Patient-derived xenograft (PDX) tumors increase growth rate with time
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Alexander T. Pearson1, Kelsey A. Finkel2, Kristy A. Warner2, Felipe Nör2,3, David Tice4, Manoela D. Martins3, Trachette L. Jackson5 and Jacques E. Nör2,6,7,8
1 Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, MI, USA
2 Department of Restorative Sciences, University of Michigan School of Dentistry, Ann Arbor, MI, USA
3 Department of Oral Pathology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
4 MedImmune, Gaithersburg, MD, USA
5 Department of Mathematics, University of Michigan School of Literature, Sciences, and the Arts, Ann Arbor, MI, USA
6 Department of Otolaryngology, University of Michigan School of Medicine, Ann Arbor, MI, USA
7 Department of Biomedical Engineering, University of Michigan College of Engineering, Ann Arbor, MI, USA
8 Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, USA
Jacques E. Nör, email:
Keywords: mathematical modeling, tumor growth, mouse models, head and neck squamous cell carcinoma, adenoid cystic carcinoma
Received: September 16, 2015 Accepted: January 06, 2016 Published: January 14, 2016
Patient-derived xenograft (PDX) models are frequently used for translational cancer research, and are assumed to behave consistently as the tumor ages. However, growth rate constancy as a function of time is unclear. Notably, variable PDX growth rates over time might have implications for the interpretation of translational studies. We characterized four PDX models through several in vivo passages from primary human head and neck squamous cell carcinoma and salivary gland adenoid cystic carcinoma. We developed a mathematical approach to merge growth data from different passages into a single measure of relative tumor volume normalized to study initiation size. We analyzed log-relative tumor volume increase with linear mixed effect models. Two oral pathologists analyzed the PDX tissues to determine if histopathological feature changes occurred over in vivo passages. Tumor growth rate increased over time. This was determined by repeated measures linear regression statistical analysis in four different PDX models. A quadratic statistical model for the temporal effect predicted the log-relative tumor volume significantly better than a linear time effect model. We found a significant correlation between passage number and histopathological features of higher tumor grade. Our mathematical treatment of PDX data allows statistical analysis of tumor growth data over long periods of time, including over multiple passages. Non-linear tumor growth in our regression models revealed the exponential growth rate increased over time. The dynamic tumor growth rates correlated with quantifiable histopathological changes that related to passage number in multiple types of cancer.
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