Memorial Sloan Kettering Cancer Center
New York, New York
Chair, Department of Medical Physics
Enid A. Haupt Endowed Chair in Medical Physics
Developing new mathematical tools to interpret and understand large sets of data in order to gain a deeper understanding of cancer.
Mathematical approaches can be used to gain insight into how complex, interacting systems drive cancer, how cancer affects a patient, and how treatments affect cancer. As part of the Mathematical Oncology Initiative, 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 interpret many kinds of data. These tools can help us gain a deeper understanding of the overall picture of cancer, including areas such as disease evolution, treatment response, identifying subtypes, patient risk of toxicity, and more. Their work will contribute to the advancement of precision medicine for cancer.
Drs. Deasy and Tannenbaum have successfully developed and applied advanced mathematical methods to complex datasets and made significant progress in addressing questions in several areas of cancer biology and treatment. They have developed a way to analyze large amounts of data of multiple types and meaningfully correlate them across data types, for example, radiomic features from CT scans and gene expression from the same tumor samples. The team has also applied a new mathematical tool to extract more refined disease subtypes from data from the two largest genomic breast cancer studies, METABRIC in the UK and The Cancer Genome Atlas (TCGA) in the US. Lastly, the team has developed a deep learning method to enhance outcomes prediction. The method was tested with data from a large study of multiple myeloma and ten major cancers in TCGA and showed superior predictive performance compared to other alternative methods.
In the coming year, the team will continue to apply their mathematical tools to myriad areas of cancer research, including refining and characterizing features of a novel and particularly lethal breast cancer subtype, predicting treatment response, testing whether CT scans correlate with tumor immune status, and improving methods for analyzing pathology slides and radiological images.
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.
The Simons Foundation Award
State University of New York
Stony Brook, New York
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