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).
Danai Dima, MD, of the Taussig Cancer Institute, Cleveland Clinic, discusses teclistamab-cqyv, a B-cell maturation antigen approved in October 2022 for patients with relapsed or refractory multiple myeloma who have received at least four prior lines of therapy. Dr. Dima and her team evaluated the real-world safety and efficacy of this agent and found encouraging evidence of efficacy in a real-world setting (Abstract 91).
Adam S. Kittai, MD, of The Ohio State University, discusses his data supporting the use of CAR T-cell therapy for patients with Richter’s transformation. Given the high response rate to CD19 CAR T-cell treatment, along with early relapse in most patients, allogeneic stem cell transplantation at response should also be considered, he says (Abstract 108).
William G. Wierda, MD, PhD, of The University of Texas MD Anderson Cancer Center, discusses results from the phase I/II BRUIN study, which shows encouraging response and overall survival in patients with Richter transformation. Although this condition remains a challenging diagnosis, pirtobrutinib represents a potential treatment option that warrants further investigation, according to Dr. Wierda (Abstract 1737).
Pieter Sonneveld, MD, PhD, of the Netherland’s Erasmus MC Cancer Institute, discusses primary results from the Perseus trial, showing that for patients with newly diagnosed multiple myeloma who are eligible for transplantation, the combination of daratumumab plus bortezomib, lenalidomide, and dexamethasone, followed by daratumumab and lenalidomide maintenance, may be a new standard of care (Abstract LBA1).
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).
