DNMT3A R882 Mutation with FLT3-ITD Positivity Is an Extremely Poor Prognostic Factor in Patients with Normal-Karyotype Acute Myeloid Leukemia after Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
The prognostic relevance of epigenetic modifying genes (DNMT3A, TET2, and IDH1/2) in patients with acute myeloid leukemia (AML) has been investigated extensively. However, the prognostic implications of these mutations after allogeneic hematopoietic cell transplantation (HCT) have not been evaluated comprehensively in patients with normal-karyotype (NK)-AML. A total of 115 patients who received allogeneic HCT for NK-AML were retrospectively evaluated for the FLT3-ITD, NPM1, CEBPA, DNMT3A, TET2, IDH1/2, WT1, NRAS, ASXL2, FAT1, DNAH11, and GATA2 mutations in diagnostic samples and analyzed for long-term outcomes after allogeneic HCT. The prevalence rates for the mutations were as follows: FLT3-ITD positivity (FLT3-ITD(pos)) (32.2%), NPM1 mutation (43.5%), CEBPA mutation (double) (24.6%), DNMT3A mutation (DNMT3A(mut)) (31.3%), DNMT3A R882(mut) (18.3%), TET2 mutation (8.7%), and IDH1/2 mutation (16.5%). The 5-year overall survival (OS) and event-free survival (EFS) rates were 57.3% and 58.1%, respectively. A multivariate analysis revealed that FLT3-ITD(pos) (hazard ratio, [HR], 2.23; P = .006) and DNMT3A R882(mut) (HR, 2.74; P = .002) were unfavorable prognostic factors for OS. In addition, both mutations were significant risk factors for EFS and relapse. People with DNMT3A R882(mut) accompanied by FLT3-ITD(pos) had worse OS and EFS, and higher relapse rates than those with the other mutations, which were confirmed in a propensity score 1:2 matching analysis. These results suggest that DNMT3A R882(mut), particularly when accompanied by FLT3-ITD(pos), is a significant prognostic factor for inferior transplantation survival outcome by increasing relapse risk, even after allogeneic HCT.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".