Saturday, December 21, 2024
12/21/2024
PROJECT SUMMARY/ABSTRACT Large granular lymphocytic leukemia (LGLL) results from a clonal expansion of antigen-driven cytotoxic T lymphocytes, or natural killer (NK) cells. LGLL is characterized by an inability to complete activation-induced cell death (AICD), inflammatory cytokine production, autoimmune disease, and cytopenias (primarily neutropenia and anemia). In the absence of molecularly targeted therapeutics, treatments include broad immunosuppressive agents that exhibit slow and incomplete responses. We have identified recurrent somatic variants along with changes in gene expression, chromatin accessibility, histone code, and DNA methylation to define the molecular pathogenesis of LGLL. These efforts revealed molecular diversity among LGLL patients that parallels the dramatic clinical heterogeneity of this disease. Common mutational events included a preponderance of somatic activation mutations in STAT3 followed by mutation of multiple epigenetic modifier genes (TET2, DNMT3A, KMT2D, SETD1B, KDM6A), with frequent co-occurrence of the two. This project will precisely define the consequences of these identified molecular alterations and their impact on LGLL biology and function. In Aim 1, we will use readouts of proliferation, STAT3 activation, cell signaling, cytokine profiling, and gene expression in cell line models and primary patient and healthy donor samples to define and compare the impact of the most frequently detected LGLL mutations. In Aim 2, we will study the consequences of the recurrent STAT3, TET2, and DNMT3A mutations on T- and NK-cell DNA methylation. We will also define the impact of these mutations on susceptibility to an epigenetic targeting agent that reduces DNA methylation. In Aim 3, we will use computational analysis of single-cell multi-omic datasets to define key molecular events downstream of recurrent somatic variants in both T- and NK-LGLL. We will identify differences in clonal structure, gene expression, chromatin accessibility, cell-cell interaction, and active transcription factors and their targets between the malignant cells and their normal counterparts, and between the major mutation groups in LGLL. Finally, we will integrate our gene expression with other reference single-cell datasets of cytotoxic T-cell antiviral responses, tumor infiltration, and cell therapy. Together, these aims will identify and characterize cell-type-specific functional changes in LGLL of high translational relevance, leading to the identification of predictive markers of treatment response and new targets for therapeutic intervention.
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