The Transcription Factor IRF8 Regulates the Sensitivity of AML Cells to LSD1 Inhibition and All-Trans Retinoic Acid
Bibliographic record
Abstract
Introduction:Acute myeloid leukemia (AML) is a hematological neoplasm with poor outcomes owing to genetic heterogeneity with many mutations in epigenetic genes. One promising target is histone demethylase LSD1/KDM1A; inhibition of LSD1 (LSD1i) induces differentiation, targets leukemic stem cells (Harris et al., 2012) and facilitates the responsiveness of AML cells to all-trans retinoic acid (ATRA) induced differentiation (Schenk et al., 2012). In patients with AML, inhibitors of LSD1 have shown modest clinical responses. The factors influencing responsiveness of AML cells to LSD1i are not known. We have previously observed in murine models of AML, that leukemias driven by overexpression of Hoxa9 and Meis1 (H9M) showed morphological differentiation in response to LSD1i and were sensitized to ATRA. Leukemias overexpressing meningioma1 (MN1) did not show these effects. Based on transcriptomic analysis, we found a differential modulation of down-stream targets of the key monocytic transcription factor IRF8 (Barth et al., 2019). In this study we investigated the role IRF8 plays in determining the responsiveness of leukemic cells to LSD1i and ATRA. Methods:In murine retrovirally-induced AML cells (Hoxa9-Meis1, H9M; MN1) treated with LSD1 inhibitor (Bomedemstat) and ATRA, we assessed proliferation via the methoxynitrosulfophenyl-tetrazolium carboxanilide (XTT) assay, and the differentiation effect by flow cytometry. Gene expression levels of key myeloid transcription factors (TFs) and targets of ATRA (Spi1, Gfi1/1b, Rara, Tgm2) were determined via quantitative PCR. We generated H9M-transformed myeloid progenitor cells on an Irf8 knockout (KO) background, each line engrafted and generated AML. Results: H9M wild-type (WT) cells had an IC50 for ATRA in the nanomolar range (47 nM) which increased 151-fold to the micromolar range (7.1 µM) in H9M Irf8 KO (p≤ 0.05). The IC50 for Bomedemstat also increased 9.7-fold in the Irf8 KO (p≤ 0.05). While both H9M WT and H9M Irf8 KO demonstrated a reduction in the proportion of c-Kit+ cells, the upregulation of CD11b was significantly dampened in H9M Irf8 KO population, leading to a reduction in CD11b mean fluorescence intensity by 2.1-fold after treatment (p≤0.0001). To determine the relationship between IRF8 expression and the sensitivity of leukemic cells to ATRA, we examined the effect of the Irf8 KO on the expression of Rara and the regulation of RARA targets upon treatment. Based on the published data (Oki, S; Ohta, T (2015): ChIP-Atlas) we identified several direct binding sites for IRF8 in key regulatory regions of Rara. When comparing the expression of Rara between H9M and H9M Irf8 KO we found a 2.1-fold lower expression of Rara in Irf8 KO cells. Consistently, H9M Irf8 KO showed decreased responsiveness of an important ATRA target gene (Tgm2) upon treatment with ATRA. We investigated changes in core myeloid transcription factors interacting with LSD1i by qPCR. H9M Irf8 KO had 2.35-fold higher expression of Gfi1 compared to H9M WT cells but showed 26-fold lower expression of Gfi1b at baseline. Upon treatment with Bomedemstat, both H9M and H9M Irf8 KO cells showed a decrease in Gfi1 expression by 4.6 / 1.7-fold and a strong upregulation of Gfi1b by 38-fold / 460-fold, respectively. However, we observed a differential regulation of Gata1 and Gata2 between the models. While Gata1 expression is increased after treatment with Bomedemstat in H9M WT cells, it decreases in H9M Irf8 KO. Gata2 is showing the opposite trend consistent with the mutual regulation of these transcription factors. Conclusion : In summary, we found that IRF8 expression modulates the response of AML cells to both LSD1 inhibition and ATRA. These findings suggest IRF8 expression is a potential biomarker for selecting patients for treatment with an LSD1 inhibitor and ATRA.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".