The Role of Lipid Metabolism and its Regulation in Acute Myeloid Leukemia Stem Cells
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a malignancy of the blood and bone marrow characterized by dysregulation of the hematopoietic hierarchy. Despite advancements in cancer research, the outcomes for patients diagnosed with AML remain poor, with a 5-year survival of 23% in Canada and 30.5% in the United States. These poor outcomes are driven in part by underlying patient heterogeneity, and the inability to fully eradicate the disease using standard therapies, resulting in refractory and relapse disease. Thus, new methods to stratify heterogenous AML populations, as well as the development of novel AML targeting therapies, are necessary to improve overall survival of these cancer patients. A targetable feature of AML which may also serve as a potential biomarker, is lipid metabolism. Lipid metabolism is highly altered in cancer, including AML, and is critical for energy metabolism, storage, structure, signaling, and mediating cell death. Therefore, to understand where lipids and lipid metabolism can be utilized in the design of treatments and prognostic markers for AML patients, I investigated the role of metabolic regulator sirtuin 3 (SIRT3) as a therapeutic target in AML, and the clinical usefulness of lipids in circulation as AML survival biomarkers. Working with primary AML samples, I demonstrate that SIRT3, a mitochondrial deacetylase, is essential in regulating fatty acid driven energy metabolism of leukemic stem cells, the initiating cells responsible for driving AML and disease relapse. A novel small molecule inhibitor of SIRT3 (YC8-02) was importantly able to inhibit disease development in engraftment and colony forming unit experiments. Then, using AML patient plasma samples, I performed a comprehensive survey of circulating lipids, identifying distinct patterns of lipids associated with heterogenous mutational features of the AML patients. I additionally discovered, through the implementation of machine learning on the plasma lipidome, that a subset of lipids, particularly sphingomyelin d44:1, could stratify patient outcomes and survival. Altogether, this work demonstrates that lipids are essential mediators of AML biology, targetable by small molecule inhibitors, and that lipids in circulation have underlying connections to AML biology and patient outcomes, implicating lipids as important AML prognostic markers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".