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

项目总结 这一目标侧重于单细胞水平上剂量反应的决定因素。我们将检验这一假设 细胞间的非遗传可变性(由网络的相对水平或活动的变化引起 成分)在确定剂量-反应曲线的形状和最大治疗效果方面至关重要 这可以在药物浓度较高的情况下实现。随机变化的影响将与之形成对比 细胞周期状态和特殊谱系(如肿瘤干细胞)。这一目标的实验也检验了 癌症联合治疗中时机和暴露顺序的重要性。将执行除AIM 1.4以外的所有任务 使用在2D培养中生长的约10-40个癌细胞株的面板，并辅以较少数量的患者- 通过翻译药理学核心(目标5)获得的衍生培养。肿瘤的影响 将根据目标3.4的进展情况，审查关于剂量反应的微环境。目标1中的研究包括 与目标2中的不同之处在于，它们专注于表型，而不是对细胞内信号进行建模。 AIM 1.1将专注于癌症细胞系的遗传多样性及其对抗癌的反应 药物，主要是研究和批准的激酶抑制剂。AIM 1.1.1将使用固定细胞显微镜 使用细胞分裂的分子标记在单细胞水平上区分药物反应表型， 诱导衰老和凋亡(以及其他形式的细胞死亡，如自噬)。反应的变化 随着药物添加时间的推移，生理状态和基因型将在不同细胞类型和内部进行研究 基因同源群体中的单个细胞。目标1.1.2。遗嘱将使用突变信息(MI)和 将AIMS 1.1.1-1.1.2中的剂量反应参数与药物特性关联的其他方法，靶标 单元类型。AIM 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 确定响应如何随时间演变并区分出现的表型的组合 终点分析结果相似。AIM 1.1.4将把这些研究扩展到患者来源的品系和培养 目标是提高我们的发现与人类疾病的相关性。 AIM 1.2将确定细胞间异质性在分数反应和剂量反应中的作用 异常浅的曲线。对相关激酶抑制剂小组的互信息分析将揭示 亚极量和浅剂量反应是否与药物、靶点或表型有关。 Aim 1.3研究了时间在药理学中的作用。AIM 1.3.1调查以下现象 涉及EGFR抑制剂和DNA损伤剂的序贯药物协同作用。AIM 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而引起的暂时性耐药性。 Aim 1.4将分析扩展到不同的治疗领域，即分枝杆菌的反应 结核分枝杆菌(Mtb)对抗生素的敏感性；这些研究跟踪了最近的数据，显示Mtb的不对称分裂 导致细胞间的异质性，从而影响药物反应。

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.