Personalised Mechanobiological Models to Predict Tumour Growth and Anti-Cancer Drug Penetration

Introduction

Personalised cancer medicine presents an exciting frontier in healthcare that tailors disease mitigation and intervention to an individual patient. However, existing technologies fail to leverage the physical forces that underpin stress-dependent tumour growth and the subsequent evolution of biomechanical resistance to anti-cancer drugs.

Scientific Understanding

Furthermore, the fundamental mechanisms governing such force-sensitivity have yet to be uncovered. This deficiency in scientific understanding of the active biomechanical behaviour of tumours and control of drug penetration has hindered the progression of anti-cancer therapy.

Project Development

In this project, an advanced computational modelling framework will first be developed to uncover the mechanisms underlying stress-dependent cell and tissue growth. This will involve coupling the thermodynamics of cellular volume control with active force generation and intracellular transport.

Experimental Analysis

Novel experimental analysis of 3D tumour spheroid growth and single cell biomechanics will reinforce the framework to gain a new understanding of how mechanical loading can prevent tumour cell division and the role of intracellular exchange in multi-cellular growth control.

Model Extension

The models will then be extended to determine the role of growth-induced stress and cell compaction in restricting drug penetration. Additionally, the project will explore whether this can be mitigated by promoting intracellular drug perfusion.

Patient-Derived Models

Finally, integrated patient-derived computational and tumour organoid models will be developed for the prediction of growth and emergent biomechanical resistance to anti-cancer drugs. This will motivate model-led mechanobiological therapy in an animal model of breast cancer.

Objective

The overarching objective of this ground-breaking project is to pioneer a personalised healthcare framework for the prediction of mechanically-regulated cancer and treatment outcomes. This has remarkable potential to drive a paradigm shift in patient-specific diagnosis and treatment of cancer.