<scp>DNA</scp> Methylation Episignature as a Novel Diagnostic Tool for Diamond‐Blackfan Anemia Syndrome
Bibliographic record
Abstract
Diamond-Blackfan Anemia Syndrome (DBAS) is a rare inherited bone marrow failure syndrome (IBMFS) characterized by impaired erythropoiesis and significant genetic heterogeneity. Diagnosis can be challenging due to clinical variability and the lack of sensitive and specific biomarkers. We investigated the evidence for a DNA methylation (DNAm) episignature in a cohort of 80 DBAS patients with causative variants in various ribosomal protein genes: DBA1 (RPS19, n = 30), DBA4 (RPS17, n = 6), DBA5 (RPL35A, n = 8), DBA6 (RPL5, n = 15), DBA7 (RPL11, n = 13), DBA10 (RPS26, n = 8). We identified a distinct and highly accurate episignature biomarker for DBAS, clearly differentiating it from both Fanconi anemia and a broad spectrum of other episignature-positive disorders. Furthermore, we developed a specific DNAm classifier for the clinically similar DBA6 and DBA7 subtypes. Applying the DBAS episignature analysis to six molecularly uncharacterized cases, three exhibited the DBAS pattern. Subsequent genome sequencing identified causative genetic variants in two (RPL5: c.325-380A>G:p.?; RPL26: c.-6 + 3_-6 + 25del:p.?), validating the test robustness. Methylation profiles from two revertant cases (RPS19:P47L and RPS17 full gene deletion) exhibited the DBAS episignature, suggesting it to be a stable epigenetic mark associated with the underlying genetic mutation, likely established early in development. In conclusion, we propose DNAm profiling as a robust diagnostic tool for DBAS, providing a biomarker applicable to all patients with clinical suspicion of the disease and critically aiding in the resolution of variants of uncertain significance and molecularly uncharacterized cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".