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]).
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).
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).
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).
Nitin Jain, MD, of The University of Texas MD Anderson Cancer Center, reviews six important abstracts on CAR T-cell treatments for B-cell acute lymphoblastic leukemia (ALL): successful 24-hour manufacture of CAR T-cell therapy; ALLCAR19, a novel fast-off rate therapy; donor-derived CD19-targeted treatment; CAR 2.0 therapy to manage post-transplant relapse; UCART22, allogeneic engineered T cells expressing anti-CD22 chimeric antigen receptor; and inotuzumab ozogamicin in pediatric CD-22–positive disease (Session 614, Abstracts 159-164).
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).
