Liu Yongjing, Feng Cong, Zhou Yincong, Shao Xiaotian, Chen Ming
Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Biomedical Big Data Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China.
Cancers (Basel). 2022 Mar 24;14(7):1645. doi: 10.3390/cancers14071645.
A tumor is a complex tissue comprised of heterogeneous cell subpopulations which exhibit substantial diversity at morphological, genetic and epigenetic levels. Under the selective pressure of cancer therapies, a minor treatment-resistant subpopulation could survive and repopulate. Therefore, the intra-tumor heterogeneity is recognized as a major obstacle to effective treatment. In this paper, we propose a stochastic clonal expansion model to simulate the dynamic evolution of tumor subpopulations and the therapeutic effect at different times during tumor progression. The model is incorporated in the CES webserver, for the convenience of simulation according to initial user input. Based on this model, we investigate the influence of various factors on tumor progression and treatment consequences and present conclusions drawn from observations, highlighting the importance of treatment timing. The model provides an intuitive illustration to deepen the understanding of temporal intra-tumor heterogeneity dynamics and treatment responses, thus helping the improvement of personalized diagnostic and therapeutic strategies.
肿瘤是一种复杂组织，由异质性细胞亚群组成，这些亚群在形态、遗传和表观遗传水平上表现出显著的多样性。在癌症治疗的选择压力下，一小部分具有治疗抗性的亚群能够存活并重新增殖。因此，肿瘤内异质性被认为是有效治疗的主要障碍。在本文中，我们提出了一种随机克隆扩增模型，以模拟肿瘤亚群的动态演变以及肿瘤进展过程中不同时间的治疗效果。该模型已整合到CES网络服务器中，以便根据用户的初始输入进行方便的模拟。基于此模型，我们研究了各种因素对肿瘤进展和治疗结果的影响，并给出了观察得出的结论，强调了治疗时机的重要性。该模型提供了直观的说明，以加深对肿瘤内异质性动态变化和治疗反应的理解，从而有助于改进个性化诊断和治疗策略。
