A systems approach to understanding overall drug response in a heterogeneous tumor-cell population

一种了解异质肿瘤细胞群总体药物反应的系统方法

• 批准号: 9912019
• 负责人: James Park
• 金额: $ 6.74万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-03 至 2023-02-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912019
• 关键词: ATAC-seq; Address; Adult; Affect; Automobile Driving; Biological; Biological Models; Biopsy; CRISPR/Cas technology; Cancer Biology; Cells; Combination Drug Therapy; Combined Modality Therapy; DNA Sequence Alteration; Data; Disease Progression; Drug Targeting; Drug Tolerance; Drug resistance; Environment; Future; Genes; Genetic; Genetic Transcription; Genome; Genomics; Glioblastoma; Glioma; Goals; Growth; Heterogeneity; In Vitro; Lead; Malignant Neoplasms; Malignant neoplasm of brain; MicroRNAs; Modeling; Molecular Profiling; Molecular Target; Multiomic Data; Mutation; Oncogenic; Organ; Organism; Outcome; Pathway Analysis; Patients; Pharmaceutical Preparations; Phenotype; Play; Population; Population Heterogeneity; Process; Property; Recurrence; Regulator Genes; Research; Resistance; Role; Small Interfering RNA; Structure; System; Testing; The Cancer Genome Atlas; Tissues; Tumor Tissue; Validation; acquired drug resistance; base; cell growth; clinically relevant; combat; drug candidate; drug discovery; effective therapy; genomic signature; improved; insight; microbial; neoplastic cell; network models; non-genetic; novel; patient response; predictive modeling; prevent; responders and non-responders; response; screening; single-cell RNA sequencing; stem-like cell; transcription factor; transcriptomics; treatment response; treatment strategy; tumor; tumor heterogeneity; tumor initiation

PROJECT SUMMARY Cellular heterogeneity, a fundamental property of multicellular systems, enables tissues, organs, and organisms to have a wide range of responses to a dynamic environment. However, such heterogeneity plays a major role in disease progression and drug resistance in multiple biological contexts, ranging from microbial systems to tumor cells in cancers. Single-cell heterogeneity, pervasive at the genomic and transcriptomic levels, within a tumor (i.e. intratumoral heterogeneity) supports multiple mechanisms through which cellular subpopulations that are inherently drug resistant arise or can acquire resistance during treatment. These issues hinder our ability to develop effective treatment strategies. The quintessential example of tumor cell heterogeneity is Glioblastoma (GBM), a highly aggressive and lethal form of primary brain cancer. To address GBM cellular heterogeneity, efforts focus on identifying novel single or combination drug therapies that may inhibit growth of glioma stem-like cells (GSCs), a clinically relevant subpopulation of tumor cells resistant to current therapies and drive tumor recurrence. To quantify the effects of drug candidates on GSCs, half-maximal inhibitory concentration (IC50) curves are used. However, the use of IC50 curves involves the implicit assumption that the tested cell population is homogeneous, which does not apply in the case of GSCs, a heterogeneous population of stem-like cells that differ in their tumor-initiation ability, molecular signatures, and therapeutic responses. Rather than simply representing a “responsive” or “non-responsive” population phenotype to a particular drug, these varied responses actually reflect the heterogeneous population structure underlying the overall GSC population. Results from our collaborators have demonstrated remarkable differences in the response of patient-derived GSCs to the drug pitavastatin, which has shown potential to inhibit GSC growth. Understanding how a tumor- cell population is structured (i.e. proportions of subpopulations within the overall population) and the regulatory mechanisms (e.g. transcription factor and miRNA regulators) that relate or distinguish these subpopulations would provide deeper insight into how tumor-cell heterogeneity contributes to overall tumor-cell population drug response. In this project, we propose a systems approach to determine and test experimentally the regulatory mechanisms that relate or distinguish cellular subpopulations and associated drug response by analyzing genomic and transcriptomic heterogeneities in a cell population, using patient-derived GSCs and their response to pitavastatin as a model system. Further, we will verify model-based predictions of transcription factor regulators using CRISPR-Cas9 gene editing in the GSC populations. The results of this project will be regulatory network models that delineate omics-scale regulatory mechanisms that relate or distinguish cellular subpopulations in GSCs having distinct drug-response phenotypes. Ultimately, these results will inform the rational selection of molecular targets to attack specific drug-resistant subpopulations.

项目总结 细胞异质性，多细胞系统的一个基本属性，使组织、器官和生物体 对动态环境有广泛的反应。然而，这种异质性起着主要作用。 在多种生物背景下的疾病进展和耐药性，从微生物系统到 癌症中的肿瘤细胞。单细胞异质性，普遍存在于基因组和转录水平，在 肿瘤(即瘤内异质性)支持多种机制，通过这些机制，细胞亚群 在治疗过程中出现或可能获得抗药性。这些问题阻碍了我们 制定有效的治疗策略。肿瘤细胞异质性的典型例子是胶质母细胞瘤 (GBM)，一种高度侵袭性和致命性的原发性脑癌。为了解决GBM细胞的异质性， 努力的重点是确定可能抑制脑胶质瘤干样生长的新的单一或联合药物疗法 细胞(GSCs)，一种临床上相关的肿瘤细胞亚群，对当前的治疗方法具有抵抗力并驱动肿瘤 复发。为了量化候选药物对GSCs的影响，半数最大抑制浓度(IC50) 使用曲线。然而，IC50曲线的使用涉及到一个隐含的假设，即测试的细胞群体 是同质性的，这不适用于GSCs，GSCs是一种不同种类的干细胞群体， 它们的肿瘤启动能力、分子特征和治疗反应不同。而不是简单地 代表对特定药物“有反应”或“无反应”的人群表型，它们各不相同 答复实际上反映了整个GSC人口背后的异质人口结构。 我们合作者的结果显示，在患者衍生的反应方面存在显著差异 药物匹伐他汀已显示出抑制GSC生长的潜力。了解肿瘤是如何- 细胞群体的结构(即亚群体在总群体中的比例)和调控 与这些亚群相关或区分的机制(如转录因子和miRNA调节因子) 将提供对肿瘤细胞异质性如何对整个肿瘤细胞群药物做出贡献的更深层次的见解 回应。在这个项目中，我们提出了一种系统的方法来确定和实验测试监管 通过分析来关联或区分细胞亚群和相关药物反应的机制 使用患者来源的GSCs及其反应在细胞群体中的基因组和转录异质性 将匹伐他汀作为模型系统。此外，我们将验证基于模型的转录因子预测 在GSC人群中使用CRISPR-Cas9基因编辑的调节器。这个项目的结果将是受监管的 描述与细胞相关或区分的组学规模调控机制的网络模型 GSCs中具有不同药物反应表型的亚群。最终，这些结果将告知 合理选择分子靶点攻击特定耐药亚群。