857
• 关键词: Antibodies; Apoptosis; Award; Bayesian learning; Biochemical; Biological Assay; Cell Cycle Arrest; Cell Extracts; Cell surface; Cells; Clinical; Clinical Investigator; Collaborations; Collection; Communities; Complex; Computational algorithm; Computing Methodologies; Data; Data Analyses; Data Collection; Data Quality; Data Set; Databases; Development; Disease; Dose; Drug Interactions; Education and Outreach; Ensure; Exposure to; Extracellular Matrix; Fuzzy Logic; Generations; Genetic Transcription; Goals; Growth Factor; Human; Image; Immune; Immune response; Immunoassay; Immunofluorescence Immunologic; Individual; Knowledge; Linear Regressions; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Messenger RNA; Methods; Modeling; Molecular Computations; Online Systems; Pathway Analysis; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacology; Phase; Phenotype; Phosphotransferases; Polypharmacy; Population; Post-Translational Protein Processing; Principal Component Analysis; Production; Proteins; Proteome; Proteomics; Research; Research Personnel; Semantics; Sentinel; Signal Transduction; Signaling Protein; Statistical Methods; Statistical Models; Stem cells; Structure; Technology; Testing; Thromboplastin; Work; analytical method; base; cell type; cellular imaging; cytokine; data visualization; drug discovery; epigenome; experimental study; genome browser; genomic data; improved; induced pluripotent stem cell; inhibitor/antagonist; innovation; kinase inhibitor; learning strategy; live cell imaging; member; network models; novel; novel strategies; operation; outreach; personalized medicine; phenotypic data; predicting response; programs; public health relevance; random forest; response; senescence; single cell analysis; small molecule; tool; translational impact; tumor; usability; web services; web site

DESCRIPTION (provided by applicant): The overall goal of the HMS LINCS Center is to delineate the fundamental principles of cellular response to perturbagens at the level of single-cells and cell populations and to make response data routinely available via web-based browse, query and programmatic interfaces. This will involve the development and testing of new measurement methods, computational algorithms, and response signatures for diverse human cell types exposed to perturbations individually and in combination. We will emphasize the systematic collection of data not currently available in existing public databases including live and fixed-cell imaging, biochemical data on signaling proteins and multi-factorial drug-response phenotypes. A focus on diverse transformed and primary cells, including those derived from healthy and diseased donors, and on clinical grade small molecules (kinase inhibitors and epigenome drugs) will increase the translational impact of our work. The proposed LINCS Center represents a continuation of a program in operation for ~3.5 years under a LINCS pilot phase U54 award. We will expand the scope and sophistication of our Center, devoting significant effort to (i) improving the quality of data analysis and visualization, particularly wih respect to the complexities of perturbagen polypharmacy (ii) accelerating the release of perturbagen-response signatures using methods that have been demonstrated to yield reliable and informative data, with a particular emphasis on primary and non-transformed cells (iii) developing and applying new measurement methods, particularly mass spectrometry for analysis of cell populations and live-cell imaging for analysis of single cells. The work will involve nine complementary but independent aims. In Data Generation, Aim 1 will perform systematic analysis of perturbagen responses at a single-cell level. Aim 2 will collect multiplex protein and mRNA data on perturbagen response using a set of complementary imaging, mass spectrometry and bead-based assays. Aim 3 will apply LINCS methods to non-transformed immune cells and induced pluripotent stem cells, and explore if signatures can guide a detailed medicinal chemistry campaign. In Data Analysis, Aim 4 will construct perturbagen-response signatures using statistical modeling, network inference and machine learning methods. Aim 5 will develop new approaches to understanding and analyzing drug interactions on multiple phenotypes in single cells. Aim 6 will develop a novel compressed sensing framework for analyzing the poly-pharmacology of kinase inhibitors. Aim 7 will enhance the query, browse and explore functions of the HMS LINCS website and database and its integration with the UCSC Genome Browser. In Community Interaction and Outreach, Aim 8 will implement diverse training and outreach activities, including collaboration with LINCS and non-LINCS research groups. In Administration, Aim 9 will ensure effective management of the Center, sustained access to tools and data produced within the LINCS Project, and compliance with program goals.

