Evolutionary dynamics of non-genetic mechanisms of drug resistance in cancer

虽然癌症传统上被认为是一种遗传性疾病，但人们越来越认识到，肿瘤细胞群体之间的非遗传性来源在疾病进展和耐药性导致治疗失败方面发挥着重要作用。例如，表观遗传现象和环境噪声等非遗传异质性的常见来源已被证明会在肿瘤细胞中诱导瞬时或可逆的耐药状态。由于这些表型开关通常以比遗传进化快得多的时间尺度工作，它们可以对肿瘤进化过程和治疗反应产生很大影响。这些观察结果引起了人们对新药临床潜力的极大兴趣，例如表观遗传疗法，这些药物针对控制这些表型开关的细胞机制；然而，到目前为止，这些疗法的成功是有限的。这些努力的一个障碍是对个体细胞水平的随机表型转变如何产生并影响肿瘤群体水平的治疗反应的机械性理解不足。这个项目旨在通过发展数学理论来阐明这些问题，这些理论将亚细胞水平的表型转变机制与种群水平的肿瘤进化动力学联系起来，在不断变化的肿瘤微环境的背景下。这一框架将被应用于了解白血病和结直肠癌的治疗耐药性的发展，并探索防止治疗失败的新治疗策略。由于类似的现象发生在抗生素治疗的细菌群体中，这样的模型也可以被用来提供对抗菌素治疗耐药性的洞察。在项目的第一部分，PI将开发和分析一个连续时间的马尔可夫过程模型，该模型由肿瘤微环境中基质细胞分泌因子的可逆表型转变驱动。在项目的第二部分，PI将开发一个新的多尺度建模框架，使用分支随机行走将随机亚细胞表观遗传动力学与种群水平的进化动力学联系起来，并使用该模型来探索表观遗传过程对癌症耐药进化的影响。这些模型将被分析，以阐明驱动种群中非遗传异质性的潜在过程如何影响整体进化转向耐药和肿瘤复发。这项工作将涉及到非标准几何上状态依赖的分枝随机游动的分析以及连续时间马尔可夫过程的分枝过程近似。特别是，将分析这些模型的极限行为、随机命中时间(例如，复发时间、基因沉默/激活时间)和灭绝概率。PI将与实验合作者密切合作，应用这些模型和结果来了解耐药由非遗传机制驱动的特定癌症系统。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Although cancer is traditionally viewed as a genetic disease, it is becoming increasingly recognized that non-genetic sources of heterogeneity amongst tumor cell populations play an important role in disease progression and drug resistance leading to treatment failure. For example, common sources of non-genetic heterogeneity such as epigenetic phenomena and environmental noise have been shown to induce transient or reversible drug-resistant states in tumor cells. Since these phenotypic switches often operate at significantly faster time scales than genetic evolution, they can exert large influences on the course of tumor evolution and response to therapy. These observations have generated tremendous interest in the clinical potential of novel drugs, such as epigenetic therapies, that target the cellular machinery controlling these phenotypic switches; however, the success of these therapies has been limited so far. One obstacle to these efforts has been an inadequate mechanistic understanding of how stochastic phenotypic transitions at the level of individual cells arise and influence tumor population-level responses to therapy. This project aims to elucidate these questions through the development of mathematical theories that link the mechanisms of phenotypic transitions at the subcellular level to population-level tumor evolutionary dynamics, within the context of a changing tumor microenvironment. This framework will be applied to understand the development of therapeutic resistance in leukemias and colorectal cancers, and to explore novel treatment strategies for preventing treatment failure. Since analogous phenomena occur in the context of bacterial populations treated with antibiotics, such models can also be leveraged to provide insights into antimicrobial therapy resistance.In the first part of the project, the PI will develop and analyze a continuous-time Markov process model of drug resistance driven by reversible phenotypic transitions in response to stromal cell-secreted factors in the tumor microenvironment. In the second part of the project, the PI will develop a novel multiscale modeling framework using branching random walks to link stochastic subcellular epigenetic dynamics to population-level evolutionary dynamics, and use this model to explore the impact of epigenetic processes on the evolution of drug resistance in cancer. These models will be analyzed to elucidate how the underlying processes driving non-genetic heterogeneity in a population influence overall evolutionary shifts towards drug resistance and tumor recurrence. This work will involve the analysis of state-dependent branching random walks on nonstandard geometries as well as branching process approximations of continuous time Markov processes. In particular, analyses of the limiting behaviors of these models, stochastic hitting times (e.g. recurrence time, gene silencing/activation times), and extinction probabilities will be conducted. The PI will work closely with experimental collaborators to apply these models and results to understand specific cancer systems in which drug resistance is driven by non-genetic mechanisms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.