Cytogenetics and gene mutations influence survival in older patients with acute myeloid leukemia treated with azacitidine or conventional care
Bibliographic record
Abstract
Older patients with newly diagnosed acute myeloid leukemia (AML) in the phase 3 AZA-AML-001 study were evaluated at entry for cytogenetic abnormalities, and a subgroup of patients was assessed for gene mutations. Patients received azacitidine 75 mg/m 2 /day x7 days ( n = 240) or conventional care regimens (CCR; n = 245): intensive chemotherapy, low-dose cytarabine, or best supportive care only. Overall survival (OS) was assessed for patients with common (occurring in ≥10% of patients) cytogenetic abnormalities and karyotypes, and for patients with recurring gene mutations. There was a significant OS improvement with azacitidine vs CCR for patients with European LeukemiaNet-defined Adverse karyotype (HR 0.71 [95%CI 0.51–0.99]; P = 0.046). Azacitidine-treated patients with -5/5q-, -7/7q-, or 17p abnormalities, or with monosomal or complex karyotypes, had a 31–46% reduced risk of death vs CCR. The most frequent gene mutations were DNMT3A (27%), TET2 (25%), IDH2 (23% [R140, 15%; R172, 8%]), and TP53 (21%). Compared with wild-type, OS was significantly reduced among CCR-treated patients with TP53 or NRAS mutations and azacitidine-treated patients with FLT3 or TET2 mutations. Azacitidine may be a preferred treatment for older patients with AML with Adverse-risk cytogenetics, particularly those with chromosome 5, 7, and/or 17 abnormalities and complex or monosomal karyotypes. The influence of gene mutations in azacitidine-treated patients warrants further study.
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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 | 0.000 | 0.000 |
| 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".