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, Dr. Deasy has 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.
Dr. Deasy and his team 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. The team 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 and found a subtype with a distinct molecular signature a may respond to different treatments than other breast cancer subtypes. 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 and identified the key role that the gene WEE1 plays in driving poor prognosis for many cases. The team also demonstrated that, for breast cancers, metastases take on molecular characteristics of the destination tissue.
In the coming year, the team will continue to apply their mathematical tools to myriad areas of cancer research including improving methods to better extract features from pathology slides and radiological images that may guide treatment options. The team plans to follow up on their previous discovery of a new breast cancer subtype by analyzing specific genes that may represent therapeutic targets for this subtype.
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.
2017
The Simons Foundation Award
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