优化患者源性肿瘤异种移植的药物反应研究设计。

IF 2.5 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2022-11-22 eCollection Date: 2022-01-01 DOI:10.1177/11769351221136056
Jessica Weiss, Nhu-An Pham, Melania Pintilie, Ming Li, Geoffrey Liu, Frances A Shepherd, Ming-Sound Tsao, Wei Xu
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引用次数: 2

摘要

采用患者源性肿瘤异种移植(PDX)模型来评估临床前抗癌药物的有效性。对于大规模的药物疗效研究,采用每名患者1只小鼠(1 × 1 × 1)的设计是可行的。基于我们的综合PDX实验,我们评估了可修改的参数,这些参数可以提高该设计的统计能力。以实际研究为参考,探讨统计效力与治疗效应大小、小鼠间变异和肿瘤测量频率之间的关系。我们的结果显示,在1 × 1 × 1设计下,在显著性水平为0.2或0.05时,可以检测到较大的效应量。我们发现,在所有研究的情况下,在α水平为0.05时达到80%功率所需的最小小鼠数量是,小效应量时每组21只小鼠,中等效应量时每组5只小鼠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts.

Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts.

Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts.

Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts.

Patient-derived tumor xenograft (PDX) models were used to evaluate the effectiveness of preclinical anticancer agents. A design using 1 mouse per patient per drug (1 × 1 × 1) was considered practical for large-scale drug efficacy studies. We evaluated modifiable parameters that could increase the statistical power of this design based on our consolidated PDX experiments. Real studies were used as a reference to investigate the relationship between statistical power with treatment effect size, inter-mouse variation, and tumor measurement frequencies. Our results showed that large effect sizes could be detected at a significance level of .2 or .05 under a 1 × 1 × 1 design. We found that the minimum number of mice required to achieve 80% power at an alpha level of .05 under all situations explored was 21 mice per group for a small effect size and 5 mice per group for a medium effect size.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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