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 st...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-11-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/11769351221136056 |
| _version_ | 1828186267760721920 |
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| author | Jessica Weiss Nhu-An Pham Melania Pintilie Ming Li Geoffrey Liu Frances A Shepherd Ming-Sound Tsao Wei Xu |
| author_facet | Jessica Weiss Nhu-An Pham Melania Pintilie Ming Li Geoffrey Liu Frances A Shepherd Ming-Sound Tsao Wei Xu |
| author_sort | Jessica Weiss |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T07:16:20Z |
| format | Article |
| id | doaj.art-a39d203b5f3d46508161c69091c20787 |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-04-12T07:16:20Z |
| publishDate | 2022-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-a39d203b5f3d46508161c69091c207872022-12-22T03:42:27ZengSAGE PublishingCancer Informatics1176-93512022-11-012110.1177/11769351221136056Optimizing Drug Response Study Design in Patient-Derived Tumor XenograftsJessica Weiss0Nhu-An Pham1Melania Pintilie2Ming Li3Geoffrey Liu4Frances A Shepherd5Ming-Sound Tsao6Wei Xu7Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, CanadaPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, CanadaDepartment of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, CanadaPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, CanadaDepartment of Medical Biophysics, University of Toronto, Toronto, ON, CanadaDepartment of Medicine, Division of Medical Oncology, University of Toronto, Toronto, ON, CanadaDepartment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, CanadaDepartment of Biostatistics, Dalla Lana School of Public Health, Toronto, ON, CanadaPatient-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.https://doi.org/10.1177/11769351221136056 |
| spellingShingle | Jessica Weiss Nhu-An Pham Melania Pintilie Ming Li Geoffrey Liu Frances A Shepherd Ming-Sound Tsao Wei Xu Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts Cancer Informatics |
| title | Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts |
| title_full | Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts |
| title_fullStr | Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts |
| title_full_unstemmed | Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts |
| title_short | Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts |
| title_sort | optimizing drug response study design in patient derived tumor xenografts |
| url | https://doi.org/10.1177/11769351221136056 |
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