Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, yet remains a challenging task. To be able to translate the results of the experiments more readily, a preferred measure to quantify this response should take into account more of the available experimental data, including both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. We test and compare KuLGaP to four widely used response measures using 329 patient-derived xenograft (PDX) models. Our results show that KuLGaP is more selective than currently existing measures, reduces the risk of false-positive calls, and improves translation of the laboratory results to clinical practice. We also show that outcomes of human treatment better align with the results of the KuLGaP measure than other response measures. KuLGaP has the potential to become a measure of choice for quantifying drug treatment in mouse models as it can be easily used via the kulgap.ca website.

中文翻译：

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评估源自患者的异种移植物的治疗反应

量化人类癌症小鼠模型对药物治疗的反应对于治疗开发和分配很重要，但仍然是一项具有挑战性的任务。为了能够更容易地转化实验结果，量化这种反应的首选措施应考虑更多可用的实验数据，包括随时间变化的肿瘤大小和重复之间的变化。我们提出了一种基于理论的测量方法 KuLGaP，用于计算治疗臂和控制臂之间的差异。我们使用 329 个患者衍生的异种移植 (PDX) 模型测试并比较 KuLGaP 与四种广泛使用的反应措施。我们的研究结果表明，KuLGaP 比目前现有的措施更具选择性，降低了误报的风险，并提高了将实验室结果转化为临床实践的能力。我们还表明，与其他响应措施相比，人类治疗的结果与 KuLGaP 措施的结果更加一致。KuLGaP 有可能成为在小鼠模型中量化药物治疗的首选措施，因为它可以通过 kulgap.ca 网站轻松使用。