A meta-analysis of tumour response and relapse kinetics based on 34, 881 patients: A question of cancer type, treatment and line of treatment. (June 2021) - Yates, James W.T.; Cheung, S.Y. Amy
Abstract: Purpose: Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design. Experimental design: Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA. Results: Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34, 881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported. Conclusions: Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used. Highlights: Often tumour response is quantified using mathematical models. These modelling studies hold vital information to inform clinical trial design. Rates of shrinkage, regrowth and resistance vary by cancer type and treatment. There is a need for integrative and standardised approaches to analysing such data.