Modeling and Predicting Therapeutic Resistance of Cancer

描述(由申请人提供)：即使对常规化疗或靶向治疗有初始治疗敏感性，肿瘤也可能变得耐药并复发。对癌症的治疗耐药性进行建模和预测的方法在开发更有效的癌症治疗方法方面非常有用。我们的长期目标是开发策略来模拟、预测和靶向癌症的治疗耐药性。我们提出的方法是利用条件转基因小鼠模型和计算建模相结合。我们假设，对癌基因失活的治疗耐药性可以建模，并因此预测为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。我们在目标1中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，以预测癌基因失活时出现的治疗耐药。然后，在目标2中，我们将在实验中询问免疫系统和进化动力学在治疗耐药出现中的作用。我们将使用活体显微镜和生物发光成像技术直接检测条件转基因小鼠模型中的免疫效应器/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在新情况下出现治疗耐药的预测。

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.