Ubezio Paolo
Biophysics Unit, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Cancer Manag Res. 2019 Sep 19;11:8529-8538. doi: 10.2147/CMAR.S215729. eCollection 2019.
Assessing the efficacy of anticancer agents in animal models remains a necessary step in the development of new treatment options and plays an important role in their optimization and comparison. Often, however, interpretation of the results is flawed by excessive trust in scores traditionally handed down, but whose origin and limitations have been lost. Here I examine the theories and assumptions underlying the most common rating scales, suggesting improvements to the old scores and proposing the adoption of multi-parameter analysis and interpretation of the results, considering different time-windows. I examined case examples of different scenarios of antiproliferative effects induced by treatment, demonstrating that common scores fail to distinguish between completely different responses to treatment or, in other circumstances, indicate a different outcome when the response is the same. I found that a combination of parameters, including the percent tumor growth between the start and end of treatment, the relative tumor burden at nadir and the absolute growth delay, may distinguish among the different cases and support a correct interpretation of the antitumor response. All these parameters can be derived from individual tumor growth curves in a simple way, without any change to common experimental procedures.
评估抗癌药物在动物模型中的疗效仍然是开发新治疗方案的必要步骤，并且在优化和比较这些方案中发挥着重要作用。然而，结果的解读常常因过度依赖传统传承下来的评分而存在缺陷，而这些评分的起源和局限性已被遗忘。在此，我审视了最常用评分量表背后的理论和假设，建议改进旧评分，并提议采用多参数分析和结果解读，同时考虑不同的时间窗。我研究了治疗诱导的抗增殖效应不同场景的案例，证明常用评分无法区分对治疗完全不同的反应，或者在其他情况下，当反应相同时却显示出不同的结果。我发现，包括治疗开始和结束时肿瘤生长百分比、最低点时的相对肿瘤负荷以及绝对生长延迟在内的参数组合，可能区分不同情况并支持对抗肿瘤反应的正确解读。所有这些参数都可以通过简单的方式从个体肿瘤生长曲线中得出，而无需改变常规实验程序。