描述（由申请人提供）：HMS LINCS中心的总体目标是在单细胞和细胞种群的水平上描述蜂窝响应的基本原理，并通过基于Web的浏览，查询和程序化接口通常可用。这将涉及对新测量方法，计算算法和响应签名的开发和测试，以分别和组合暴露于扰动的各种人类细胞类型。我们将强调现有公共数据库中目前尚不可用的数据的系统收集，包括实时和固定细胞成像，有关信号蛋白的生化数据以及多因素药物反应表型。关注多样化的转化和原代细胞，包括来自健康和患病供体的细胞，以及临床级的小分子（激酶抑制剂和表观基因组药物），将增加我们工作的翻译影响。拟议的Lincs中心代表了在Lincs Pilot阶段U54奖项下运营计划的延续。我们将扩大中心的范围和复杂性，致力于（i）提高数据分析和可视化的质量，尤其是与伴有抗逆转录夜根多药的复杂性（II）加速伴有伴有抗响应签名的方法的复杂性（ii），该方法使用已证明的新的和不适用的数据，并与特定的数据一起开发了新的和无用的数据。测量方法，尤其是用于分析细胞群体和用于分析单细胞的活细胞成像的质谱法。 这项工作将涉及九种互补但独立的目标。在数据生成中，AIM 1将在单细胞水平上对心形脑反应进行系统分析。 AIM 2将使用一组互补成像，质谱和基于珠的测定法收集多重蛋白和mRNA数据。 AIM 3将在非转化的免疫细胞和诱导多能干细胞上应用lincs方法，并探索是否可以指导详细的药物化学运动。在数据分析中，AIM 4将使用统计建模，网络推理和机器学习方法构建伴有响应标志。 AIM 5将开发新的方法来理解和分析单个细胞中多种表型的药物相互作用。 AIM 6将开发出一种新型的压缩传感框架，以分析激酶抑制剂的多药理学。 AIM 7将增强HMS Lincs网站和数据库的查询，浏览和探索功能及其与UCSC基因组浏览器的集成。在社区互动和外展中，AIM 8将实施各种培训和外展活动，包括与Lincs和非Lincs研究小组的合作。在管理中，AIM 9将确保对中心的有效管理，持续访问Lincs项目中生产的工具和数据，并遵守计划目标。

项目成果

Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics.

• DOI: 10.1038/nbt.3882
• 发表时间: 2017-06-07
• 期刊: Nature biotechnology
• 影响因子: 46.9
• 作者: Hafner M;Niepel M;Sorger PK
• 通讯作者: Sorger PK

A High Content Screen in Macrophages Identifies Small Molecule Modulators of STING-IRF3 and NFkB Signaling.

• DOI: 10.1021/acschembio.7b01060
• 发表时间: 2018-04-20
• 期刊: ACS chemical biology
• 影响因子: 4
• 作者: Koch PD;Miller HR;Yu G;Tallarico JA;Sorger PK;Wang Y;Feng Y;Thomas JR;Ross NT;Mitchison T
• 通讯作者: Mitchison T

GRcalculator: an online tool for calculating and mining dose-response data.

• DOI: 10.1186/s12885-017-3689-3
• 发表时间: 2017-10-24
• 期刊: BMC cancer
• 影响因子: 3.8
• 作者: Clark NA;Hafner M;Kouril M;Williams EH;Muhlich JL;Pilarczyk M;Niepel M;Sorger PK;Medvedovic M
• 通讯作者: Medvedovic M

Proteomic profiling across breast cancer cell lines and models.

• DOI: 10.1038/s41597-023-02355-0
• 发表时间: 2023-08-04
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Kalocsay, Marian;Berberich, Matthew J.;Everley, Robert A.;Nariya, Maulik K.;Chung, Mirra;Gaudio, Benjamin;Victor, Chiara;Bradshaw, Gary A.;Eisert, Robyn J.;Hafner, Marc;Sorger, Peter K.;Mills, Caitlin E.;Subramanian, Kartik
• 通讯作者: Subramanian, Kartik