Jeffrey E. Rubnitz, MD, PhD, of St. Jude Children’s Research Hospital, discusses study findings suggesting that pharmacogenomic differences between Black and White patients should be considered when tailoring induction regimens to improve outcomes of all patients and bridge the racial disparity gap in acute myeloid leukemia treatment (Abstract 386).
Michael Wang, MD, of The University of Texas MD Anderson Cancer Center, discusses phase III results from the Sympatico study, which shows the combination of ibrutinib and venetoclax improved progression-free survival vs ibrutinib plus placebo in patients with relapsed or refractory mantle cell lymphoma. According to Dr. Wang, these findings demonstrate a favorable benefit-risk profile for ibrutinib plus venetoclax in this patient population (Abstract LBA2).
Sanjal H. Desai, MBBS, of the University of Minnesota, discusses results from a multicenter cohort, which shows that, for transplant-eligible patients with relapsed or refractory classical Hodgkin lymphoma, PD-1–based salvage therapy at any point before transplantation is associated with improved progression-free survival, compared with brentuximab vedotin or chemotherapy-based salvage regimens (Abstract 182).
Harinder Gill, MD, MBBS, of The University of Hong Kong, discusses findings showing the use of an “AAA” regimen (pure oral arsenic trioxide combined with all-trans retinoic acid) in a risk-adapted strategy that minimized chemotherapy was highly effective and safe in patients with newly diagnosed acute promyelocytic leukemia of all risk categories and age groups. However, he cautions, early deaths remain an obstacle to realizing a cure for all with this disease (Abstract 157).
Bijal D. Shah, MD, of Moffitt Cancer Center and Research Institute, discusses a matching-adjusted indirect comparison of brexucabtagene autoleucel and pirtobrutinib in patients with relapsed or refractory mantle cell lymphoma who have been previously treated with a BTK inhibitor (Abstract 5136).
Andrew Srisuwananukorn, MD, of The Ohio State University Comprehensive Cancer Center, discusses a novel artificial intelligence model that can distinguish between prefibrotic primary myelofibrosis and essential thrombocythemia. This proposed model may assist clinicians in identifying patients who may benefit from disease-specific therapies or enrollment in clinical trials (Abstract 901).
