Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells

使用仿生细胞模拟癌症信号级联的异质性

• 批准号: 9886240
• 负责人: Cheemeng Tan
• 金额: $ 18.8万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9886240
• 关键词: 3-Dimensional; ATP phosphohydrolase; Address; Affect; Animal Model; Antineoplastic Agents; Benchmarking; Biological Assay; Biological Process; Biomimetics; Cell Culture Techniques; Cell membrane; Cells; Chronic Myelomonocytic Leukemia; Computer Models; Cytolysis; Development; Disease; Dose; Drug Combinations; Drug Screening; Drug usage; Engineering; Environment; Exhibits; Expression Profiling; Failure; Fluorescence; Fluorescence Resonance Energy Transfer; Genes; Genetic Transcription; Goals; Guanosine Triphosphate Phosphohydrolases; Heterogeneity; In Vitro; Lab-On-A-Chips; Libraries; MEKs; Malignant Neoplasms; Measures; Membrane; Methods; Modeling; Molecular Target; Mutation; Nature; Pathway interactions; Pharmaceutical Preparations; Physiological; Platelet-Derived Growth Factor alpha Receptor; Platelet-Derived Growth Factor beta Receptor; Point Mutation; Proteins; Ras Signaling Pathway; Ras/Raf; Reporter; Reporting; Research; Signal Pathway; Signal Transduction; Signaling Protein; Structure; Surface; System; T-Lymphocyte; Technology; Testing; Time; Translating; Treatment Failure; Tube; Variant; Vesicle; Work; Xenograft procedure; anticancer treatment; base; cancer cell; cancer heterogeneity; cancer therapy; combinatorial; computer framework; drug candidate; drug efficacy; flexibility; high-throughput drug screening; in vivo; interest; microbial; miniaturize; public health relevance; receptor; reconstitution; response; screening; tumor; tumor heterogeneity

PROJECT SUMMARY Modeling heterogeneity of a cancer-signaling cascade using biomimetic cells, PI Tan Developing biomimetic systems that capture the heterogeneity of cancer-signaling cascades will enhance the development of effective anti-cancer therapy and reduce the attrition rate of drug candidates. A cancer- signaling cascade consists of receptors that propagate signals to protein networks, which then modulate expression profiles of gene networks. A signaling cascade can exhibit tremendous heterogeneity in cancers due to variation in the concentration, composition, and sequence of its protein constituents. Such heterogeneity has been known to diminish the efficacy of certain anti-cancer drugs. To date, however, there is a general lack of engineered systems that emulate the heterogeneity of cancer-signaling cascades directly. Even though cell cultures and xenografts capture physical structure of tumors, they do not directly control the heterogeneity of a targeted signaling cascade. Here, we propose to overcome the bottleneck by engineering a biomimetic-cell approach that reconstitutes variants of a cancer-signaling cascade to mimic its heterogeneity. Each biomimetic cell is a synthetic system that will be constructed from the bottom-up by mimicking cell membranes and one instance of a heterogeneous cancer-signaling cascade. As a proof of concept, we will investigate the proposed idea using the Ras signaling cascade activated by the platelet-derived-growth-factor receptor beta (PDGFRβ). The proposal consists of two main steps 1) Reconstitute the core PDGFRβ signaling cascade inside biomimetic cells. 2) Model heterogeneity of the PDGFRβ signaling cascade inside biomimetic cells. We will construct a library of biomimetic cells, each containing one unique instance of the signaling cascade, using a bio-printer that will mix each protein constituents of the signaling cascade at well-defined composition and concentrations. The biomimetic-cell library and a computational model will be integrated to study robustness of anti-cancer drugs in inhibiting the heterogeneous signaling cascade. The proposed idea challenges the main paradigm in studies of cancer-signaling cascades by reconstituting (partially or fully) at least 100,000 unique and physiologically relevant instances of a cancer-signaling cascade inside biomimetic cells, which will be miniaturized for high-throughput drug screening. The proposed work is significant because it enables multi- dimensional screening of drugs against well-defined heterogeneity of the targeted cancer-signaling cascade. If successful, this study will yield a validated, generalizable approach for studying the impact of heterogeneous cancer pathways on the efficacy of anti-cancer drugs.

项目摘要 使用仿生细胞模拟癌症信号级联的异质性，PI Tan 开发能够捕获癌症信号级联的异质性的仿生系统将增强 开发有效的抗癌疗法，降低候选药物的流失率。一种癌症- 信号级联由将信号传播到蛋白质网络的受体组成，然后蛋白质网络调节 基因网络的表达谱。信号级联可以在癌症中表现出巨大的异质性， 其蛋白质成分的浓度、组成和序列的变化。这种异质性 会降低某些抗癌药物的疗效。然而，到目前为止，普遍缺乏 直接模拟癌症信号级联的异质性的工程系统。虽然细胞 尽管培养物和异种移植物捕获肿瘤的物理结构，但它们不直接控制肿瘤的异质性。 靶向信号级联。在这里，我们建议通过设计仿生细胞来克服瓶颈 一种重建癌症信号级联的变体以模拟其异质性的方法。每一种仿生 细胞是一个合成系统，将通过模仿细胞膜和一个 一个异质性癌症信号级联的例子。作为概念验证，我们将调查拟议的 使用由血小板衍生生长因子受体β（PDGFRβ）激活的Ras信号级联的想法。 该方案包括两个主要步骤：1）在仿生体内重构核心PDGFRβ信号级联 细胞2)仿生细胞内PDGFRβ信号级联的模型异质性。我们将建立一个 一个仿生细胞库，每个细胞都包含一个独特的信号级联实例，使用生物打印机 将信号级联的每种蛋白质组分以明确定义的组成和浓度混合。 仿生细胞库和计算模型将被整合以研究抗癌的鲁棒性 抑制异质性信号级联的药物。所提出的想法挑战的主要范式， 通过重建（部分或全部）至少100，000个独特的和 仿生细胞内癌症信号级联的生理相关实例，这将是 小型化用于高通量药物筛选。这项工作意义重大，因为它使多- 针对靶向癌症信号级联的明确定义的异质性进行药物的维度筛选。如果 成功，这项研究将产生一个有效的，普遍的方法，研究异质性的影响， 癌症通路对抗癌药物疗效的影响。