Trends in Outcomes over Three Decades after Upfront Autologous Stem Cell Transplant for Multiple Myeloma at MD Anderson Cancer Center
Bibliographic record
Abstract
Introduction Remarkable advances have been made in the treatment of multiple myeloma (MM) with the advent of novel therapies and the use of post-transplant maintenance. We report trends in outcomes of MM patients who received upfront autologous hematopoietic stem cell transplantation (autoHCT) at our institution over more than three decades. Methods We conducted a single-center retrospective study of patients with newly diagnosed MM undergoing upfront autoHCT between 1988 to 2021. Patients were grouped by the year of autoHCT: 1988-2000 (n=249), 2001-2005 (n=373), 2006-2010 (n=568), 2011-2015 (n=815) and 2016-2021 (n=1036). High-risk cytogenetic abnormalities were defined as del17p, t(4;14), t(14;16), 1q21 gain or amplification by fluorescence in situ hybridization. Progression free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method, and group differences were assessed using the log-rank test. Associations between demographic and clinical factors and survival outcomes were evaluated using Cox proportional hazards regression analysis. Results A total of 3041 patients were included in our analysis. Median age at autoHCT increased from 52 years (1988-2000) to 62 years (2016-2021), with only 1% of transplanted patients being ≥ 65 years of age in 1988-2000, compared to 38% in 2016-2021 (p<0.001). The proportion of African-American patients increased from 9% in 1988-2000 to 19% in 2016-2021 (p=0.004). The incidence of high-risk cytogenetics increased from 15% in 1988-2000 to 40% in 2016-2021 (p<0.001). The comorbidity burden, as measured by the hematopoietic cell transplantation-specific comorbidity index (HCT-CI), increased over time, with 17% of patients having HCT-CI>3 in 1988-2000 compared to 28% in 2016-2021 (p<0.001). Induction regimens evolved over time from predominately conventional chemotherapy (39%) in 1988-2000 to immunomodulatory drug (IMiD) and proteasome inhibitor (PI)-based regimens, with 74% receiving an IMiD-PI containing triplet [39% bortezomib, lenalidomide and dexamethasone (VRD) and 35% carfilzomib, lenalidomide and dexamethasone (KRD)] between 2016-2021 (p<0.001). Maintenance therapy was used in >80% from 2011 onwards (Table 1). Response rates prior to autoHCT steadily improved with 4% and 10% patients achieving ≥CR and ≥VGPR between 1988-2000, compared to 19% and 65% between 2016-2021, respectively. At day 100 post-transplant, 24% and 49% patients achieved ≥CR and ≥VGPR between 1988-2000 compared to 41% and 81% between 2016-2021, respectively. At best post-transplant response, 33% and 53% patients achieved ≥CR and ≥VGPR between 1988-2000 compared to 63% and 91% between 2016-2021, respectively. The median PFS in the entire study population was 38.3 months (95% CI 36.4-40.3), improving from 22.3 months between 1988-2000 to 58.6 months between 2016-2021 (hazard ratio [95% CI]: 0.42 [0.36-0.50], p<0.001; Figure 1A). Notably, patients with high-risk cytogenetics also had an improvement in PFS in recent years, with a median PFS of 28.0 months (0.38 [0.26-0.55], p<0.001) and 36.8 months (0.32 [0.22-0.46], p<0.001) in 2011-2015 and 2016-2021, respectively, compared to only 13.7 months in 2001-2005. Patients aged ≥65 years also had an improvement in median PFS from 33.6 months (95% CI 23.1-44.2) between 2001-2005 to 52.8 months (95% CI 40.0-68.5, p<0.001) between 2016-2021. Median OS was 99.4 months (95% CI 94.2-104.0) in the entire study population, steadily improving from 55.1 months to not reached (0.41 [0.33-0.52], p<0.001) in 1988-2000 and 2016-2021, respectively (Figure 1B). Similarly, in those with high-risk cytogenetics, OS improved with a median OS of 32.9 months in 2001-2005 compared to 66.5 months (0.39 [0.26-0.61], p<0.001) in 2016-2021. Between 1988-2000, day 100 non-relapse mortality (NRM) was 6%, whereas from 2001 onwards NRM remained ≤ 1% (p<0.001). Conclusions This single-center analysis of over 3,000 newly diagnosed MM patients undergoing upfront autoHCT demonstrates significant improvements in the depth of response and survival outcomes over the past three decades, even in patients with high-risk disease. NRM remained <1% despite increasing age and comorbidity burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".