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).
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).
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]).
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).
Ann-Kathrin Eisfeld, MD, of The Ohio State University Comprehensive Cancer Center, discusses SEER data showing that patients with acute myeloid leukemia who are Black and younger than age 60 may have poor survival outcomes, a disparity that should be addressed and further studied to establish molecular risk profiles (Abstract 6).
Jyoti Nangalia, MBBChir, of Wellcome Sanger Institute and the University of Cambridge, discusses how her team used large-scale whole-genome sequencing to precisely time the origins of a blood cancer and measure how it grew. The information could provide opportunities for early diagnosis and intervention (Abstract LBA-1).
