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

Informations projet

MechanoGAP

N° de convention de subvention: 101116234

DOI

10.3030/101116234

Date de signature de la CE 14 Novembre 2023

Date de début 1 Janvier 2024

Date de fin 31 Decembre 2028

Financé au titre de

• European Research Council (ERC)

Coût total

€ 1 499 693,00

Contribution de l’UE

€ 1 499 693,00

Coordonné par

UNIVERSITY OF GALWAY
Ireland

Periodic Reporting for period 1 - MechanoGAP (Personalised Mechanobiological Models to Predict Tumour Growth and Anti-Cancer Drug Penetration)

Période du rapport: 2024-01-01 au 2026-06-30

Résumé du contexte et des objectifs généraux du projet

Personalised cancer medicine presents an exciting frontier in healthcare that tailors intervention to an individual patient. Standard treatments such as chemotherapy are only effective in a subset of cancer patients, but personalised medicine can potentially tailor drug selection to individual patient needs. Significant technological advances have been developed for patient tissue analysis, animal models, and simulation to predict tumour risk and outcomes. However, personalised medicine is motivated from a biological and clinical perspective, and existing technology fails to leverage the important role of the physical forces that can affect tumour behaviour. This key deficiency presents a major barrier to development of next-generation personalised healthcare technology, and risks motivating inappropriate drug regimens with extreme side effects.
This motivates looking to the emerging research area of cancer mechanobiology, a field of science that studies how physical forces and mechanical properties of the tumour environment influence biological processes - how cancer cells and tissues sense, respond to, and are shaped by mechanical stimulation. Crucially, to advance the current understanding of tumour development and response to treatment, new computational and experimental models must be developed that are rooted in a deep understanding of the biophysical processes underlying cell growth and survival. Addressing this deficiency in scientific understanding could drive a paradigm shift in patient-specific diagnosis and treatment. The overall aim of this project is therefore to develop mechanobiology-informed models for the prediction of tumour progression and drug resistance, that will interrogate and uncover the biophysical processes underlying these activities. Ultimately, the goal is to develop a personalised healthcare framework for the optimisation of new combinational drug therapies.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

We first aimed to understand the key role of mechanobiology in breast cancer development, and particularly the importance of cancer and cell subtype. The physical environment surrounding a tumour, including how stiff or soft the surrounding tissue is, has an important influence on how cancer cells grow, divide, and respond to treatment. Our research programme is working to understand these mechanical influences at multiple scales, from individual cells to tumour clusters, combining laboratory experiments with advanced computer modelling. We have been developing these computer models to better understand what controls when and how cells decide to grow and divide. Our findings suggest that cell division is not simply controlled by chemical signals alone. Physical pressure acting on cells can interfere with this process, effectively restricting growth. This creates a link between how a cell senses its environment and in turn how this influences its ability to divide. We have also applied new methods in artificial intelligence to build efficient computational models capable of simulating tumour behaviour across a wide range of conditions.
We generated lab-based tumour models in engineered environments of controlled stiffness, finding that stiffer environments can suppress tumour growth over time. We are applying these models to understand mechanisms of chemotherapy resistance, which will help us to identify ways to improve drug efficacy. To study the biomechanics of individual tumour cells and their drug resistance profile, we have developed small scale devices capable of inducing cell deformation. We then applied computational tools such as finite element analysis to uncover precise biomechanical property measurements from the experimental data. Importantly, tumours contain heterogeneous populations of cells with distinct behaviours and drug sensitivity. We are thus investigating how certain cancer cell subtypes exploit mechanical adaptability and signalling with neighbouring cells to evade treatment. Understanding this diversity is a step toward more targeted and effective therapeutic strategies. We are now preparing to extend this work to patient-derived tumour models.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

This research programme addresses a fundamental but underappreciated aspect of cancer, which is the role of physical forces in controlling tumour development and influencing the response to therapy. The potential impacts of this work span basic science, technology development, and ultimately patient care. By uncovering the mechanical actions that govern how tumours grow and respond to their physical environment, this research has the potential to reframe how we think about cancer progression. Understanding why some tumours become resistant to chemotherapy not because of genetic mutation but because of their mechanical surroundings opens new avenues for intervention. Rather than focusing on new drug development, our work could uncover efficient routes to improving treatment outcomes by helping existing drugs to work more effectively, particularly in surgically-untreatable tumours.
The insights generated by our research programme will also have relevance beyond cancer. Understanding how physical forces regulate cell growth and tissue organisation is a fundamental question in biology with implications for other fields including developmental biology, regenerative medicine, and tissue engineering. The computational and experimental tools developed in this programme will be openly applicable across these domains. Our programme is also pioneering advanced computational models that combine physical, biological, and clinical data to simulate how tumours evolve and respond to treatment. These models will be designed to be efficient, generalisable, and ultimately applicable to individual patients for personalised care. While this research programme focuses on breast cancer as a model system, the framework being developed should be transferable. Tumour types that are currently very difficult to treat, including cancers of the pancreas, lung, and brain, share many of the same biomechanical challenges. The tools and insights generated by our work are intended as a foundation for addressing those unmet needs.