Post-Transplant Outcomes in High-Risk Compared with Non–High-Risk Multiple Myeloma: A CIBMTR Analysis
Bibliographic record
Abstract
Conventional cytogenetics and interphase fluorescence in situ hybridization (FISH) identify high-risk multiple myeloma (HRM) populations characterized by poor outcomes. We analyzed these differences among HRM versus non-HRM populations after upfront autologous hematopoietic cell transplantation (autoHCT). Between 2008 and 2012, 715 patients with multiple myeloma identified by FISH and/or cytogenetic data with upfront autoHCT were identified in the Center for International Blood and Marrow Transplant Research database. HRM was defined as del17p, t(4;14), t(14;16), hypodiploidy (<45 chromosomes excluding -Y) or chromosome 1 p and 1q abnormalities; all others were non-HRM. Among 125 HRM patients (17.5%), induction with bortezomib and immunomodulatory agents (imids) was higher compared with non-HRM (56% versus 43%, P < .001) with similar pretransplant complete response (CR) rates (14% versus 16%, P .1). At day 100 post-transplant, at least a very good partial response was 59% in HRM and 61% in non-HRM (P = .6). More HRM patients received post-transplant therapy with bortezomib and imids (26% versus 12%, P = .004). Three-year post-transplant progression-free (PFS) and overall survival (OS) rates in HRM versus non-HRM were 37% versus 49% (P < .001) and 72% versus 85% (P < .001), respectively. At 3 years, PFS for HRM patients with and without post-transplant therapy was 46% (95% confidence interval [CI], 33 to 59) versus 14% (95% CI, 4 to 29) and in non-HRM patients with and without post-transplant therapy 55% (95% CI, 49 to 62) versus 39% (95% CI, 32 to 47); rates of OS for HRM patients with and without post-transplant therapy were 81% (95% CI, 70 to 90) versus 48% (95% CI, 30 to 65) compared with 88% (95% CI, 84 to 92) and 79% (95% CI, 73 to 85) in non-HRM patients with and without post-transplant therapy, respectively. Among patients receiving post-transplant therapy, there was no difference in OS between HRM and non-HRM (P = .08). In addition to HRM, higher stage, less than a CR pretransplant, lack of post-transplant therapy, and African American race were associated with worse OS. In conclusion, we show HRM patients achieve similar day 100 post-transplant responses compared with non-HRM patients, but these responses are not sustained. Post-transplant therapy appeared to improve the poor outcomes of HRM.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".