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Enregistrement W2990380228 · doi:10.1182/blood-2019-131450

Diffuse Large B-Cell Lymphomas with a Molecular PMBCL Expression Signature Represent a Distinct Molecular Subtype Associated with Poor Clinical Outcome

2019· article· en· W2990380228 sur OpenAlexaff
Gerben Duns, Elena Viganò, Daisuke Ennishi, Clémentine Sarkozy, Stacy Hung, Elizabeth A. Chavez, Katsuyoshi Takata, Anja Mottok, Randy D. Gascoyne, Ryan D. Morin, Kerry J. Savage, David W. Scott, Christian Steidl

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensSimon Fraser UniversityBC Cancer AgencySpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésLymphomaCancer researchDiffuse large B-cell lymphomaMedicineImmunophenotypingPhenotypeBiologyOncologyInternal medicinePathologyGeneImmunologyGeneticsFlow cytometry

Résumé

récupéré en direct d'OpenAlex

Introduction: The recently developed DLBCL90 NanoString assay robustly distinguishes primary mediastinal large B-cell lymphoma (PMBCL) from diffuse large B-cell lymphoma (DLBCL), as well as cell-of-origin (COO) subtypes of DLBCL (ABC, GCB, unclassified) and cases with a Double-Hit (DHIT) signature (Ennishi D., JCO 2019). When this assay was applied to biopsies from 343 patients with de novo DLBCL uniformly treated with R-CHOP, nineteen of these cases had a molecular PMBCL signature (mPMBCL), despite the fact that they were diagnosed as DLBCL based on their morphology, immunophenotype and clinical features. Here, we aimed to comprehensively characterize the molecular and clinicopathologic features of these mPMBCL cases. Methods: Survival estimates were calculated using Kaplan-Meier analysis, using time to progression (TTP) and disease specific survival (DSS) as endpoints. We applied whole-exome sequencing, copy number analysis (SNP6.0) and RNAseq to identify somatic mutations, copy number aberrations and differentially expressed genes, respectively. FISH was applied to assess the presence of rearrangements affecting MYC, BCL2 and BCL6. We used data previously obtained within our centre from a PMBCL cohort (n=73) to compare mPMBCL with "bona fide" PMBCL (PMBCL) tumors (Mottok et al, Blood 2019). Results: Median age at diagnosis was significantly higher for mPMBCL compared to PMBCL (62 vs 37 years, p<.001). Only 3 out of 19 mPMBCL presented with an anterior mediastinal mass, a clinical hallmark of PMBCL. Importantly, these cases demonstrated extensive extrathoracic disease, which is unusual for PMBCL. Pleural and/or pericardial effusion was seen in only 11% of mPMBCL compared to 50% of PMBCL (p<.001). Bone marrow involvement was seen in 21% of mPMBCL and not observed in any PMBCL (p<.001). The mPMBCL had a trend toward worse outcomes compared to R-CHOP-treated PMBCL (n=61) (2 year TTP: 62% vs 77%, log-rank p=.12; DSS: 74% vs 84%, p=.10). By COO assignment of the DLBCL90 assay, 16 out of 19 mPMBCL were categorized as GCB-DLBCL. Compared to the other GCB-DLBCL in our DLBCL cohort (excluding DHIT-signature-positive cases), the mPMBCL had inferior outcomes (2-year TTP: 62% vs 87%, log-rank p=.03; DSS: 72% vs 89%, p=.02). There were no significant differences in baseline clinical characteristics between mPMBCL and GCB-DLBCL. Comparison of the mutational landscape of mPMBCL to PMBCL demonstrated perturbations in the central hallmarks of PMBCL pathogenesis: JAK/STAT signaling, NF-ĸB pathway activation and immune evasion. Genomic aberrations affecting JAK-STAT signaling were shared between mPMBCL and PMBCL, with SNVs or indels affecting IL4R, STAT6 and SOCS1 found in 37%, 37%, and 89% of mPMBCL and 36%, 40% and 69% of PMBCL, respectively. Moreover, copy number analysis revealed JAK2 amplifications in 44% of mPMBCL (71% of PMBCL) and differential gene expression analysis showed increased levels of CD274 (PDL1), PDCD1LG2 (PDL2) and genes belonging to the JAK-STAT-signaling network in mPMBCL. In contrast, these genetic aberrations were rarely observed in a recent whole-exome sequencing study on 304 primary DLBCL tumors (Chapuy B., Nat.Med. 2018). Mutations were also observed in NF-ĸB pathways but the patterns of mutations were distinct between mPMBCL (BIRC3 and BTK) and PMBCL (NKBIE) suggesting convergent biology with alternative mechanisms of pathway dysregulation. Similarly, mPMBCL harbored different mutations (CD83) implicated in immune evasion compared with PMBCL (B2M). Finally, we compared the mutational landscape of mPMBCL with recently described genetically-defined subgroups of DLBCL. Interestingly, a large majority of mPMBCL harbored at least one of the mutations characteristic of "Cluster 4" (incl. CD83, HIST1H1E, SGK1), a subset of DLBCLs defined by Chapuy et al that predominantly includes GCB-DLBCLs. Conclusion: We have identified and characterized a subgroup of DLBCL that expresses the PMBCL gene expression signature. Similar to bona fide PMBCL, these tumors are characterized by genomic aberrations that affect JAK-STAT, NF-ĸB signaling and immune response. However, our data suggest that dysregulation of the latter two pathways is established through distinct evolutionary modes that are reflected in differential mutation patterns and anatomical and clinical presentations. Our findings provide potential novel therapeutic avenues for this subset of lymphoma. Disclosures Sarkozy: Takeda: Research Funding. Savage:BMS, Merck, Novartis, Verastem, Abbvie, Servier, and Seattle Genetics: Consultancy, Honoraria; Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding. Scott:Celgene: Consultancy; Roche/Genentech: Research Funding; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding. Steidl:Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Juno Therapeutics: Consultancy; Tioma: Research Funding; Roche: Consultancy; Bayer: 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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

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 Opus0,011
Tête enseignante GPT0,270
Écart entre enseignants0,259 · 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

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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