Evolutionary Dynamics of Brain, Lung, and Hematopoietic Tumors

现代癌症研究和进化理论中的三个关键问题包括：（a）理解突变随时间的进展，（B）识别这些肿瘤的起源细胞，（c）以及癌细胞群体对治疗的差异反应。在这项资助提案中，我们创建了一个研究人员联盟，将癌细胞进化动力学的数学建模与体外和体内建模相结合，以验证和迭代修改数学框架。在第一个项目中，我们将使用进化数学模型来预测神经胶质瘤和白血病发展过程中突变积累的顺序。我们将用这些肿瘤的基因工程小鼠模型来验证我们的预测，其中这些突变的时间顺序是实验上可操纵的。在第二个项目中，我们将使用进化数学模型来预测神经胶质瘤和白血病最可能的起源细胞。同样，我们将使用胶质瘤发生和白血病发生的小鼠模型，使我们能够验证和完善数学模型，并确定哪些癌基因实际上会允许预测的细胞作为这些肿瘤的起源。在最后的专题中，我们将使用进化论来描述肺腺癌对靶向治疗和髓母细胞瘤对放射治疗的不同反应。我们将使用数学框架来预测在特定给药策略期间出现耐药性的风险，并确定最大限度地防止耐药性演变的最佳方法。数学框架将再次修订和验证与体外和体内建模。这三个项目利用单个细胞测量核心设施，允许同时测量单个细胞的多个值，这些值影响数学建模参数。在这个过程中，我们将建立一个互动的研究人员团队，与其他PS-OC，NIH以及外部生物和数学社区合作。此外，我们还制定了核心教育和培训课程，以便在这两个学科的交界处培训下一代调查人员。

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.