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：
9135930
负责人：
DEAN W FELSHER
金额：
$ 61.48万
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/9135930
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 Health Immune Immune system Immunocompetent Lesion Malignant Neoplasms Measures Mediating Methods Modeling Molecular Oncogenes Resistance Role System Tetanus Helper Peptide Therapeutic Therapeutic Agents Transgenic Mice Transplantation angiogenesis base bioluminescence imaging cancer therapy chemotherapy cytokine effective therapy imaging modality improved in vivo intravital microscopy killings mathematical model microscopic imaging mouse model mutant neoplastic cell novel oncogene addiction pressure prevent programs response role model simulation targeted treatment therapy resistant treatment response 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）：即使对常规化疗或靶向治疗具有初始治疗敏感性，肿瘤也可能产生耐药性并复发。模拟和预测癌症治疗耐药性的方法对于开发更有效的癌症治疗方法非常有用。我们的长期目标是制定模型、预测和靶向癌症治疗耐药性的策略。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抗性进行建模，从而预测其是由细胞自主和免疫介导的选择压力驱动的肿瘤细胞克隆进化的结果。我们的目标 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，以预测癌基因失活后出现的治疗耐药性。然后，在目标 2 中，我们将通过实验探究免疫系统和进化动力学在治疗耐药性出现中的作用。我们将通过活体显微镜和生物发光成像直接检查我们的条件转基因小鼠模型中的免疫效应子/细胞因子和克隆进化。最后，在目标 3 中，我们将在体内验证我们的数学模型对新情况下出现治疗耐药性的预测。