Computer Model Predicts Tumour Growth


The model is the product of mathematical formulas based on the first principals of physics, such as conservation of mass, and it has allowed researchers to recreate tumour growth in a computer. Through subsequent repetitive testing against real tumours, they have also linked their computerised tumours to real-world brain tumours, or "gliomas," and can now watch tumour growth on a computer screen.

Creating such a model is significant because it could help design specific, targeted treatments for individualised therapy. "This helps us design a treatment," said lead author Elaine Bearer. "By testing potential therapies in the computer, we can get our new drugs much faster to patients."

To conduct the study, Bearer and her collaborators developed a mathematical formula that incorporated a number of equations describing the process of tumour evolution and growth. The master computational model was built on formulas that predict how much oxygen tumour cells consume and the rate of oxygen diffusion, and quantitative measures of cell growth and metabolic rates. The model is a series of interdependent differential equations. Each equation includes variables, or numerical values that can be experimentally manipulated.

For example, to test if oxygen consumption rates influence tumour growth, the values assigned to those rates can be changed and the outcome observed on the computer screen — not unlike playing a computer game. The result: A three-dimensional matrix of a glioma that can be adjusted to see what its growth stage will be over time, including speed of growth, size and shape.

Researchers validated their computational model with glioma specimens. Bearer studied about 40 different human brain tumour samples. The samples, sliced and sealed in a specimen slide, were as large as a chick pea or as small as the head of a pin. The tumour specimens used in the studies had been removed for diagnosis or surgical treatment of the tumour.

Researchers compared their virtual computational tumour with the actual human brain tumour samples at different stages of tumour evolution. Through many rounds of checking computer output against the real-life tumours, researchers created a computational model that mimicked natural biological tumours in all respects.

They focused specifically on a glioma because it does not invade the body through a basement membrane as epithelial-based cancers do, such as tumours that grow in the colon, breast or prostate. In the brain, tumours grow without having to digest a basement membrane to invade adjacent tissue. A basement membrane is essentially boundary of a given tissue that separates cells from the surrounding connective tissue.

Bearer said she hopes the research will allow doctors to find drug targets for glioma. She also envisions using the computational model to find targets for personalised medical therapies, enabling it to quickly identify molecular targets, and then select from existing treatments or design new treatments to stop the tumour.

MEDICA.de; Source: Brown University