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Enregistrement W4405040604 · doi:10.1182/blood-2024-199227

Multiomic Profiling Reveals Pleiotropic Effects of Somatic IRF4-C99R Mutations in Primary Mediastinal Large B-Cell Lymphomas

2024· article· en· W4405040604 sur OpenAlexaff
Shinya Rai, Gerben Duns, Fabian Frontzek, Jasper Wong, Yifan Yin, Shannon Healy, Michael Y. Li, Makoto Kishida, Elena Viganò, Manabu Fujisawa, Bruce W. Woolcock, Adèle Telenius, Barbara Meissner, Merrill Boyle, Hisae Nakamura, Luke O’Brien, Andrew Lytle, Pedro Farinha, Graham W. Slack, Laura K. Hilton, Ryan D. Morin, Leandro Venturutti, Tomohiro Aoki, Kerry J. Savage, David W. Scott, Christian Steidl

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensPrincess Margaret Cancer CentreSimon Fraser UniversityUniversity of British ColumbiaSpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésSomatic cellBiologyB cellCancer researchProfiling (computer programming)IRF4GeneticsImmunologyAntibodyGeneTranscription factor

Résumé

récupéré en direct d'OpenAlex

Introduction: Interferon Regulatory Factor 4 (IRF4) is a transcription factor that plays a critical role in the regulation of pre-B-cell development, the germinal center reaction as well as plasma cell differentiation. IRF4 has emerged as a master regulator of cancer-specific gene expression programs in various lymphoid malignancies. Prior studies uncovered a hotspot missense mutation in the DNA-binding domain of IRF4 (C99R) that alters the affinity of binding to specific DNA motifs and leads to disease-specific changes in classic Hodgkin lymphoma (Schleussner et al, 2023). The C99R mutations were also found to be recurrently detected in primary mediastinal large B-cell lymphoma (PMBCL) (Mottok et al, 2019, Noerenberg et al, 2023); however, its functional role in PMBCL pathogenesis remains unknown. Materials and methods: We introduced the C99R mutation into two PMBCL-derived cell lines (U2940, Karpas-1106P) by applying CRISPR prime editing. We then performed ATAC-seq, ChIP-seq, and RNA-seq analyses on our isogenic model systems (IRF4WT and IRF4C99R). We also analyzed single cell (sc) transcriptome data of nuclei isolated from formalin-fixed paraffin-embedded tissues samples of primary PMBCL (n=4) with/without this mutation, including control tissues from reactive lymph node (n=2), and normal thymus (n=2). Results: A meta-analysis of publicly available sequencing data revealed IRF4C99R hotspot mutations in 29 out of 413 PMBCL samples (7%), compared to only 2 out of 1,999 diffuse large B-cell lymphoma samples (0.1%) (p<0.00001). To characterize the role of this mutation, we compared the global chromatin accessibility profiles between IRF4WT and IRF4C99R PMBCL cells by ATAC-seq and identified regions with commonly enhanced (1,334 regions) and reduced (2,796 regions) accessibility across the isogenic models. Specifically, in IRF4C99R cells, the accessibility of flanking regions at specific EICE- and canonical/non-canonical AICE-motifs were elevated but clearly reduced at ISRE motifs. The direct IRF4 binding to these distinct motifs was validated by IRF4 ChIP-seq. In line with these results, differential gene expression analysis revealed down-regulation of key regulators of plasma cell differentiation such as PRDM1 and interferon gamma (IFNγ) signaling-related genes such as TNIK in IRF4C99R cells, both genes were regulated by IRF4 at ISRE DNA motifs. Conversely, a key cytokine in PMBCL CCL17 (TARC) was upregulated at both the mRNA and protein levels in IRF4C99R cells, as validated by qPCR and ELISA, respectively. Previous studies reported downregulation of TARC by IFNγ signaling, a finding that we reproduced by IFNγ stimulation experiments in our PMBCL models. Moreover, we showed that inhibition of TNIK abrogated IFNγ-mediated downregulation of TARC, demonstrating a mechanistic link between IRF4C99R and increased TARC expression. Finally, we found the receptor tyrosine kinase encoding gene EPHB1, regulated at both EICE and AICE motifs, was the highest upregulated gene in IRF4C99R cells (adj. p<0.01). Of note, the higher expression of both TARC and EPHB1 mRNA in primary PMBCL tumors carrying the IRF4C99R (n=3) compared to IRF4WT (n=63) were validated by Illumina Whole-Genome DASL assay. We next performed in vitro binding assays and found EPHB1 specifically interacts with the membrane-bound ligand Ephrin-B2. Functional studies showed Ephrin B2-mediated adhesion and migration were significantly increased in IRF4C99R cells. Moreover, we retrovirally engineered Eu-Myc mouse lymphoma cells to overexpress EPHB1 in an in-vivo syngeneic model. After intraperitoneal injection into C57BL6 mice, the cells with EPHB1 overexpression promoted enhanced tumor growth with increased infiltration of spleen, liver and thymus. Finally, sc transcriptome analysis showed significantly higher EPHB1 expression in primary PMBCL tumor cells with the IRF4C99R mutation (n=2, p<0.001) as compared to cases with IRF4WT (n=2), and the expression was rarely detected in normal B cells. Ephrin-B2 ligand expression was detectable in the endothelial cells of PMBCL tumors. Conclusion: Our findings highlight pleiotropic effects of the heterozygous IRF4C99R mutation leading to hallmarks of PMBCL lymphomagenesis including a plasmacytic differentiation block, upregulation of the key cytokine TARC, and providing a potential explanation of disease specific tropism through the Ephrin-B2/EPHB1 axis.

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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

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)0,0010,000

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 GPT0,244
Écart entre enseignants0,237 · 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'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

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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