Modeling and Predicting Therapeutic Resistance of Cancer

癌症治疗耐药性的建模和预测

基本信息

批准号：
8766569
负责人：
DEAN W FELSHER
金额：
$ 62.24万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-16 至 2019-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8766569
关键词：
Acute T Cell Leukemia Aftercare Automobile Driving Bypass CD4 Positive T Lymphocytes Cell Aging Cell Death Cell Survival Cell physiology Cells Clonal Evolution Computer Simulation Defect Development Differential Equation Event Exhibits Genetic Goals Immune Immune system Immunocompetent Lesion Malignant Neoplasms Measures Mediating Methods Modeling Molecular Oncogenes Resistance Role Simulate System Tetanus Helper Peptide Therapeutic Therapeutic Agents Therapeutic Studies Transgenic Mice Transplantation angiogenesis base bioluminescence imaging cancer therapy chemotherapy cytokine effective therapy imaging modality improved in vivo intravital microscopy killings mathematical model mouse model mutant neoplastic cell novel oncogene addiction pressure prevent programs public health relevance response role model simulation therapeutic target tumor

项目摘要

DESCRIPTION (provided by applicant): Even when there is initial therapeutic sensitivity to a conventional chemotherapy or targeted therapy, tumors can become resistant and recur. Methods that model and predict therapeutic resistance of cancer can be extremely useful in the development of more effective treatments for cancer. Our long-term goal is to develop strategies to model, predict, and target therapeutic resistance of cancer. Our proposed approach is to utilize conditional transgenic mouse models combined with computational modeling. We hypothesize that therapeutic resistance to oncogene inactivation can be modeled and thus predicted as a consequence of clonal evolution of tumor cells driven by both cell autonomous and immune-mediated selective pressures. Our approach in Aim 1 is to build a mathematical model that incorporates the roles of the immune system and of evolutionary dynamics to predict the emergence of therapeutic resistance upon oncogene inactivation. Then, in Aim 2 we will experimentally interrogate the roles of the immune system and of evolutionary dynamics in the emergence of therapeutic resistance. We will directly examine immune effectors/cytokines and clonal evolution in our conditional transgenic mouse model with intravital microscopy and bioluminescence imaging. Finally, in Aim 3 we will validate in vivo our mathematical model's predictions of the emergence of therapeutic resistance under novel circumstances.

描述（由申请人提供）：即使对常规化学疗法或靶向疗法具有初始治疗敏感性，肿瘤也会变得抗性和复发。建模和预测癌症治疗性抗性的方法在开发更有效的癌症治疗方法方面非常有用。我们的长期目标是制定策略，以建模，预测和靶向癌症的治疗性抗性。我们提出的方法是利用条件转基因小鼠模型与计算建模相结合。我们假设可以对癌基因失活的治疗性进行建模，从而预测是由于细胞自主和免疫介导的选择性压力驱动的肿瘤细胞的克隆进化。我们目标1的方法是建立一个数学模型，该模型结合了免疫系统和进化动力学的作用，以预测过灭活后治疗性抗性的出现。然后，在目标2中，我们将在实验中询问免疫系统的作用以及进化动力学在治疗抗性的出现中。我们将在条件转基因小鼠模型中直接检查具有内部转基因小鼠模型和生物发光成像中的免疫效应子/细胞因子和克隆进化。最后，在AIM 3中，我们将验证我们的数学模型在新情况下对治疗性抗性出现的预测。