权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Exploiting Ecology and Evolution to Prevent Therapy Resistance in EGFR-Driven Lung Cancer

利用生态学和进化来预防 EGFR 驱动的肺癌的治疗耐药性

基本信息

批准号：
10737854
负责人：
Jacob Gardinier Scott
金额：
$ 11.93万
依托单位：
CLEVELAND CLINIC LERNER COM-CWRU
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-12-01 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10737854
关键词：
Area Automobile Driving Biological Assay Biology Cancer Etiology Cause of Death Cell Line Cessation of life Clinic Clinical Clinical Oncology Computer Models Coupled Data Dependence Directed Molecular Evolution Disease Drug Design Drug resistance Ecology Environment Epidermal Growth Factor Receptor Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Equation Evolution Experimental Designs Family Fibroblasts Game Theory Goals Individual Investigation Knowledge Laboratories Learning Libraries Malignant Neoplasms Malignant neoplasm of lung Maps Mathematics Measures Modeling Molecular Molecular Profiling Mutate Mutation Non-Small-Cell Lung Carcinoma Oncology Outcome Patients Pharmaceutical Preparations Phenotype Play Population Process Protocols documentation Publications Research Research Project Grants Resistance Secondary to Shapes Solid Neoplasm Techniques Testing Therapeutic Time Toxic effect Translating Translations Tyrosine Kinase Inhibitor anticancer research cancer cell cancer type clinical application clinical development clinically relevant design drug discovery drug sensitivity effective therapy experience experimental study improved in vivo innovation insight life history mathematical model multidisciplinary neoplastic cell novel strategies novel therapeutics objective response rate personalized medicine pressure prevent replicator resistance mechanism targeted agent targeted treatment time use treatment strategy tumor tumor heterogeneity tumor progression

项目摘要

ABSTRACT Lung cancer is the leading cause of cancer deaths in the USA, with an estimated 158,000 deaths in 2016. The direct cause of the majority of these deaths is the eventual emergence of resistance to initially effective therapies. This evolution of drug resistance represents one of the greatest unmet needs in oncology. While most research is focused on the individual molecular alterations that confer this resistance, we instead propose to focus on the eco-evolutionary processes that generate these alterations. To study the Darwinian evolution and ecological interactions occurring within heterogeneous tumors, we will tightly integrate bespoke mathematical models and experimental techniques designed to inform them. Focusing on EGFR mutated non-small cell lung cancer, a cancer type with a highly efficacious targeted therapy with which we have experience in our lab, we will approach this problem with three, orthogonal, integrated mathematical-experimental Aims. First, to understand the ecological interactions occurring at the inter-cellular level in heterogeneous tumors, we will couple our experience with evolutionary game theory with our first-in-class evolutionary game assay, which we have designed to specifically for this purpose. Here, we hope to learn to target the interactions that drive resistance – a novel strategy which could open up entirely new avenues of drug design. Second, we will allow evolution to show us the convergent phenotypes it creates in the face of specific selective pressures through long-term directed evolution. During this long-term evolution, we will measure phenotype, in the form of drug sensitivity to a panel of chemotherapeutics and targeted therapies at regular intervals, creating the first temporal collateral sensitivity map in any solid tumor. By pooling common phenotypes observed throughout the evolutionary life history, we will then use interactomic and seed-based protocols to generate molecular signatures of these states of sensitivity, which we will validate in publicly available data and in an in library of PDX lines. Finally, we will delve deeply into the relevant time scales of the ecological and evolutionary processes we study in the first two aims. We plan to apply the replicator-mutator framework of evolutionary game theory to a spatial transform that we pioneered in cancer. To validate and parameterize these models, we will also test the evolutionary stability of the ecological dynamics we measure with our game assay by performing the assay through evolutionary time during a long-term evolution experimental. Each of our three orthogonal aims is supported by recent high impact publications, and each represent tightly coupled experimental and computational protocols. Our clean, well- designed integration, together with innovative focus on the direct study of the evolutionary biology, promises to shed light on this difficult area of cancer research, and offers the possibility of providing generalizable insights.

摘要