Modification of the Histone Landscape with JAK Inhibition in Myeloproliferative Neoplasms
Bibliographic record
Abstract
Dysregulation of epigenetic processes is increasingly understood to play a role in the pathogenesis of myeloproliferative neoplasms (MPNs). Ruxolitinib, a JAK/STAT inhibitor, has proved a useful addition to the therapeutic arsenal for these disorders, but has limited disease modifying activity. We determined the effect of JAK inhibition on the histone landscape of MPN cells in cell line models of MPNs and validated using samples from the MAJIC randomised clinical trial of ruxolitinib in polycythaemia vera and essential thrombocythaemia. We demonstrated an epigenetic modifying effect of ruxolitinib using a histone modification assay. The majority of 21 histone H3 modifications were upregulated, with H3K27me3 and H3K36me2 significant in the combined cell line results. Chromatin immunoprecipitation and sequencing (CHIP-seq) for three marks of interest, H3K4me1, H3K4me3 and H3K27ac, was consistent with the histone modification assay showing a significant increase in H3K4me3 and H3K27ac peaks at promoter regions, both marks of active transcription. In contrast, RNA sequencing demonstrates a coordinated reduction in gene expression in a number of cell pathways including PI3K-AKT signalling, transcriptional misregulation in cancer and JAK-STAT signalling in spite of these histone changes. This highlights the complex mechanisms of transcriptional control within the cells which was reflected in analysis of the histone landscape in patient samples following ruxolitinib treatment.
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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".