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Enregistrement W2985936603 · doi:10.1182/blood-2019-124235

Molecular Correlates of Central Nervous System Relapse in Diffuse Large B-Cell Lymphoma

2019· article· en· W2985936603 sur OpenAlexaff
Robert Kridel, Keren Isaev, Daisuke Ennishi, Brian Skinnider, Karen Mungall, Andrew J. Mungall, Mehran Bakhtiari, Rosemarie Tremblay‐LeMay, Barbara Meissner, Susana Ben‐Neriah, Merrill Boyle, Diego Villa, Marco A. Marra, Christian Steidl, Randy D. Gascoyne, Kerry J. Savage, David W. Scott

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
Thématique14-3-3 protein interactions
Établissements canadiensUniversity Health NetworkSpinal Cord Injury BCCanada's Michael Smith Genome Sciences CentrePrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésDiffuse large B-cell lymphomaMedicineOncologyInternal medicineInternational Prognostic IndexLymphomaGene expression profilingPathologyGene expressionBiologyGene

Résumé

récupéré en direct d'OpenAlex

Introduction: Central nervous system (CNS) relapse is a rare phenomenon in diffuse large B-cell lymphoma (DLBCL), occurring in less than 5% of all patients, but is associated with disproportionate morbidity and mortality. Indeed, the median survival of patients diagnosed with CNS relapse is as low as 2-4 months. Individual risk factors for CNS relapse are well established, and include clinical parameters such as stage, number/type of extranodal sites and elevated lactate dehydrogenase. These and other clinical risk factors have been integrated into a risk score that is reproducible and easy to calculate (CNS International Prognostic Index). Moreover, molecular attributes such as double-hit translocation status, MYC/BCL2 dual protein expression and the activated B-cell-like subtype have been associated with a higher risk of CNS relapse. However, while experts recommend prophylactic interventions for high-risk patients, the major shortcoming of available risk tools is their limited sensitivity. Herein, we evaluated whether gene expression and/or mutational profiles can identify those patients that will ultimately experience CNS relapse, and whether intratumoral heterogeneity impedes accurate prognostication. Methods: We accrued diagnostic FFPET samples from 230 newly diagnosed DLBCL patients, selected to fall into 3 clinical groups: 1) cases with CNS relapse/CNS involvement at diagnosis (n=58); 2) cases with systemic relapse but without CNS relapse (n=64) and 3) cases without any relapse (n=108). These 230 samples were subjected to microarray-based gene expression profiling and differential gene expression analysis. Pathway analysis was performed using Gene Set Enrichment Analysis on ranked gene lists. We assembled a partially overlapping dataset with mutation data of 45 genes in 213 diagnostic samples (n=65 with CNS relapse, 62 with systemic relapse and 86 without relapse). Lastly, we performed exome sequencing in 5 pairs (peripheral and CNS parenchymal tumors) of patients with CNS relapse or CNS involvement at diagnosis, and reconstructed clonal phylogenies using PyClone. Results: Focusing on gene expression data at first, we did not observe significant differential expression between CNS relapse and non-relapse cases at the individual gene level. This was in contrast to the comparison between systemic relapse vs. non-relapse cases where 368 genes were differentially expressed (adjusted P<0.05). In terms of pathway analysis, minimal gene set enrichment was seen in CNS relapse cases, whereas functional annotations such as translation, ribosome biogenesis and MYC targets were significantly enriched in cases with systemic relapse. In keeping with these observations, the percentage of cases that were positive for the recently published double hit signature was highest in cases with systemic relapse (64% vs. 39% in CNS relapse cases and 27% in cases without relapse, P=0.012). However, CNS relapsing cases were defined by down-regulation of numerous immune signatures (e.g. interferon and multiple T cell signatures), suggesting that an intact immune response may have a protective effect on CNS relapse. Considering mutation data, we found that TP53 and SGK were most commonly mutated in systemic relapse cases, while TNFRSF14 and KTM2D were most commonly mutated in non-relapse cases (all adjusted P<0.05). The only gene mutation with a borderline significant trend for enrichment in CNS relapse cases was MYD88 (adjusted P=0.05). We then performed exome sequencing of 5 tumor pairs. A subset of high-confidence somatic variants and tumor purity were used as input for PyClone to infer clonal population structures. In all pairs, we documented the existence of common ancestral mutations, as well as significant clonal divergence, with CNS-exclusive mutations not identified in diagnostic specimens. Conclusion: In summary, we have documented that CNS and systemic relapse result from distinct biological processes that, in part, may be associated with the underlying taxonomy of DLBCL. Our findings further show that CNS relapse results from the dissemination of sub-clones that may not be readily sampled at the time of diagnosis, and that intratumoral heterogeneity may limit our ability to predict CNS relapse. Large-scale, integrative analyses and in-depth characterization of clonal trajectories hold the promise to increase our ability to predict dissemination of DLBCL into the CNS. Disclosures Kridel: Gilead Sciences: Research Funding. Villa:Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen, Gilead: Consultancy, Honoraria. Steidl:Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Juno Therapeutics: Consultancy; Bristol-Myers Squibb: Research Funding; Roche: Consultancy; Tioma: Research Funding; Bayer: Consultancy; Seattle Genetics: Consultancy. 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; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; 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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,037
Score d'incertitude au seuil0,542

Scores Codex et Gemma par catégorie

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)0,0000,000

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 Opus0,003
Tête enseignante GPT0,192
Écart entre enseignants0,189 · 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 tête enseignante, 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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