The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia.
Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland; School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland.
Pharmacol Ther. 2020 Aug;212:107555. doi: 10.1016/j.pharmthera.2020.107555. Epub 2020 Apr 19.
As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.
随着我们提供深入的、针对肿瘤内分子改变的个体化特征描述的能力迅速提高，很明显需要新的方法来利用这些数据的力量，并为每个患者带来全部的获益。系统生物学方法开始在这一领域崭露头角，成为整合大量网络层面数据并提取药物反应的一致、临床相关预测的潜在方法。然而，这一领域的初步前景尚未实现。在这里，我们认为，为了开发这些针对个体药物反应的精确模型并相应地调整治疗方法，我们将需要开发能够捕捉药物反应信号网络的动态性质以及关键的个体化信息（如突变状态或表达谱）的数学模型。我们还回顾了该领域常用的建模方法，并概述了最近将系统生物学应用于癌症治疗精准医学方法的实例。
