Molecular characterization of <scp>AML‐MRC</scp> reveals <scp><i>TP53</i></scp> mutation as an adverse prognostic factor irrespective of <scp>MRC</scp>‐defining criteria, <scp><i>TP53</i></scp> allelic state, or <scp><i>TP53</i></scp> variant allele frequency
Bibliographic record
Abstract
Abstract Background Acute myeloid leukemia with myelodysplasia‐related changes (AML‐MRC) generally confers poor prognosis, however, patient outcomes are heterogeneous. The impact of TP53 allelic state and variant allele frequency (VAF) in AML‐MRC remains poorly defined. Methods We retrospectively evaluated 266 AML‐MRC patients who had NGS testing at our institution from 2014 to 2020 and analyzed their clinical outcomes based on clinicopathological features. Results TP53 mutations were associated with cytogenetic abnormalities in 5q, 7q, 17p, and complex karyotype. Prognostic evaluation of TP53MUT AML‐MRC revealed no difference in outcome between TP53 double/multi‐hit state and single‐hit state. Patients with high TP53MUT variant allele frequency (VAF) had inferior outcomes compared to patients with low TP53MUT VAF. When compared to TP53WT patients, TP53MUT patients had inferior outcomes regardless of MRC‐defining criteria, TP53 allelic state, or VAF. TP53 mutations and elevated serum LDH were independent predictors for inferior OS and EFS, while PHF6 mutations and transplantation were independent predictors for favorable OS and EFS. NRAS mutation was an independent predictor for favorable EFS. Conclusions Our study suggests that TP53MUT AML‐MRC defines a very‐high‐risk subentity of AML in which novel therapies should be explored.
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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".