Titles and Affiliations
Chair, Department of Medical Physics
Enid A. Haupt Endowed Chair in Medical Physics
Research area
Developing new mathematical tools to interpret and understand large sets of data in order to gain a deeper understanding of cancer.
Impact
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.
Progress Thus Far
Dr. Deasy and his team have uncovered new insights into why some breast cancers are especially aggressive and harder to treat. They discovered a high-risk form of breast cancer, found in about 12–13 percent of patients, that is linked to poor long-term outcomes and appears to suppress the body’s immune response. This insight could help clinicians better identify which patients are at highest risk and may benefit from new treatment approaches. The team also developed a new way of understanding cancer diversity by comparing structural patterns of tumors and healthy tissue, which not only explains differences between cancers but also predicts how tumors will respond to certain drugs. Their work further showed that when breast cancer spreads, it often takes on the characteristics of the organ to which it spreads, rather than the original tumor, offering new clues about the metastatic process. In addition, the team identified pregnancy-specific proteins as potential drivers of cancer and possible targets for future therapies. Together, their findings are opening up new possibilities for predicting breast cancer outcomes and guiding treatment.
What’s next
In the coming year, the team will focus on both improving powerful computational tools and applying them to key cancer challenges. The team will refine their platform to better combine large-scale biological data and use advanced mathematical methods to improve cancer imaging, such as measuring blood flow in breast tumors and treatment effects in brain metastases. They will also explore how cancers respond to therapies over time and test “digital twin” models—dynamic, virtual replicas of physical processes, that use real data to mirror their real-word counterparts—for example molecular changes in tissues, to help predict the best time to change treatments. Finally, the team will combine imaging and biological data to better classify breast cancer subtypes, with the goal of improving diagnosis and treatment strategies.
Biography
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.
