Identification of Genes Frequently Mutated In FL and DLBCL with Transcriptome, Genome and Exome Sequencing
Notice bibliographique
Résumé
Abstract Abstract 804 Introduction: Follicular lymphoma (FL) and diffuse large B cell lymphoma (DLBCL) are the two most common types of non Hodgkin lymphoma (NHL). It is widely accepted that DLBCL can be divided into two major subtypes using gene expression profiling: germinal center B-cell (GCB) and activated B-cell (ABC). Both FL and the GCB subtype of DLBCL derive from germinal center B cells and have been found to share some common mutational events such as translocations leading to the deregulation of the BCL2 oncogene and mutations affecting a single tyrosine (Y641) in the histone methyltransferase EZH2. In contrast, ABC DLBCL tumors are characterized by mutations leading to the constitutive activity of NFkB. The clear differences in treatment response between subtypes allow this distinction to be used as a prognostic indicator and may ultimately lead to therapies that target individual features of each subtype. However, besides the gene expression and mutational signatures that differentiate the DLBCL subtypes, there is a paucity of molecular prognostic markers in these NHLs. Further, there is limited knowledge about the genetic events that drive the GCB subtype of DLBCL, which, if better understood, may enable the design of targeted therapeutics. Methods: To identify mutations driving lymphomagenesis and in particular, aggressive cases of NHL, we applied Illumina second-generation sequencing technology to the analysis of tumor genomes and constitutional DNAs from a FL and a DLBCL tumor and the exomes from two additional DLBCLs. In these “omes”, we identified somatic protein-altering point mutations in more than 250 genes including genes known to be involved in cancer, for example TP53, FAS and TNFAIP3 (A20). Many of these mutations may represent passenger rather than driver mutations, the latter of which are involved in disease progression. To identify the likely driver mutations, we sought to identify the genes that are recurrent targets of somatic mutation in these cancers. To this end, we further analyzed the transcriptome sequences we generated using RNA-seq from 95 primary DLBCLs,13 FL cases and 10 DLBCL-derived cell lines. Results: 105 of the genes found mutated in the FL and DLBCL genomes were observed to be recurrent targets of somatic mutation in these diseases. Some of these were known targets of aberrant somatic hypermutation (SHM) including BCL2, PIM1, and IRF4 and others have been previously identified as targets of recurrent mutation in lymphoma, such as EZH2, CD79B and CARD11. One of the most frequently mutated genes was MLL2, a histone methyltransferase never before implicated in lymphomagenesis. MLL2 showed a pattern of mutation characteristic of a dosage-sensitive tumor suppressor gene. Another frequently mutated gene was MEF2B, a calcium-regulated transcriptional co-activator/repressor that cooperates with histone modifying enzymes to epigenetically regulate the expression of genes. We found that mutations affecting MEF2B occur in 11.7% of FL and 9% of DLBCL, with the majority (73%) of these mutations affecting three amino acids (K4, Y69, and D83). Analysis of these 105 recurrently mutated genes for prognostic signatures is ongoing. Conclusions: High-throughput sequencing platforms have enabled the identification of recurrent targets of somatic mutations never suspected to be involved in lymphoma. Some of these mutated genes may have prognostic value while others may represent targets for the rational design of novel therapeutics. 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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».