ArticleScientific reports2021
Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- BCLXSignal transduction and targeted therapy · 2026Article
- Multiomic analysis of clonal development reveals new regulators of leukemic cell growth.Genes & development · 2026Article
- Dual FAK and EPHA2 targeting by brigatinib tackles PARP inhibitor adaptive survival response in high-grade serous ovarian cancer.Science translational medicine · 2026Article
- CTPS1 is an unexplored vulnerability in breast and ovarian cancer.Theranostics · 2026Article
- Suppression of LKB1-mutant lung adenocarcinoma by natural killer cells from females.Journal of the National Cancer Institute · 2025Article
- High-fidelity Cas9-mediated targeting of KRAS driver mutations restrains lung cancer in preclinical models.Nature communications · 2025Article
- Antitumor activity of rucaparib plus PLX038A in serous endometrial carcinoma.Journal of experimental & clinical cancer research : CR · 2025Article
- Proper likelihood functions for parameter estimation in S-shaped models of unperturbed tumor growth.Scientific reports · 2025Article
- Arginase-1-specific T cells target and modulate tumor-associated macrophages.Journal for immunotherapy of cancer · 2025Article
- The E2F4 transcriptional repressor is a key mechanistic regulator of colon cancer resistance tobioRxiv : the preprint server for biology · 2025Article
- Investigating proteogenomic divergence in patient-derived xenograft models of ovarian cancer.Scientific reports · 2025Article
- Investigation of selective glucocorticoid receptor modulation in high-grade serous ovarian cancer PDX models.Journal of gynecologic oncology · 2025Article
- Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies.Cancer research communications · 2024Article
- Targeting neddylation and sumoylation in chemoresistant triple negative breast cancer.NPJ breast cancer · 2024Article
- Leveraging homologous hypotheses for increased efficiency in tumor growth curve testing.Scientific reports · 2023Article
- CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.Bioinformatics (Oxford, England) · 2023Article
- Assessing the performance of different outcomes for tumor growth studies with animal models.Animal models and experimental medicine · 2022Article
- Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors.Translational oncology · 2022Article
- Repurposing Ceritinib Induces DNA Damage and Enhances PARP Inhibitor Responses in High-Grade Serous Ovarian Carcinoma.Cancer research · 2022Article
- Multiomic analysis identifies CPT1A as a potential therapeutic target in platinum-refractory, high-grade serous ovarian cancer.Cell reports. Medicine · 2021Article
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14 authors.
Funding
Abstract
Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling of tumor growth data. Biologically plausible structures for the covariation between repeated tumor burden measurements are explained. Graphical, tabular, and information criteria tools useful for choosing the mean model functional form and covariation structure are demonstrated in a Case Study of five PDX models comparing cancer treatments. Power calculations were performed via simulation. Linear mixed effects regression models applied to the natural log scale were shown to describe the observed data well. A straight growth function fit well for two PDX models. Three PDX models required quadratic or cubic polynomial (time squared or cubed) terms to describe delayed tumor regression or initial tumor growth followed by regression. Spatial(power), spatial(power) + RE, and RE covariance structures were found to be reasonable. Statistical power is shown as a function of sample size for different levels of variation. Linear mixed effects regression models provide a unified and flexible framework for analysis of PDX repeated measures data, use all available data, and allow estimation of tumor doubling time.
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