Is Tandem ASCT Needed in MM Patients with High Risk Cytogenetics in the Era of Maintenance Therapy? Results from the Canadian Myeloma Research Group (CMRG) Database
Bibliographic record
Abstract
Background: Recent studies evaluating tandem autologous transplantation for multiple myeloma (MM) show conflicting results in terms of efficacy. However subgroup analysis suggests that those with high-risk disease may benefit the most from tandem transplant. We used the CMRG database to compare single versus tandem ASCT for patients with MM with high-risk cytogenetics. Methods: The primary objective was to compare PFS in MM patients with high-risk cytogenetics (p53 deletion, t(4;14), t(14;16)) identified from the CMRG database undergoing front-line single or tandem ASCT from 01/2010 to 06/2019. Secondary objectives compared OS, ORR, and outcomes based on whether post-transplant maintenance was given. OS and PFS rates were calculated from the date of first ASCT using the Kaplan-Meier method. ORR was assessed by Chi-square using best response post ASCT. Results: There were 302 single and 125 tandem transplants, followed by maintenance therapy in 190 (63%) and 96 (77%) respectively. Translocation (4;14) was seen in 209 (49%), t(14:16) in 61 (15.6%) and delP53 in 222 (52%) with more than one abnormality in 65 patients. The most common induction regimen consisted of cyclophosphamide, bortezomib, and steroids, (83%) followed by bortezomib and dexamethasone (8%) and dexamethasone alone (4.7%). Forty-seven patients (11%) required reinduction prior to first ASCT with regimens including RVD (49%), Rd (23%) and others (D/DT/VD-PACE, CyBor-D, KRD, VD, IxaRD, 28%). Maintenance was prescribed to 286 patients with regimens including lenalidomide ± dexamethasone (65%), lenalidomide + proteasome inhibitor ± dexamethasone (22%), proteasome inhibitor ± dexamethasone (11%) and others (2%). Patient characteristics are summarised in table 1. The overall response rate was 93.9% (94.5% for single ASCT and 92% for tandem ASCT). The PFS at 3 years was 41.1% (single) and 45.7% (tandem) with median PFS 26 vs 35 months respectively (p=0.0621). Three year OS was 71.5% (single) and 83.8% (tandem), median OS 83 vs 89 months (p=0.0060). Both PFS and OS were improved with the use of maintenance therapy, regardless of single vs tandem transplant. PFS at 3 years was 52.1% for those receiving maintenance therapy compared to 21.7% for no maintenance (median 42 vs 16 months, p<0.0001). Overall survival was 79.5% with maintenance vs 63.6% without (median 92 vs 60 months, p<0.0001). Figures 1 shows PFS and OS for single or tandem transplant, with or without maintenance therapy. There was no difference in PFS or OS after a single or tandem transplant when maintenance was given. PFS for single or tandem ASCT with maintenance at 3 years was 53.7% and 46.3% respectively (p=0.527). Three year OS rates were 76.7% and 85.6% (p=0.0962). However, PFS was better with tandem compared to single ASCT when no maintenance was given. PFS at 3 years for single transplant with no maintenance was 19.0% (median 13 months) vs 48.9% (median 23.7 months) for tandem without maintenance (p=0.0084), while OS were not statistically different (62.4% vs 74.7%, median 60 months vs not reached, p=0.5271). Conclusions: Tandem ASCT does improve outcomes for MM with high-risk cytogenetics. However, the main benefit was seen in patients who did not receive maintenance therapy. Our data demonstrate the potent anti-myeloma effect of post-ASCT maintenance and raise the question of the optimal role of tandem ASCT in the modern treatment era. Disclosures Duggan: Novartis: Honoraria; Amgen: Consultancy; Celgene: Consultancy; Astra Zeneca: Consultancy; Jannsen: Consultancy. Reece:Janssen, Bristol-Myers Squibb, Amgen, Takeda: Consultancy, Honoraria; Janssen, Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria, Research Funding; Otsuka: Research Funding. Song:Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen, Celgene,Takeda: Consultancy, Honoraria; Otsuka: Honoraria. Jimenez-Zepeda:Janssen, Celgene, Amgen, Takeda: Honoraria. McCurdy:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Sanofi: Honoraria; GSK: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Louzada:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Mian:Takeda: Consultancy, Honoraria; Celgene: Consultancy; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Sanofi: Consultancy. Sebag:Celgene: Honoraria; Takeda: Honoraria; Amgen: Honoraria; Janssen: Honoraria, Research Funding. White:Takeda: Honoraria; Sanofi: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria; Karyopharm: Honoraria; Antengene: Honoraria; GSK: Honoraria. Stakiw:Lundbeck: Honoraria; Celgene: Honoraria; BMS: Honoraria; Roche: Research Funding; Janssen: Honoraria, Research Funding; Amgen: Honoraria; Novartis: Honoraria. Leblanc:Celgene Canada; Janssen Inc.; Amgen Canada; Takeda Canada: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Kotb:Takeda: Honoraria; Sanofi: Research Funding; Janssen: Honoraria; Amgen: Honoraria; Celgene: Honoraria; Karyopharm: Current equity holder in publicly-traded company; Merck: Honoraria, Research Funding. Venner:Janssen, BMS/Celgene, Sanofi, Takeda, Amgen: Honoraria; Celgene, Amgen: Research Funding.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".