Virtual tissue simulator: a computational path towards personalized medicine in cancer treatment

What is it about?

In the past, everyone with the same type of cancer used to get the same treatment, however the “one size fits all” approach does not work for everyone. This inherent variability of cancer lends itself to the growing field of personalized medicine, which uses information on patients physiology and specifics of the tumor in question to help targeting the treatment. Within this broad context we introduce a computational model that reproduces a variety of spatial patterns of tissue regeneration and tumor growth. These results are a promising step in the direction of a personalized estimation of tissue dynamics from a limited number of measurements carried out at diagnosis. This could significantly contribute to improve our understanding of how cancer develops and to outline different therapeutic strategies to improve patient outcomes.

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Photo by CDC on Unsplash

Photo by CDC on Unsplash

Why is it important?

Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to the disease heterogeneity, standard treatments, such as immunotherapy, chemotherapy or radiation, are effective in only a subset of the patient population. Moreover, tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer has paved the way to the growing field of personalized medicine. With that in mind, the computational model introduced in this paper is our first step in the development of a “Virtual Lab” that simulates the tumor growth based on the patient’s personal factors, and its response to different types of treatments. It will not only contribute to cancer research but we are confident that, once the goal of implementing patient specific factors is reached, it will also can be used by clinicians as a platform for conducting virtual clinical trials to improve their decision making process. In both approaches, it would mean an improvement in drug design, a reduction in treatment costs and the most important, a reduction of patient risks and side effects, which are still significant today.

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