Risk stratification of low-dose cytarabine and venetoclax in patients with AML ineligible for intensive chemotherapy
Bibliographic record
Abstract
ABSTRACT: Prognostic risk categorization aids treatment selection for patients with acute myeloid leukemia (AML). Although the European LeukemiaNet (ELN) classifications (2017 and 2022) for AML have been used to stratify outcomes for patients receiving intensive chemotherapy, their application to patients receiving less intensive therapy, such as azacitidine plus venetoclax, has been less satisfactory. In response, a 4-gene classifier that stratifies older patients with AML unfit for intensive chemotherapy into those with higher benefit (wild type), intermediate benefit (FLT3-internal tandem duplication [ITD] or NRAS/KRAS mutation), or lower benefit (TP53 mutation) after azacitidine plus venetoclax treatment was developed. We hypothesized that this 4-gene classifier may also have prognostic utility in patients receiving low-dose cytarabine (LDAC) plus venetoclax. Surprisingly, neither the ELN 2022 criteria nor the 4-gene azacitidine-venetoclax classifier model adequately stratified prognosis in a cohort of 139 patients receiving LDAC plus venetoclax. Patients with concurrent NPM1 and FLT3-ITD/RAS variants performed surprisingly well with LDAC plus venetoclax (complete remission [CR]/CR with incomplete blood count recovery [CRi] rate, 92%; median overall survival [OS], 29.67 months). Data-driven (sequential bootstrapping and tree-based) and empirical analyses identified complex karyotype and/or presence of TP53 mutation as prognostically relevant molecular/cytogenetic risk markers. Patients with complex karyotype and/or TP53 mutation displayed poor clinical outcomes (CR/CRi, 25%; median OS, 3.48 months). Notably, 74% of the study population lacked these poor prognostic markers and had a 67% CR/CRi rate with a median OS of 14.92 months. Overall, these data support the importance of molecular subclassification in defining treatment outcomes to venetoclax-based therapies. These trials were registered at www.clinicaltrials.gov as #NCT02287233 and #NCT03069352.
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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".