Christian Marinaccio, PhD Candidate, of Northwestern University, describes research he is conducting in the laboratory of John D. Crispino, PhD, which shows the loss of the tumor suppressor gene LKB1/STK11 facilitates progression of myeloproliferative neoplasms to acute myeloid leukemia (Abstract 1).
Andrew D. Zelenetz, MD, PhD, of Memorial Sloan Kettering Cancer Center, offers his expert views on five treatment studies in mantle cell lymphoma focusing on the next-generation BTK inhibitor LOXO-305; lisocabtagene maraleucel; minimal residual disease monitoring following autologous stem cell transplantation with or without rituximab maintenance; the antibody-drug conjugate VLS-101; and venetoclax, lenalidomide, and rituximab (Abstracts 117, 118, 120, 121, 122).
Curtis Lachowiez, MD, of The University of Texas MD Anderson Cancer Center, discusses an interim analysis of a phase Ib/II study showing that venetoclax plus chemotherapy represents an effective regimen, particularly in patients with newly diagnosed and relapsed or refractory acute myeloid leukemia. The regimen appears to be an effective bridge to hematopoietic stem cell transplantation (Abstract 332).
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).
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).
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).
