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.