Lena E. Winestone, MD, MSHP, on Health-Care Disparities in Hematologic Cancers: Real-World Data
2020 ASH Annual Meeting & Exposition
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).
The ASCO Post Staff
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).
The ASCO Post Staff
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).
The ASCO Post Staff
Steven M. Horwitz, MD, of Memorial Sloan Kettering Cancer Center, discusses data from the largest multicenter retrospective analysis of allogeneic hematopoietic transplantation, which supports its curative potential in patients with mature T-cell lymphoma, a group marked by poor survival and limited treatment options (Abstract 41).
The ASCO Post Staff
David T. Teachey, MD, of the University of Pennsylvania and Children’s Hospital of Philadelphia, discusses data showing that cranial radiation might be eliminated in most children with T-cell acute lymphoblastic leukemia and that bortezomib may improve survival in children with T-cell lymphoblastic lymphoma (Abstract 266).
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).
