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Nat Rev Clin Oncol. 2014 Feb;11(2):109-18. doi: 10.1038/nrclinonc.2013.244. Epub 2014 Jan 21.
The emerging paradigm of Precision Oncology 3.0 uses panomics and sophisticated methods of statistical reverse engineering to hypothesize the putative networks that drive a given patient's tumour, and to attack these drivers with combinations of targeted therapies. Here, we review a paradigm termed Rapid Learning Precision Oncology wherein every treatment event is considered as a probe that simultaneously treats the patient and provides an opportunity to validate and refine the models on which the treatment decisions are based. Implementation of Rapid Learning Precision Oncology requires overcoming a host of challenges that include developing analytical tools, capturing the information from each patient encounter and rapidly extrapolating it to other patients, coordinating many patient encounters to efficiently search for effective treatments, and overcoming economic, social and structural impediments, such as obtaining access to, and reimbursement for, investigational drugs.
精准肿瘤学 3.0 的新兴模式利用泛组学和复杂的统计反向工程方法来假设驱动特定患者肿瘤的潜在网络，并使用靶向治疗组合来攻击这些驱动因素。在这里，我们回顾了一种称为快速学习精准肿瘤学的模式，其中每个治疗事件都被视为一种探针，它同时治疗患者，并为验证和完善治疗决策所依据的模型提供机会。快速学习精准肿瘤学的实施需要克服一系列挑战，包括开发分析工具、从每个患者的就诊中获取信息并迅速将其推广到其他患者、协调许多患者的就诊以有效地寻找有效治疗方法，以及克服经济、社会和结构障碍，例如获得和报销试验性药物。
