在这篇综述中,我们总结了我们最近的努力,试图通过使用神经网络来表征从癌细胞基因型和环境到其表型的映射的不同方面来理解异质性在癌症进展中的作用。我们的核心前提是,癌症是一个不断发展的系统,需要突变和选择,这些过程发生的主要渠道是癌细胞,其行为在多个生物学尺度上受到调节。选择压力主要由肿瘤生长的微环境驱动,这直接作用于细胞表型。反过来,表型由基因型调节的细胞内途径驱动。整合所有这些过程是一项艰巨的任务,需要弥合许多生物学尺度(即基因型,通路,表型和环境),我们将在这篇综述中只触及表面。我们将专注于使用神经网络作为连接这些不同生物尺度的手段的模型,因为它们使我们能够轻松地创建用于选择的异质性,并且重要的是这种异质性可以在不同的生物学尺度上实现。更具体地说,我们考虑了三个不同的神经网络,它们桥接了这些尺度的不同方面以及与微环境的对话,(I)微环境对进化动力学的影响,(ii)在药物诱导的扰动下从基因型到表型的作图,以及(iii)在不同微环境条件下正常细胞和癌细胞中的途径活性。
In this review we summarise our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only scratch the surface of in this review. We will focus on models that use neural networks as a means of connecting these different biological scales, since they allow us to easily create heterogeneity for selection to act upon and importantly this heterogeneity can be implemented at different biological scales. More specifically, we consider three different neural networks that bridge different aspects of these scales and the dialogue with the micro-environment, (i) the impact of the micro-environment on evolutionary dynamics, (ii) the mapping from genotype to phenotype under drug-induced perturbations and (iii) pathway activity in both normal and cancer cells under different micro-environmental conditions.