Genomic Profile and Clinical Outcomes in Acute Myeloid Leukemia with Monosomal Karyotype
Bibliographic record
Abstract
The biology of Monosomal Karyotype Acute Myeloid Leukemia (MK AML) remains unclear, and its mutational profile has not been exclusively assessed. We sought to determine the genomic profile of MK AML patients and its correlation with overall survival (OS). We conducted a retrospective study involving 664 AML patients, identifying 156 (23.5%) with MK AML. The most common monosomies were -17 (41%) and -7 (37%), with 149 (95%) and 138 (88%) having myelodysplasia-related (MR) cytogenetics and complex karyotype (CK), respectively. Frequent mutations included TP53 (69%), DNMT3A (19%), TET2 (13%), and IDH1 (7%). Patients with MK AML with TP53 mutation (TP53 Mut) had shorter OS compared to those with TP53 wild-type (WT) (median OS, 3.9 versus 9.2 months, p = 0.002). Our validation study further supports this finding. There was no significant difference in OS related to the presence or absence of CK (p = 0.252), MR mutations (p = 0.252), DNMT3A (p = 0.264), TET2 (p = 0.264), and IDH1 (p = 0.183) alterations. Co-mutation with novel EPI6 and TAZI signature alterations did not significantly impact OS among MK AML TP53 Mut patients, suggesting that TP53 Mut remains the dominant driver of outcome in this subgroup. In conclusion, MK AML is a genotypically diverse and high-risk group, with MK AML TP53 Mut indicating worse prognosis.
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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 | 0.000 |
| 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".