Comparison of classical tumour growth models for patient derived and cell-line derived xenografts using the nonlinear mixed-effects framework

Abstract

In this study we compare seven mathematical models of tumour growth using nonlinear mixed-effects which allows for a simultaneous fitting of multiple data and an estimation of both mean behaviour and variability. This is performed for two large datasets, a patient-derived xenograft (PDX) dataset consisting of 220 PDXs spanning six different tumour types and a cell-line derived xenograft (CDX) dataset consisting of 25 cell lines spanning eight tumour types. Comparison of the models is performed by means of visual predictive checks (VPCs) as well as the Akaike Information Criterion (AIC). Additionally, we fit the models to 500 bootstrap samples drawn from the datasets to expand the comparison of the models under dataset perturbations and understand the growth kinetics that are best fitted by each model. Through qualitative and quantitative metrics the best models are identified the effectiveness and practicality of simpler models is highlighted

Keywords:

• Cancer
• mixed-effects
• mathematical modelling

Acknowledgments

We thank Matthew Fidler for his help and advice on modelling and fitting in NLMIXR.

Disclosure statement

D.V. and K.C.B. are full-time employees and shareholders of AstraZeneca. DV was a PostDoc fellow of the AstraZeneca PostDoc programme. J.W.T.Y. was an employees of AstraZeneca.

Additional information

Funding

Funded by AstraZeneca. Dimitrios Voulgarelis is a fellow of the AstraZeneca Postdoc Programme.