Cellular Heterogeneity and Aging in MDS and AML

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The goal of this application is to increase the efficacy of existing treatments for myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) by identifying and characterizing RNA processing phenotypes that predict age-related disease progression and drug resistance. Current therapies for both of these devastating diseases are effective in only a small number of patients and predicting responses remains challenging. Improving response rates represents a substantial unmet need for Veterans. Like many other cancers, MDS and AML are most prevalent in aging populations. The Adaptive Oncogenesis model holds that this association is the result of changing selective pressures acting on heterogeneous populations of cells as we age. MDS and AML are also characterized by extensive heterogeneity. This heterogeneity is evident in the molecular profiling of cells from individual patients, as well as in variable response to treatments across patients. Preliminary data show that mRNA processing and regulation are also highly variable in these diseases. As changes to these processes can play a critical role in cancer, this proposal seeks to understand how variability in mRNA processing and regulation drives disease progression and resistance to drug treatment in MDS and AML. The proposed studies use new sequencing methods to capture the full range of mRNA processing and regulation heterogeneity in MDS and AML at single-cell resolution. Using primary human and MDS and AML specimens, as well as murine models of AML, these studies will allow us to identify specific mRNA processing and regulation phenotypes that allow malignant cells to take advantage of changes to tissue environments associated with aging and drug treatment. Targeting the molecular mechanisms underlying these phenotypes will improve treatments and enhance the prediction of clinical outcomes for Veterans with MDS and AML. In addition to studies using primary specimens and animal models, this application will leverage “big data” resources to identify clinically relevant mRNA processing and regulation signatures from that can inform the clinical management of patients. This award will support the continued development of software tools to interpret changes in RNA processing from single-cell sequencing data. Taken together, the proposed studies will provide important new insights into the interplay between mRNA isoform heterogeneity, drug resistance, and aging in MDS/AML and will identify novel therapeutic targets that can be used to improve the treatment of Veterans with these diseases.