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).
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).
Farhad Ravandi, MD, of The University of Texas MD Anderson Cancer Center, offers his expert perspective on key treatment studies in acute myeloid leukemia on the use of gilteritinib, consolidation chemotherapy, venetoclax, cladribine, azacitidine, quizartinib, decitabine, and CPX-351 (Session 616 [Abstracts 24- 29]).
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).
Sara Zarnegar-Lumley, MD, of Vanderbilt University Medical Center, discusses an analysis of a large cohort confirming the age-associated prevalence of IDH mutations in patients, across the age spectrum, with acute myeloid leukemia and therapeutic implications. IDH-mutated genes were found to co-occur frequently with other mutations, some of which favorably impact outcomes in patients younger than 60 (Abstract 388).
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).
