Genetic risk stratification and outcomes among treatment-naive patients with AML treated with venetoclax and azacitidine
Bibliographic record
Abstract
ABSTRACT: The European LeukemiaNet (ELN) acute myeloid leukemia (AML) genetic risk classification systems are based on response to intensive chemotherapy; their ability to discriminate outcomes in older patients treated with venetoclax-azacitidine may be suboptimal. This pooled analysis of the phase 3 VIALE-A trial (NCT02993523) and phase 1b study (NCT02203773) examined prognostic stratification according to the 2017 and 2022 ELN risk classifications and derived new molecular signatures differentiating venetoclax-azacitidine-treated patients based on overall survival (OS). Overall, 279 patients treated with venetoclax-azacitidine and 113 patients treated with placebo-azacitidine were analyzed. The ELN 2017 or 2022 prognostic criteria classified most patients as adverse-risk AML (60.2% and 72.8% for venetoclax-azacitidine and 65.5% and 75.2% for placebo-azacitidine, respectively). Although outcomes with venetoclax-azacitidine improved across all ELN risk groups compared with placebo-azacitidine, ELN classification systems poorly discriminated venetoclax-azacitidine outcomes. By applying a bioinformatic algorithm, new molecular signatures were derived differentiating OS outcomes with venetoclax-azacitidine. The mutational status of TP53, FLT3 internal tandem duplication (FLT3-ITD), NRAS, and KRAS categorized patients into higher-, intermediate-, and lower-benefit groups (52%, 25%, and 23% of patients, respectively), each associated with a distinct median OS (26.5 months [95% confidence interval (CI), 20.2-32.7]; 12.1 months [95% CI, 7.3-15.2]; and 5.5 months [95% CI, 2.8-7.6], respectively). ELN prognostic classifiers did not provide clinically meaningful risk stratification of OS outcomes in patients treated with venetoclax-azacitidine. TP53, FLT3-ITD, NRAS, and KRAS mutation status allows the classification of these patients into 3 risk groups with distinct differences in median OS. These trials were registered at www.clinicaltrials.gov as #NCT02993523 and #NCT02203773.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".