Li Chunhe
Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.
Phys Chem Chem Phys. 2017 Mar 15;19(11):7642-7651. doi: 10.1039/c6cp07767f.
Cancer immunotherapy, an approach of targeting immune cells to attack tumor cells, has been suggested to be a promising way for cancer treatment recently. However, the successful application of this approach warrants a deeper understanding of the intricate interplay between cancer cells and the immune system. Especially, the mechanisms of immunotherapy remain elusive. In this work, we constructed a cancer-immunity interplay network by incorporating interactions among cancer cells and some representative immune cells, and uncovered the potential landscape of the cancer-immunity network. Three attractors emerge on the landscape, representing the cancer state, the immune state, and the hybrid state, which can correspond to escape, elimination, and equilibrium phases in the immunoediting theory, respectively. We quantified the transition processes between the cancer state and the immune state by calculating transition actions and identifying the corresponding minimum action paths (MAPs) between these two attractors. The transition actions, directly calculated from the high dimensional system, are correlated with the barrier heights from the landscape, but provide a more precise description of the dynamics of a system. By optimizing the transition actions from the cancer state to the immune state, we identified some optimal combinations of anticancer targets. Our combined approach of the landscape and optimization of transition actions offers a framework to study the stochastic dynamics and identify the optimal combination of targets for the cancer-immunity interplay, and can be applied to other cell communication networks or gene regulatory networks.
癌症免疫疗法是一种通过靶向免疫细胞来攻击肿瘤细胞的方法，最近被认为是一种很有前景的癌症治疗方式。然而，这种方法的成功应用需要对癌细胞与免疫系统之间复杂的相互作用有更深入的了解。特别是，免疫疗法的机制仍然难以捉摸。在这项工作中，我们通过纳入癌细胞与一些代表性免疫细胞之间的相互作用构建了一个癌症 - 免疫相互作用网络，并揭示了癌症 - 免疫网络的潜在格局。在这个格局上出现了三个吸引子，分别代表癌症状态、免疫状态和混合状态，它们分别对应免疫编辑理论中的逃逸、清除和平衡阶段。我们通过计算转移作用并确定这两个吸引子之间相应的最小作用路径（MAPs）来量化癌症状态和免疫状态之间的转变过程。从高维系统直接计算得到的转移作用与格局中的势垒高度相关，但能更精确地描述系统的动力学。通过优化从癌症状态到免疫状态的转移作用，我们确定了一些抗癌靶点的最佳组合。我们结合格局和转移作用优化的方法提供了一个框架，用于研究随机动力学并确定癌症 - 免疫相互作用的最佳靶点组合，并且可以应用于其他细胞通信网络或基因调控网络。
