LSD1 inhibition modulates transcription factor networks in myeloid malignancies
Bibliographic record
Abstract
Acute Myeloid Leukemia (AML) is a type of cancer of the blood system that is characterized by an accumulation of immature hematopoietic cells in the bone marrow and blood. Its pathogenesis is characterized by an increase in self-renewal and block in differentiation in hematopoietic stem and progenitor cells. Underlying its pathogenesis is the acquisition of mutations in these cells. As there are many different mutations found in AML that can occur in different combinations the disease is very heterogeneous. There has been some progress in the treatment of AML through the introduction of targeted therapies and a broader application of the stem cell transplantation in its treatment. However, many mutations found in AML are still lacking defined interventions. These are in particular mutations and dysregulation in important myeloid transcription factors and epigenetic regulators that also play a crucial role in normal hematopoietic differentiation. While a direct targeting of the partial loss-of-function or change in function observed in these factors is very difficult to imagine, recent data suggests that the inhibition of LSD1, an important epigenetic regulator, can modulate interactions in the network of myeloid transcription factors and restore differentiation in AML. Interestingly, the impact of LSD1 inhibition in this regard is quite different between normal and malignant hematopoiesis. The effect of LSD1 inhibition involves transcription factors that directly interact with LSD1 such as GFI1 and GFI1B, but also transcription factors that bind to enhancers that are modulated by LSD1 such as PU.1 and C/EBPα as well as transcription factors that are regulated downstream of LSD1 such as IRF8. In this review, we are summarizing the current literature on the impact of LSD1 modulation in normal and malignant hematopoietic cells and the current knowledge how the involved transcription factor networks are altered. We are also exploring how these modulation of transcription factors play into the rational selection of combination partners with LSD1 inhibitors, which is an intense area of clinical investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".