Long-Term Results of Cytarabine-Containing Induction Followed By Consolidation with Autologous Stem Cell Transplant and Rituximab Maintenance As Primary Treatment for Mantle Cell Lymphoma
Notice bibliographique
Résumé
Abstract Introduction: Mantle cell lymphoma (MCL) often follows an aggressive course and remains incurable with standard therapies. First-line chemotherapy followed by consolidation with high-dose chemotherapy (HDT) and autologous stem cell transplant (ASCT) has become a standard of care in eligible patients (pts). As relapse remains the main cause of treatment failure, strategies such as intensifying induction therapy with high-dose cytarabine or adding rituximab maintenance (RM) have been tested to reduce the relapse rate (RR) post-ASCT. We evaluated the effect of the addition of cytarabine and RM on the outcome of pts undergoing ASCT. Methods: We conducted a retrospective analysis of consecutive MCL pts who underwent ASCT after first-line chemotherapy at the Princess Margaret Cancer Centre between 2000-2013. Pts received induction with CHOP, RCHOP, or RCHOP alternating with RDHAP (RCHOP/RDHAP), followed by HDT with or without total body irradiation (TBI). All pts had a documented response to induction using Cheson 1999 criteria. After ASCT, pts received maintenance with single-agent rituximab 375 mg/m2 or were simply observed. Results: 98 MCL pts were treated: median age was 56 years (36-66), 15 pts (15%) had blastoid or pleomorphic subtype, 85 pts (87%) had stage IV disease. MIPI was high risk in 18 pts (19%). Induction therapy: CHOP 14 pts (14%), RCHOP 57 (58%), and RCHOP/RDHAP 27 (28%). After induction CR was obtained in 44%, PR in 56% pts. CR rates were: CHOP 7 (50%), RCHOP 25 (44%), RCHOP/RDHAP 12 (44%) (P=ns). 89% pts had collected > 5*106 CD34/kg after RCHOP, and 78% after RCHOP/RDHAP (P=ns). Overall 66/98 pts (67%) had > 5*106 CD34/kg collected with 1 apheresis (Table 1). HDT was melphalan+etoposide for 63% pts, cytarabine+melphalan for 31% pts; 77 (79%) also received TBI. Median time from diagnosis to ASCT was 7.5 months (2.5, 33.4). Post-ASCT responses: CR 92 pts (94%), PR 4 (4%), 2 (2%) PD. Median time to ANC ≥0.5 were 10 days (CHOP), 11 days (RCHOP), and 11 days (RCHOP/RDHAP), while median days to PLT≥20 were 9 (CHOP), 11.5 (RCHOP), and 13 (RCHOP/RDHAP). Post-ASCT, 31% of pts had normal blood counts at 3 months which improved to 52% at 1 year post-ASCT. Maintenance data were available for 95/98 pts. RM was given to 72 pts (74%). Median follow-up from date of transplant for the entire cohort was 3.22 years (range 0.7 - 14.1). The 2-year and 5-year PFS were 85.8% (76.7-91.5) and 52.2% (37.7-64.7), respectively. 32 pts relapsed after ASCT (32.65%). Relapse occurred in 3 (11%) pts after RCHOP/RDHAP, 19 (33%) after RCHOP, and 10 (71%) after CHOP. Median time to relapse was 9 years (95%CI: 4.7-NR). 2-year and 5-year RR were 14.54% and 41.65%, respectively. Median OS was 9.15 years (95%CI: 7.3-NR), 2-year OS was 88.8% (80.2-93.8), and 5-year OS was 74.9% (61.7-84.2%). For patients observed without treatment post-ASCT, median PFS was 2.87 years (1.22-4.63) and median OS 5.19 years (1.66-NR), while for those receiving RM, PFS was 9.06 years (4.97-NR, p<0.001) and median OS has not yet been reached (7.30- NR, p=0.009). Conclusions: Response rate and PFS were similar between different induction regimens. The outcomes of responding pts following ASCT appear superior to previous strategies. Our patients enjoyed a very long PFS and median OS is surprisingly long as well. Within the limits of a retrospective study, our data support the use of rituximab maintenance, showing a significant benefit in both PFS and OS. Table 1. ASCT data Stem cell collection CHOP RCHOP RCHOP/RDHAP P value Pts collecting > 2x106 /Kg CD34+ cells in 1 day 4/14 (29%) 44/57 (77%) 17/27 (67%) 0.002 Pts collecting 2-5 x106 /Kg CD34+ cells N/A 6/57 (11%) 6/27 (22%) Pts collecting >5 x106 /Kg CD34+ cells N/A 51/57 (89%) 21/27 (78%) 0.153 Engraftment median (range) Days to ANC ≥ 0.5 10 (9,11) 11 (9, 12) 11 (9, 12) Pts with ANC ≥ 0.5 ≤ 11 days 13/13 (100%) 45/52 (87%) 21/25 (84%) 0.33 Days to PLT ≥ 20 9 (7, 15) 11.5 (9, 17) 13 (9, 26) Pts with PLT ≥ 20 ≤ 12 days 12/13 (92%) 37/52 (71%) 8/25 (32%) 0.0001* Days from ASCT to discharge 13 (11,26) 14 (11,30) 13 (11,23) PTs requiring RBC Transfusions 11 (79%) 43 (75%) 22 (81%) 0.821 Number of RBC Transfusions 2 (0, 4) 2 (0, 7) 3(0, 6) Pts requiring PLT Transfusions 12 (86%) 52 (93%) 25 (93%) 0.759 Number of PLT Transfusions 1 (0, 4) 2 (1, 9) 3 (1, 5) * comparison CHOP Vs R-CHOP: not significant, p=0.113 Legend. ASCT: autologous stem cell transplant; Pts: patients; ANC: absolute neutrophils count; PLTs: platelets; RBC: red blood cells. Disclosures Kuruvilla: Hoffmann LaRoche: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Gilead: Consultancy; Janssen: Consultancy, Honoraria; Merck: Honoraria; Bristol-Myers Squibb: Honoraria; Lundbeck: Honoraria; Karyopharm: Honoraria.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».