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

基本信息

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
项目类别：
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

项目摘要

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（通过应用程序证明）：自噬是一个复杂的填充物。生存。策略忘记了cancif中的星际目标。生理和致癌应激源，以及系统的RNAi扰动。通过可衍生的模型的假设，我们将在野生型和突变的KRAS背景中控制自动抑制剂，从而通过可引人入胜的假设来控制双重的模型。 . Utophagic Flux and Cell Survival and Death. For the Effort, we will iment an innovative data-driven approach. ships Between Measured Inputs (Signaling Readouts) and Outputs (Autophagic Flux, Survival, and Death) in Datasets. derived cell 预测抑制自噬驱动的肺罐头的治疗益处。癌细胞行为的最终目标是为“癌症战争”贡献有价值的武器。