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Enregistrement W2988594495 · doi:10.1182/blood-2019-130052

The U1 Spliceosomal RNA: A Novel Non-Coding Hotspot Driver Mutation Independently Associated with Clinical Outcome in Chronic Lymphocytic Leukemia

2019· article· en· W2988594495 sur OpenAlexaff
Ferran Nadeu, Shimin Shuai, Ander Díaz‐Navarro, Irene López, Silvia Martín, Hiromichi Suzuki, Romina Royo, Guillem Clot, Julio Delgado, Tycho Baumann, Junyan Lu, Alba Navarro, Marta Kulis, Sachin Kumar, Ana Gutiérrez‐Fernández, Miguel Alcoceba, Marcos González, Enrique Colado, Ángel Ramírez Páyer, Cristina Capdevila, Marta Aymerich, Rosó Marés, Mònica López‐Guerra, Laura Magnano, Pablo Mozas, Alfredo Rivas‐Delgado, María José Terol, Anna Enjuanes, Wolfgang Huber, Armando López‐Guillermo, Sı́lvia Beà, José I. Martín‐Subero, Thorsten Zenz, Michael D. Taylor, Dolors Colomer, Xosé S. Puente, Lincoln Stein, Elı́as Campo

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensSickKids FoundationHospital for Sick ChildrenUniversity of TorontoOntario Institute for Cancer Research
Organismes subventionnairesnon disponible
Mots-clésIGHV@BiologyGeneticsChronic lymphocytic leukemiaSpliceosomeRNARNA splicingSmall nuclear RNAMutationEpigeneticsIntronGeneLeukemiaNon-coding RNA

Résumé

récupéré en direct d'OpenAlex

Introduction: Genomic studies of chronic lymphocytic leukemia (CLL) have uncovered >80 potential driver mutations. The vast majority of these mutations affect coding regions, and just two potential drivers have been identified in non-coding elements. Aim: To describe the biological and clinical impact of a recurrent A>C mutation at the third base of the small nuclear RNA U1, the non-coding component of the spliceosome involved in the recognition of the 5' splice site (5'SS). Methods: Whole-genome sequencing (WGS) and RNA-seq from 318 CLL patients were used to identify and characterize a highly recurrent A>C point mutation occurring at position 3 of the U1 snRNA gene (g.3A>C mutation). The U1 wild-type and mutant forms were introduced into three CLL cell lines (JVM3, HG3, MEC1) to validate in vitro the predicted effect of this alteration. We screened two independent cohorts including a total of 1,314 CLL patients for the presence of the mutation using the rhAmp SNP genotyping assay, and integrated the U1 mutational status with well-known driver alterations, IGHV and epigenetic subgroups, and clinical parameters. Results: The U1 mutation was found in 8/78 (10.3%) CLL cases analyzed by WGS. Given its role in 5'SS recognition by base-pairing, we reasoned that this mutation was likely to alter the splicing and expression patterns of CLL. We were able to confirm widespread specific alterations in the transcriptome by comparing RNA-seq data between wild-type and g.3A>C mutated samples. Applying this knowledge to an algorithm aimed to infer the U1 mutational status from expression data, we were able to identify 4 mutated cases among 240 additional cases that had RNA-seq but no WGS. In total, 12/318 (3.8%) CLL patients analyzed by WGS and/or RNA-seq harbored this mutation. This g.3A>C U1 mutation changes the preferential A-U base-pairing between U1 and 5'SS to C-G base-pairing, creating novel splice junctions and altering the splicing pattern of 3,193 introns in 1,519 genes. In addition to altered splicing, 869 genes were differentially expressed between mutated and wild-type cases. We identified specific cancer genes (e.g. MSI2, POLD1, or CD44) and pathways (B-cell receptor signaling, promotion of apoptosis, telomere maintenance, among others) altered by the U1 mutation. To confirm a causal link between this mutation and splicing changes, we introduced exogenous U1 genes with or without the mutation into three cell lines. Subsequent RNA-seq of these cell lines recapitulated the altered splicing and expression patterns observed in CLL patients. We next screened for the presence of the U1 mutation 1,057 patients (cohort 1) using the rhAmp assay and it was found in 30 (2.8%) cases. The distribution of the mutation was similar in Binet stages and CLL vs monoclonal B-cell lymphocytosis. However, the U1 mutation was almost always found in IGHV unmutated CLL (29/30, p=9.0e-11) and within the naïve-like CLL epigenetic subgroup (p=3.7e-7). None of the U1 mutated cases had mutations in the SF3B1 splicing factor. Considering only pre-treatment CLL samples, U1 mutation was associated with a shorter time to first treatment independently of the Binet stage, IGHV mutational status, epigenetic subgroups, and mutations in the well-known CLL drivers SF3B1, NOTCH1, ATMor TP53. In cohort 2 (n=257), this mutation was found in 13 (5.1%) patients, confirming its enrichment in IGHV unmutated cases, naïve-like epigenetic subgroup, and splicing modulation. Despite the relatively small number of pre-treatment samples carrying the U1 mutation (7/178) and short follow-up of the patients (median 2.6 years), the effect of this mutation on time to first treatment in cohort 2 was compatible with the one observed in cohort 1. Finally, we screened for the U1 mutation a cohort of diffuse large B-cell lymphoma (n=108), mantle cell lymphoma (n=101), follicular lymphoma (n=87), splenic marginal zone lymphoma (n=12), acute myeloid leukemia (n=52), and myelodysplastic syndrome (n=67). The mutation was not present in any of the samples analyzed. Conclusions: Here we have reported that the third base of the small nuclear RNA U1 is recurrently mutated in CLL, proved its effect in splicing and gene expression, and shown that this mutation is independently associated with faster disease progression. The g.3A>C U1 mutation represents a novel non-coding driver alteration in CLL with potential clinical and therapeutic implications. Disclosures Ramirez Payer: GILEAD SCIENCES: Research Funding. Terol:Astra Zeneca: Consultancy; Gilead: Research Funding; Abbvie: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy. Lopez-Guillermo:Celgene: Consultancy, Research Funding; Janssen: Research Funding; Roche: Consultancy, Research Funding; Gilead: Consultancy, 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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,295
Score d'incertitude au seuil0,973

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,029
Tête enseignante GPT0,325
Écart entre enseignants0,296 · 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 tête enseignante, 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

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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