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).
Lena E. Winestone, MD, MSHP, of the University of California, San Francisco and Benioff Children’s Hospital, reviews different aspects of bias in treatment delivery, including patient selection for clinical trials; racial and ethnic disparities in survival for indolent non-Hodgkin diffuse large B-cell lymphomas; and end-of-life hospitalization of patients with multiple myeloma, as well as outcome disparities (Abstracts 207-212).
Ari M. Melnick, MD, of Weill Cornell Medicine, discusses the BCL10 mutation in patients with activated B-cell–like diffuse large B-cell lymphoma, and his study results which showed that the mutation should be considered as a biomarker for ibrutinib resistance so that alternative targeted treatments can be prioritized (Abstract 3).
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).
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).
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).
