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Enregistrement W2593273640 · doi:10.1182/blood.v126.23.sci-10.sci-10

Clonal Events in Normal Aging Hematopoiesis

2015· article· en· W2593273640 sur OpenAlexaff
Lambert Busque

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensHôpital Maisonneuve-Rosemont
Organismes subventionnairesnon disponible
Mots-clésMyeloidBiologyPopulationHaematopoiesisSomatic evolution in cancerBone marrow failureBone marrowImmunologyGeneticsInternal medicineStem cellCancerMedicine

Résumé

récupéré en direct d'OpenAlex

Chronological aging of the hematopoietic compartment is associated with decreased bone marrow cellularity, reduced lymphopoiesis, increased anemia, a myeloid proliferation bias and an increased incidence of myeloid cancers. Beerman et al. proposed that this age-related myeloid lineage favoritism may be explained by clonal expansion of intrinsically myeloid-biased hematopoietic stem cells with robust self-renewal potential(1). This age-associated clonal expansion was initially suspected by X-chromosome inactivation (XCI) studies performed in the normal aging population, which documented a skewed XCI pattern in a significant proportion of women over 60 year-old(2). More recently, genome wide approaches led several groups to document au augmented prevalence of acquired clonal copy number changes (3,4,5) or clonal somatic mutations with increasing age (6,7,8,9). The most frequently mutated genes are the same as those documented in myeloid cancers, such as TET2, DNMT3A, ASXL1, PPM1D, GNAS, TP53, JAK2 and SF3B1 among others. The prevalence of these age-associated mutations may reach > 10% of older individuals, and is associated with an 11-12 fold increased relative risk of developing hematological malignancies. However, the actual problematic is to define the prognostic significance of these clonal mutations in the aging population. Steensma et al. proposed to consider these mutations as «Clonal Hematopoiesis of Indeterminate Potential (CHIP)»(10). The goal of our research group is to define the oncogenic penetrance of CHIP by applying a precision medicine approach in a large prospective cohort (n=4000) of aging individuals comprised of related and unrelated subjects. The variables under investigation include, clonality by XCI in women, deep sequencing (NGS) of myeloid cancer associated genes, epigenetic markers (5hmC, 5mC), telomere length, blood counts, heritability and outcome. PRELIMINARY RESULTS. XCI analyses Acquired skewing of XCI predominantly affects the myeloid lineage with a prevalence of 41.4% for PMN and is age dependent (r=0.15, P<10-4), in contrast to T cells 22.5%. These results support the idea of an age-associated clonal myeloid expansion. NGS of myeloid gene panel. We documented a prevalence of 17.9% of mutated individuals. Mutations were mainly documented in TET2 and DNMT3A which accounted for 90% of all identified mutations. Other significantly mutated genes included JAK2, ASXL1, CBL, TP53 and KRAS. Double mutations were identified in 2.5% of individuals (14% of the mutated individuals) and half of them had concomitant mutation in TET2 and DNMT3A. Age and XCI skewing was similar between subjects with mutation in TET2 or DNMT3A, but slightly higher in double mutants. Epigenetic markers. Subjects with mutation in TET2 had a significant reduction in 5hmC level that correlated with Variable Allele Frequency (VAF) of the mutation. No specific global epigenetic phenotype was documented in the DNMT3A mutation subgroup. We also documented an age-associated reduction in 5hmC that was independent of acquired mutation in the TET2 gene. Taken together these results indicate that age-associated clonal mutations involves predominantly two genes (TET2 and DNMT3A), suggesting that alteration of epigenetic maintenance is a central to the initiation of clonal dominance. Completion of investigation of the aging cohort and prospective follow-up will help characterize the link between aging hematopoiesis and the development of myeloid cancers. 1. Beerman I, Maloney WJ, Weissmann IL, et al. Stem cells and the aging hematopoietic system. Curr Opin Immunol. 2010;22(4):500-506. 2. Busque L, Mio R, Mattioli J, et al. Non-random X-inactivation patterns in normal females: lyonization ratios vary with age. Blood. 1996;88(1):59-65. 3. Forsberg LA, Rasi C, Razzaghian HR, et al. Age-related somatic structural changes in the nuclear genome of human blood cells. AJHG, 2012;90:217-228. 3. Laurie CC, Laurie CA, Rice K, et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat Genet. 2012;44(6):642-650. 4. Jacobs KB, Yeager M, Zhou W, et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nat Genet. 2012;44(6):651-658. 5. Busque L, Patel JP, Figueroa ME, et al. Recurrent somatic TET2 mutation in normal elderly individuals with clonal hematopoiesis. Nat Genet. 2012;444(11):1179-1181. 6. Xie M, Lu C, Wang J, et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med. 2014;20(12):1472-1478. 7. Genovese G, Kähler AK, Handsaker RE, et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med. 2014;371(26):2477-2487. 8. Jaiswal S, Fontanillas P, Flannick J, et al. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014;371(26):2488-2498. 9.Steensma DP, Bejar R, Jaiswal S, et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood. 2015;126(1):9-16 Disclosures Busque: Pfizer: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,032
Tête enseignante GPT0,308
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2015
Routes d'admission1
Résumé présentoui

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