Jyoti Nangalia, MBBChir, of Wellcome Sanger Institute and the University of Cambridge, discusses how her team used large-scale whole-genome sequencing to precisely time the origins of a blood cancer and measure how it grew. The information could provide opportunities for early diagnosis and intervention (Abstract LBA-1).
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).
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).
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).
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).
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).
