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Allen Tannenbaum, PhD
Distinguished Professor, Computer Science and Applied Mathematics/Statistics
State University of New York
Stony Brook, New York
Goal: To develop new tools to interpret and understand large sets of tumor data in order to predict response to therapy and guide precision cancer care.
Impact: Drs. Tannenbaum and Deasy are utilizing advanced mathematical methods to interpret tumor of data in new ways that can help determine breast cancer subtype (and even identify new subtypes) as well as estimate the intensity of drug response. Their work may shed new light on biological characteristics of breast cancer that could inform treatments.
What’s next: The team will apply their respective expertise in mathematics and biophysics to integrate different data types from high quality publicly available datasets to further explore the capacity of their methods to better define breast cancer subtypes and predict drug response.
Mathematics is emerging as a new tool to understand cancer behavior. For example, cancer growth can be modeled using relatively simple mathematical equations. To understand the inner principles driving cancer and treatment response, however, new mathematical models and tools need to be developed. This effort takes a multi-disciplinary effort of mathematicians, biologists, oncologists, and other scientists to develop new tools to interpret, model, and understand the massive amount of scientific data that have been generated in cancer research over the last decade.
Full Research Summary
Research area: Developing mathematical models and tools that would reveal the inner principles driving breast cancer and response to treatment.
Impact: Mathematical approaches can be used to study cancer growth and how to control it and may even help identify the most effective drugs for breast cancer patients. Drs. Tannenbaum and Deasy have assembled a team of mathematicians, biologists, oncologists, and other scientists to develop mathematical models and tools that can be used to understand disease evolution, breast cancer treatment response, and variable patient risk of toxicity. Their work will contribute to the advancement precision medicine in cancer.
Current investigation: The team is developing and applying new mathematical tools that will help them understand the similarity of data from one tumor to another. This will allow them to predict treatment response or aid in prognosis.
What they’ve learned so far: Using their mathematical models, the team has been able to determine breast cancer subtype, including reporting a new subtype, that would otherwise not have been discovered. These methods can also be applied to estimate the intensity of drug response in laboratory test datasets.
What’s next: In the next year, Drs. Tannenbaum and Deasy will focus their efforts on being able to better define breast cancer subtypes and predict drug response. These methods will be able to integrate different data types, such as copies of genes together with gene activity, to gain a more complete picture of tumor response to cancer therapies.
Allen Tannenbaum is an applied mathematician and presently Distinguished Professor of Computer Science and Applied Mathematics & Statistics at the State University of New York at Stony Brook. He is also Visiting Investigator of Medical Physics at Memorial Sloan Kettering Cancer Center in New York City. Dr. Tannenbaum has done research in numerous areas including robust control, computer vision, and medical imaging, having more than 500 publications. He pioneered the field of robust control with the solution of the gain margin and phase margin problems. He was one of the first to introduce partial differential equations in computer vision and biomedical imaging co-inventing an affine-invariant heat equation for image enhancement. Tannenbaum and collaborators further formulated a new approach to optimal mass transport theory. In recent work, he has developed techniques using graph curvature ideas for studying cancer networks. His work has won several awards including IEEE Fellow, O. Hugo Schuck Award of the American Automatic Control Council, and the George Taylor Award for Distinguished Research.