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Updated: Nov 2, 2025

Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research
Published on: May 5, 2021
Experimental design of preclinical experiments: number of PDX lines vs subsampling within PDX lines
Jeanette E Eckel-Passow1, Gaspar J Kitange2, Paul A Decker1
1Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA.
Background:
Appropriately designed preclinical patient-derived xenograft (PDX) experiments are important to accurately inform human clinical trials. There is little experimental design guidance regarding choosing the number of PDX lines to study, and the number of mice within each PDX line.
Methods:
Retrospective data from IDH-wildtype glioblastoma preclinical experiments evaluating a uniform regimen of fractionated radiation (RT), temozolomide (TMZ) chemotherapy, and concurrent RT/TMZ across 27 PDX lines were used to evaluate experimental designs and empirically estimate statistical power for ANOVA and Cox regression.
Results:
Increasing the number of PDX lines resulted in more precise and reproducible estimates of effect size. To achieve 80% statistical power using ANOVA, experiments using a single PDX line required subsampling of 6 mice per PDX for each treatment group to detect a difference in survival of 135 days, and 9 mice per PDX to detect a difference of 100 days. Alternatively, a design that used 10 PDX lines had greater than 80% power to detect a difference of 135 days with a single mouse per PDX per treatment group, a difference of 100 days with 2 mice per PDX per treatment, and 35 days with more than 10 mice per PDX per treatment. Power for Cox regression was slightly smaller than ANOVA for very small experiments regardless of effect size and slightly higher than ANOVA for detecting a smaller effect size of 35 days difference in survival for moderate-to-large experiments.
Conclusions:
Experimental designs using few mice across many PDX lines can provide robust results and account for inter-tumor variability.
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