56 | GENOMIC ANALYSIS OF MATURE B‐CELL LYMPHOMAS
Notice bibliographique
Résumé
K. Dreval, M. Cruz, and L. K. Hilton equally contributing authors. Introduction: The B-cell lymphomas represent a genetically heterogeneous and complex collection of malignancies. Among diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma (BL) and follicular lymphoma (FL), over 140 recurrently mutated genes have been established. Subdivisions of these pathologies into molecular or genetic subgroups with distinct biological features is of interest but it remains unclear whether genome-wide analyses have identified a sufficient number of relevant genetic features. To search for additional drivers and refine our understanding of common mutation patterns across the mature B-cell lymphomas, we performed a comprehensive meta-analysis of all available published and locally generated whole genome sequencing (WGS) and exome data. Methods: Sequencing data was assembled from a total of 2603 DLBCL, 784 FL, 433 BL, 202 MCL, 213 CLL and 808 cases spanning other mature B-cell lymphoma pathologies. This includes WGS and exome data from 1992 and 3051 samples, respectively. All WGS and exome data was analyzed for somatic mutations and structural variants using LCR-modules, our suite of open-source pipelines. RNA-seq data, available from 1837 cases, was analyzed for gene expression, alternative splicing and detecting oncogene rearrangements. Significantly-mutated genes (SMGs) were identified using a combination of MutSigCV, OncodriveFML and dNdSCV. Non-coding loci enriched for mutations were comprehensively identified using FishHook. Results: Using a pooled analysis of all DLBCL, BL and FL samples with paired normals, we identified 133 SMGs. Of these genes, 106 were among previously reported high confidence SMGs, with the remaining 27 not previously attributed to these pathologies. The mutation incidence among these new genes was low (median: 2.46), as expected. Notable examples are genes with potential roles in chromatin remodeling (ARID1B, INO80), immune evasion (FCGR2B), DNA damage response (RBM38), and BCR signaling (CD79A). While most of the novel genes were more commonly mutated in DLBCL, CDKN2C and FIP1L1 mutations were more abundant in BL. Despite the volume of data, this analysis did not reproduce 37 of the genes that have previously been attributed to at least one of these entities. Most of these represent targets of aSHM, such as BTG1, CIITA and ACTG1, which may be enriched for passenger mutations. Recurrence analysis identified a total of 105 mutation hotspots in 66 genes. This also recovered additional genes with significant hotspots that were not globally significant, including TLR2, BCOR, BCR and MEF2C. We found 130 non-coding loci that were enriched for mutations, with most regions having the highest mutation burden in DLBCL. Conclusions: Genomic Analysis of Mature B-cell Lymphomas (GAMBL) analysis has extended the list of lymphoma genes to 170 and has revealed the existence of mutation hotspots in more than a third of these genes and many non-coding loci with regulatory potential. Keywords: bioinformatics; computational and systems biology; genomics, epigenomics, and other -omics; aggressive B-cell non-Hodgkin lymphoma No potential sources of conflict of interest.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».