Paul G. Richardson, MD, on Multiple Myeloma: Roundup of Three Key Studies
2020 ASH Annual Meeting & Exposition
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
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
Sagar Lonial, MD, of the Emory University School of Medicine, summarizes key papers presented in a session he co-moderated on how second-generation CAR T cells can be used to treat patients with multiple myeloma (Session 653).
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
Sara Zarnegar-Lumley, MD, of Vanderbilt University Medical Center, discusses an analysis of a large cohort confirming the age-associated prevalence of IDH mutations in patients, across the age spectrum, with acute myeloid leukemia and therapeutic implications. IDH-mutated genes were found to co-occur frequently with other mutations, some of which favorably impact outcomes in patients younger than 60 (Abstract 388).
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).
