Model Uses Nonclinical Calculations to Forecast the Metastasis Path of Breast Cancer

Researchers developed a mathematical model to forecast metastatic breast cancer survival rates using techniques usually reserved for weather prediction, financial forecasting, and surfing the Web. Their model was published in Breast Cancer (doi:10.1038/npjbcancer.2015.18).

For decades, medical schools have taught doctors that the best way to treat cancer and metastatic progression is to memorize a list of tumors and their typical migration patterns.

"This is akin to back in the days when weather reporting depended solely on a barometer and experience," said coauthor Jorge Nieva, MD, an associate professor of clinical medicine at the Keck School of Medicine at University of Southern California (USC).

"Medical students are taught very fundamental cancer progression patterns. What the modeling does is it brings the sort of complexity of modern-day weather forecasting to trying to understand where tumors go, when they go, and how they get to that location. This type of mathematical modeling is wholeheartedly different from what most medical students learn today."

The study looked at 25 years of data regarding 446 patients with breast cancer at Memorial Sloan Kettering Cancer Center. It focused on a subgroup of women whose initial diagnosis was localized disease that later relapsed with metastatic disease.

The model shows that cancer metastasis is neither random nor unpredictable. Survival depends significantly on the location of the first metastatic site or spatiotemporal patterns. In other words, USC researchers uncovered a framework that explains how tumor cells circulate through a patient's bloodstream over time to settle in various organs. That path varies depending on tumor makeup and treatment decisions.

"There's nothing like this in the cancer world; there's nothing really like this in the disease progression community even though the techniques are well-developed in other contexts," said lead author Paul Newton, PhD, an aerospace and mechanical engineering professor in the USC Viterbi School of Engineering. "Our long-term goal is to build comprehensive predictive computational simulations of metastatic cancer. Ultimately what we want to do is tailor those models to individual patients using their individual characteristics."

"If somebody is reading about breast cancer on Wikipedia, the likelihood that she is going to jump to a lung cancer page or a bone cancer page is much higher than the likelihood of her jumping to the Costco web site," said Newton, who is also a professor at the Norris Comprehensive Cancer Center in the Keck School of Medicine at USC as well as professor of mathematics.

"These probabilities of jumping from 1 page to another are not all equal. Where you jump to next depends strongly on where you currently are. This observation lies at the heart of our model."

Patients with breast cancer die when tumors have colonized an average of 4 metastatic sites, the study found. Women had the poorest chances of long-term survival if they had more than 2 initial metastatic locations; they fared much better if migrating tumor cells first landed on 1 organ.

Roughly 35% of patients with breast cancer developed first metastasis to the bone, while less than 5% contracted their first metastasis in the brain, Newton said. The 5-year survival of the bone group is more than 90%, whereas the brain group had survival characteristics of 20% or less, he said.

The future of cancer care could be squads consisting of a biologist, a mathematician, a physicist, and a computer programmer to complement the current medical teams, Newton said.

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