Evolutionary Dynamics of Brain, Lung, and Hematopoietic Tumors

脑、肺和造血肿瘤的进化动力学

• 批准号: 8333458
• 负责人: Franziska Michor
• 金额: $ 225.06万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8333458
• 关键词: Applications Grants; Brain Neoplasms; Cells; Communities; Core Facility; Development; Discipline; Dose; Educational Curriculum; Evolution; Genetically Engineered Mouse; Glioma; Hematopoietic Neoplasms; In Vitro; Individual; Lung Adenocarcinoma; Lung Neoplasms; Measurement; Mutation; Oncogenes; Population; Process; Radiation therapy; Research Personnel; Resistance; Risk; Time; Training; Training and Education; United States National Institutes of Health; Work; anticancer research; cancer cell; in vivo Model; leukemia; mathematical model; medulloblastoma; mouse model; next generation; prevent; response; theories; tumor; Applications Grants; Brain Neoplasms; Cells; Communities; Core Facility; Development; Discipline; Dose; Educational Curriculum; Evolution; Genetically Engineered Mouse; Glioma; Hematopoietic Neoplasms; In Vitro; Individual; Lung Adenocarcinoma; Lung Neoplasms; Measurement; Mutation; Oncogenes; Population; Process; Radiation therapy; Research Personnel; Resistance; Risk; Time; Training; Training and Education; United States National Institutes of Health; Work; anticancer research; cancer cell; in vivo Model; leukemia; mathematical model; medulloblastoma; mouse model; next generation; prevent; response; theories; tumor

Three of the critical issues in modern cancer research and evolutionary theory include (a) an understanding of the progression of mutations over time, (b) an identification of the cell of origin of these tumors, (c) and the differential response of cancer cell populations to therapy. In this grant proposal, we have created a consortium of investigators that will blend mathematical modeling of the evolutionary dynamics of cancer cells with in vitro and in vivo modeling to validate, and iteratively revise the mathematical frameworks. In the first project we will use evolutionary mathematical modeling to predict the order in which mutations are accumulated during glioma and leukemia development. We will validate our predictions with genetically engineered mouse modeling of these tumors where the temporal order of these mutations is experimentally manipulable. In the second project we will use evolutionary mathematical modeling to predict the most likely cell of origin for gliomas and leukemias. Again, we will use mouse modeling of gliomagenensis and leukemiagenesis that allows us to validate and refine the mathematical models and determine what oncogenes will in fact allow the predicted cells to serve as the origin for these tumors. In the final project, we will use evolutionary theory to describe the differential response to targeted therapy in lung adenocarcinomas and to radiation therapy in medulloblastomas. We will use the mathematical framework to predict the risk of resistance emerging during a particular dosing strategy and determine the optimal approach that will maximally prevent the evolution of resistance. The mathematical framework will again be revised and validated with in vitro and in vivo modeling. These three projects utilize a single cell measurement core facility that allows simultaneous measurements of individual cells for multiple values that impact the mathematical modeling parameters. In the process, we will build a team of interactive investigators that work with other PS-OCs, the NIH, and the outside biologic and mathematical communities. In addition, we have established core education and training curricula to train the next generation of investigators at the interface of these two disciplines.

现代癌症研究和进化论中的三个关键问题包括（a）对突变的进展，（b）对这些肿瘤起源细胞的鉴定以及癌细胞群体对治疗的差异反应的鉴定。在这项赠款建议中，我们创建了一个研究人员联盟，该联盟将将癌细胞的进化动力学与体外和体内建模进行数学建模融合，以验证并迭代地修改数学框架。在第一个项目中，我们将使用进化数学建模来预测神经胶质瘤和白血病发育过程中突变的顺序。我们将通过这些肿瘤的基因工程小鼠建模来验证我们的预测，在这些肿瘤上这些突变的时间顺序在实验上是可以操纵的。在第二个项目中，我们将使用进化数学建模来预测神经胶质瘤和白血病的原点细胞。同样，我们将使用胶质性和白化的小鼠建模，使我们能够验证和完善数学模型，并确定哪些致癌基因实际上允许预测的细胞作为这些肿瘤的起源。在最后一个项目中，我们将使用进化论来描述肺腺癌中对靶向治疗的差异反应以及对髓母细胞瘤中的放射治疗。我们将使用数学框架来预测特定剂量策略中抗药性的风险，并确定最大程度地阻止阻力演变的最佳方法。数学框架将再次通过体外和体内建模进行修订和验证。这三个项目利用单个单元格测量核心设施，该设施允许单个单元格的多个值对数学建模参数的多个值进行测量。在此过程中，我们将建立一个与其他PS-OC，NIH以及外部生物学和数学社区合作的互动调查人员团队。此外，我们还建立了核心教育和培训课程，以在这两个学科的界面上培训下一代研究人员。