Allogeneic hematopoietic stem cell transplantation in patients with therapy‐related hematologic malignancies developing after multiple myeloma
Bibliographic record
Abstract
INTRODUCTION: Increasing survival of patients with multiple myeloma (MM) has resulted in an increased recognition of therapy-related hematological malignancies (t-MDS/AML, t-ALL, and t-CMML). There are limited data on the role of allogeneic hematopoietic stem cell transplantation (HCT) in this patient population. PATIENTS AND METHODS: We retrospectively reviewed patients who underwent HCT for t-MDS/AML, t-ALL, and t-CMML developing after receiving treatment for MM at our center. Patients were analyzed for myeloma characteristics and therapy, time to diagnosis of therapy-related hematological neoplasms, clinical, laboratory characteristics, transplant details, relapse-free survival (RFS), and overall survival (OS). RESULTS: Twenty patients underwent HCT for therapy-related hematological malignancies after MM (t-MDS/AML = 13, t-ALL = 6, t-CMML = 1). Median(range) age at time of transplant was 62.5 (49-73) years and 70% (n = 14) were male. The most common cytogenetic abnormality was complex/monosomal karyotype in 30% (n = 6) followed by monosomy/deletion of chromosome 5 or 7 in 15% (n = 3) of patients each. Donors were human leukocyte antigen matched (10/10 or 6/6) siblings in 30% (n = 6), unrelated in 60% (n = 12) and haploidentical in 10% (n = 2) patients. Estimated 2-year OS and RFS for the whole cohort were 53.1% and 47.2% respectively. There was a trend toward better survival in patients with t-ALL when compared to t-MDS/AML; however, the difference was not statistically significant. We did not find any pre-transplant or post-transplant factors that were predictive of survival outcomes after multivariate analysis. CONCLUSIONS: Allogeneic HCT provides substantial long-term disease-free survival in a proportion of patients with MM-associated therapy-related hematological malignancies. Multicenter studies with more patients and longer follow-up may provide additional information about factors affecting outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".