延迟免疫应答在肿瘤免疫系统相互作用中的作用
Effects of delayed immune-response in tumor immune-system interplay
摘要：
肿瘤构成了一个广泛的疾病家族，其动力学特征是多个时空尺度的共存。因此，肿瘤细胞在生态学上与其他类型的细胞相互作用，例如内皮细胞或免疫系统效应器，产生和交换各种化学信号。因此，肿瘤生长是混合建模的理想对象，其中离散随机过程在低浓度下建模药剂，并且平均场方程模型化学信号。在以前的工作中，我们提出了一个混合版本的著名的Panetta-Kirschner平均场模型的肿瘤细胞，效应细胞和白细胞介素-2。我们的混合模型表明-在其原始公式的推论的变化-免疫监视，即肿瘤消除免疫系统，可能会发生通过一种大的随机振荡的副作用。然而，该模型没有考虑到，由于化学运输和细胞分化/分裂，肿瘤诱导的免疫效应物的募集不是瞬时的，而是表现出滞后期。为了捕捉这一点，我们在这里集成了一个平均场方程白细胞介素-2的二维延迟随机过程描述这种延迟的相互作用。通过将分段确定性马尔可夫过程（用于混合部分）与广义半马尔可夫时钟结构（用于考虑延迟）耦合来获得实现底层随机过程的轨迹的算法。我们（一）通过模拟和参数敏感性分析技术与肿瘤质量的增长与延迟，（二）我们定量确定概率根除时间，和（iii）我们证明，在振荡制度，存在一个启发式的随机分岔导致延迟诱导的肿瘤根除，这既不是预测的平均场，也不是由混合非延迟模型。
英文摘要：
Tumors constitute a wide family of diseases kinetically characterized by the co-presence of multiple spatio-temporal scales. So, tumor cells ecologically interplay with other kind of cells, e.g. endothelial cells or immune system effectors, producing and exchanging various chemical signals. As such, tumor growth is an ideal object of hybrid modeling where discrete stochastic processes model agents at low concentrations, and mean-field equations model chemical signals. In previous works we proposed a hybrid version of the well-known Panetta-Kirschner mean-field model of tumor cells, effector cells and Interleukin-2. Our hybrid model suggested -at variance of the inferences from its original formulation- that immune surveillance, i.e. tumor elimination by the immune system, may occur through a sort of side-effect of large stochastic oscillations. However, that model did not account that, due to both chemical transportation and cellular differentiation/division, the tumor-induced recruitment of immune effectors is not instantaneous but, instead, it exhibits a lag period. To capture this, we here integrate a mean-field equation for Interleukins-2 with a bi-dimensional delayed stochastic process describing such delayed interplay. An algorithm to realize trajectories of the underlying stochastic process is obtained by coupling the Piecewise Deterministic Markov process (for the hybrid part) with a Generalized Semi-Markovian clock structure (to account for delays). We (i) relate tumor mass growth with delays via simulations and via parametric sensitivity analysis techniques, (ii) we quantitatively determine probabilistic eradication times, and (iii) we prove, in the oscillatory regime, the existence of a heuristic stochastic bifurcation resulting in delay-induced tumor eradication, which is neither predicted by the mean-field nor by the hybrid non-delayed models.
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