Outcomes of Multiple Myeloma Patients With Prior Solid Tumors Undergoing Autologous Transplantation
Bibliographic record
Abstract
Upfront autologous hematopoietic cell transplantation (autoHCT) remains standard of care for eligible patients with newly diagnosed multiple myeloma (MM). Comorbidities are routinely evaluated to determine eligibility and estimate mortality after autoHCT, including the history of prior solid tumor (PST). While PST is considered high-risk for worse survival based on widely used risk indices, its independent impact on transplant outcomes in MM remains unclear. To elucidate the prognostic impact of PST in patients with MM undergoing upfront autoHCT. We conducted a single-center retrospective analysis of consecutive MM patients who underwent upfront autoHCT between 1997 and 2021, categorizing them into those with (PST+) and without (PST-) prior solid organ malignancy. Primary endpoints were progression-free survival (PFS) and overall survival (OS). Among 2853 patients included in this analysis, 274 (10%) were PST+ and 2579 (90%) were PST-. The PST+ patients were older (67 vs. 60 years; P < .001), predominantly male (66% vs. 58%; P = .010), were more often transplanted in the year 2010 or later (78% vs. 69%; P = .003) and were more likely to have high-risk cytogenetic abnormalities (30% vs. 24%; P = .06). There was no significant difference in pre-transplant hematologic response (P = .33), day-100 post-transplant (P = .35) or the best post-transplant response (P = .27) between the PST+ and PST- groups. Similarly, there were no differences in pre-transplant (P = .34) or best post-transplant (P = .44) MRD status between the two groups. After a median follow-up of 53.8 months (range 0.2-262), the median PFS was comparable (36.7 months in PST+ vs. 39.9 months in PST-, P = .31), yet the median OS was significantly shorter in the PST+ group (81.3 months vs. 104.0 months, P = .020). Multivariable analysis confirmed PST as an independent predictor of inferior OS (HR 1.34, P = .011). MM patients undergoing upfront autoHCT with PST had worse OS compared to those without PST, despite similar response rates and PFS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".