Abstract 4792: Elucidating stem cell-specific metabolic pathways in normal and malignant hematopoiesis to target human acute myeloid leukemia stem cells
Bibliographic record
Abstract
Abstract Metabolic alterations are a cancer hallmark but the metabolic requirements of hematopoietic stem cells (HSC) in general, and leukemic stem cells (LSCs) specifically are poorly understood, particularly beyond energy metabolism. By analyzing a comprehensive transcriptional roadmap of human hematopoiesis, and comparing this to a LSC signature developed from 84 primary human acute myeloid leukemia (AML) samples, we uncovered two unexpected findings: (1) several metabolic pathways, specifically in bioactive lipids, distinguish HSC from progenitors and are essential for their function; and LSC are more similar to HSC overall, but also possess specific metabolic pathways that are more comparable to those of normal progenitors. We compiled a comprehensive list of 67 genes involved in fatty acid metabolism based on literature and database information and found that 36 of these were differentially expressed between HSC and progenitors, with 24 of them higher in HSC. In particular, sphingolipid enzymes are differentially regulated throughout the hematopoietic hierarchy. Interesting, sphingosine-1-phosphate (S1P) plays a role in HSC egress and ceramide vs S1P levels serve as a rheostat to regulate growth and cell survival. We defined these 24 genes as a lipid stem signature, and find that it is enriched in the LSC gene expression profiles from our 84 primary AML cohort by GSEA analysis indicating that fatty acid metabolism is differentially regulated in LSCs and non-LSCs, as it is for HSCs vs. normal progenitors. Our signature was found to be prognostic for patient survival in a Dutch cohort of 181 cytogenetically normal AMLs. To determine if lipid metabolism plays a functional role in AML biology, we screened a fatty acid compound library using a novel AML cell line (8227) that retains hierarchical organization and assessed cell viability, phenotypic LSC content, and differentiation. We identified myriocin, which targets serine palmitoyltransferase (1st step of sphingolipid synthesis) in this screen. Myriocin decreased 8227 viability and altered differentiation in vitro and reduced leukemia burden in vivo following transplantation into NOD/SCID mice transgenic for human cytokines. Treatment of mice bearing primary AML xenografts, including those from therapy resistant and relapsed patients, with myriocin or its derivative FTY720, a S1P mimetic, resulted in reduction of leukemic engraftment. Moreover, serial transplantation of AML samples by limiting dilution showed decreased LSC frequency of FTY720- and myriocin-treated cells in secondary mice compared to vehicle-treated cells. Importantly, myriocin or FTY720 treatment do not disrupt engraftment in mice bearing normal hematopoietic grafts. Thus, sphingolipid biology in LSC is different from that of HSC, and we present a novel AML therapeutic strategy targeting bioactive sphingolipids as a means to eradicate LSC while sparing HSC. Citation Format: Stephanie Z. Xie, Elisa Laurenti, John E. Dick. Elucidating stem cell-specific metabolic pathways in normal and malignant hematopoiesis to target human acute myeloid leukemia stem cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4792. doi:10.1158/1538-7445.AM2014-4792
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".