Outcomes of Tandem Hematopoietic Stem Cell Autologous Transplant in High-Risk Myeloma Patients: A Single-Center Experience
Bibliographic record
Abstract
Abstract Introduction: Studies have demonstrated that multiple myeloma (MM) is a clinically, genetically complex and heterogeneous disease. Cytogenetic alterations identify high-risk patients in MM and are associated with a poor prognosis. They include at least one of the following at diagnosis: t(4;14), t(14;16), t(14;20), del(17p), 1q amp and 1q amp + del(1p). Autologous stem cell transplantation (ASCT) and the development of novel agents have considerably increased the median survival of MM patients. Patients with high-risk cytogenetics are associated with worse survival, and studies have shown improvement in progression-free survival with tandem ASCT when compared to single ASCT. Methods: This is a retrospective single-center study evaluating MM patients with high-risk cytogenetics at our center who have undergone a tandem autologous transplant from January 1, 2017, to December 31, 2020. Primary objective was overall response rates (ORR) and relapse rates. Secondary objectives looked at progression-free survival (PFS), overall survival (OS) using the Kaplan-Meier method. Results: From January 1, 2017, to December 31, 2020, 25 high-risk patients underwent tandem ASCT. Key patient characteristics are shown in table 1. Translocation (4:14) was seen in 8/25 (32%) patients; t(14:16) in 5/25 (20%) patients; del17p in 7/25 (28%), 1q amp in 8/25 (32%) patients, del1p + 1q amp in 5/25 (20%) patients. In terms of double hit and triple hit disease, 9/25 (36%) patients had two high risk changes and 3/25 (12%) with had three high risk changes. ISS staging wise, 5/25 (20%) patients were ISS stage I, 5/25 (20%) patients ISS stage 2 and 9/25 (36%) patients were ISS stage 3. The most common induction regimen consisted of cyclophosphamide, bortezomib, and dexamethasone (CyBorD). One patient each transitioned to daratumumab, lenalidomide and dexamethasone and PAD/CVD due to poor response. Maintenance therapy was given to 21/25 (84%) tandem ASCT patients with 5 (20%) patients receiving lenalidomide, 4 (16%) patients receiving proteasome inhibitor and 12 (48%) patients received dual maintenance. In terms of response, we recorded CR (complete response) vs VGPR (very good partial response) vs PR (partial response) after induction, 2-3 months after ASCT #1, 2-3 months after ASCT #2, 12 months after ASCT #2, and 12 months after maintenance. Following induction, 16 patients had achieved VGPR, and 9 of patients had achieved PR. After ASCT #1, 5 (55.5%) PR patients deepened their response to a VGPR. 1-3 months post ASCT #2, 3 (33.3%) PR patients deepened to a VGPR, and 2 (12.5%) VGPR patients achieved CR. 12 months after ASCT #2, 2 of the previous VGPR patients (12.5%) achieved CR, and 1 PR (11.1%) patient achieved VGPR. At the median follow-up time of 60 months, 44% of patients had relapsed. 1 patient relapsed within 1-3 months post ASCT#1. Nine other patients relapsed post ASCT #2 and maintenance except one patient who relapsed at time of ASCT #2. Univariable analysis identified hemoglobin as a statistically significant association with risk of progression or death. All patients who received any maintenance after tandem transplant were progression-free at 60 months (p<0.001). Conclusion: In our retrospective study, results suggest that tandem ASCT allows for deepening of responses. Together with maintenance therapy, this contributes to the durability of further PFS prolongation in high-risk MM patients. Figure 1 Figure 1. Disclosures Lam: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria, Membership on an entity's Board of Directors or advisory committees; Hoffmann-La Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Meyers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Beigene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; SeaGen: 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; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Honoraria, Membership on an entity's Board of Directors or advisory committees. Phua: Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; NovoNordisk: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria; Amgen: Honoraria. Louzada: Amgen: Honoraria; Pfizer: Honoraria; Celgene: Honoraria; Janssen: 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".