Unveiling the Genetic Mosaic of Pediatric AML: Insights from Southwest China
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».