Mutations Affecting RNA Binding Proteins Are a Novel Feature of Mantle Cell Lymphoma
Notice bibliographique
Résumé
Objectives Mantle cell lymphoma (MCL) is an uncommon B-cell non-Hodgkin lymphoma that is incurable with standard therapies. The genetic drivers of this cancer have not been firmly established and the features known to contribute to differences in clinical course remain limited. We sought to extend our understanding of the molecular etiology of this malignancy using an integrative genomic analysis of diagnostic biopsies. Methods We performed exome sequencing on 51 frozen MCL tumors and analyzed these alongside previously published exome cohorts. We sequenced tumour genomes and matched constitutional DNA from 34 frozen MCLs, along with matched constitutional DNA, to more broadly identify the pattern of non-coding mutations. Based on mutations identified in this discovery cohort, we re-sequenced 18 recurrently-mutated genes in 212 archival MCLs, each having clinical follow-up data. We also performed RNA-seq on 110 of these cases and analyzed these data for alternative splicing and differential expression, including the differential splicing of HNRNPH1 in the context of recurrent intronic mutations. We investigated the functional and phenotypic effect of mutations and deregulated HNRNPH1 protein through ectopic expression of full-length HNRNPH1 and a mini-gene containing the exons and introns affected by mutations. Using custom droplet digital PCR (ddPCR) assays, we validated alternative splicing patterns in HNRNPH1 itself and other targets identified through re-analysis of available CLIP-seq data. Results In addition to confirming the prognostic association of TP53 and NOTCH1 mutations in MCL, we identified two additional genes associated with outcome: EWSR1 with poor outcome (HR = 5.6) and MEF2B with good outcome (HR = 0.2). By comparing mutation patterns to diffuse large B-cell lymphoma (DLBCL), we identified an MCL-specific missense hot spot in MEF2B, non-specific truncating mutations in EWSR1, and truncating mutations affecting the DAZAP1 C-terminus in both MCL and DLBCL. The DAZAP1 mutations are predicted to alter protein sub-cellular localization and disrupt protein-protein interactions. We also identified the focal recurrence of non-coding mutations surrounding a single exon of the HNRNPH1 gene that were largely restricted to MCL. These mutations affected a region bound by HNRNPH1 protein and disrupted the preferred binding motif of this protein. Intronic mutations were significantly associated with alternative splicing of the HNRNPH1 mRNA and appear to disrupt a negative regulatory loop that normally limits the level of HNRNPH1. Using cell-based assays, we have evaluated the role of HNRNPH1 in cell survival and proliferation. Our interrogation of alternative splicing events in downstream targets implicate HNRNPH1 as a master splicing regulator which may broadly perturb the transcriptome and proteome to favor lymphomagenesis in MCL. Conclusions We discovered three novel MCL-related genes with roles in RNA trafficking or splicing, namely EWSR1, DAZAP1, and HNRNPH1. Mutations in these RNA-binding proteins were identified in 49 of 291 (17%) samples analyzed. Our results improve the current understanding of the MCL mutational landscape, highlight the similarities and differences between MCL and DLBCL, and strongly implicate a role for aberrant regulation of RNA metabolism in MCL pathobiology. We elucidated a functional role for recurrent non-coding HNRNPH1 mutations specific to MCL and identified multiple downstream targets. We continue to explore putative trans targets of HNRNPH1, a novel oncoprotein in MCL. Disclosures Steidl: Seattle Genetics: Consultancy; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; Bayer: Consultancy; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Juno Therapeutics: Consultancy; Tioma: Research Funding. Connors:Bristol-Myers Squibb: Consultancy; Seattle Genetics: Honoraria, Research Funding; Takeda Pharmaceuticals: Honoraria. Villa:Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen, Gilead: Consultancy, Honoraria. Johnson:Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; Seattle Genetics: Honoraria; Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding. Scott:Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Celgene: Consultancy; Roche/Genentech: Research Funding.
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 | 0,001 | 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 ».