Computational Model of Autophagy-Mediated Survival in Chemoresistant Lung Cancer

点击翻译按钮获取中文摘要

 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."