Gray Zone Lymphoma (GZL) with Features Intermediate Between Classical Hodgkin Lymphoma (cHL) and Diffuse Large B-Cell Lymphoma (DLBCL): Analysis of Tumor Immunophenotype (IP) and Critical Examination of Therapy with Associated Impact on Outcome
Notice bibliographique
Résumé
Abstract Background: The WHO recognizes a category of B-cell lymphoma unclassifiable with features intermediate between DLBCL and cHL, also known as GZL. This is a challenging disease entity to treat due to disease heterogeneity and lack of pathologic or clinical prognostic indicators as well as absence of standard management guidelines for untreated or relapsed/refractory patients (pts). Methods: We performed a multicenter retrospective analysis of newly diagnosed GZL pts treated from 2001-2012 across 19 North American academic centers. Diagnosis was established by institutional expert pathology review. This work is an expansion and update of prior reported data (Evens AM et al, ASH 2013, #847) with 16 cases added to the original report. Additionally, new data were examined including histopathologic and IP analysis and detailed frontline and salvage therapy. Prognostic factors associated with survival on univariate and multivariate Cox regression analyses were examined. Results: Characteristics of 112 GZL pts included: median age 39 years (19-86); M:F 1.8:1; ECOG PS 0-1 87%; B symptoms 40%; anemia 59%; hypoalbuminemia 30%; bulky disease (≥10cm) 24%; non-mediastinal presentation 57%; bone marrow pos 11%; Stage III/IV 52%; IPI 0-2 77%; and IPS 0-2 in 81%. The most prevalent tumor IPs were: 93% CD20+ (100/108), 91% CD30+ (98/108), 78% CD79+ (43/55), 97% Pax5+ (67/69), 97% Oct2+ (27/28), and 94% MUM1+ (32/32). CD15 (44%, 45/101) and CD45 (69%, 48/70) were variable. Only 13% and 24% of pts were CD10+ (4/30) and EBV+ (13/55), respectively. Notably, IP did not differ based on clinical presentation (ie, mediastinal vs non-mediastinal). The most common frontline treatments were R-CHOP n=52, ABVD +/- R n=34, and R-EPOCH n=11. 71% of CD20+ pts were treated with rituximab as part of frontline therapy. At 31-month median follow-up, 2-year PFS and OS for all pts were 40% and 88%, respectively. The only pathologic factor correlating with outcome was CD20 positivity (PFS: HR 0.34, 95% CI 0.16-0.73, P=0.006). Characteristics correlating with PFS were anemia (HR 0.51, 95% CI 0.29-0.91, P=0.022), low albumin (HR 0.57, 95% CI 0.32-1.00, P=0.05), and IPI (continuous: HR 1.48, 95% CI 1.19-1.82, P=0.0003). For therapy, 2-year PFS and OS for R-CHOP were 46% and 84%, respectively; ABVD+/-R 25% and 96%, respectively; and R-EPOCH 68% and 83%, respectively (Fig. 1). R-EPOCH predicted improved PFS (0.047), however this effect was abrogated after controlling for IPI, anemia and hypoalbuminemia (P=0.2). Pts who received rituximab with frontline therapy had improved 2-year PFS (51% vs 19%, respectively, P=0.012). Interestingly, the significance of CD20 persisted on Cox regression controlling for rituximab (rituximab HR 0.55, 95% 0.33-0.93, P=0.025; CD20 0.35, 95% CI 0.16-0.75, P=0.007). Furthermore, the effect of rituximab remained significant after controlling for IPI, anemia, and hypoalbuminemia (HR 0.35, 95% CI 0.18-0.69, P=0.002). Overall, 58% of pts relapsed with median time to relapse of 7 months (0-64); the mean number of salvage therapies was 3. Regimens at 1st relapse included: R-ICE (n=33), R-ESHAP (n=6), R-EPOCH (n=7), ABVD (n=1) and brentuximab vedotin (n=4). Beyond first relapse, the most common treatments were: brentuximab vedotin (n=7) and radiation (n=5). 61% of relapsed pts proceeded to stem cell transplantation (SCT) (38% allogeneic, 62% autologous). 20/27 (74%), 20/32 (63%), and 3/3 (100%) of pts who relapsed after frontline ABVD, R-CHOP and R-EPOCH, respectively, had SCT at relapse. 2-year OS was superior for pts who had SCT (88% vs 67%, P=0.014; Fig. 2), which persisted on multivariable regression (SCT: HR0.14, 95% CI 0.02-0.95, P=0.044; IPI continuous: HR 2.04, 95% CI 1.00-4.16, P=0.05; anemia: HR 2.40, 95% CI 0.32-18.25, P=0.4; low albumin: HR 5.54, 95% CI 1.08-28.44, P=0.04). Conclusions: To the best of our knowledge, this represents the largest series of GZL reported to date. Presence of CD20 appeared to be an independent prognostic factor and treatment with a rituximab-based DLBCL-specific regimen for frontline therapy was associated with the most optimal PFS. In addition, pts who underwent SCT at relapse appeared to have superior OS, however caution should be given to this finding given likely selection bias. Continued examination of this unique lymphoma is warranted. Figure 1. PFS comparison of frontline therapeutic regimens for GZL. Figure 1. PFS comparison of frontline therapeutic regimens for GZL. Figure 2. Impact of SCT on OS for patients with relapsed/refractory GZL Figure 2. Impact of SCT on OS for patients with relapsed/refractory GZL Disclosures Bartlett: Seattle Genetics, Inc.: Other, Research Funding; Takeda Pharmaceuticals International Co.: Research Funding; Pfizer: Research Funding; Pharmacyclics: Research Funding; Novartis: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; ImaginAb: Research Funding; Genentech: Research Funding; Janssen: Research Funding; AstraZeneca: Research Funding. Mato:Genentech, Celgene, Millenium: Speakers Bureau. Advani:Seattle Genetics, Inc.: Research Funding, Travel expenses Other; Genentech: Research Funding; Janssen Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Takeda International Pharmaceuticals Co.: Research Funding. Blum:Janssen, Pharmacyclics : 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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».