Author: Poonam Desai (2024)
Advisory Committee: Arif Harmanci, PhD
PhD thesis: McWilliams School of Biomedical Informatics at UTHealth Houston.
ABSTRACT
In recent years, translational initiatives have gained significant traction within the medical community. Clinical parameters alone have been insufficient to fully understand the intricate mechanisms governing patient responses to disease and therapy. Additionally, single-cell technologies have rapidly advanced to a point where generation of large patient-based datasets is financially and logistically feasible. The integration of clinical information with multi-omic single-cell technologies enables the formation of data-driven hypotheses pertaining to the mechanisms of disease progression and therapeutic resistance. Acute Myeloid Leukemia (AML) is a rapidly-progressing cancer of the bone marrow with poor prognoses and dismal outcomes. Of the adult patients that are diagnosed with AML, 40% are refractory to first-line therapies and 60% of patients relapse within one year. The only cure for AML is the allogeneic stem cell transplant, which is mediated by the anti-leukemic activity of donor T cells, indicating that patient T cells may possibly be used to eliminate leukemia. While immunotherapy focusing on patient T cell activation has been transformative in the treatment of some cancers, it has not seen the same success in the treatment of AML. In fact, we currently do not have detailed knowledge of T cell biology in AML, which is critical for understanding patient response and resistance to these therapies. Here, we aim to develop translational bioinformatics approaches to comprehend T cell functionality in AML through integration of multi-omic single-cell data. We investigated paired single-cell RNA and single-cell T cell receptor (TCR) sequencing of a large cohort of patient bone marrow samples covering a range of mutations and chromosomal abnormalities. Additionally, we investigated single-cell T cell secretome data from peripheral blood and bone marrow samples. We annotated T cell subsets in detail through a combination of manual and informatics approaches. Our analyses revealed significant differences between T cells from healthy donors, newly diagnosed patients, and relapsed/refractory patients at the transcriptome, TCR, and secretome levels. Overall, we demonstrated the effectiveness of multi-omic integration to further our understanding of patient T cell responses to disease and therapy.