Unveiling the Genetic Mosaic of Pediatric AML: Insights from Southwest China
Bibliographic record
Abstract
Background: Pediatric acute myeloid leukemia (pAML) is the second most common type of childhood leukemia, behind acute lymphoblastic leukemia. High-throughput technologies have enabled the identification of increasing molecular alterations linked to AML prognosis, revealing genomic heterogeneity among individual patients and providing clinically valuable diagnostic and prognostic information. This study systematically analyzed the correlation between high-frequency mutated genes and prognosis in pAML by performing whole-transcriptome sequencing (WTS) of bone marrow samples from newly diagnosed AML children in Southwest China and mapping their genetic profiles. Methods: pAML patients treated at the Department of Hematology and Oncology, Children’s Hospital of Chongqing Medical University, from January 2015 to October 2024, were enrolled, and WTS was performed. The study described the frequency, pathogenicity classification, and risk stratification of mutation genes and fusion genes, and constructed a genetic landscape. For high-frequency pAML mutations, the impact on early induction remission rate (CR) and long-term event-free survival (EFS) was evaluated. Results: A total of 134 pediatric AML patients from Southwest China were included, with a male-to-female ratio of 74:60 and a median diagnosis age of 5.96 years. Based on pathogenicity classification using WTS, fusion genes were categorized into level 1, level 2, and level 3 genes, as well as mutation genes. The study identified five fusion genes of level 1, the most frequent being RUNX1::RUNX1T1 (32/134, 23.88%), KMT2A rearrangements (29/134, 21.64%), and CBFB::MYH11 (13/134, 9.7%). Sixteen mutation genes of level 1 were detected, seven of which recurred in over 5% of patients, including NRAS (31/134, 23.13%), FLT3 (25/134, 18.66%), KIT (24/134, 17.91%), CEBPA (14/134, 10.45%), WT1 (13/134, 9.7%), KRAS (11/134, 8.2%), and PTPN11 (7/134, 5.22%). Sex-based analysis revealed that PTPN11 mutations were significantly more frequent in males (9.45% vs. 0%, p = 0.023), as were KIT mutations (24.32% vs. 10.00%, p = 0.044). Risk-stratified analysis showed that WT1 mutations (14.13% vs. 0%, p = 0.031) and FLT3-ITD mutations (13.19% vs. 0%, p = 0.042) were enriched in intermediate- and high-risk groups, whereas CEBPA (25.64% vs. 5.43%, p = 0.012), KIT (35.90% vs. 10.87%, p = 0.003), and KIT-E8 (20.51% vs. 1.10%, p < 0.001) mutations were more prevalent in low-risk groups. Prognostic analysis indicated that PTPN11 and KIT mutations did not affect CR or EFS across sexes, nor did WT1, CEBPA, or KIT mutations influence outcomes by risk stratification. However, FLT3-ITD-positive patients had significantly lower CRs (χ2 value = 11.965, p = 0.007), although EFS differences were nonsignificant. In contrast, WT1 mutations were associated with inferior EFS compared to wild-type (p = 0.036). Furthermore, the univariate and multivariate Cox regression revealed consistent results with the above findings, indicating that WT1 mutation was an independent adverse prognostic factor for EFS (HR = 2.400, 95% CI: 1.101–5.233, p = 0.028). The results of univariate and multivariate logistic regression analyses also confirmed that FLT3-ITD mutation was an independent predictor of initial treatment response in our cohort (OR = 10.699, 95% CI: 2.108–54.302, p = 0.004). Conclusions: This study delineated the genetic landscape of pAML in Southwest China and explored the prognostic value of gene fusions and mutations in early and long-term outcomes. These findings provide a foundation for understanding the genetic heterogeneity of pAML and offer evidence for the development of precision medicine approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".