“Tumor on a chip”: A physiological mimicking method for personalized therapy in cancer

It is well established that tumors display substantial heterogeneity in their type, site, and stage. Due to the many complexities of cancer, the development of reliable tumor tissue culture models that can mimic a range of malignancy behaviors more accurately would be of great value to researchers. Such models can be clinically relevant as predictive drug-performance tools, enabling doctors to prescribe the most effective treatment for each individual without having to engage in trial and error.

Current tumor culture models do not reproduce the complexity observed in the three-dimensional (3D) tissue architecture of living organs or incorporate mechanical forces that can substantially influence cancer cell behavior. Animal models intrinsically contain a more complete representation of the in vivo tumor microenvironment complexity, yet their use is less straightforward and not always an accurate representation of the human pathophysiology.

A promising approach to modeling cancer is based on the development of microfluidic chips that enable the recapitulation of tissue–tissue interfaces and the physiologically relevant physical microenvironment of cancers, while sustaining perfusion in vitro. Microfluidics, which enable the accurate control of cell culture conditions, can ideally reproduce specific aspects of the tumor microenvironment, such as the continuous transport of nutrients and oxygen as well as the removal of cellular waste products.

My research is focused on developing a 3D cancer model that mimics the tumor and its microenvironment. I am working on developing a “tumor on a chip” model comprised of tumor cells extracted from individual patients. These cells are assembled into spheroids (3D-cell aggregates) functioning as an ex vivo tumor model so that different therapies can be tried on them. Additionally, genomic analysis of tumor mutations will be conducted on these ex vivo tumor models, more accurately presenting the primary tumor genome. In this way, a drug’s success in eradicating a patient’s tumor may be predicted in a cost-effective manner and also increase treatment efficiency.