Posts Tagged ‘optimal treatment’

New approaches to cancer therapy using mathematics

Reporter: Irina Robu, PhD

Our bodies are made up of trillions of cells grouped together to form tissues and organs such as muscles, bones, the lungs and the liver. Genes inside each cell tell it when to grow, work, divide and die. Usually, our cells follow these commands and we stay healthy. Nevertheless, occasionally the instructions get mixed up, triggers our cells to grow and divide out of control or not die when they should. As more and more of these abnormal cells grow and divide, they can form a lump in the body called a tumor.

Cancer therapy thrives in shrinking tumors and frequently fails in the long run; however, a small number of cancer cells are resistant to treatment. The cancer cells expand to fill the space left by the cells that were destroyed.  Using mathematical analysis and numerical simulations, Dr. Noble and Dr. Viossat, a mathematician at Université Paris-Dauphine proposed new approach to validate the concept of using a combination of biological, computational and mathematical models and they show how spatial constraints within tumors can be exploited to suppress resistance to targeted therapy.

Lately, mathematical oncologists have designed a new method to tackling this problem based on evolutionary principles. Known as adaptive therapy, this as-yet unproven strategy aims to stop or delay the failure of cancer treatment by manipulating competition between drug-sensitive and resistant cells. It uses relatively low doses and has the additional potential benefits of reducing side effects and enhance quality of life.

As a way to solve the problem, Dr. Noble and Dr. Viossat organized a workshop for mathematical modelers to determine the state of art of adaptive therapy, discuss future directions and foster collaborations. The virtual event was attended by one hundred persons who participated in more than twenty talks, interacting via the Sococo virtual meeting platform.

Dr. Noble plans to continue developing mathematical models to improve cancer treatment. His long-term objective is to project optimal treatment regimens for each tumor type and each patient.



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