Computational Model of Autophagy-Mediated Survival in Chemoresistant Lung Cancer

自噬介导的化疗耐药肺癌生存的计算模型

• 批准号: 9547104
• 负责人: William S Hlavacek
• 金额: $ 48.42万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9547104
• 关键词: Achievement; Address; Ally; Apoptosis; Autophagocytosis; Biological; Biological Process; Biology; Cancer cell line; Cell Cycle Kinetics; Cell Death; Cell Line; Cell Survival; Cells; Cellular biology; Cessation of life; Complex; Computational algorithm; Computer Simulation; Computers; Data; Data Set; Devices; Digestion; Drug resistance; Engineering; Equilibrium; Foundations; Genetic; Goals; Hypoxia; Individual; Interdisciplinary Study; Intervention; KRAS2 gene; Kinetics; Laboratories; Link; Lipids; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Measures; Mediating; Modeling; Molecular; Molecular Target; Monte Carlo Method; Mutation; Non-Small-Cell Lung Carcinoma; Nuclear Fission; Nuclear Weapon; Nutrient; Oncogenic; Output; Pathway interactions; Patients; Phenotype; Physics; Physiological; Play; Process; Proteins; Public Health; RNA Interference; Reaction; Recycling; Research; Research Personnel; Research Project Grants; Resolution; Role; Scientist; Signal Transduction; Site; Specific qualifier value; Starvation; Stress; Structure; System; Testing; Therapeutic; Treatment Efficacy; War; World War II; assault; base; cancer cell; cell behavior; design; drug development; environmental stressor; genetic predictors; improved; inhibition of autophagy; inhibitor/antagonist; innovation; models and simulation; mutant; novel; programs; public health relevance; response; stressor; therapeutic evaluation; therapeutic target; tool; tumor progression; weapons

 DESCRIPTION (provided by applicant): Autophagy is a complex intracellular recycling program associated with tumor progression and cancer cell survival. Researchers still lack strategies to effectively target this process, and an understanding of when to apply such strategies. Oncogenic stress, such as that elicited by mutant KRAS, can activate autophagy to promote cancer cell survival. Importantly, KRAS mutations are linked to 40% of lung cancer deaths in the U.S. each year. Therefore, we propose an innovative, multidisciplinary research project that investigates autophagy in connection with KRAS: we will integrate predictive computational modeling and high-quality cell-based measurements to accurately model the autophagic process in KRAS-driven lung cancer. We anticipate that our model will help identify the most effective therapeutic strategies for targeting autophagy in cancer. Specific Aim #1: Validate a mechanistic model of the core autophagy pathway to predict targets for the effective inhibition of autophagy. We have specified a mechanistic model through "rules" that capture the key biological processes comprising the autophagy pathway. To validate this model, we measured how the individual steps of autophagy respond to physiological and oncogenic stressors, and systematic RNAi perturbations. Here, we propose to tune the model to align with quantitative data, and test predictions of the rate-limiting steps. This framework will explore the possibility that autophagy is controlled by a bistable switch, an intriguing model-derived hypothesis with therapeutic relevance. As part of this aim, we will identify effective autophagy inhibitors in wildtype and mutant KRAS backgrounds. Specific Aim #2: Model the relationship of autophagy and cell fate to test therapeutic predictions for KRAS-driven lung cancer. The autophagy model will be extended to investigate the relationship between autophagic flux and cell survival and death. For this effort, we will implement an innovative data-driven approach, which involves defining relationships between measured inputs (signaling readouts) and outputs (autophagic flux, survival, and death) in datasets. We will use this model and patient-derived cell lines to predict the therapeutic benefit of inhibiting autophagy in KRAS-driven lung cancer. Our collaborative research brings mechanistic modeling and cell biology experts together for a project that is highly relevant and valuable to public health. Mechanistic modeling was used by Los Alamos National Laboratory after World War II to assist with complex nuclear fission devices like the atomic bomb. We will use modeling to predict complex cancer cell behavior, with the ultimate goal of contributing a valuable weapon to the "war on cancer."

 描述（由申请人提供）：自噬是一种与肿瘤进展和癌细胞存活相关的复杂的细胞内回收程序。研究人员仍然缺乏有效针对这一过程的策略，也不了解何时应用此类策略。致癌应激，例如突变型 KRAS 引起的应激，可以激活自噬以促进癌细胞存活。重要的是，KRAS 突变与美国每年 40% 的肺癌死亡有关。因此，我们提出了一个创新的多学科研究项目，研究与 KRAS 相关的自噬：我们将整合预测计算模型和高质量的基于细胞的测量，以准确模拟 KRAS 驱动的肺癌中的自噬过程。我们预计我们的模型将有助于确定针对癌症自噬的最有效的治疗策略。具体目标#1：验证核心自噬途径的机制模型，以预测有效抑制自噬的靶标。我们通过“规则”指定了一个机制模型，捕获构成自噬途径的关键生物过程。为了验证这个模型，我们测量了自噬的各个步骤如何响应生理和致癌应激源以及系统性 RNAi 扰动。在这里，我们建议调整模型以与定量数据保持一致，并测试限速步骤的预测。该框架将探讨 自噬可能是由双稳态开关控制的，这是一个有趣的模型衍生假设，具有治疗相关性。作为这一目标的一部分，我们将在野生型和突变型 KRAS 背景中鉴定有效的自噬抑制剂。具体目标 #2：模拟自噬和细胞命运的关系，以测试 KRAS 驱动的肺癌的治疗预测。自噬模型将被扩展以研究自噬通量与细胞存活和死亡之间的关系。为此，我们将实施一种创新的数据驱动方法，其中涉及定义数据集中测量的输入（信号读数）和输出（自噬通量、生存和死亡）之间的关系。我们将使用这个模型和患者来源的细胞 预测 KRAS 驱动的肺癌中抑制自噬的治疗效果。我们的合作研究将机械建模和细胞生物学专家聚集在一起，开展一个与公共卫生高度相关且有价值的项目。第二次世界大战后，洛斯阿拉莫斯国家实验室使用机械建模来协助原子弹等复杂的核裂变装置。我们将利用模型来预测复杂的癌细胞行为，最终目标是为“抗癌战争”贡献宝贵的武器。