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Enregistrement W2980035451 · doi:10.1182/blood.v114.22.619.619

ABC and GCB DLBCLs Display Unique Biologically Distinct and Clinically Relevant Epigenetic Signatures.

2009· article· en· W2980035451 sur OpenAlexaff
Rita Shaknovich, Huimin Geng, Nathalie A. Johnson, Leandro Cerchietti, María E. Figueroa, Lucas Tsikitas, Olivier Elemento, Joseph M. Connors, Laurie H. Sehn, Randy D. Gascoyne, Ari Melnick

Notice bibliographique

RevueBlood · 2009
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensUniversity of British ColumbiaBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésEpigeneticsDNA methylationDiffuse large B-cell lymphomaGene expression profilingGerminal centerBiologyCancer researchDNA microarrayGeneLymphomaGene expressionOncologyGeneticsB cellMedicineImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 619 Diffuse Large B-cell Lymphoma (DLBCL) is a complex biological entity with heterogeneous genetic, biological and clinical features. Gene expression profiling studies have attempted to resolve some of this heterogeneity. For example, DLBCL patients harboring gene signatures associated with expression of germinal center B-cell genes (GCB) or activated B-cell genes (ABC) vary in their response rate to standard chemo-immunotherapy regimens. Since epigenetic gene regulation can play a fundamental role in determining the phenotype of normal and malignant tissues we asked whether ABC and GCB DLBCLs also display unique epigenetic signatures that might be clinically useful and further explain the biology of these tumors. For this purpose we examined the DNA methylation level of 50,000 cytosine residues distributed among 14,000 gene promoters in a cohort of 159 patients with DLBCL, all of whom were uniformly treated with R-CHOP, using the HELP assay and high-density oligonucleotide microarrays. For a subset of these patients (n=69), we also performed Affymetrix gene expression profiling. First, a Bayesian predictor of ABC/GCB subtypes was trained from a published expression profiling study of 203 DLBCL patients. The predictor was then applied to our cohort of 69 patients. At a probability cutoff of 0.9, 20 patients were classified as ABC, 40 were classified as GCB and 9 could not be classified (i.e. “type III DLBCL”). As expected from published studies, the differences in progression free survival (PFS) and overall survival (OS) of these ABC vs. GCB patients thus determined was highly significant, with p=0.0026 (log rank) and p=0.043 (log rank) respectively, with a much worse prognosis for ABC patients compared to GCB ones. We then asked whether the ABC and GCB subtypes could be predicted from the DNA methylation profiles of the same 69 patients. By performing a t-test we found that 239 genes were differentially methylated between ABC and GCB (p<0.0001) and also displayed >30% difference in methylation level. This DNA methylation signature was incorporated into a new Bayesian predictor, which we showed to predict ABC and GCB DLBCL subtypes from DNA methylation profiles with a 91% accuracy. Using a cross-validation procedure, we estimated that the classification performance on independent cases to be ∼87%. The predicted ABC and GCB cases retained the clinical predictive power of the gene expression profile when applied to the remaining 90 patients that did not have gene expression profiles, confirming its clinical relevance (difference in PFS p=0.0148, log rank). Gene set enrichment analysis showed that the ABC DNA methylation signature was enriched in genes involved in antigen dependent B and T-cell responses and in TNF inflammatory responses (p<1.01E-4 and <6.01E-4 respectively). A computational analysis of promoter DNA sequences of the genes involved in this signature revealed over-representation of binding sites for transcription factors including MYB, STAT5A, MAZ, and JUN and Sp1; many of these factors have a known role in B cell development and function. The role of Sp1 in these tumors is under further examination. Among the 239 genes that were differentially methylated and the 411 genes that were differentially expressed between ABC and GCB there was an overlap of 16 genes (greater than expected by chance Fisher Exact p=0.005). A predictor based on the methylation profiles of these 16 genes was on its own 92% accurate in identifying ABC vs. GCB cases among our cohort of DLBCLs. Although there was a general trend for inverse correlation in expression between the 239 differentially methylated genes, these 16 overlapping genes displayed marked and extreme inverse correlation. This was validated by single locus quantitative methylation sequencing (MassArray) and QPCR. The 16 genes included genes known to play critical roles in B-cell differentiation, proliferation and metabolism but not previously implicated in DLBCL. Gain and loss of function assays of a subset of these genes in ABC and GCB DLBCL cell lines show that they have tumor suppressor functions in DLBCL. Our results demonstrate for the first time that ABC and GCB DLBCLs are epigenetically distinct diseases; they identify new biological differences and candidate tumor suppressor genes between these tumors and demonstrate that a DNA methylation classifier can be used to distinguish GCB and ABC DLBCL subtypes. Disclosures: No relevant conflicts of interest to declare.

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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,005

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
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
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 Opus0,012
Tête enseignante GPT0,273
Écart entre enseignants0,262 · 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

Citations0
Publié2009
Routes d'admission1
Résumé présentoui

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