Multiscale modelling of glioma growth and treatments

Computational simulations of tumour growth before (left), one day after (middle) and 100 days after surgery (color bar shows intensity). This shows that the underlying mathematical model predict the multifocal regrowth of tumour after surgery as observed clinically

Gliomas, the most common primary brain tumours, are diffusive and highly invasive. The standard treatment for brain tumours consists of a combination of surgery, radiation therapy and chemotherapy. Currently, I am developing a multiscale mathematical model for glioma growth using several patient-specific information to assist its treatment planning and delivery.

Multiscale modelling of cancer and its treatments

Cancer as a Multiscale Disease: Mathematical Modeling Approach. Multiscale mathematical models can help in studying cancer evolution and serve as in silico test base for comparing and optimizing various multi-modality anticancer treatments.

Plots of the spatial distribution of the cells in different stages of the cell cycle. (i) Plots from hybrid multiscale CA model and the colours represent various stages of cell-cycle (ii) Plots from hybrid cellular potts model (using Compucell3D) and the colour legend shows the types of the tumour cells

Simulation results from a hybrid model: Spatial profiles of glucose, chemoattractant, ECM, and MMP at various time points

Cancer is a heterogeneous disease often requiring complex alterations of a normal cell to drive it to malignancy and ultimately to a metastatic state. These alterations are largely due to aberrant expression of a set of genes or pathways such as p53 pathways and hypoxia pathways rather than a single gene. We have recently studied the effects of the miR-451-AMPK-mTOR pathway to study how up- or down-regulation of components in these pathways affects cell proliferation and migration.

Modelling the effects of tumour heterogeneities on tumour growth and treatment responses

Binary images of one of the eight glioma xenographt cross-sections, illustrating tumour blood vessels, perfused vessels, hypoxic area, and total tumour area, respectively. It is necessary to understand the tumour heterogeneities in order to study cancer progression and plan effective treatment strategies. A growing tumour can change its microenvironment in its own favour by suppressing anti‐tumour factors and producing excess growth factors. There are also increasing evidences in support of the hypothesis that the tumour microenvironment plays an important role in conferring drug resistance, a major cause of relapse contributing to the incurability of cancer.

Modelling drug resistance and its implications in cell-cycle phase specific chemotherapy

Plots showing the spatial evolution of cancer cells within a two-population model when single dose of cell-cycle phase-specific chemotherapeutic drugs are given.

The development of drug resistance by cancer cells continues to be a key impediment in the successful delivery of these multi-drug therapies. Recent studies have indicated that intra-tumoural heterogeneity has a significant role in driving resistance to chemotherapy in many human malignancies. Multiple factors, including the internal cell-cycle dynamics and external microenvironement contribute to the intra-tumoral heterogeneity. Our recent studies has indicated the role on slow-cycling tumour sub-populations in developing resistance to conventional chemotherapeutic drug.

Modelling radiation bystander effects and its implications in clinical radiotherapy

Plots showing the spatio-temporal evolution of host-tumour dynamics with and without radiation treatment

Radiation-induced bystander effects are defined as those biological effects expressed, after the irradiation, by cells that are not directly exposed to the radiation. As a consequence of these bystander signals, the affected cells may die or show chromosomal instability as well as further abnormalities. Consequently, the bystander effect has several important implications for radiation protection, radiotherapy and diagnostic radiology. Currently, I am developing a hybrid model incorporating the multiple effects of radiation and radiation induced bystander effects.

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