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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".