Meletios A. Dimopoulos, MD, of the University of Athens, discusses data from the phase III APOLLO study, which evaluated the use of subcutaneous daratumumab plus pomalidomide and dexamethasone, vs pomalidomide and dexamethasone alone, in patients with relapsed or refractory multiple myeloma (Abstract 412).
Jorge E. Cortes, MD, of the Georgia Cancer Center at Augusta University, reviews four important studies of treatment advances in chronic myeloid leukemia (CML): nilotinib vs dasatinib in newly diagnosed disease; final 5-year results from the BFORE trial on bosutinib vs imatinib for chronic phase (CP) CML; data from the OPTIC trial on ponatinib for CP-CML; and a novel class of mutated cancer-related genes associated with the Philadelphia translocation (Abstracts 45, 46, 48, 49).
Ann-Kathrin Eisfeld, MD, of The Ohio State University Comprehensive Cancer Center, discusses SEER data showing that patients with acute myeloid leukemia who are Black and younger than age 60 may have poor survival outcomes, a disparity that should be addressed and further studied to establish molecular risk profiles (Abstract 6).
Smita Bhatia, MD, MPH, and Radhika Gangaraju, MD, both of the Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, discuss findings that showed survivors of bone marrow transplants are at a 7- to 12-fold higher risk of coronary heart disease than a sibling comparison group. They recommend aggressive management of cardiovascular risk factors to prevent morbidity from heart disease in this patient population (Abstract 73).
Steven M. Horwitz, MD, of Memorial Sloan Kettering Cancer Center, discusses data from the largest multicenter retrospective analysis of allogeneic hematopoietic transplantation, which supports its curative potential in patients with mature T-cell lymphoma, a group marked by poor survival and limited treatment options (Abstract 41).
Hassan Awada, MD, of the Taussig Cancer Institute, Cleveland Clinic Foundation, discusses the use of newer machine-learning techniques to help decipher a set of prognostic subgroups that could predict survival, thus potentially improving on traditional methods and moving acute myeloid leukemia into the era of personalized medicine (Abstract 34).
