Impact of the Dark Zone Signature on Central Nervous System Relapse in a Real‐World Diffuse Large B‐cell Lymphoma Population
Notice bibliographique
Résumé
Introduction: Central nervous system (CNS) relapse in diffuse large B-cell lymphoma (DLBCL) is associated with dismal outcomes necessitating the identification of high-risk patients (pts). To refine CNS risk stratification, a better understanding of the role of molecular risk factors is required. We previously described the dark zone signature (DZsig), which refines the cell of origin (COO) classification and identifies pts within germinal centre B-cell-like (GCB) DLBCL with inferior outcomes. Within DZsig expressing tumours (DZsig+) of DLBCL morphology, ∼40% harbour ‘double hit’ MYC and BCL2 rearrangements (DH), whereas ∼60% lack DH (Ennishi et al JCO 2018, Alduaij et al Blood 2022). Here we report the incidence and characteristics of CNS relapse in DZsig+ DLBCL in relation to DH status and the CNS International Prognostic Index (CNS-IPI) in an unselected, real-world DLBCL population. Methods: All pts with de novo tumours of DLBCL morphology diagnosed in British Columbia, Canada, during 2005–2010 with evaluable diagnostic biopsies, without confirmed CNS involvement at diagnosis and treated with curative intent, were included. Evaluable biopsies were profiled by fluorescence in situ hybridization (FISH), immunohistochemistry and digital gene expression profiling (GEP) to assign COO and DZsig. Cumulative incidence of CNS relapse was estimated while accounting for the competing risk of death from other causes. Results: Of 1149 pts, 804 had evaluable GEP results, 797 had no CNS involvement at diagnosis and 670 were treated with curative intent, mostly R-CHOP (Table 1). With a median follow-up of 12.4 years (y), the cumulative incidence of CNS relapse at 2 y in DZsig+ was significantly higher than in non-DZsig GCB (6.4% vs. 1.0% p = 0.03, Figure 1) regardless of the presence of DH by FISH (DZsig+ without DH 6.8% vs. DZsig+ with DH 6.7% p = 0.99). CNS relapse events in DZsig+ occurred more frequently in pts with a high CNS-IPI (2 y risk: 20% high vs. 3.4% low/intermediate (int) p = 0.02). In a multivariable competing risk analysis that included COO and CNS-IPI, high CNS-IPI was significantly associated with CNS relapse (hazard ratio with 95% confidence interval [CI]: 3.7 [1.1–12.6] p = 0.035) with a trend towards higher risk in DZsig+ relative to non-DZsig GCB (3.2 [0.95–10.5] p = 0.06). All CNS relapses in DZsig+ occurred early (<1 y from diagnosis) and more frequently involved the leptomeninges than non-DZsig GCB or ABC (p = 0.01, Table 1). The research was funded by: Canadian Cancer Society Research Institute (704848 and 705288), Genome Canada (4108), Genome British Columbia (171LYM), the Canadian Institutes of Health Research (GPH- 129347 and 300738), the Terry Fox Research Institute (1061 and 1043), and the British Columbia Cancer Foundation. The presenter is supported by the Kuwait Ministry of Health, the Leukemia and Lymphoma Society of Canada/Canadian Institutes of Health Research Clinician Scientist Fellow award and the Michael Smith Health Research, British Columbia Research Trainee award. Keywords: Aggressive B-cell non-Hodgkin lymphoma, Diagnostic and Prognostic Biomarkers Conflicts of interests pertinent to the abstract. D. Villa Honoraria: Roche, Abvie, Beigene, Janssen, AZ, BMS/Celgene, Kite/Gilead, ONO Therapeutics, Zetagen Research funding: Roche, AZ (to the institution) A. S. Gerrie Honoraria: Abbvie, AstraZeneca, Janssen, Sandoz Research funding: Abbvie, AstraZeneca, Janssen L. H. Sehn Consultant or advisory role: Teva, Roche/Genentech Chugai, AbbVie, Acerta, Amgen, Apobiologix,AstraZeneca, BMS/Celgene, Debiopharm,Genmab, Gilead, Incyte, Janssen, Kite,Karyopharm, Lundbeck, Merck, Morphosys,Novartis, Sandoz, Seattle Genetics, Servier,Takeda, TG Therapeutics, Verastem Honoraria: AbbVie, Acerta, Amgen, Apobiologix,AstraZeneca, BMS/Celgene, Gilead, Incyte,Janssen, Kite, Karyopharm, Lundbeck, Merck, Morphosys, Sandoz, Seattle Genetics, Servier,Takeda, TG Therapeutics, Verastem, Chugai, Teva, Roche/Genentech Research funding: Teva, Roche/Genentech D. W. Scott Consultant or advisory role: Abbvie, AstraZeneca, Incyte, Janssen Honoraria: AstraZeneca Research funding: Janssen, Roche Other remuneration: NanoString- Patents and Royalties K. Savage Employment or leadership position: Beigene and Regeneron Consultant or advisory role: Seagen Honoraria: BMS, Merck, Astra Zeneca, Janssen, Abbvie Other remuneration: Regeneron (DSMC), Beigene (Steering committee)
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».