Longitudinal sequencing of <i><scp>RUNX</scp>1</i> familial platelet disorder: new insights into genetic mechanisms of transformation to myeloid malignancies
Bibliographic record
Abstract
The mechanisms by which patients with RUNX1 familial platelet disorder with propensity to myeloid malignancies (FPDMM) develop myeloid malignancies (MM) are not fully understood. We report the results of targeted next-generation sequencing on three patients with RUNX1 FPDMM who developed acute myeloid leukaemia or myelodysplastic syndromes (AML/MDS). DNA samples were collected from bone marrow, peripheral blood and buccal swabs at different time points. One patient had clonal haematopoiesis, represented by an SRSF2 p.P95R variant, prior to his AML diagnosis, when he developed an additional NRAS p.G12D variant. His sister presented to us with MDS, with a TET2 p.S471fs and identical NRAS p.G12D variant. The third patient, from another family, had an additional RUNX1 p.R204X and an NFE2 p.Q139fs variant at AML diagnosis. This constitutes the first report of NFE2 variants in AML without extramedullary disease and NRAS variants in AML/MDS in the setting of FPDMM. A systematic review of the literature including our findings distinguishes two genetic landscapes at AML transformation from FPDMM characterized by either the presence or absence of somatic abnormalities in RUNX1 with or without variants in genes usually associated with MM. Whether clonal haematopoiesis precedes transformation only in patients without somatic abnormalities in RUNX1 needs further confirmation.
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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 | 0.001 | 0.001 |
| 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".