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Enregistrement W3097778032 · doi:10.1182/blood-2020-137258

Double-Hit Signature with <i>TP53</i> Abnormalities Predicts Poor Survival in Patients with Germinal Center Type Diffuse Large B-Cell Lymphoma Treated with R-CHOP

2020· article· en· W3097778032 sur OpenAlexaff
Joo Y. Song, Anamarija M. Perry, Alex F. Herrera, Lu Chen, Pam Skrabek, Michel R. Nasr, Rebecca A. Ottesen, Janet Nikowitz, Victoria Bedell, Joyce Murata‐Collins, Yuping Li, Christine McCarthy, Raju Pillai, Jinhui Wang, Xiwei Wu, Jasmine Zain, Leslie Popplewell, Larry W. Kwak, Auayporn Nademanee, Joyce C. Niland, David W. Scott, Qiang Gong, Wing C. Chan, Dennis D. Weisenburger

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensSpinal Cord Injury BCUniversity of British ColumbiaCancerCare Manitoba
Organismes subventionnairesnon disponible
Mots-clésDiffuse large B-cell lymphomaBCL6Germinal centerLymphomaFluorescence in situ hybridizationBiologyCancer researchComparative genomic hybridizationImmunophenotypingOncologyInternal medicinePathologyMedicineB cellImmunologyGeneticsGeneFlow cytometry

Résumé

récupéré en direct d'OpenAlex

Background: In diffuse large B-cell lymphoma (DLBCL), the presence of MYC and BCL2 and/or BCL6 translocations, so-called double-hit lymphoma (DH), has been associated with an aggressive clinical course. Recently, it was reported that gene expression profiling (GEP) could also identify cases with the biological and clinical characteristics of DH lymphoma, including some without the requisite translocations (DHITsig-positive cases)1. The purpose of this study was to develop a molecular subtyping schema for germinal center B-cell type (GCB) DLBCL using genomic studies such as fluorescence in situ hybridization (FISH) cytogenetic analysis, GEP, and mutation analysis to risk-stratify patients with GCB DLBCL. Method and Results: We performed a detailed genomic analysis of 87 cases of de novo GCB DLBCL to identify characteristics that are associated with survival in those treated with R-CHOP. The cases were extensively characterized by combining the results of immunohistochemistry, cell-of-origin GEP (Nanostring), DH GEP (DLBCL90)1, FISH cytogenetic analysis for DH lymphoma, copy number analysis (CNA), and targeted deep sequencing using a custom mutation panel of 334 genes. These studies were used to divide the cases into four groups. GCB1: DHITsig-positive with TP53 inactivation (DHIT+TP53): DLBCL with TP53 mutations and/or deletions has a poor prognosis in patients treated with R-CHOP. We found 7 cases (8% of all cases) of GCB DLBCL that were DHITsig-pos with TP53 abnormalities. By FISH analysis, two cases had a triple-hit (TH), one was DH with MYC/BCL2, and 2 cases had a MYC translocation only. Cases in GCB1 had the worst overall survival (OS; Hazard Ratio (HR)=9.2, P=0.0018) and shortest progression-free survival (PFS; HR=6.1, P=0.002) compared to other groups (Figures 1 A/B). However, cases with TP53 abnormalities that were DHITsig-neg did not have the same poor survival. GCB2: DHITsig-positive (DHITsig-pos): The other 8 cases (9%) who were DHITsig-pos from the DBLCL90 GEP but lacked TP53 abnormalities showed a predilection (88%) for having an EZH2 mutation and/or BCL2 translocation (EZB of Schmitz et al2). These cases also had a high frequency of MYC mutations (63%) but lacked mutations in SGK1 and had a low frequency of mutations in linker histone genes (e.g. HIST1H1E). By FISH analysis, 3 cases were DH lymphoma with MYC/BCL2, 2 cases were TH lymphoma, and 1 case had a MYC translocation only. Typically DHITsig-pos cases have a poor OS when compared to DHITsig-neg cases1, however this group demonstrated good survival in our study, after removing the cases with TP53 abnormalities. GCB3: DHITsig-negative and EZH2 mutation and/or BCL2 translocation (EZB-like): We had 28 cases (32%) that were DHITsig-neg and had an EZH2 mutation and/or BCL2 translocation. These were categorized as EZB-like with some overlapping features with the DLBCL in Cluster 3 of Chapuy et al3. The survival of this group was intermediate compared to the other groups (Figures 1A/B). GCB4: DHITsig-negative and not EZB-like (GCB Other): The largest group of cases (51%) were DHITsig-neg and lacked EZH2 mutations and BCL2 translocations. These cases had frequent mutations in SGK1 (16%) and histone modifying genes (50%), as well as TET2 mutations (25%). These cases have similarities to Cluster 4 of Chapuy et al3 and the ST2 group from Wright et al4. The survival of this group was excellent (Figures 1 A/B). These groups were validated in an independent cohort of 188 cases of GCB DLBCL4 (Figures 1 C/D). Conclusions: We have identified four distinct biologic subgroups of GCB DLBCL with different survival rates, and with similarities to the genomic classifications from recent large retrospective studies of DLBCL. Patients with the DH signature but no abnormalities of TP53 (GCB2), and those lacking EZH2 mutation and BCL2 translocation (GCB4), had an excellent prognosis. However, patients with an EZB-like profile (GCB3) had an intermediate prognosis, whereas those with TP53 inactivation combined with the DH signature (GCB1) had an extremely poor prognosis. We propose this as a practical schema to risk-stratify patients with GCB DLBCL. This schema provides a promising new approach to identify high-risk patients for new and innovative therapies. Figure 1 Disclosures Herrera: AstraZeneca: Research Funding; Karyopharm: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Merck: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Other: Travel, Accomodations, Expenses, Research Funding; Gilead Sciences: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Immune Design: Research Funding; Pharmacyclics: Research Funding. Zain:Kyowa Kirlin: Research Funding; Mundai Pharma: Research Funding; Seattle Genetics: Research Funding. Popplewell:Pfizer: Research Funding; Novartis: Research Funding; Roche: Research Funding. Kwak:Celltrion Healthcare: Membership on an entity's Board of Directors or advisory committees; CJ Healthcare: Consultancy; Sellas Life Sciences Grp: Consultancy; Enzychem Life Sciences: Membership on an entity's Board of Directors or advisory committees; Antigenics: Other: equity; InnoLifes, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pepromene Bio: Consultancy, Membership on an entity's Board of Directors or advisory committees; Xeme Biopharma/Theratest: Other: equity; Celltrion, Inc.: Consultancy. Scott:NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy; Janssen: 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 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 enseignants0,001
Score d'incertitude au seuil0,002

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
Bibliométrie0,0010,000
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 Opus0,008
Tête enseignante GPT0,196
Écart entre enseignants0,188 · 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

Citations2
Publié2020
Routes d'admission1
Résumé présentoui

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