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

项目摘要 细胞异质性是多细胞系统的一个基本特性， to have a wide宽range范围responses反应to a dynamic动态environment环境.然而，这种异质性起着重要作用 在多种生物学背景下的疾病进展和耐药性中，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组水平，在一个 肿瘤（即肿瘤内异质性）支持多种机制， 在治疗过程中产生或获得耐药性。这些问题阻碍了我们的能力， 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)是一种高度侵袭性和致命的原发性脑癌。为了解决GBM细胞异质性， 努力集中在确定新的单一或联合药物治疗，可以抑制神经胶质瘤干细胞样生长， 细胞（GSC），一种对当前疗法具有抗性的临床相关肿瘤细胞亚群， 复发为了量化候选药物对GSC的影响，使用半最大抑制浓度（IC 50） 使用曲线。然而，IC50曲线的使用涉及隐含的假设，即测试的细胞群 是同质的，这不适用于GSC的情况，GSC是干细胞样细胞的异质群体， 不同的肿瘤起始能力，分子特征，和治疗反应。而不是简单 代表对特定药物的"应答"或"非应答"群体表型，这些变化 反应实际上反映了整个GSC群体的异质性群体结构。 我们的合作者的结果表明，患者来源的反应存在显着差异。 GSC对匹伐他汀药物的反应，匹伐他汀已显示出抑制GSC生长的潜力。了解肿瘤是如何- 细胞群体是结构化的（即总群体中亚群的比例），并且调节细胞群体的结构。 与这些亚群相关或区分的机制（例如转录因子和miRNA调节因子） 将提供更深入的了解肿瘤细胞异质性如何有助于整体肿瘤细胞群体药物 反应在这个项目中，我们提出了一个系统的方法来确定和实验测试的监管 通过分析与细胞亚群和相关药物反应相关或区分细胞亚群和相关药物反应的机制， 细胞群体中的基因组和转录组异质性，使用患者来源的GSC及其应答 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群体中使用CRISPR-Cas9基因编辑的调节剂。这个项目的结果将是规范性的 描述组学规模调节机制的网络模型， GSC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。

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.