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Enregistrement W1551703232 · doi:10.1182/blood.v122.21.626.626

A Meta-Analysis Of Hodgkin Lymphoma Reveals 19p13.3 (TCF3) As a Novel Susceptibility Loc

2013· article· en· W1551703232 sur OpenAlexaff
Wendy Cozen, Dalin Li, Maria Timofeeva, Arjan Diepstra, Dennis J. Hazelett, Manon Delahaye-Sourdeix, Christopher K. Edlund, Klaus Rostgaard, David J. Van Den Berg, Lude Franke, Karin E. Smedby, Sally L. Glaser, Harm-Jam Westra, Leslie L. Robison, Thomas M. Mack, Hervé Ghesquières, Amie E. Hwang, Alexandra Nieters, Sílvia de Sanjosé, Victoria K. Cortessis, Tracy Lightfoot, Nikolaus Becker, Marc Maynadié, Lenka Foretová, Eve Roman, Yolanda Benavente, Bharat N. Nathwani, Bengt Glimelius, Anthony Staines, Paolo Boffetta, Brian K. Link, Lambertus A. Kiemeney, Stephen M. Ansell, Ravi Bhatia, Louise C. Strong, Pilar Galán, Lars J. Vatten, Thomas M. Habermann, Eric J. Duell, Annette Lake, Rianne Veenstra, Lydia Visser, Kevin Y. Urayama, Dorothy Montgomery, Valérie Gaborieau, Lawrence M. Weiss, Graham Byrnes, Mark Lathrop, Hans‐Olov Adami, Mads Melbye, James R. Cerhan, Alice Gallagher, G M Taylor, Susan L. Slager, Paul Brennan, David V. Conti, Gerhard A. Coetzee, Kenan Onel, Ruth F. Jarrett, Henrik Hjalgrim, Anke van den Berg, James McKay

Notice bibliographique

RevueBlood · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensMcGill University and Génome Québec Innovation Centre
Organismes subventionnairesnon disponible
Mots-clésGenome-wide association studySNPPopulation stratificationMeta-analysisNodular sclerosisImputation (statistics)Genetic associationExpression quantitative trait lociOncologyBiologySingle-nucleotide polymorphismGeneticsCase-control studyLinkage disequilibriumPopulationHeritabilityMedicineInternal medicineGenotypeLymphomaGeneHodgkin lymphomaMissing data

Résumé

récupéré en direct d'OpenAlex

Abstract Background Recent genome wide association studies (GWAS) of Hodgkin lymphoma (HL) have identified several associations at both HLA and non-HLA loci. However, much of HL heritability remains unexplained. Methods To identify novel risk loci, we performed a meta-analysis of 3 HL GWAS including a total of 1,810 cases and 7,879 controls. Results were replicated in an independent set of 1,163 cases and 2,580 controls, for a total of 3,097 and 11,097 cases and controls combined, respectively. participants in discovery and replication stages were of European descent. quality control and imputation we conducted a meta-analysis addressing 1,004,829 variants (λ= 1.10, λ1000= 1.03). Associations between SNP genotypes and HL risk were evaluated under a log-additive model of inheritance adjusting for sex, study center and significant principal components to control for population stratification. We performed an analysis with all HL cases and then conducted stratified analyses by histological subtype (classical, nodular sclerosis and mixed cellularity), age at diagnosis (nodular sclerosis among those diagnosed at 15- 35 years in all studies, and those diagnosed at 35 and older years in the European Study only) and EBV tumor status (negative and positive). We then used a bioinformatic approach (FunciSNP) to identify potential functional variants associated with HD risk correlated with risk loci of interest. We extracted publically available ENCODE data on biofeatures to identify potential functional motifs associated with the index SNP or correlated SNPs. Finally, we measured expression levels of the two alternative mRNA transcripts in lymphoblastoid cell lines (LCLs) from 49 post-therapy HL patients and 25 unaffected controls. RT-PCR was carried out in triplicate. Relative expression levels were calculated relative to TBP as housekeeping gene. Linear models were used to assess correlation between genotype and TCF3expression levels. Results The meta-analysis identified a novel susceptibility variant at chromosome 19p13.3 (rs1860661) associated with HL risk (Odds Ratio [OR]= 0.78, P=2.0*10-8, I2=0%). variant is located in intron 2 of TCF3 (also known as E2A), a regulator of B- and T-cell lineage commitment. was also significantly associated with HL (OR= 0.85, P=0.002) in the replication series of 1,281 cases and 3,218 controls. the combined analysis consisting of the discovery and replication sets, rs1860661was strongly associated with HL (OR=0.81,=3.5*10-10), with no evidence of heterogeneity between contributing studies (Phom=0.41, I2=0%). The number of G alleles defined by rs1860661 was significantly associated with a reduced risk of each HL subtype except EBV positive HL. rs1860661 and two correlated SNPs, rs10413888 (r2=0.90) and rs8103453 (r2=0.89) identified by FunciSNP analysis map in or near marks of open chromatin and in DNAse hypersensitivity sites in TCF3 in CD20+ B cell lines., the protective minor alleles of these SNPs as defined by the G-G-G haplotype map to the binding sites for ZBTB7a (rs10413888 and rs1860661) and (rs8103453) transcription factors, likely improving the binding efficiency to the sites which may result in increased transcription rates of TCF3. TCF3 is encoded by two alternative transcripts (E12 and E47). Higher expression levels of TCF3-E47, whose transcription start site is located close to rs1860661, was associated with the rs1860661-G allele in controls (P=0.02), but not in HL patients (P=0.22). Conclusion/Discussion TCF3 is essential for the commitment of lymphoid progenitors to both B-cell and T-cell lineage development. A molecular and phenotypic hallmark of classical HL is the loss of the B-cell phenotype in HRS cells, including lack of demonstrable B-cell receptor and most B-cell specific markers such as CD19 or CD20. HRS cells have a low level of TCF3, particularly homodimers of the isoform E47, due to expression of the ABF-1 and ID2 inhibitors that bind to TCF3. Thus, higher TCF3 levels in HRS precursor cells may lead to enhanced retention of the B cell phenotype, thereby conferring a protective effect. These data suggest a link between the 19p13.3 locus including TCF3 and HL risk, indicating that TCF3 could be relevant to HL etiology and pathogenesis. Disclosures: Link: Genentech: Consultancy; Millenium: Consultancy; Pharmacyclics: Consultancy; Spectrum: 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 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)0,008
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,011
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0050,021
Bibliométrie0,0050,006
Études des sciences et des technologies0,0010,000
Communication savante0,0030,001
Science ouverte0,0020,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,063
Tête enseignante GPT0,294
Écart entre enseignants0,231 · 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'étudeMéta-analyse
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

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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