The risk of second primary malignancies in patients receiving T-cell directed therapies for multiple myeloma: a systematic review
Bibliographic record
Abstract
Whilst T-cell directed therapies have revolutionized the therapeutic landscape in triple-class refractory multiple myeloma, ongoing long-term safety concerns remain, including the risk of second primary malignancy (SPM) development. We systematically evaluated the incidence and distribution of SPMs in R/R MM patients post T-cell-directed therapy. MEDLINE, EMBASE, and Cochrane CENTRAL databases were searched for clinical trial and real-world studies reporting outcomes for patients infused with either chimeric antigen receptor (CAR) T-cell or bispecific antibody therapies reported until March 2025. Patient-specific characteristics and SPM outcomes were extracted from eligible studies with calculation of point estimate confidence intervals (CIs) achieved via the Clopper-Pearson Exact Method. A total of 12 studies (7 RCTs and 5 RWS) were eligible for analysis, encompassing a total of 2743 adult R/R MM patients. Eleven studies were related to CAR T-cell therapy, with only 1 study reporting on bispecific antibody therapy. The pooled SPM point estimate for CAR T-cell therapy was 6.3%, with hematological malignancies representing the most common subtype. This highlights the potential risk of SPMs in patients eligible for T-cell directed therapy. Further robust, prospective clinical trial and pharmacovigilance data will continue to inform the true level of risk in this cohort of patients.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".