Unique epigenomic signatures identify biologically significant subtypes of MDS and predict response to azacitidine
Bibliographic record
Abstract
Abstract Myelodysplastic syndromes (MDS) are characterized by aberrant DNA methylation, and mutations in epigenetic modifiers are frequently found in these patients. Although DNA methyltransferase inhibitors (DNMTi) are used to treat MDS, response variability remains a challenge in the clinic, with limited predictive markers. Through comprehensive genomic, epigenomic, and transcriptomic analyses, we have gained valuable insights into the intricate interplay between genetic and epigenetic alterations in MDS. We describe aberrantly hyper and hypomethylated regions in MDS, extending beyond promoter regions and affecting long-distance regulatory elements. Using these aberrant DNA methylation patterns, we classified MDS patients into epigenetic subtypes correlated with known molecular drivers. This epigenetic classification includes a novel group of patients characterized only by their shared DNA methylation profile and lacking any genetic drivers. Furthermore, we identified a robust DNA methylation signature capable of distinguishing DNMTi responders from non-responders prior to receiving treatment. Leveraging these DMRs, we developed robust classifiers capable of predictive response to DNMTi by integrating DNA methylation, gene expression, mutations, and laboratory parameters. Our findings highlight the potential of epigenetic-based classifiers for personalized treatment approaches for MDS patients. Key Points DNA methylation patterns define biologically meaningful MDS subtypes and uncover a new group lacking known mutations. A methylation-based signature at diagnosis predicts azacitidine response, supporting its use in guiding personalized MDS therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".