Release of IFNγ by Acute Myeloid Leukemia Cells Remodels Bone Marrow Immune Microenvironment by Inducing Regulatory T Cells
Bibliographic record
Abstract
Abstract Purpose: The stromal and immune bone marrow (BM) landscape is emerging as a crucial determinant for acute myeloid leukemia (AML). Regulatory T cells (Treg) are enriched in the AML microenvironment, but the underlying mechanisms are poorly elucidated. Here, we addressed the effect of IFNγ released by AML cells in BM Treg induction and its impact on AML prognosis. Experimental Design: BM aspirates from patients with AML were subdivided according to IFNG expression. Gene expression profiles in INFγhigh and IFNγlow samples were compared by microarray and NanoString analysis and used to compute a prognostic index. The IFNγ release effect on the BM microenvironment was investigated in mesenchymal stromal cell (MSC)/AML cell cocultures. In mice, AML cells silenced for ifng expression were injected intrabone. Results: IFNγhigh AML samples showed an upregulation of inflammatory genes, usually correlated with a good prognosis in cancer. In contrast, in patients with AML, high IFNG expression was associated with poor overall survival. Notably, IFNγ release by AML cells positively correlated with a higher BM suppressive Treg frequency. In coculture experiments, IFNγhigh AML cells modified MSC transcriptome by upregulating IFNγ-dependent genes related to Treg induction, including indoleamine 2,3-dioxygenase 1 (IDO1). IDO1 inhibitor abrogated the effect of IFNγ release by AML cells on MSC-derived Treg induction. In vivo, the genetic ablation of IFNγ production by AML cells reduced MSC IDO1 expression and Treg infiltration, hindering AML engraftment. Conclusions: IFNγ release by AML cells induces an immune-regulatory program in MSCs and remodels BM immunologic landscape toward Treg induction, contributing to an immunotolerant microenvironment. See related commentary by Ferrell and Kordasti, p. 2986
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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".