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
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).
The ASCO Post Staff
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]).
The ASCO Post Staff
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).
The ASCO Post Staff
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).
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).
