Modelling, inference and simulation of personalised breast cancer treatment

Abstract

Mathematical modelling and simulation tools are an attractive and time- and cost- effective approach to determine optimal therapy for individual cancer patients.

Current models can address pharmacokinetics and pharmacodynamics of anticancer medicine at various spatial and temporal scales. Simulations can then be performed to explore many treatment regimens to identify optimal plans with minimal toxicity. To individualise a model to each individual patient, its parameters require separate estimation and validation, and the runtime of simulations remains too slow for a practical clinical use.

In this project, I demonstrate that - a mathematical model designed for a specific type of cancer, integrating routinely-collected data from a clinical trial is feasible, - the model is robust enough to simulate and predict various responses using individual data, and - the collected data are sufficient for the purpose of validation and personalisation of the model

We use data from a recently published neoadjuvant clinical phase II trial in patients with advanced breast tumours where histological, magnetic resonance imaging (MRI) and molecular data were collected before, during and at the end of neoadjuvant treatment.

Overall, our study demonstrates the effectiveness and the potential of simulation-based personal treatment optimisation. It lays the basis for future program in delivering robust clinic companion diagnostic tool.