Age-Associated Acquired Mutations in Hematopoietic Cells Predominantly Affect Epigenetic Regulators TET2 and DNMT3A and Are Associated with Distinct Biological and Hematological Profiles
Notice bibliographique
Résumé
Abstract BACKGROUND. Recent studies using whole genome approaches have shown that acquired mutations in genes such as DNMT3A, TET2, ASXL1, JAK2 and TP53 are observed in a subset of elderly subjects without hematologic malignancies, and are associated with an increased relative risk of developing myeloid cancers, including acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS). The goal of this study is to precisely determine, using a more sensitive approach, the prevalence of somatic mutations and their allelic frequencies in elderly subjects, and assess the impact of somatic mutations on biological characteristics. METHODS. Cohort: 1101 hematologically healthy women aged 70 to 101 years old were recruited from the general population. All patients in the cohort were characterized with a i) medical questionnaire; ii) complete blood counts (CBC), and iii) X-chromosome inactivation (XCI) patterns in polymorphonuclear (PMN), using HUMARA to assess the clonality of myeloid-derived cells. Sequencing: All individuals were sequenced using a targeted approach at high coverage (95% >500x) on an Ion Proton NGS instrument. Libraries were generated using an amplicon-based panel covering 19 recurrently mutated genes in myeloid malignancies (ASXL1, BRAF, CBL, CEBPA, DNMT3A, FLT3, GATA2, IDH1, IDH2, JAK2, KIT, KRAS, NPM1, NRAS, PTPN11, RUNX1, TET2, TP53 and WT1) with 237 amplicons over 22kb. Statistical analyses: Mutational status was correlated with the biological characteristics of subjects using linear regression analysis (GLM). All age-dependent variables were corrected for age (XCI skewing PMN↑, monocytes↑, hemoglobin↓, lymphocytes↓ and platelets↓). RESULTS. Mutations. We identified 249 somatic mutations in 210 of the 1101 individuals (19%) with variant allele frequencies (VAF) ranging from 3.6% to 74.8%. Mutations were found in DNMT3A (n=131), TET2 (n=100), ASXL1 (n=5), JAK2 (N=4), TP53 (n=4), CBL (n=2), IDH1 (n=1), RUNX1 (n=1) and CEBPA (n=1). TET2 and DNMT3A mutations accounted for 93% of all somatic mutations. More than one somatic mutation was observed in 32 individuals (3%), with one subject harboring 5 mutations. Correlation between mutational status and biological characteristics. The presence of mutations significantly increased with age (all genes: P =0.00024, TET2: P =0.00016, TET2 and DNMT3A (double mutations): P =0.03268), although no age effect was documented for DNMT3A mutation alone. The presence of a mutation in TET2, but not in other disease alleles correlated with increased myeloid skewing (P= 0.0008). The presence of a mutation (all genes) or a mutation in DNMT3A correlated with a decreased mean corpuscular volume (MCV) (P= 0.046). TET2 mutations were associated with decreased total white blood cell counts (P =0.0262) and PMN counts (P =0.0137). DNMT3A correlated with increased total white blood cell counts (P =0.035) and increased lymphocyte counts (P =0.0256). No mutational phenotype correlated with alterations in monocyte counts. Despite these global mutation-associated hematological phenotypes, we observed several individuals with a high VAF (>30%) in DNMT3A or TET2 with completely normal CBC. CONCLUSION: Acquired mutations in the normal aging population occur at very high frequency, most commonly TET2 and DNMT3A. This suggests that mutation-driven epigenetic dysregulation is key in the development of age-associated hematological cancers. TET2 and DNMT3A mutations are associated with distinctive characteristics such as increased clonal dominance, aging and lower PMN counts for TET2; increased lymphocyte counts and decreased MCV for DNMT3A. This suggests that these two genes may have a different impact on transformation to malignant phenotype. Prospective evaluation of this cohort and sequential analysis of blood specimens will provide informative insight on the sequence of events leading to age-associated hematological cancers. Disclosures Mollica: Pfizer: Consultancy; BMS: Consultancy; Novartis: Consultancy. Busque:Novartis: Consultancy; BMS: Consultancy; PFIZER: Consultancy.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».