Sample-size calculation for preclinical dose-response experiments using heterogeneous tumour models.
Sample-size calculation for preclinical dose-response experiments using heterogeneous tumour models.
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DOI：
10.1016/j.radonc.2021.02.032
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发表时间：
2021-05
影响因子：
5.7
通讯作者：
Loeck, Steffen
中科院分区：
文献类型：
--
作者：
Ciecior, Willy;Ebert, Nadja;Borgeaud, Nathalie;Thames, Howard D.;Baumann, Michael;Krause, Mechthild;Loeck, Steffen
关键词：
在临床前放射肿瘤学研究中，局部肿瘤控制被认为是最相关的终点，因为它反映了癌症干细胞的失活。临床前肿瘤控制测定可以比较不同放射治疗策略之间的剂量反应曲线，例如评估额外的靶向药物和免疫治疗干预措施，或不同放射方式之间的剂量反应曲线。为了模拟人类肿瘤群体的生物异质性并适应个性化肿瘤学方法，越来越多的临床前研究结合更大的肿瘤模型进行。为了设计研究方案并获得可靠的结果，必须制定前瞻性样本量规划来考虑这种异质性队列。开发了一种基于蒙特卡罗的方法来估计 1:1 两臂前瞻性肿瘤控制试验的样本量。基于重复的逻辑回归分析、预定义的剂量水平、对所包括的肿瘤模型的剂量反应曲线的假设以及剂量修改因素（DMF），计算每个剂量组给定数量的动物的功效。提出了两个应用：(i) 对于头颈鳞状细胞癌 (HNSCC) 模型 FaDu 的简单肿瘤控制测定，每臂 7 个剂量水平中的每一个都需要 10 只动物，才能显示 1.25 的 DMF，功效为 0.82，且不脱落（总共：140 只动物）。 (ii) 在一项更复杂的实验中，将六种不同的肺肿瘤模型与异质群体结合起来，每组 11 个剂量水平中的每一个都需要 21 只动物，才能在不退出的情况下显示 1.25 的 DMF，功效为 0.81（总共：462 只动物）。与六种单独的肿瘤控制检测相比，分析异质队列可将所需的动物数量减少 50% 以上。提出了一种估计异质群体中比较肿瘤控制测定所需动物数量的方法，还允许在实验组中纳入不同的治疗作为个性化方法。该软件是公开可用的，可用于基于逻辑回归的 S 形剂量反应曲线的计划比较。
In preclinical radio-oncological research, local tumour control is considered the most relevant endpoint as it reflects the inactivation of cancer stem cells. Preclinical tumour-control assays may compare dose-response curves between different radiotherapy strategies, e.g., assessing additional targeted drugs and immunotherapeutic interventions, or between different radiation modalities. To mimic the biological heterogeneity of human tumour populations and to accommodate for approaches of personalized oncology, preclinical studies are increasingly performed combining larger panels of tumour models. For designing the study protocols and to obtain reliable results, prospective sample-size planning has to be developed that accounts for such heterogeneous cohorts. A Monte-Carlo-based method was developed to estimate the sample size of a comparative 1:1 two-arm prospective tumour-control assay. Based on repeated logistic regression analysis, pre-defined dose levels, assumptions on the dose-response curves of the included tumour models and on the dose-modifying factors (DMF), the power is calculated for a given number of animals per dose group. Two applications are presented: (i) For a simple tumour-control assay with the head and neck squamous cell carcinoma (HNSCC) model FaDu, 10 animals would be required for each of 7 dose levels per arm to reveal a DMF of 1.25 with a power of 0.82 without drop out (total: 140 animals). (ii) In a more complex experiment combining six different lung tumour models to a heterogeneous population, 21 animals would be required for each of 11 dose levels per arm to reveal a DMF of 1.25 with a power of 0.81 without drop out (total: 462 animals). Analyzing the heterogeneous cohort reduces the required animal number by more than 50% compared to six individual tumour-control assays. An approach for estimating the required animal number for comparative tumour-control assays in a heterogeneous population is presented, allowing also the inclusion of different treatments as a personalized approach in the experimental arm. The software is publicly available and can be applied to plan comparisons of sigmoidal dose-response curves based on logistic regression.
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