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突变和扩增驱动的耐药机制及其对肿瘤复发影响的比较

A Comparison of Mutation and Amplification-Driven Resistance Mechanisms and Their Impacts on Tumor Recurrence.

作者信息

Li Aaron, Kibby Danika, Foo Jasmine

出版信息

ArXiv. 2023 Aug 21:arXiv:2305.19423v4.

Abstract

Tumor recurrence, driven by the evolution of drug resistance is a major barrier to therapeutic success in cancer. Resistance is often caused by genetic alterations such as point mutation, which refers to the modification of a single genomic base pair, or gene amplification, which refers to the duplication of a region of DNA that contains a gene. Here we investigate the dependence of tumor recurrence dynamics on these mechanisms of resistance, using stochastic multi-type branching process models. We derive tumor extinction probabilities and deterministic estimates for the tumor recurrence time, defined as the time when an initially drug sensitive tumor surpasses its original size after developing resistance. For models of amplification-driven and mutation-driven resistance, we prove law of large numbers results regarding the convergence of the stochastic recurrence times to their mean. Additionally, we prove sufficient and necessary conditions for a tumor to escape extinction under the gene amplification model, discuss behavior under biologically relevant parameters, and compare the recurrence time and tumor composition in the mutation and amplification models both analytically and using simulations. In comparing these mechanisms, we find that the ratio between recurrence times driven by amplification vs. mutation depends linearly on the number of amplification events required to acquire the same degree of resistance as a mutation event, and we find that the relative frequency of amplification and mutation events plays a key role in determining the mechanism under which recurrence is more rapid. In the amplification-driven resistance model, we also observe that increasing drug concentration leads to a stronger initial reduction in tumor burden, but that the eventual recurrent tumor population is less heterogeneous, more aggressive, and harbors higher levels of drug-resistance.

摘要

由耐药性演变驱动的肿瘤复发是癌症治疗成功的主要障碍。耐药性通常由基因改变引起,如点突变(指单个基因组碱基对的修饰)或基因扩增(指包含一个基因的DNA区域的复制)。在此,我们使用随机多类型分支过程模型研究肿瘤复发动态对这些耐药机制的依赖性。我们推导了肿瘤灭绝概率以及肿瘤复发时间的确定性估计值,肿瘤复发时间定义为初始对药物敏感的肿瘤在产生耐药性后超过其原始大小的时间。对于扩增驱动和突变驱动耐药性的模型,我们证明了关于随机复发时间收敛到其均值的大数定律结果。此外,我们证明了在基因扩增模型下肿瘤逃脱灭绝的充分必要条件,讨论了生物学相关参数下的行为,并通过分析和模拟比较了突变模型和扩增模型中的复发时间和肿瘤组成。在比较这些机制时,我们发现由扩增与突变驱动的复发时间之比线性依赖于获得与一个突变事件相同程度耐药性所需的扩增事件数量,并且我们发现扩增和突变事件的相对频率在确定复发更快的机制中起关键作用。在扩增驱动的耐药性模型中,我们还观察到增加药物浓度会导致肿瘤负荷在初始时有更强的降低,但最终复发的肿瘤群体异质性更低、更具侵袭性且具有更高水平的耐药性。

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