Mixed effects modelling of tumour growth in response to radiation and immunogenic combinations

Abstract

Cancer remains one of the major causes of death worldwide in spite of the large resources on research into therapies in the last 20 - 30 years. Preclinical tumour studies aim to identify potential drug combinations, which could be more efficacious. The results of these preclinical studies can then warrant further investigation in clinical trials, with the aim of confirming efficacy and translational relevance. Recent years have provided potential combination therapies such as radiotherapy (RT) in combination with DNA damage response inhibitors (DDRi) or immune checkpoint inhibition (ICI), but there is a very low amount of information regarding dosage and schedule optimisation strategies. Mathematical modelling can be used to identify optimal doses and schedules in the context of preclinical experiments as well as clinical studies. Current modelling of the impacts of radiation in combination with DDRi or ICI are limited to a few specific preclinical experiments, with little evidence of external validation. These models are developed from data collected during experiments in highly responsive xenograft or syngeneic tumour models that do not capture the response of the typical tumour in the clinic. Furthermore, at the beginning of the PhD project, while models of RT in combination with DDRi or ICI have been developed, there were no published models describing the impact of RT/DDRi/ICI in triple combination therapies. The overarching aim of this thesis was to develop mixed effects models that described the impact of RT/DDRi/ICI in the context of the syngeneic tumour model – MC38, in order to identify optimal doses and schedules of RT/DDRi/ICI to assist in preclinical experiments. In addition, these mixed effects models would also be used to validate potential biomarkers, which could confirm that the immune response drives tumour rejection after RT, or RT/DDRi in MC38 tumours, these biomarkers could then be used to assist in parameterisation for further model development. Preclinical studies involving MC38 syngeneic tumour models had indicated that at the given dosage and schedule, RT/DDRi/ICI had no observable benefit in tumour response compared with RT/ICI. The first aim of this project was to develop a mixed effects model, which captured the differential responses to RT/DDRi/ICI and corresponding de-escalated therapies. The mixed effects model was developed to capture the lack of observable benefit of RT/DDRi/ICI compared with RT/ICI, and then to simulate alternative potencies of RT/ICI which could show a relative improvement in efficacy of RT/ICI combined with AZD0156 (ATMi) compared with RT/ICI combinations. After successful validation of the model, this work revealed that reducing the potency of ICI by 68% could lead to an improved chance of observing the benefits of incorporating ATMi as part of a tritherapy. This would require additional experimental validation with MC38 models to identify the appropriate dosage. For further model development, it is important to identify appropriate, biologically relevant mechanisms that can be incorporated into the model. Mixed effects modelling strategies can assist in identifying drug mechanisms-of-action. These mechanisms can be parameterised in order to develop more complex mathematical models that more appropriately capture the pharmacodynamic effects of different treatment modalities on tumour control. However, the datasets available only contained data regarding tumour immunophenotypes at day 7. Thus, the next aim was to confirm whether the mixed effects model developed previously, could be used to correlate the expected growth rate of the tumour at day 7 (DD7) with survival time using a Cox regression model. A Cox regression analysis indicated that DD7 was strongly associated with survival time in MC38 syngeneic tumour models treated with RT. However, the relationship between DD7 and survival in mice treated with RT/DDRi was less predictive of response due to the lack of variabil

Date of Award	1 Aug 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Hitesh Mistry (Supervisor), Leon Aarons (Supervisor) & Kayode Ogungbenro (Supervisor)