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With countless biological details emerging from cancer experiments, there is a growing need for minimal mathematical models which simultaneously advance our understanding of single tumors and metastasis, provide patient-personalized predictions, whilst avoiding excessive hard-to-measure input parameters which complicate simulation, analysis and interpretation. Here we present a model built around a co-evolving resource network and cell population, yielding good agreement with primary tumors in a murine mammary cell line EMT6-HER2 model in BALB/c mice and with clinical metastasis data. Seeding data about the tumor and its vasculature from in vivo images, our model predicts corridors of future tumor growth behavior and intervention response. A scaling relation enables the estimation of a tumor's most likely evolution and pinpoints specific target sites to control growth. Our findings suggest that the clinically separate phenomena of individual tumor growth and metastasis can be viewed as mathematical copies of each other differentiated only by network structure.

Data Model Mathematics Average Statistics Cancer Health Care Medicine

One of the most difficult things about treating cancer is that each case is a very individual process; tumors recruit blood vessels in order to grow, yet also send out new vessels of their own. This harnessing of the body’s resources is what eventually allows a tumor to metastasize — be carried into other parts of the body where growth continues. On the other hand, sometimes tumors don’t grow at all. Predicting each patient’s unique response to cancer and its progression is a large part of the battle, as an accurate estimate is required to begin appropriate treatment. The field of Oncology is always seeing exciting developments, and this latest one is no different — its benefits touch future and existing cancer patients, alike. Knowing that hindsight is 20/20, it would be a lot easier if there was a “fast-forward button” with which doctors could view each unique case before it develops.
Physicist Sehyo Choe and colleagues at the University of Heidelberg, Germany have developed such a button in the form of a mathematical model. By inputting data about the tumor and its current location of blood vessels, the model allows doctors and researchers to see how the tumor will grow and move — if at all — giving them a very accurate helping hand when it comes to prompt and accurate treatment. Tested on mice, the model accurately predicted the progression of all cancer-stricken subjects, giving researchers that amazing “fast-forward” capability.

Data Model Mathematics Average Statistics Cancer Health Care Medicine