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How Math is Helping Researchers Understand and Treat Cancer

By BCRF | March 13, 2020

BCRF highlights how mathematical models are accelerating research discoveries to improve breast cancer outcomes

Math has long played an important role in breast cancer research. Take one of the field’s most significant impacts on cancer treatment: the Norton-Simon hypothesis. Developed in 2006 by Drs. Richard Simon and Larry Norton, the Norton-Simon hypothesis is derived from mathematical modeling of how cancer cells grow and how chemotherapy kills those cells.

It has since fundamentally changed how chemotherapy is delivered. Today, dose-dense chemotherapy, a strategy of delivering more doses in less time, has been shown to improve survival rates and is an example of the real world verifying mathematical predictions.

Math is helping researchers understand how cancers grow and how cancer cells function. This knowledge equips scientists with tools to predict treatment response and, in turn, improve those treatments overall.

How math is being used in cancer research

Math allows us to dive deeper into cancer cells. We are generating more information about genes, proteins, functions, and connections every day, but this vast amount of data is too great for individuals to analyze.

Investigators are applying complex math and geometric network algorithms to look for patterns in data and figure out which specific genes or gene networks might be involved in a drug response. Researchers hope this could lead to new biomarker tests or more effective combinations of treatments.

Much of cancer research is done in biological systems—cultured cell lines and other models of cancer. This line of research, though, has its limitations—not the least of which are time and money.

Mathematical modeling allows scientists to explore complex virtual biological systems, challenge those virtual systems, and test predictions. What takes years in biological systems, can take only weeks or months with mathematical modeling. Current investigative methodologies produce large amounts of data, even on a single breast cancer. More advanced mathematical methods are needed both for handling these large amounts of data and for developing predictive and diagnostic tools.

How BCRF is supporting mathematical oncology

In 2016, BCRF launched the Mathematical Oncology Initiative with the goal of applying mathematical concepts to accelerate discoveries in understanding how tumors develop and respond to treatment.

With initial support from the Henry and Marilyn Taub Foundation, BCRF investigators Drs. Joseph Deasy and Allen Tannenbaum launched a coordinated group of projects applying new mathematical tools to better understand the evolution of cancer—the changes that occur during its progressionas well as what makes one cancer different from another.

With generous support from the Simons Foundation, BCRF is able to continue to support the work of Drs. Deasy and Tannenbaum and have added a new investigator to its Mathematical Oncology Initiative: Dr. Nir Peled (Soroka Medical Institute, Israel).

This expanded collaboration converges several critical but largely underdeveloped threads of cancer research. In doing so, researchers hope to design better diagnostic and prognostic tools and treatment strategies to optimize cancer management—and ideally cure and/or prevent cancers.