Quantifying the Propagation of Resistance to Chemotherapy in Cancer

耐药性是提高癌症患者总体反应和存活率的主要障碍。虽然大多数肿瘤最初对药物治疗反应良好，但大多数会在治疗后的某个时间点复发。治疗失败可能归因于治疗前固有的肿瘤异质性或治疗开始后诱导的肿瘤异质性。该项目旨在开发数学模型，用于研究药物特定治疗方案下单一或多药耐药的出现和时空传播。该项目开发的工具将为设计个性化治疗提供基础，该个性化治疗可以根据化疗耐药性的动态变化进行调整。该项目涉及研究生的培训以及课程和推广计划。PI将专注于开发、模拟和分析新的数学模型，以描述肿瘤内异质性介导的癌症耐药动态。基于生物的数学模型包括：离散-连续混合模型、积分-微分方程组、随机模型和偏微分方程组。该项目开启了新的研究机会，这些机会是由PI与临床医生和实验者建立的合作指导的。这项资助项目的重点是研究的数学方面。这个项目的成功完成将为我们未来在实验和临床研究中的转化性研究提供广泛的数学方法。

Drug resistance represents a major obstacle to improving the overall response and survival of cancer patients. While most tumors initially respond well to drug therapies, the majority will relapse at a certain point following treatment. Therapeutic failure may be attributed to intrinsic tumor heterogeneity prior to therapy or induced tumor heterogeneity after initiation of therapy. This project aims at developing mathematical models for studying the emergence and the spatial and temporal propagation of single or multidrug resistance under drug-specific treatment protocols. The tools developed in this project will provide a foundation for designing personalized therapy that can be adjusted based on the dynamics of the resistance to chemotherapy. The project involves training of graduate students as well as curriculum and outreach initiatives.The PI will focus on developing, simulating, and analyzing new mathematical models for describing the dynamics of drug resistance in cancer, as mediated by intratumoral heterogeneity. Biologically-based mathematical models will include: hybrid discrete-continuous models, integro-differential equations, stochastic models, and partial-differential equations. The project opens new research opportunities that are guided by PI's established collaborations with clinicians and experimentalists. The focus of this funded project is on the mathematical aspects of the study. A successful completion of this project will provide us with a wide range of mathematical approaches for future translational research in experimental and clinical studies.