Germline SAMD9 and SAMD9L mutations are associated with extensive genetic evolution and diverse hematologic outcomes
Bibliographic record
Abstract
Germline SAMD9 and SAMD9L mutations cause a spectrum of multisystem disorders that carry a markedly increased risk of developing myeloid malignancies with somatic monosomy 7. Here, we describe 16 siblings, the majority of which were phenotypically normal, from 5 families diagnosed with myelodysplasia and leukemia syndrome with monosomy 7 (MLSM7; OMIM 252270) who primarily had onset of hematologic abnormalities during the first decade of life. Molecular analyses uncovered germline SAMD9L (n = 4) or SAMD9 (n = 1) mutations in these families. Affected individuals had a highly variable clinical course that ranged from mild and transient dyspoietic changes in the bone marrow to a rapid progression of myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML) with monosomy 7. Expression of these gain-of-function SAMD9 and SAMD9L mutations reduces cell cycle progression, and deep sequencing demonstrated selective pressure favoring the outgrowth of clones that have either lost the mutant allele or acquired revertant mutations. The myeloid malignancies of affected siblings acquired cooperating mutations in genes that are also altered in sporadic cases of AML characterized by monosomy 7. These data have implications for understanding how SAMD9 and SAMD9L mutations contribute to myeloid transformation and for recognizing, counseling, and treating affected families.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".