New BCRF Mathematical Oncology Initiative

Launched with support from The Henry and Marilyn Taub Foundation

BCRF is pleased to announce the Mathematical Oncology Initiative with inaugural support from The Henry and Marilyn Taub Foundation. The generous support is in tribute to Henry and Marilyn’s niece Sandra Taub, who lost her battle to breast cancer in 2006. Sandra’s parents, Arlene and Joseph Taub, are generous supporters of BCRF and Arlene is an active member of the BCRF Advisory Board. Friends of BCRF since 1995, The Henry and Marilyn Taub Foundation is the ideal partner to launch this exciting initiative with BCRF. The Mathematical Oncology Initiative will begin first at Memorial Sloan Kettering Cancer Center and will be led by Drs. Larry Norton, Joseph Deasy, and Allen Tannenbaum. It will bring together mathematicians, computer scientists and physicists to partner with biologists and oncologists with the goal of better understanding cancer and metastasis to improve treatments and outcomes for people facing cancer.

Mathematics Drives Advances

Throughout human history, mathematical approaches have consistently driven the discoveries that advanced civilization. Examples include Newton’s theory of universal gravitation—and calculus in general—that laid the groundwork for our understanding of the universe and space exploration; Faraday’s laws that formed the foundation of electricity that, in the span of a century, revolutionized western civilization and economies; and Einstein’s laws in physics—so fundamental to our understanding of the universe today that new mathematical methods would need to be devised in order to calculate their collective impact on our society.

Medicine and oncology are the new frontiers for mathematical problem solving. As we enter the era of precision (or personalized) medicine, we face new challenges that require innovation and collaboration to realize the promise and potential of next-generation technologies. An example would be the mathematical law called the Norton-Simon Hypothesis that has saved many thousands of lives. By applying mathematical modeling of tumor growth and drug response, a paradigm-changing approach to chemotherapy delivery known as dose-density was developed using the Norton-Simon Hypothesis. The Mathematical Oncology Initiative once again sparks novel mathematical methods that can break down the impediments to personalized cancer care, more effective disease monitoring and the prevention of drug resistance.

The Initiative Focus

The focus of the Mathematical Oncology Initiative is to devise better ways to understand disease evolution and patient response to treatments. To properly develop the field of mathematical oncology, and thereby make truly groundbreaking advances against breast and other cancers, a coordinated group of initial projects at Memorial Sloan Kettering Cancer Center will be funded. As the Initiative broadens additional funding will be secured and additional investigators and institutions will be incorporated. Past discoveries have proven that no single mathematical technique is appropriate to address all areas of cancer prevention and care.

The initial coordinated set of projects at Memorial Sloan Kettering Cancer Center will scrutinize molecular data sets from cancer patients using advanced computational and statistical modeling techniques. The resulting new methods can be used to predict novel treatment approaches that can be tested in the laboratory and ultimately in the clinic. The current projects include:

1. Disease response. Predictive modeling of a tumor’s response to anti-cancer therapy (like radiation, chemotherapy, or targeted drugs) will help to personalize dose and schedule of treatments.

2. Cellular signaling. Applying network mathematics, investigators will identify robust activation of signaling pathways (or lack thereof) that may be driving resistance to targeted drug treatments.

3. Genetic treatment-risk. Bioinformatics techniques will be applied to analyze tissue response and provide information on key genetic factors of risk variability, including specific, targetable protein networks. The primary goal is to achieve risk prediction models that can be used clinically to stratify treatments. An additional outcome of this initiative will be the identification of new targets for drug development.

4. Image analysis. Machine-learning based computer vision methods, including completely novel image metrics, will aid in classification of disease and monitor treatment response over time in a quantitative fashion.