David T. Teachey, MD, on Pediatric Leukemia and Lymphoma: New Findings on Cranial Radiation and Bortezomib
2020 ASH Annual Meeting & Exposition
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
Steven M. Horwitz, MD, of Memorial Sloan Kettering Cancer Center, discusses phase II data from the Primo trial, which support continued evaluation of duvelisib as a treatment option for relapsed or refractory peripheral T-cell lymphoma due to consistent response rates (Abstract 44).
Paul G. Richardson, MD, of Dana-Farber Cancer Institute, gives his expert perspective on three important studies in multiple myeloma: long-term results from the IFM 2009 trial on early vs late autologous stem cell transplant in patients with newly diagnosed disease; the effect of high-dose melphalan on mutational burden in relapsed disease; and daratumumab plus lenalidomide, bortezomib, and dexamethasone in transplant-eligible patients with newly diagnosed disease (Abstracts 143, 61, and 549).
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
Corey Cutler, MD, MPH, of Dana-Farber Cancer Institute, discusses results from a multicenter trial that compared reduced-intensity allogeneic hematopoietic cell transplantation to hypomethylating therapy or best supportive care in patients aged 50 to 75 with advanced myelodysplastic syndromes (Abstract 75).
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).
