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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

基本信息

批准号：
10558581
负责人：
William S Hlavacek
金额：
$ 66.96万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10558581
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 Compensation Complex Computer Models Coupled Data Dimerization Drug Combinations Drug Controls Drug Targeting Drug resistance Dryness 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 Predisposition Property Protein Isoforms Protein Kinase Proteins RAS driven cancer Recovery Regulation Resistance Scaffolding Protein Signal Pathway Signal Transduction Signaling Protein Specificity System Systems Biology Testing Therapeutic Thermodynamics Validation Work Xenograft procedure cell transformation clinical efficacy drug efficacy drug sensitivity experimental study in vivo inhibitor kinase inhibitor knock-down mathematical model melanoma molecular dynamics 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 synergism targeted treatment therapy resistant tool tumor

项目摘要

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.

项目总结/文摘