Aim/Project 1. Dose-response at single-cell and population levels

目标/项目 1. 单细胞和群体水平的剂量反应

• 批准号: 8769535
• 负责人: PETER Karl SORGER
• 金额: $ 35.99万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8769535
• 关键词: Antibiotic Resistance; Antibiotics; Antineoplastic Agents; Apoptosis; Apoptotic; Area; Autophagocytosis; Biological Assay; Cancer cell line; Cell Culture Techniques; Cell Cycle; Cell Death; Cell Line; Cell division; Cells; Cellular Morphology; Communicable Diseases; Complex; DNA Damage; Data; Dose; Drug Addiction; Drug Combinations; Drug Synergism; Drug Targeting; Drug resistance; Drug usage; End Point Assay; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Exhibits; Follow-Up Studies; Genotype; Goals; Heterogeneity; Holidays; Immune; Immunofluorescence Microscopy; Inhibitory Concentration 50; Laboratories; Life; MAP Kinase Gene; Measures; Methods; Microscopy; Modeling; Molecular; Monitor; Mycobacterium tuberculosis; Noise; Patients; Pharmaceutical Preparations; Pharmacology; Phenotype; Physiological; Play; Population; Property; Proteins; Regulator Genes; Relative (related person); Research Personnel; Resistance; Resources; Role; Sampling; Shapes; Signal Transduction; Testing; Therapeutic; Therapeutic Effect; Time; Translating; Translations; Tumor Stem Cells; Variant; base; cancer cell; cancer stem cell; cell fixing; cell type; cellular engineering; cellular imaging; combination cancer therapy; follow-up; genetic regulatory protein; human disease; kinase inhibitor; molecular marker; neglect; non-genetic; research study; response; senescence; tumor microenvironment

PROJECT SUMMARY This aim focuses on determinants of dose-response at a single-cell level. We will test the hypothesis that non-genetic cell-to-cell variability (arising from variation in the relative levels or activities of network components) is critical in determining the shape of dose-response curves and the maximum therapeutic effect that can be achieved at high drug concentrations. The impact of stochastic variation will be contrasted with that of cell cycle state and of special lineages (e.g. tumor stem cells). Experiments in this Aim also examine the importance of timing and order-of-exposure in combination cancer therapy. All but aim 1.4 will be performed using panels of ~10-40 cancer cell lines grown in 2D culture supplemented by a smaller number of patient- derived cultures obtained through the Translational Pharmacology Core (Aim 5). The influence of the tumor microenvironment on dose-response will be examined based on progress with Aim 3.4. Studies in Aim 1 are distinguished from those in Aim 2 by their focus on phenotypes as opposed to modeling intracellular signaling. Aim 1.1 will focus on genetically diverse panels of cancer cell lines and their responses to anti-cancer drugs, primarily investigational and approved kinase inhibitors. Aim 1.1.1 will use fixed cell microscopy to discriminate among drug response phenotypes at a single-cell level using molecular markers of cell division, induction of senescence and apoptosis (and other forms of cell death such as autophagy). Variation in response with time after drug addition, physiological state and genotype will be studied across cell types and within single cells in a genetically homogenous population. Aim 1.1.2. will wills use mutational information (MI) and other methods to associate dose response parameters from Aims 1.1.1-1.1.2 with features of the drug, target of cell type. Aim 1.1.3 will supplement fixed-cell analysis with live-cell imaging of selected drug-cell line combinations to determine how response evolves over time and distinguish among phenotypes that appear similar by endpoint assays. Aim 1.1.4 will extend these studies to patient-derived lines and cultures with the goal of increasing the relevance of our findings to human disease. Aim 1.2 will determine the role of cell-to-cell heterogeneity on fractional response and dose-response curves that are unusually shallow. Mutual information analysis of panels of related kinase inhibitors will reveal whether submaximal and shallow dose-response associates with drug, target or phenotype. Aim 1.3 examines the role of time in pharmacology. Aim 1.3.1 investigates the phenomenon of sequential drug synergy involving EGFR inhibitors and DNA damaging agents. Aim 1.3.2 investigates transient drug resistance induced by paradoxical responses to compounds that are thought to be pro-apoptotic. Aim 1.4 extends the analysis to a different therapeutic area, the response of Mycobacterium tuberculosis (Mtb) to antibiotics; these studies follow up recent data showing that asymmetric division by Mtb results in a cell-to-cell heterogeneity that impacts drug response.

项目摘要 这一目标的重点是在单细胞水平的剂量反应的决定因素。我们将检验这个假设， 非遗传细胞间变异性（由网络的相对水平或活动的变化引起） 组分）在确定剂量-反应曲线的形状和最大治疗效果中是关键的 在高药物浓度下可以实现。随机变化的影响将与 细胞周期状态和特殊谱系（例如肿瘤干细胞）。本目标中的实验还检查了 联合癌症治疗中暴露时间和顺序的重要性。除目标1.4外，将执行所有目标 使用在2D培养物中生长的约10 - 40个癌细胞系的组， 通过转化药理学核心（目标5）获得的衍生培养物。肿瘤的影响 将根据目标3.4的进展情况检查微环境对剂量-反应的影响。目标1中的研究是 与Aim 2中的那些区别在于它们专注于表型，而不是建模细胞内信号传导。 目标1.1将集中在遗传多样性的癌症细胞系及其对抗癌药物的反应 药物，主要是研究和批准的激酶抑制剂。目标1.1.1将使用固定细胞显微镜， 使用细胞分裂的分子标志物在单细胞水平上区分药物反应表型， 诱导衰老和细胞凋亡（以及其他形式的细胞死亡，例如自噬）。响应变化 随着药物添加后时间的推移，将在细胞类型和细胞内研究生理状态和基因型。 基因同质群体中的单个细胞。目标1.1.2。将使用突变信息（MI）， 将目标1.1.1 - 1.1.2中的剂量反应参数与药物特征、靶点 细胞类型。目标1.1.3将用选定药物细胞系的活细胞成像补充固定细胞分析 组合，以确定反应如何随时间演变，并区分出现的表型， 通过终点测定类似。目的1.1.4将这些研究扩展到患者来源的细胞系和培养物， 目标是增加我们的发现与人类疾病的相关性。 目的1.2将确定细胞间异质性对部分应答和剂量应答的作用 异常浅的曲线。相关激酶抑制剂组的互信息分析将揭示 次最大和浅剂量反应是否与药物、靶点或表型相关。 目的1.3考察时间在药理学中的作用。目标1.3.1研究了 涉及EGFR抑制剂和DNA损伤剂的连续药物协同作用。目标1.3.2调查 由对被认为是促细胞凋亡的化合物的矛盾反应诱导的短暂耐药性。 目的1.4将分析扩展到不同的治疗领域，分枝杆菌的反应 结核病（Mtb）抗生素;这些研究跟进最近的数据表明，不对称分裂的Mtb 导致影响药物反应的细胞间异质性。