Prognostic significance of cytogenetic and molecular features in pediatric acute myeloid leukemia: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pediatric acute myeloid leukemia (AML) is a clinically and genetically heterogeneous malignancy with variable outcomes. Accurate risk stratification based on cytogenetic and molecular markers is essential for guiding therapy. However, the prognostic impact of several key genomic alterations remains inconsistent across studies. This meta-analysis aims to evaluate the prognostic significance of cytogenetic and molecular abnormalities in pediatric AML and clarify their association with survival outcomes. METHODS: A systematic search was conducted across PubMed, EMBASE, Scopus, Web of Science, and CENTRAL up to May 5, 2025. Studies were included if they reported survival outcomes in pediatric patients (≤ 18 years) with de novo AML and evaluated cytogenetic or molecular markers. Data were extracted and synthesized using a random-effects model. Hazard ratios (HRs) or risk ratios (RRs) were pooled for overall survival (OS), event-free survival (EFS), disease-free survival (DFS), complete remission (CR), and relapse risk (RR). Heterogeneity was assessed using I² statistics, and risk of bias was evaluated using the Newcastle-Ottawa Scale. RESULTS: Thirty-nine studies encompassing over 1,645 pediatric patients were included in the meta-analysis. WT1 overexpression was significantly associated with OS (RR = 1.38, 95% CI: 1.17-1.63). KIT mutations were linked to inferior OS (RR = 0.69, 95% CI: 0.57-0.84), but not to CR, DFS, or relapse risk. FLT3-ITD mutations showed no consistent prognostic effect (RR = 0.97, 95% CI: 0.65-1.46), with substantial heterogeneity (I² = 83%). CEBPA mutations did not significantly impact EFS (RR = 1.00, 95% CI: 0.93-1.07), and neither RAS mutations nor EVI1 overexpression demonstrated prognostic relevance. Publication bias was minimal, and sensitivity analyses confirmed the robustness of pooled estimates. CONCLUSION: WT1 overexpression and KIT mutations (in selected cytogenetic contexts) are validated as adverse prognostic indicators in pediatric AML. Conversely, FLT3-ITD and CEBPA mutations require nuanced interpretation due to variable effects and methodological heterogeneity. These findings support the integration of molecular profiling into pediatric AML risk stratification and underscore the need for harmonized, prospective studies to refine prognostic models in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".