Chen Duyu, Jiao Yang, Torquato Salvatore
Department of Chemistry, Princeton University, Princeton, New Jersey, United States of America; Physical Science in Oncology Center, Princeton University, Princeton, New Jersey, United States of America.
Physical Science in Oncology Center, Princeton University, Princeton, New Jersey, United States of America; Materials Science and Engineering, Arizona State University, Tempe, Arizona, United States of America.
PLoS One. 2014 Oct 16;9(10):e109934. doi: 10.1371/journal.pone.0109934. eCollection 2014.
Malignant cancers that lead to fatal outcomes for patients may remain dormant for very long periods of time. Although individual mechanisms such as cellular dormancy, angiogenic dormancy and immunosurveillance have been proposed, a comprehensive understanding of cancer dormancy and the "switch" from a dormant to a proliferative state still needs to be strengthened from both a basic and clinical point of view. Computational modeling enables one to explore a variety of scenarios for possible but realistic microscopic dormancy mechanisms and their predicted outcomes. The aim of this paper is to devise such a predictive computational model of dormancy with an emergent "switch" behavior. Specifically, we generalize a previous cellular automaton (CA) model for proliferative growth of solid tumor that now incorporates a variety of cell-level tumor-host interactions and different mechanisms for tumor dormancy, for example the effects of the immune system. Our new CA rules induce a natural "competition" between the tumor and tumor suppression factors in the microenvironment. This competition either results in a "stalemate" for a period of time in which the tumor either eventually wins (spontaneously emerges) or is eradicated; or it leads to a situation in which the tumor is eradicated before such a "stalemate" could ever develop. We also predict that if the number of actively dividing cells within the proliferative rim of the tumor reaches a critical, yet low level, the dormant tumor has a high probability to resume rapid growth. Our findings may shed light on the fundamental understanding of cancer dormancy.
导致患者死亡的恶性肿瘤可能会长期处于休眠状态。尽管已经提出了诸如细胞休眠、血管生成休眠和免疫监视等个体机制,但从基础和临床角度来看,对癌症休眠以及从休眠状态到增殖状态的“转换”仍需更全面的理解。计算建模使人们能够探索各种可能且现实的微观休眠机制及其预测结果的情景。本文的目的是设计这样一个具有突发“转换”行为的休眠预测计算模型。具体而言,我们推广了先前用于实体瘤增殖生长的细胞自动机(CA)模型,该模型现在纳入了多种细胞水平的肿瘤 - 宿主相互作用以及不同的肿瘤休眠机制,例如免疫系统的影响。我们新的CA规则在微环境中引发了肿瘤与肿瘤抑制因子之间的自然“竞争”。这种竞争要么导致一段时间的“僵持”,在此期间肿瘤最终要么获胜(自发出现)要么被根除;要么导致在这种“僵持”发展之前肿瘤就被根除的情况。我们还预测,如果肿瘤增殖边缘内活跃分裂细胞的数量达到一个临界但较低的水平,休眠肿瘤很有可能恢复快速生长。我们的研究结果可能有助于对癌症休眠的基本理解。