Meletios A. Dimopoulos, MD, on Multiple Myeloma: Daratumumab, Pomalidomide, and Dexamethasone
2020 ASH Annual Meeting & Exposition
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).
The ASCO Post Staff
Steven M. Horwitz, MD, of Memorial Sloan Kettering Cancer Center, discusses phase II data from the Primo trial, which support continued evaluation of duvelisib as a treatment option for relapsed or refractory peripheral T-cell lymphoma due to consistent response rates (Abstract 44).
The ASCO Post Staff
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).
The ASCO Post Staff
Caron A. Jacobson, MD, of the Dana-Farber Cancer Institute, discusses results from the ZUMA-9 C2 study, an ongoing trial that is exploring axicabtagene ciloleucel in patients with relapsed or refractory large B-cell lymphoma (Abstract 2100).
The ASCO Post Staff
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).
The ASCO Post Staff
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).