Brain/Spine model for cancer mechanobiology – multiscale simulations

Brain metastases, or secondary brain tumours, occur in 10% to 30% of adults with cancer. Treatment for people who developed  brain cancer metastasis is often surgery, radiation therapy or both. Clinical evidence suggests however that  stereotactic radio-surgery (SRS), or in combination with whole brain radiotherapy (WBRT), can offer improved cancer patient survival and decreased morbidity. Despite this, prediction of response to treatment on a patient-specific basis still remains elusive, while there are no reliable tools for clinical decision making in patients with brain metastases. As such, our research efforts aim to address this challenge through a multidisciplinary approach that encompasses medicine and cancer biology, physics and engineering.

On the clinical branch, we are focusing the retrospective and prospective clinical efforts on brain cancer patient metastases, with patients referred to for SRS or stereotactic fractionated radiotherapy (SFRT). On the technological branch, we are developing a technologically integrated digital platform for predicting and assessing the efficacy of external beam radiation treatments (SRS, SFRT) in patients with brain metastases. Thus, novel image processing and radiomics algorithms, as well as cutting-edge in silico models of the brain are being developed and will be integrated into a digital platform. The platform will be validated and assessed for its accuracy, reliability, repeatability and reproducibility against pertinent clinical data.

This project, PROTEAS, is an active collaboration between UCY and the Bank of Cyprus Oncology Centre, and is funded by the Cyprus Cancer Research Institute (CCRI_2020_FUN_001 – BRIDGES IN RESEARCH EXCELLENCE).

Relevant work:

de Montigny et al. 2020. Methods, doi: 10.1016/j.ymeth.2020.01.006

LEFT to  RIGHT: From organ-scale to cell-scale (multiscale) modelling of glioblastoma multiforme (GBM) development. Our in silico platform will encompass patient-specific information of the brain microstructure (from MRIs) to predict stage-II tumour progression. (doesn’t allow playback on other websites)

Hybrid model couples FEM to simulate the balance of chemical cues (e.g. oxygen, growth factors, cytokines) and cell populations (from a macroscopic perspective) together with ABM to explicitly simulate cell phenotypic behaviour. Simulation shows glioma cell growth and invasion (coloured in orange when in normoxia, and in red when in hypoxia) within host of glial cells (in blue) and neurons (in green). Necrotic cells are shown in blue.
Pictures from (A) to (D) zoom out from the centre of the glioma tumour while (E) depicts the entire simulation domain, where the transparent grey surface in the latter figure illustrates the tumour outline. The coloured contour in the plane of (F) demonstrates the oxygen deficiency (grey denotes physiological oxygen saturation levels, while black denotes very low oxygen saturation levels) that ultimately leads to host cell death and glioma cell necrosis. The overall size of the domain of analysis in (F) is [9 mm]^3.

Abnormalities of solid tumours from a biomechanics perspective involve stiffening of the tissue and accumulation of mechanical stresses, which are recognised to affect cancer development, invasiveness, tumour morphology and metastasis. It’s been known that elevated mechanical stresses supress cancer-cell proliferation, however, the majority of in silico models describe tumour development as an isotropic growth process. Nonetheless, spinal tumours that grow within multiple host tissues of different mechanical properties, they are observed to exhibit anisotropic growth patterns.

Through a collaborative effort with Professor Stylianopoulos and his group (, we are developing tumour- and microenvironment-specific biomechanical models (for isotropic or/and anisotropic growth). In our recent Proc Royal Society A paper, we demonstrated the in silico models’ capacity to predict the evolution of mechanical stresses and interstitial fluid pressure in intramedullary spinal tumours and brain tumours, while they have being also able to realistically capture the morphology of such carcinomas.

Relevant work:

Katsamba et al. 2020. Proceedings of the Royal Society A, doi:10.1098/rspa.2019.0364

A: Patient-specific spine model using MRI data – the sagittal plane of the spinal cord MR scan is shown, where the region between the T1 and T2 vertebrae is modelled in silico using finite elements. B+C: The tumour (light green) is located inside the spinal cord (blue), the two vertebrae (red and green), the intervertebral disc (violet) and the spinal canal (light blue). D. Three-dimensional discretization using tetrahedral FEs of the spine model. Source:
A: Medical image depicts the development a real-case spinal tumour. B: Finite element simulation results compare the prediction of the isotropic versus anisotropic growth model, which demonstrate that the latter model produces in silico realistic tumour growth in the spinal chord.

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