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Full text release has been delayed at the author's request until August 16, 2026

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O-GlcNAcylation and Response Prediction in Acute Myeloid Leukemia: A Data-Driven Approach

Schauner, Robert D
http://orcid.org/0000-0002-6336-7331
http://rave.ohiolink.edu/etdc/view?acc_num=case171957070607595

Abstract Details

2024, Doctor of Philosophy, Case Western Reserve University, Pathology.
AML is the most common acute leukemia in adults with an overall poor prognosis and high relapse rate. Multiple factors including genetic abnormalities, differentiation defects and altered cellular metabolism contribute to AML development and progression. Though the roles of oxidative phosphorylation and glycolysis are defined in AML, the role of the HBP, which regulates the O-GlcNAcylation of cytoplasmic and nuclear proteins, remains poorly defined. We studied the expression of the key enzymes involved in the HBP in AML blasts and stem cells at the single-cell and bulk level. We found higher expression levels of the key enzymes in the HBP in AML as compared to healthy donors in whole blood. We also observed elevated OGT and OGA expression in AML stem and bulk cells as compared to normal HSPCs. Gene set analysis showed substantial enrichment of the NF-κB pathway in AML cells expressing high OGT levels. We found AML bulk cells and stem cells show enhanced OGT protein expression and global O-GlcNAcylation compared to normal HSPCs, validating our in-silico findings. Our study suggests the HBP may prove a potential target, alone or in combination with other therapeutic approaches, to impact both AML blasts and stem cells. Moreover, as insufficient targeting of AML stem cells by traditional chemotherapy is thought to lead to relapse, blocking HBP and O-GlcNAcylation in AML stem cells may represent a novel promising target to control relapse. Additionally, prognostic biomarker discovery approaches based upon bulk analysis are unable to capture key attributes of rare subsets of cells that play a critical role in patient outcomes. Single-cell RNA sequencing is a powerful technique that enables the assessment of rare subsets of cells, but this technique is not amenable to clinical diagnostics. One area where improved prognostic biomarkers are important is for the management of pediatric AML patients with a FLT3-ITD genetic abnormality. We utilized single-cell data from the rare LSCs to generate a machine-learning model using in-silico deconvolution on bulk RNA sequencing data as a feature extractor. The resulting model is strongly predictive alone and, when using other clinical factors, predicts outcomes better than previously reported models. This work highlights the ability to utilize single cell data to inform the development of biomarkers that can be applied to bulk datasets.
Brian Cobb (Committee Chair)
David Wald (Advisor)
Tae Hyun Hwang (Advisor)
Stanley Huang (Committee Member)
Li Lily Wang (Committee Member)
Clive Hamlin (Committee Member)
117 p.
Biostatistics; Immunology; Oncology
single-cell RNA sequencing; acute myeloid leukemia; hexoseamine biosynthetic pathway; biomarkers; applied machine learning

Recommended Citations

Citations

  • Schauner, R. D. (2024). O-GlcNAcylation and Response Prediction in Acute Myeloid Leukemia: A Data-Driven Approach [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case171957070607595

    APA Style (7th edition)

  • Schauner, Robert. O-GlcNAcylation and Response Prediction in Acute Myeloid Leukemia: A Data-Driven Approach. 2024. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case171957070607595.

    MLA Style (8th edition)

  • Schauner, Robert. "O-GlcNAcylation and Response Prediction in Acute Myeloid Leukemia: A Data-Driven Approach." Doctoral dissertation, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case171957070607595

    Chicago Manual of Style (17th edition)

case171957070607595
© 2024, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.