Epigenetic Profiling of Primary DLBCLs Reveals Novel DNA Methylation-Based Clusters and New Underlying Mechanisms of Lymphomagenesis
Notice bibliographique
Résumé
Abstract Abstract 556 Epigenetic profiling of primary DLBCLs reveals novel DNA methylation-based clusters and new underlying mechanisms of lymphomagenesis. Nyasha Chambwe, Matthias Kormaksson, Subhajyoti De, Franziska Michor, Nathalie Johnson, David W. Scott, Randy D. Gascoyne, Ari Melnick, Fabien Campagne and Rita Shaknovich. Diffuse Large B Cell Lymphomas (DLBCLs) are a heterogeneous group of diseases from the clinical, molecular and genetic standpoints. While gene expression profiling has identified clinically and biologically relevant DLBCL subtypes, there is still considerable heterogeneity beyond what has been resolved through transcriptional and genetic profiling. It is increasingly clear that lesions in epigenetic regulatory proteins and transcription factors are a hallmark of DLBCL, which suggests that aberrant epigenetic programming is likely to be a significant factor in these tumors. Our recent preliminary data suggests that aberrant DNA methylation is also widespread in DLBCL and contributes to the abnormal expression patterns of GCB and ABC DLBCLs. Moreover recent data in the setting of AML show that DNA methylation profiles delineate disease subtypes not captured through transcriptional or genetic profiling. We hypothesized that DNA methylation profiles would allow us to identify new, biologically significant DLBCL subtypes. We therefore examined the DNA methylation of over 140 patients with DLBCL using the HELP assay covering multiple CpGs at over 14,000 gene loci. We next performed an unbiased (unsupervised) analysis of probesets that display significant variability (n=3,005), using K-means consensus clustering. This procedure identified four robust DLBCL subtypes based on epigenetic profiles. To identify the genes that define these four clusters we next performed supervised analysis of the DLBCL subtypes including the normal counterpart germinal center B-cells as a normal control, using three independent statistical methods. 46 genes defined cluster A, 236 genes defined cluster B, 376 genes defined cluster C and 1271 genes defined cluster D (selected genes displayed change in methylation of at least 30% at BH corrected p-value < 0.05). Each of these epigenetically defined DLBCL subtypes featured aberrant DNA methylation of genes and pathways with potential relevance to pathogenesis. For example cluster A was notable for aberrant DNA methylation of REL, STAT3, CD30; cluster B for aberrant methylation of the TNFa and IFN1 networks; cluster C of IDH2, FOXG1 genes; and cluster D of CDKN2A, ATF3, FOXL3 genes. Other defining characteristics of Cluster D was enrichment for ABC DLBCLs (Fisher exact test, p=0.007) and most remarkably, marked intra-tumor and inter-individual heterogeneity of DNA methylation patterning. This latter feature is suggestive of potential epigenetic clonal complexity and failure to properly control the boundaries of methylated regions of the genome in these tumors. Clusters A and B revealed enrichment for GC features and cluster B had increased expression of MUM1 (Fisher exact test, p=0.007 and p=0.002). Furthermore, we noted that higher expression of DNMT3B and DNMT3L were associated with hypermethylation in DLBCL samples, while higher expression level of AICDA was associated with aberrant hypomethylation in DLBCLs as compared to normal GC B-cells. Higher expression levels of epigenetic modifiers EZH2 and MBD4 was associated with greater heterogeneity of DNA methylation patterning compared to the normal methylation pattern in germinal center B cells. Collectively, the data indicate that DLBCLs are composed of entities defined by specific DNA methylation profiles that only partially overlap with the ABC and GCB classification. The DLBCL subtypes display perturbation of genes likely to play significant biological roles, and aberrant methylation patterning can be traced in part to aberrant expression of epigenetic regulators including DNA methyltransferases, AICDA and EZH2. 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 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) | 0,001 | 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 ».