Characterizing Transcriptional and Epigenetic Signatures Induced By FLT3-ITD Activation
Bibliographic record
Abstract
Abstract Molecular studies have shown that specific somatic mutations impact therapeutic response and overall outcome in acute myeloid leukemia (AML) and have informed the development of molecularly targeted therapies. Previous studies have shown that the FLT3-ITD mutant disease allele predicts a poor prognosis in AML. Despite this important insight and the established role of FLT3-ITD mutations in AML pathogenesis, the impact of this mutation on gene regulation has not been extensively investigated. We hypothesized that transcriptional and epigenetic studies using genetically accurate murine models, cell lines, and primary AML samples would allow us to identify how FLT3 activation induces changes in gene expression and chromatin state. To assess the impact of FLT3-ITD associated FLT3 activation on gene expression, we performed RNA-sequencing studies on two FLT3-ITD cell lines (MOLM-13 and MV4-11) in the presence/absence of AC-220, a potent, specific FLT3 inhibitor. We also treated mice expressing a constitutive FLT3-ITD knock-in allele with AC-220 or vehicle, and performed RNA-sequencing on purified granulocyte-macrophage progenitors (GMPs). We assessed the impact of transient (4-12 hours drug treatment) and chronic (10-14 days) FLT3 inhibition on gene expression; we hypothesized that chronic drug exposure would result in more robust FLT3-mutant gene expression changes. In each case, the effects of FLT3-ITD activation/inhibition on gene expression were compared to RNA-seq data from FLT3-ITD mutant patients from TCGA. We first investigated the impact of short-term and chronic drug exposure on FLT3-ITD dependent gene expression in vitro. Comparison of short-term drug and vehicle treated cells revealed 159 differentially expressed (DE) genes (Benjamini-Hochberg false discovery rate (BH FDR) p < 0.05 and an absolute log2 fold change (FC) > 0.8). By contrast, we found that chronic FLT3 inhibition identified 743 DE genes. Comparison between the acutely and chronically treated cell lines revealed overlap of only 19 genes, suggesting important differences between the acute and steady-state effects of FLT3-inhibition. We found more significant overlap between chronic FLT3-inhibitor gene expression and FLT3-ITD specific gene expression in TCGA, demonstrating that long-term drug exposure more robustly delineates mutant-specific effects on gene expression. We next investigated the impact of short and long term FLT3-inhibition on gene expression in vivo. Analysis of DE genes identified 93 genes in the acutely treated mice vs. vehicle, and 274 genes in chronically treated mice (BH FDR p < 0.05 and absolute log2 FC of > 0.5). Only 12 DE genes were shared between these two perturbations compared with vehicle control. We then compared these gene expression signatures to FLT3-ITD specific gene expression from TCGA; we noted a significant inverse correlation between the signatures of chronic FLT3 inhibition in vivo with FLT3-ITD specific gene expression in TCGA (R2=0.47), but no correlation between the gene expression changes of acute FLT3 inhibition and FLT3-ITD TCGA patients (R2=0.09). We next integrated the FLT3 signatures from our in vivo work and TCGA with ChIP-sequencing for H3K4me3 and H3K27me3 in primary samples with FLT3-ITD compared to normal controls. We found that 3.6% of DE genes in our in vivo system, and 7.2% of DE genes in TCGA, had significant changes in H3K4me3 or H3K27me3. The most common alteration in chromatin state observed with FLT3 activation was an increase in H3K4me3 and transcriptional activation, with a smaller set of genes showing increased H3K27me3 and reduced expression, consistent with FLT3-mediated epigenetic repression. Motif analysis showed that DE loci with significant changes in chromatin state were enriched for ELF5, NF-E2, Pu.1, and Bach1 binding sequences, implicating these transcription factors in mediating FLT3-dependent gene expression effects. By studying the global transcriptional changes that occur with chronic, steady-state FLT3 inhibition in in vitro and in vivo systems, we identified a set of gene expression changes characteristic of FLT3-activation. In addition, integrating changes in gene expression and chromatin state allowed us to identify loci with alterations in epigenetic state in the setting of FLT3-ITD associated FLT3 activation, and to identify candidate transcription factors that mediate FLT3-dependent effects on gene expression. Disclosures No relevant conflicts of interest to declare.
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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".