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Enregistrement W2991549326 · doi:10.1182/blood-2019-127375

Mutational Landscape of Grey Zone Lymphoma

2019· article· en· W2991549326 sur OpenAlexaff
Clémentine Sarkozy, Stacy Hung, Katsuyoshi Takata, Elizabeth A. Chavez, Tomohiro Aoki, Gerben Duns, Graham W. Slack, Adèle Telenius, Tomoko Miyata‐Takata, Elena Viganò, Thierry Jo Molina, Diane Damotte, Susana Ben‐Neriah, Christiane Copie‐Bergman, Camille Laurent, Anja Mottok, Gilles Salles, Kerry J. Savage, David W. Scott, Alexandra Traverse‐Glehen, Christian Steidl

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensBC Cancer AgencySpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésExome sequencingExomeLymphomaIndelBiologyPathologyMedicineCancer researchGeneticsGeneSingle-nucleotide polymorphismMutation

Résumé

récupéré en direct d'OpenAlex

Introduction: Grey zone lymphoma (GZL), a B-cell lymphoma with features intermediate between large B-cell lymphoma (LBCL) and classical Hodgkin lymphoma (cHL), is a rare and poorly defined entity. To decipher its mutational landscape and discover new therapeutic targets, we performed exome sequencing of 31 GZL cases. Methods: GZL cases from the LYSA group (N=139) and BC Cancer (N=30) were centrally reviewed and classified as previously published (Sarkozy et al, Am J Surg Pathol 2019). Whole-exome sequencing was performed on 31 cases with available fresh frozen tissue, using laser micro-dissection (LMD, MMI technology) to enrich for tumor cells and obtain matching normal DNA from microenvironment cells. DNA was extracted (Agencourt® DNAdvance kit) and genomic libraries were constructed with the Ovation ultra-low kit (Nugen®). Exome capture was performed using Agilent SureSelectXT V6+UTR followed by paired-end sequencing (NextSeq®). Somatic nucleotide variants (SNVs) and indels were identified using VarScan, Strelka and Mutect. Parameters affecting the sensitivity and specificity of variant calling were optimized using 7 "gold standard" cases for which DNA from peripheral blood cells was additionally available. Possible oncogenic drivers were identified based on rate of recurrence, MutSigCV and literature review. Results: Among the 31 GZL cases, the median age was 41 y (14-83) with a sex ratio of 15M:16F; 21 cases had mediastinal involvement, including 15 within the thymic area; EBER in-situ hybridization (ISH) was positive in 8 cases. Seven (23%) cases were classified as group-0 (cHL morphology with 100% CD20 expression), 22 (71%) with an intermediate morphology as group-1 (N=9, cHL-like morphology) or group-2 (N=13, LBCL-like morphology) and 2 (6%) as group-3 (LBCL with 100% of CD30 expression). The mean coverage was 96X (42-203) for tumor samples. One case was excluded due to failure in the LMD process. Among the 30 cases, 6628 variants across 4826 genes were found, including 2903 coding mutations (325 indels and 2808 SNVs, mean of 104/sample, range: 15-678), 721 affecting the 5' UTR and 2774 the 3' UTR. A total of 152 genes were identified as being potential oncogenic drivers, with a mean of 11 mutated genes per case (range 2-36). The most recurrently mutated genes were SOCS1 (33%), B2M (23%), GNA13 (20%), LRRN3 (17%), and ZNF217, NCOR1, ITPKB, IRF2BP2, CSF2RB, and CSMD3 (13% each). The epigenetic SWI/SNF and transcription regulation pathway (including NCOR1/2, ARID1A, KMT2D, KMT2A) was affected in 73% of the cases, JAK/STAT in 70% and NF-kB in 19%. As assessed by CNVkit and GISTIC, the most recurrent gains/amplifications identified were in 9p24.1 (JAK2, CD274, PDCD2LG2; 69%) and 2p16.1 (REL, BCL11A; 62%), and losses in 11q14.3 (ATM; 48%) and 12q24.33 (NCOR2; 48%). Based on mutational signature analysis, individual base substitutions were linked to mutagenic processes, with the highest contributions associated with aging (29%) and defective DNA mismatch repair (27%); moreover, mutations attributable to AID/APOBEC activity (5%), were found to be significantly enriched in EBV- vs. EBV+ cases (p = 0.013). EBV+ cases had fewer total variants (mean 98 vs 258, p=0.08) and potential oncogenic variants (mean 7 vs 15, p=0.03) compared to EBV- cases. EBV+ cases also lacked mutations in the NF-kB pathway and MHC-class I components (B2M and HLA-B: 0% vs 43% in EBV-, p=0.06) but had mutations in STAT3, DHX58, ACTB and ATP13A4 (6/7 cases) not present in the 23 EBV- cases. LRRN3 and GNA13 mutations were significantly associated with thymic area involvement (40% vs 0%, p=0.01). Furthermore, fluorescence-ISH indicated that 20% (1/5) of EBV+ cases had a rearrangement in the CIITA locus (16p13.13) vs 53% (9/17) in EBV- cases. Patients with an intermediate morphology had more oncogenic variants than those in group 0 and 3 (mean of 15 vs 6 variants/case, p=0.01 affecting 12 vs 5 genes, p=0.004). Finally, NCOR1 (N=4) and NCOR2 (N=2) mutations were exclusively found in cases with intermediate morphology (23% vs 0% for those with group 0 or 3 morphology). Conclusion: These data suggest that GZL is a highly heterogenous disease harboring somatic driver events shared with PMBCL and HL. We also discovered novel gene mutations pointing to the importance of previously unrecognized pathways in the pathogenesis of GZL. The distinct mutational pattern in EBV+ GZL suggests divergent evolutionary trajectories. Disclosures Sarkozy: Takeda: Research Funding. Salles:Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; BMS: Honoraria; Amgen: Honoraria, Other: Educational events; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. 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:Juno Therapeutics: Consultancy; Tioma: Research Funding; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Seattle Genetics: Consultancy; Bayer: 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)0,001
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 enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
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 Opus0,008
Tête enseignante GPT0,229
Écart entre enseignants0,222 · 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

Citations6
Publié2019
Routes d'admission1
Résumé présentoui

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