Hassan Awada, MD, on AML and Machine Learning: Improving Prognostication
2020 ASH Annual Meeting & Exposition
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).
The ASCO Post Staff
Caron A. Jacobson, MD, of the Dana-Farber Cancer Institute, discusses results from the ZUMA-9 C2 study, an ongoing trial that is exploring axicabtagene ciloleucel in patients with relapsed or refractory large B-cell lymphoma (Abstract 2100).
The ASCO Post Staff
Ari M. Melnick, MD, of Weill Cornell Medicine, discusses the BCL10 mutation in patients with activated B-cell–like diffuse large B-cell lymphoma, and his study results which showed that the mutation should be considered as a biomarker for ibrutinib resistance so that alternative targeted treatments can be prioritized (Abstract 3).
The ASCO Post Staff
Andrew D. Zelenetz, MD, PhD, of Memorial Sloan Kettering Cancer Center, offers his expert views on five treatment studies in mantle cell lymphoma focusing on the next-generation BTK inhibitor LOXO-305; lisocabtagene maraleucel; minimal residual disease monitoring following autologous stem cell transplantation with or without rituximab maintenance; the antibody-drug conjugate VLS-101; and venetoclax, lenalidomide, and rituximab (Abstracts 117, 118, 120, 121, 122).
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
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).