Ree Anne Hansen, Meltzer Sebastian, Flatmark Kjersti, Dueland Svein, Kalanxhi Erta
Department of Oncology, Akershus University Hospital, P.O. Box 1000, 1478 Lørenskog, Norway.
Institute of Clinical Medicine, University of Oslo, P.O. Box 1171 Blindern, 0318 Oslo, Norway.
Int J Mol Sci. 2014 Dec 9;15(12):22835-56. doi: 10.3390/ijms151222835.
Organ toxicity in cancer therapy is likely caused by an underlying disposition for given pathophysiological mechanisms in the individual patient. Mechanistic data on treatment toxicity at the patient level are scarce; hence, probabilistic and translational linkages among different layers of data information, all the way from cellular targets of the therapeutic exposure to tissues and ultimately the patient's organ systems, are required. Throughout all of these layers, untoward treatment effects may be viewed as perturbations that propagate within a hierarchically structured network from one functional level to the next, at each level causing disturbances that reach a critical threshold, which ultimately are manifested as clinical adverse reactions. Advances in bioinformatics permit compilation of information across the various levels of data organization, presumably enabling integrated systems biology-based prediction of treatment safety. In view of the complexity of biological responses to cancer therapy, this communication reports on a "top-down" strategy, starting with the systematic assessment of adverse effects within a defined therapeutic context and proceeding to transcriptomic and proteomic analysis of relevant patient tissue samples and computational exploration of the resulting data, with the ultimate aim of utilizing information from functional connectivity networks in evaluation of patient safety in multimodal cancer therapy.
癌症治疗中的器官毒性可能是由个体患者特定病理生理机制的潜在易感性引起的。关于患者层面治疗毒性的机制数据稀缺;因此,需要不同层次数据信息之间的概率性和转化性联系,从治疗暴露的细胞靶点到组织,最终到患者的器官系统。在所有这些层次中,不良治疗效果可被视为在层次结构网络中从一个功能水平传播到下一个功能水平的扰动,在每个水平上引起达到临界阈值的干扰,最终表现为临床不良反应。生物信息学的进展允许跨不同数据组织层次汇编信息,大概能够基于综合系统生物学预测治疗安全性。鉴于癌症治疗生物学反应的复杂性,本通讯报道了一种“自上而下”的策略,从在确定的治疗背景下系统评估不良反应开始,接着对相关患者组织样本进行转录组学和蛋白质组学分析以及对所得数据进行计算探索,最终目的是利用功能连接网络中的信息评估多模式癌症治疗中的患者安全性。