“PRECISION MEDICINE INFORMED BY UNBIASED LEARNING ALGORITHMs WILL ENABLE US TO DEVELOP IMPROVED PREVENTION, MONITORING, AND TREATMENT STRATEGIES FOR LEUKEMIA AND OTHER CANCERS…”
Dr. Duane Hassane, Director of the Leukemia Genomics at the Englander Institute for Precision Medicine and Assistant Professor of Computational Biomedicine in the Department of Medicine tells us about his work with blood cancers.
In a few words, what is your work focused on?
Understanding how leukemia and other blood cancers develop and evolve. We mainly focus on acute myeloid leukemia (AML), which is the most prevalent acute leukemia in adults.
Why is it a problem?
Acute leukemias and acute myeloid leukemia (AML) in particular are often fatal, rapid onset blood cancers. The standard of care for AML been unchanged mostly for the past four decades. About 25% of AML patients will survive 5 years.
Why does it matter to you?
AML is generally a disease of aging. The types of DNA lesions that drive its development tend to be age-related. Other exposures such as chemotherapy and radiation further promote the development of AML. Thus, as people simultaneously live longer and survive other cancers, we can expect the absolute risk of AML to escalate on a population level. Given the rapid onset of AML and its high fatality rate, the development better therapies and prevention strategies are especially important and urgently needed.
What is the current situation?
The standard of care for AML has gone largely unchanged for the last 40 years consisting mainly of harsh multi-agent chemotherapy regimens consisting of cytarabine and anthracyclines. Chemotherapy may be one of the most effective ways to put the brakes on cancer cells that are rapidly but it generally affects cells that are actively dividing. AML has been well-shown to harbor a non-dividing and chemotherapy-resistant population of leukemia stem cells that survive treatment, evolve, and regenerate leukemia. Thus, in addition to targeting these cells, we have focused on detecting these surviving cells, termed “minimal residual disease” (MRD). The presence of MRD is an important predictor of relapse. Due to advancements made by precision medicine and a genome-scale understanding of AML, 2017 has been especially promising for AML with the FDA approval of two targeted inhibitors directed at AMLs that harbor mutations in the FLT3 and IDH2 genes.
What are the potential solutions?
My lab has developed novel approaches to targeting and detecting AML. Most recently, in collaboration with physicians of the Leukemia Program of Weill Cornell Medicine and New York Presbyterian Hospital, we have shown through deep sequencing of blood that low levels of acquired druggable AML mutations may be present a decade before patients become diagnosed with AML. The term for this phenomenon is “clonal hematopoiesis” and has attracted popular attention lately. Thus, with widespread genomic screening, we may be able identify persons who benefit from an intervention – although this approach remains to be tested.
Can we currently prevent it?
At the moment, there is no known prevention strategy but that may change. Among the mutations occurring in AML patients before the onset of their disease, we found mutations in IDH2, which is targetable by the FDA-approved drug enasidenib.
What would you like to see in the near future?
I would like to see an “economy of scale” making prevention and monitoring strategies applicable to as many people and diseases as possible. The prevention and monitoring data that we are gathering in AML is promising and the paradigm likely extends to a number of cancers. There are also data showing possible benefit of deep sequencing on non-cancer conditions including cardiovascular disease. I am thus involved in pan-cancer initiatives to help make this possible. In the end, on a population-level, precision medicine informed by unbiased learning algorithms will enable us to develop improved prevention, monitoring, and treatment strategies for leukemia and other cancers and diseases: a collaboration of genomics, big data, statistics and A.I., researchers, patients, and physicians.
Who else is involved working with you?
My clinical collaborators are Drs. Pinkal Desai, Sangmin Lee, Michael Samuel, Ellen Ritchie, and Dr. Gail Roboz. In addition, we also work closely with Dr. Monica L. Guzman (Associate Professor of Pharmacology in Medicine) and Dr. Ari M. Melnick (Professor of Medicine). We are also working with multiple myeloma and bone marrow transplant programs led by Drs. Ruben Niesvizky and Koen Van Besien, respectively. On the molecular diagnostic side, we are working with Drs. Michael Kluk and Wayne Tam. New developments are on the horizon as well with Dr. Peter Martin in lymphoma.
What are the future steps for you and your team?
To gain as much insight as we can into the progression and prevention of AML and other cancers. We plan to further push the limits of genomic technology and to leverage big data, machine learning, and AI tools to help physicians and researchers identify clinically informative patterns in data more efficiently than ever before.
More on Dr. Hassane…
Dr. Hassane is the principal investigator or co-investigator on grants from the Leukemia and Lymphoma Society and the National Institutes of Health and has authored or co-authored high impact peer-reviewed manuscripts and book chapters. His findings have been presented and highlighted at international meetings. Dr. Hassane’s laboratory aims to understand the genetic heterogeneity of leukemia, myelodysplastic syndromes, and myeloproliferative disease to improve monitoring and control of relapse and disease progression and prevention. His efforts employ a variety of state-of-the-art technologies, including deep massively parallel gene sequencing. His research is applied in Weill Cornell’s minimal residual disease and precision medicine programs, building the next generation of advanced diagnostics for leukemia.