Attempts to eradicate cancer are often compared to a "moonshot" — the successful effort that sent the first astronauts to the moon.
But imagine if, instead of Newton's second law of motion, which describes the relationship between an object's mass and the amount of force needed to accelerate it, we only had reams of data related to throwing various objects into the air.
This, says Thomas Yankeelov, approximates the current state of cancer research: data-rich, but lacking governing laws and models.
The solution, he believes, is not to mine large quantities of patient data, as some insist, but to mathematize cancer: to uncover the fundamental formulas that represent how cancer, in its many varied forms, behaves.
"We're trying to build models that describe how tumors grow and respond to therapy," said Yankeelov, director of the Center for Computational Oncology at The University of Texas at Austin (UT Austin) and director of Cancer Imaging Research in the LIVESTRONG Cancer Institutes of the Dell Medical School. "The models have parameters in them that are agnostic, and we try to make them very specific by populating them with measurements from individual patients."