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Enregistrement W2989507932 · doi:10.1182/blood-2019-127421

NFKBIZ3′ UTR Mutations Confer Selective Growth Advantage and Activate Genes with Therapeutic Implications in Diffuse Large B-Cell Lymphoma

2019· article· en· W2989507932 sur OpenAlexaff
Sarah E. Arthur, Nicole Thomas, Christopher Rushton, Miguel Alcaide, Shannon Healy, Anja Mottok, David W. Scott, Christian Steidl, Ryan D. Morin

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensSpinal Cord Injury BCSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésDiffuse large B-cell lymphomaBiologyUntranslated regionMutationGeneThree prime untranslated regionCancer researchSignal transductionB cellGeneticsLymphomaMessenger RNAMolecular biologyImmunology

Résumé

récupéré en direct d'OpenAlex

Introduction: The activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) is characterized by activation of NF-κB signaling and increased mortality. Recurrent mutations affecting genes such as MYD88, CD79A/B and TNFAIP3 have been shown to be involved in activation of the NF-κB pathway in ABC DLBCL; however there still remain cases with no known genetic basis for this pathway activation suggesting that our understanding of the drivers of ABC DLBCL remains incomplete. Previously, NFKBIZ was shown to be amplified in ~10% of ABC DLBCLs and to contribute to activation of NF-κB signaling. We have recently described a novel pattern of mutations affecting the 3′ UTR of NFKBIZ resulting in an overall mutation rate of 34% for this gene (UTR mutations or amplifications) in ABC DLBCL. These NFKBIZ UTR mutations are mutually exclusive with MYD88 mutations, thus suggesting they may also lead to activation of NF-κB signaling. NFKBIZ encodes the IκB-ζ protein, which interacts with NF-κB transcription factors and is thought to regulate canonical NF-κB signaling. We hypothesized that NFKBIZ UTR mutations affect the normally rapid degradation of mRNA by disrupting secondary structures recognized by RNA-binding proteins such as ribonucleases. The resulting elevated mRNA levels would in turn lead to accumulation of IκB-ζ protein as a novel mechanism to promote cell growth and survival in ABC DLBCL. Methods: NFKBIZ 3′ UTR mutations were introduced into a DLBCL cell line (WSU-DLCL2) using the CRISPR-Cas9 system. NFKBIZ mRNA and protein levels were evaluated using custom designed droplet digital PCR assays and western blot analyses. Cells were stimulated with LPS to induce NFKBIZ expression and mRNA and protein levels were measured in wild-type (WT) and CRISPR-mutant lines to compare rates of mRNA decay and protein expression. A competitive growth assay with WT and CRISPR-mutant lines was performed to assess whether UTR mutations provide a growth advantage in culture (in vitro) and in mouse xenografts (in vivo). The composition of the pool of WT and mutant lines was determined by comparing WT and mutant DNA sequence proportions. RNA-sequencing was then performed on WT and a subset of CRISPR-mutant cell lines to identify genes up-regulated by IκB-ζ in mutant lines. Findings from these models were compared to the effects of NFKBIZ over-expression in patient tissues. Results: Introduction of NFKBIZ mutations into a DLBCL cell line confirmed that UTR deletions lead to increased mRNA and protein levels. LPS stimulation showed prolonged mRNA elevation in mutant lines, consistent with our model wherein these mutations disrupt post-transcriptional regulatory mechanisms. NFKBIZ UTR deletions gave DLBCL cells a selective growth advantage over WT both in vitro (cell culture) and in vivo (xenograft mouse model). RNA-sequencing of mutant and WT lines revealed possible transcriptional targets of IκB-ζ including some NF-κB targets and genes commonly over-expressed in ABC DLBCL (BATF, MAML2, and TNFRSF13B). HCK, a gene known to be activated by MYD88 was also upregulated in NFKBIZ mutant lines. This is consistent with our hypothesis that mutations in MYD88 and the NFKBIZ UTR are mutually exclusive because they activate similar pathways. HCK is also a target of ibrutinib, suggesting the potential utility of ibrutinib in these patients. Novel targets of IκB-ζ were also discovered through this analysis including CD274, the gene encoding PD-L1. This could be a novel mechanism for DLBCL tumours to express PD-L1 and therefore suggest that these tumours may be susceptible to anti-PD1/PD-L1 immunotherapies. Conclusions: This work highlights the role of NFKBIZ and 3′ UTR mutations in driving ABC DLBCL. We demonstrate that these mutations can lead to over-expression of NFKBIZ and provide a selective growth advantage to cells both in vitro and in vivo. In addition, we described multiple targets of IκB-ζ that may have implications in treatment susceptibility and/or resistance in ABC DLBCL. These findings contribute to a better understanding of the genetic basis of DLBCL, which is necessary to guide personalized therapeutic strategies. Disclosures Scott: Roche/Genentech: Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy. Steidl:Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Juno Therapeutics: Consultancy; Tioma: Research Funding; Bayer: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,007
Tête enseignante GPT0,238
Écart entre enseignants0,230 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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