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: 9769647
• 负责人: William S Hlavacek
• 金额: $ 45.63万
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769647
• 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; Structural Models; 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; predictive modeling; 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."



项目成果

A Potent and Selective ULK1 Inhibitor Suppresses Autophagy and Sensitizes Cancer Cells to Nutrient Stress.

• DOI: 10.1016/j.isci.2018.09.012
• 发表时间: 2018-10-26
• 期刊: iScience
• 影响因子: 5.8
• 作者: Martin KR;Celano SL;Solitro AR;Gunaydin H;Scott M;O'Hagan RC;Shumway SD;Fuller P;MacKeigan JP
• 通讯作者: MacKeigan JP

PI3K-C2α knockdown decreases autophagy and maturation of endocytic vesicles.

• DOI: 10.1371/journal.pone.0184909
• 发表时间: 2017
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Merrill NM;Schipper JL;Karnes JB;Kauffman AL;Martin KR;MacKeigan JP
• 通讯作者: MacKeigan JP

Prediction of Optimal Drug Schedules for Controlling Autophagy.

控制自噬的最佳药物方案的预测。

• DOI: 10.1038/s41598-019-38763-9
• 发表时间: 2019
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Shirin,Afroza;Klickstein,IsaacS;Feng,Song;Lin,YenTing;Hlavacek,WilliamS;Sorrentino,Francesco
• 通讯作者: Sorrentino,Francesco

Leaving the lysosome behind: novel developments in autophagy inhibition.

将溶酶体留在后面：自噬抑制的新发展。

• DOI: 10.4155/fmc.15.166
• 发表时间: 2016-01
• 期刊: Future medicinal chemistry
• 影响因子: 4.2
• 作者: Solitro AR;MacKeigan JP
• 通讯作者: MacKeigan JP

Identification of Kinases Responsible for p53-Dependent Autophagy.

负责 p53 依赖性自噬的激酶的鉴定。

• DOI: 10.1016/j.isci.2019.04.023
• 发表时间: 2019
• 期刊: iScience
• 影响因子: 5.8
• 作者: Celano,StephanieL;Yco,LisetteP;Kortus,MatthewG;Solitro,AbigailR;Gunaydin,Hakan;Scott,Mark;Spooner,Edward;O'Hagan,RonanC;Fuller,Peter;Martin,KatieR;Shumway,StuartD;MacKeigan,JeffreyP
• 通讯作者: MacKeigan,JeffreyP