Piretto Elena, Delitala Marcello, Ferraro Mario
Department of Mathematics, Università di Torino, via Carlo Alberto, 10, Torino 10123, Italy; Politecnico di Torino, Department of Mathematical Sciences, corso Duca degli Abruzzi 24, Torino 10129, Italy.
Politecnico di Torino, Department of Mathematical Sciences, corso Duca degli Abruzzi 24, Torino 10129, Italy.
J Theor Biol. 2018 Jun 7;446:149-159. doi: 10.1016/j.jtbi.2018.03.014. Epub 2018 Mar 13.
Drug resistance is one of the major obstacles to a successful treatment of cancer and, in turn, has been recognized to be linked to intratumoral heterogeneity, which increases the probability of the emergence of cancer clones refractory to treatment. Combination therapies have been introduced to overcome resistance, but the design of successful combined protocols is still an open problem. In order to provide some indications on the effectiveness of medical treatments, a mathematical model is proposed, comprising two cancer populations competing for resources and with different susceptibilities to the action of immune system cells and therapies: the focus is on the effects of chemotherapy and immunotherapy, used singularly or in combination. First, numerical predictions of the model have been tested with experimental data from the literature and next therapeutic protocols with different doses and temporal order have been simulated. Finally the role of competitive interactions has been also investigated, to provide some insights on the role of competitive interactions among cancer clones in determining treatment outcomes.
耐药性是癌症成功治疗的主要障碍之一，反过来，它被认为与肿瘤内异质性有关，肿瘤内异质性增加了出现对治疗难治的癌症克隆的可能性。联合疗法已被引入以克服耐药性，但成功的联合方案设计仍然是一个悬而未决的问题。为了提供一些关于医学治疗有效性的指标，提出了一个数学模型，该模型包括两个争夺资源且对免疫系统细胞作用和治疗具有不同敏感性的癌症群体：重点是单独或联合使用的化疗和免疫疗法的效果。首先，用文献中的实验数据测试了该模型的数值预测，接下来模拟了不同剂量和时间顺序的治疗方案。最后，还研究了竞争相互作用的作用，以深入了解癌症克隆之间的竞争相互作用在决定治疗结果中的作用。
