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Author Spotlight: Analyzing Bone Marrow Microenvironment in Murine Hematological Malignancies
Published on: November 10, 2023
Identifying patients with genetic predisposition to acute myeloid leukemia
Ellyn Obrochta1, Lucy A Godley1
1Section of Hematology/Oncology, Department of Medicine, The University of Chicago Comprehensive Cancer Center, The University of Chicago, 5841 S. Maryland Avenue, MC 2115, Chicago, IL 60637, USA; Section of Hematology/Oncology, Department of Human Genetics, The University of Chicago Comprehensive Cancer Center, The University of Chicago, 5841 S. Maryland Avenue, MC 2115, Chicago, IL 60637, USA.
Abstract:
Germline syndromes in myeloid leukemias are being discovered increasingly in patients, and their identification is essential for proper medical management to yield positive health outcomes for patients and their families. There needs to be a greater appreciation of germline predisposition driving the development of hematologic malignancies within the field of myeloid malignancies. Characterization of the influence of germline mutations on the development of myeloid malignancies is ongoing by utilization of next generation sequencing data and prognostic panels. Here, we propose modifications to the utilization and analysis of genetic results, specifically to have a high index of clinical suspicion for germline predisposition, to use assays that are comprehensive for detection of these variants, and a few caveats to interpreting sequencing data. Presented are the benefits and shortcomings of prognostic panels and clinical examples of the utilization of the prognostic panel used within the Department of Pathology at The University of Chicago. The examples demonstrate that panels performed for prognostication on DNA derived from malignant cells are able to identify patients with germline syndromes, but they can lack coverage for genes that confer inherited susceptibility. Furthermore, the panels are often not designed to find duplication and deletion mutations, which calls for a need to improve assay design and bioinformatic approaches to interpret such variants using these data.
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