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]).
Corey Cutler, MD, MPH, of Dana-Farber Cancer Institute, discusses results from a multicenter trial that compared reduced-intensity allogeneic hematopoietic cell transplantation to hypomethylating therapy or best supportive care in patients aged 50 to 75 with advanced myelodysplastic syndromes (Abstract 75).
Andrew D. Zelenetz, MD, PhD, of Memorial Sloan Kettering Cancer Center, discusses phase II results from a single-center study that explored a novel approach for high-risk patients with mantle cell lymphoma. Among patients with TP53 wild-type disease, the data suggested this treatment was effective (Abstract 119).
Tycel J. Phillips, MD, of the University of Michigan Rogel Cancer Center, discusses phase II data from the CITADEL-204 study, showing that patients with relapsed or refractory marginal zone lymphoma who were not previously treated with a Bruton’s tyrosine kinase inhibitor achieved rapid and durable responses with single-agent parsaclisib. Comparable results were also observed in patients with nodal, extranodal, or splenic disease (Abstract 338).
Matthew S. Davids, MD, of Dana-Farber Cancer Institute, summarizes three key studies from a session he co-moderated on ibrutinib plus venetoclax for first-line treatment of patients with chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL), long-term responses to these agents for relapsed and refractory CLL, and undetectable minimal residual disease following fixed-duration treatment with venetoclax and rituximab for CLL (Abstracts 123, 124, and 125).
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).
