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Joseph O. Deasy, PhD
Chair, Department of Medical Physics
Enid A. Haupt Endowed Chair in Medical Physics
Memorial Sloan Kettering Cancer Center
New York, 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. Deasy and Tannenbaum 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. Deasy and Tannenbaum 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. Deasy and Tannenbaum 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.
Dr. Joseph O. Deasy is Chair of the Department of Medical Physics, and holder of the Enid A. Haupt Endowed Chair in Medical Physics, at Memorial Sloan Kettering Cancer Center, New York.
Dr. Deasy is an attending physicist at Memorial Sloan Kettering Cancer Center (MSK). He received his PhD in Physics from the University of Kentucky in 1992. Thereafter he completed a NIH-funded post-doctoral fellowship at the University of Wisconsin-Madison, with mentors Rock Mackie and Jack Fowler. Before arriving at MSK in 2010, Dr. Deasy spent 11 years in the Department of Radiation Oncology, Washington University in St. Louis, first in the physics division under the direction of James Purdy, and later as the first Director of the Division of Bioinformatics and Outcomes Research. Dr. Deasy is the co-author of about 140 peer-reviewed publications and has been the principal investigator of several NIH grants. Dr. Deasy’s current interests are in applying mathematical modeling and machine learning to the analysis of imaging, genomic, and treatment datasets in order to understand the relationship between treatment, patient, and disease characteristics and the probability of disease progression and treatment response.