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 靶向RAF/MEK/ERK通路的小分子抑制剂已成为精密医学中的有力工具， 但在RAS和BRAF突变的癌症中，它们的临床疗效差异很大。即使是在 由于这些抑制剂易患癌症，因此很少产生持久的反应。研究抗药性的原因，这是 包括RAF抑制剂激活ERK信号转导通路，揭示了复杂的分子适应 由RAF和ERK信号转导通路组成的复杂网络。这些复杂性限制了我们的能力 了解和预测靶向治疗的有效性，特别是联合治疗--尽管几十年来 紧张的学习，包括数学建模。准确的预测不仅需要理解 蛋白激酶调节的分子复杂性和复杂的系统水平的网络行为 这两个水平的控制是如何耦合的。准确预测的挑战 因此，靶向治疗及其组合的有效性需要分子的融合 和系统生物学的方法。这里提出的系统生物学项目的目的是确定最优的 通过将两者的理解结合在一起的机制模型来组合激酶抑制剂： 1)激酶抑制剂的构象选择性--影响其结构、热力学和动力学性质 靶向激酶(S)；和2)系统级网络特性，包括反馈环、突变和 激酶/支架的丰度，这可以改变反馈环，并允许正常无关紧要的激酶 同种异构体，以补偿同种异构体特异性的激酶抑制。将这些功能结合在一起需要新奇的 对直接连接分子/结构和网络方面的细胞信号进行建模以预测 抑制剂及其组合可以有效地抑制致癌信号，同时使信号失活或延迟 恢复、生长和抗药性。我们建议开发这样的下一代多尺度模型 致癌ERK信号转导和药物反应，并为发现 整合结构、热力学和动力学信息的有效药物组合--并结合 对系统级动力学进行长时间尺度建模的短时间尺度分子动力学(MD)。我们将测试 我们的模型通过集成和迭代建模和实验研究来进行严格的预测。实验 研究将在具有明确突变差异的成对等基因癌细胞系中开始。一次模型预测 合适的健壮，我们将进展到癌细胞系的面板，然后到体内细胞系衍生的异种移植， 然后到患者来源的异种移植和黑色素瘤的基因工程小鼠模型(GEMM)-AS 临床综合预测的先兆。我们将确定两次(或更多次)击打一种激酶的策略 具有不同构象选择性的抑制剂--在我们的初步数据中似乎很有希望--通常是 可应用，可与抑制途径内的不同靶点相结合，并可在 详细的机械水平使用我们的多尺度模型。