Multiscale Modeling to Optimize Inhibition of Oncogenic ERK Pathway Signaling

多尺度建模优化致癌 ERK 通路信号传导的抑制

• 批准号: 10337242
• 负责人: William S Hlavacek
• 金额: $ 67.44万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-07 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10337242
• 关键词: Accounting; Affect; BAY 54-9085; BRAF gene; Behavior; Binding; Biochemical; Biological Models; Caco-2 Cells; Cancer cell line; Cell Line; Cell Proliferation; Cell model; Cells; Clinic; Clinical; Colorectal Cancer; Complex; Computer Models; Coupled; Data; Drug Combinations; Drug Controls; Drug Targeting; Drug resistance; Effectiveness; Feedback; Foundations; Free Energy; Genetic; Genetically Engineered Mouse; Goals; Growth; In Vitro; KSR gene; Kinetics; Knock-out; Link; MAP Kinase Gene; MEKs; Malignant Neoplasms; Melanoma Cell; Modeling; Molecular; Molecular Biology; Molecular Conformation; Mutate; Mutation; Oncogenic; Pathway interactions; Pharmaceutical Preparations; Phosphotransferases; Positioning Attribute; Property; Protein Isoforms; Protein Kinase; Proteins; Recovery; Regulation; Resistance; Scaffolding Protein; Signal Pathway; Signal Transduction; Signaling Protein; Specificity; System; Systems Biology; Testing; Therapeutic; Thermodynamics; Time; Validation; Work; Xenograft procedure; base; cell transformation; clinical efficacy; dimer; drug efficacy; drug sensitivity; experimental study; in vivo; inhibitor; kinase inhibitor; knock-down; mathematical model; melanoma; molecular dynamics; molecular scale; multi-scale modeling; next generation; novel; novel strategies; overexpression; patient derived xenograft model; precision medicine; predictive modeling; prevent; protein expression; response; scaffold; small molecule inhibitor; standard of care; success; targeted treatment; therapy resistant; tool; tumor

PROJECT SUMMARY/ABSTRACT Small molecule inhibitors targeting the RAF/MEK/ERK pathway have become potent tools in precision medicine, but their clinical efficacy is highly variable across the diversity of RAS- and BRAF-mutated cancers. Even in susceptible cancers, these inhibitors rarely give durable responses. Studying the causes of resistance, which include ‘paradoxical’ ERK pathway activation by RAF inhibitors, has revealed complex molecular adaptations in the complicated networks comprised of RAF and ERK pathway kinases. These complexities limit our ability to understand and predict effectiveness of targeted therapies, especially in combination – despite decades of intense study, including mathematical modeling. Accurate predictions require understanding not only of the molecular complexities of protein kinase regulation and the intricate systems-level behavior of the networks that kinase constitute, but also of how these two levels of control are coupled. The challenge of accurately predicting effectiveness of targeted therapies and their combinations therefore demands an amalgamation of molecular and systems biology approaches. The systems biology project proposed here aims to identify optimal combinations of kinase inhibitors through mechanistic models that integrate understanding of both: 1) Conformation selectivity of kinase inhibitors – affecting structural, thermodynamic and kinetic properties of the targeted kinase(s); and 2) Systems-level network properties, including feedback loops, mutations and kinase/scaffold abundances, which can modify feedback loops and allow normally inconsequential kinase isoforms to compensate for isoform-specific kinase inhibition. Combining these features necessitates novel approaches to modeling cell signaling that directly link molecular/structural and network facets to predict which inhibitors and their combinations can efficiently suppress oncogenic signaling while disabling or delaying signal recovery, growth, and drug resistance. We propose to develop such next-generation multiscale models of oncogenic ERK signaling and drug responses, and to establish a new conceptual foundation for discovering effective drug combinations by integrating structural, thermodynamic and kinetic information – and combining short time-scale molecular dynamics (MD) with long time-scale modeling of systems-level dynamics. We will test our model predictions rigorously by integrating and iterating modeling and experimental studies. Experimental studies will begin in paired isogenic cancer cell lines with defined mutational differences. Once model predictions are suitably robust, we will progress to panels of cancer cell lines, then to cell line-derived xenografts in vivo, and then to patient-derived xenografts and genetically engineered mouse models (GEMMs) of melanoma – as a presage to clinically integrated predictions. We will determine if the strategy of hitting a kinase by two (or more) inhibitors with distinct conformation selectivity – as appears promising in our preliminary data – is generally applicable, can be combined with inhibition of different targets within a pathway, and can be understood at a detailed mechanistic level using our multiscale models.

项目摘要/摘要 针对RAF/MEK/ERK途径的小分子抑制剂已成为精密医学的有效工具， 但是它们的临床效率在RAS和BRAF突变的癌症的多样性中差异很大。甚至在 易感癌症，这些抑制剂很少会产生持久的反应。研究抵抗的原因，这是 包括RAF抑制剂激活的“悖论” ERK途径，揭示了复杂的分子适应 RAF和ERK途径激酶的复杂网络完成。这些复杂性限制了我们的能力 了解和预测有针对性疗法的有效性，尤其是在组合中 - 多斯特数十年 激烈的研究，包括数学建模。准确的预测不仅需要了解 蛋白激酶调节的分子复杂性和网络的复杂系统级行为 激酶构成，也构成了这两个级别的控制级别。准确预测的挑战 因此，靶向疗法及其组合的有效性需要分子合并 和系统生物学方法。这里提出的系统生物学项目旨在确定最佳 通过整合对这两者的理解的机械模型来组合激酶抑制剂： 1）激酶抑制剂的构象选择性 - 影响结构，热力学和动力学特性 靶向激酶； 2）系统级网络属性，包括反馈循环，突变和 激酶/支架丰度，可以修改反馈回路并允许通常无关紧要的激酶 同工型补偿同工型特异性激酶抑制。将这些功能结合在一起必要的新颖 建模细胞信号传导的方法，该电池信号传导直接连接分子/结构和网络方面以预测哪个 抑制剂及其组合可以有效抑制致癌信号传导，同时禁用或延迟信号 恢复，生长和耐药性。我们建议开发此类下一代的多尺度模型 致癌ERK信号和药物反应，并建立一个新的概念基础来发现 通过整合结构，热力学和动力学信息并结合有效的药物组合 短时间分子动力学（MD），具有系统级动力学的长时间建模。我们将测试 我们的模型通过整合和迭代建模和实验研究来严格预测。实验 研究将从具有明确的突变差异的配对的等源性癌细胞系开始。一旦模型预测 非常健壮，我们将发展到癌细胞系的面板，然后在体内到细胞系衍生的异种移植物， 然后到患者衍生的Xenographictics和Genetsgript的小鼠模型（GEMM），如 临床整合预测的预示。我们将确定通过两个（或更多）打击激酶的策略 具有独特构象选择性的抑制剂（在我们的初步数据中似乎有希望）通常是 适用，可以与途径内不同目标的抑制结合，可以在 使用我们的多尺度模型的详细机械级别。