Diminished AHR Signaling Drives Human Acute Myeloid Leukemia Stem Cell Maintenance
Bibliographic record
Abstract
Abstract Eliminating leukemic stem cells (LSC) is a sought after therapeutic paradigm for the treatment of acute myeloid leukemia (AML). While repression of aryl hydrocarbon receptor (AHR) signaling has been shown to promote short-term maintenance of primitive AML cells in culture, no work to date has examined whether altered AHR signaling plays a pathologic role in human AML or whether it contributes at all to endogenous LSC function. Here, we show AHR signaling is repressed in human AML blasts and preferentially downregulated in LSC-enriched populations within leukemias. A core set of AHR targets are uniquely repressed in LSCs across diverse genetic AML subtypes. In vitro and in vivo administration of the specific AHR agonist FICZ significantly impaired leukemic growth, promoted differentiation, and repressed self-renewal. Furthermore, LSCs suppressed a set of FICZ-responsive AHR target genes that function as tumor suppressors and promoters of differentiation. FICZ stimulation did not impair normal hematopoietic stem and progenitor (HSPC) function, and failed to upregulate a prominent LSC-specific AHR target in HSPCs, suggesting that differential mechanisms govern FICZ-induced AHR signaling manifestations in HSCs versus LSCs. Altogether, this work highlights AHR signaling suppression as a key LSC-regulating control mechanism and provides proof of concept in a preclinical model that FICZ-mediated AHR pathway activation enacts unique transcriptional programs in AML that identify it as a novel chemotherapeutic approach to selectively target human LSCs. Significance: The AHR pathway is suppressed in leukemic stem cells (LSC), therefore activating AHR signaling is a potential therapeutic option to target LSCs and to treat acute myeloid leukemia.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".