A team of scientists from the University of Edinburgh, Harvard University, and John Hopkins University have created a 3D model to visualize the growth and differentiation of cancer cells inside a tumor. The simulation, detailed in a recent research paper published in Nature, uses mathematical algorithms to show the progression of a tumor over time. It also details a tumor’s metastasis in a 3D spatial setting. Unlike other models that isolate cellular processes and use predictions that are highly idealized, this new model encompasses a variety of molecular processes and provides one of the most comprehensive pictures of a tumor to date.
The simulation allows researchers to see how cells grow, die, mutate and move about when a tumor is forming. The team used vivid colors to identify individual genetic mutations and track how they expand and move. Large clumps of color in the model represent the most successful mutations that often will migrate to different parts of the body. By changing parameters, the model provides a look at both slow-growing tumors and rapidly evolving tumors that increase quickly in size and metastasize. It also includes a treatment variable that provides researchers with information on how a tumor may react to a particular 
therapy. Future improvements to the model also may help identify factors that cause a tumor to return.
Researchers hope this model will help predict the growth and spread of cancer cells so doctors can better choose appropriate treatments. Once a therapy is selected, doctors then can test the treatment using the model to see if it will be effective. Not only will it help doctors, this modeling method also will make it easier on patients who won’t have to try different therapies to see which one works. Instead, individuals battling cancer will be given the most effective treatment first.
Despite its promise in visualizing cancer, the model may not be a panacea for cancer treatment. Researcher Bartek Waclaw from the school of physics and astronomy at the University of Edinburgh is cautiously optimistic about the usefulness of the tool. He concedes that it is “a necessary idealisation” that “neglects certain processes and simplifies others.” Because of these compromises, it cannot “fully predict the behavior of a real tumor.”
