Computational Model of Autophagy-Mediated Survival in Chemoresistant Lung Cancer

 描述（由申请人提供）：自噬是与肿瘤进展和癌细胞存活相关的复杂细胞内再循环程序。研究人员仍然缺乏有效针对这一过程的策略，以及对何时应用这些策略的理解。致癌应激，如突变KRAS引起的，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：我们将整合预测性计算建模和高质量的基于细胞的测量，以准确地模拟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：建立核心自噬途径的机制模型，以预测有效抑制自噬的靶点。我们已经通过“规则”指定了一个机械模型，这些规则捕获了包括自噬途径在内的关键生物过程。为了验证这一模型，我们测量了自噬的各个步骤如何响应生理和致癌应激因子以及系统性RNAi干扰。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。本框架将探讨 自噬可能是由一个开关控制的，这是一个有趣的模型衍生的假说，具有治疗意义。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：对自噬和细胞命运的关系进行建模，以测试KRAS驱动的肺癌的治疗预测。自噬模型将被扩展到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，包括定义数据集中测量的输入（信号读出）和输出（自噬通量，生存和死亡）之间的关系。我们将使用这种模型和患者来源的细胞 线来预测抑制KRAS驱动的肺癌中的自噬的治疗益处。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，共同完成一个对公共卫生具有高度相关性和价值的项目。第二次世界大战后，洛斯阿拉莫斯国家实验室使用机械模型来协助复杂的核裂变装置，如原子弹。我们将使用建模来预测复杂的癌细胞行为，最终目标是为“抗癌战争”贡献一个有价值的武器。"

 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."