Abstract
BackgroundMouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. In this study, we establish such as framework and provide general guidelines on the design, analysis and application of MCTs.MethodsWe systematically analyzed tumor growth data from a large collection of PDX, CDX and syngeneic mouse tumor models to evaluate multiple efficacy end points, and to introduce statistical methods for modeling MCTs.ResultsWe established empirical quantitative relationships between mouse number and measurement accuracy for categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between methods on calling drug responses as objective response. We then introduced linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring drug’s mechanisms of action. We introduced additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered mouse population. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that power is close for designs with similar total number of mice. Finally, we showed that MCTs can explain discrepant results in clinical trials.ConclusionsMethods proposed in this study can make the design and analysis of MCTs more rational, flexible and powerful, make MCTs a better tool in oncology research and drug development.
Highlights
Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed
Overall survival (OS) and progression-free survival (PFS) are estimated by tumor volume increase, Cox proportional hazards models are used for survival analysis, response categories are defined by tumor volume change and objective response rate (ORR) is calculated [6, 13, 16]
Since there are mice of same mouse models in both arms, they themselves can serve as control across arms for better measurement of drug efficacy. (2) tumor volumes are routinely measured every few days; (3) mouse models are usually characterized with genomic/ pharmacology/histopathology annotations; (4) MCTs are done in labs that reduces/removes various noise and inconvenience encountered in clinical trials, such as dropouts, long trial time and concomitant medication
Summary
Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. While most reported MCTs used PDXs, MCTs using other mouse models, such as syngeneic models, are widely performed as well Because of their resemblance to clinical trials, MCTs are often analyzed by methods for clinical trials. (1) In an oncology clinical trial, a patient is enrolled in only one arm, while in a MCT, multiple mice bearing tumor from the same mouse model are made so that mice can be placed in all arms. (2) tumor volumes are routinely measured every few days; (3) mouse models are usually characterized with genomic/ pharmacology/histopathology annotations; (4) MCTs are done in labs that reduces/removes various noise and inconvenience encountered in clinical trials, such as dropouts, long trial time and concomitant medication Since there are mice of same mouse models in both arms, they themselves can serve as control across arms for better measurement of drug efficacy. (2) tumor volumes are routinely measured every few days; (3) mouse models are usually characterized with genomic/ pharmacology/histopathology annotations; (4) MCTs are done in labs that reduces/removes various noise and inconvenience encountered in clinical trials, such as dropouts, long trial time and concomitant medication
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