Impact of CD34/CD309 expression in circulating endothelial progenitor cells on prognosis and response to bortezomib therapy in multiple myeloma
Bibliographic record
Abstract
Multiple myeloma (MM) is a prevalent yet incurable hematologic malignancy. Despite the proven efficacy of proteasome inhibitors in treating MM, resistance to Bortezomib-based treatments persists in a subset of patients. This case control study explores the potential of circulating endothelial progenitor cells (EPCs) as biomarkers for predicting response to Proteasome Inhibitor based therapy combined with Dexamethasone in MM patients. This study was conducted on 105 MM patients receiving bortezomib plus dexamethasone therapy and 90 healthy individuals as a control group. Utilizing 8-color multi-parameter flow cytometry, we assessed the levels of circulating EPCs, identified through CD34 FITC and CD309 PE markers at diagnosis and after one treatment cycle (4 weeks). Our findings revealed that patients exhibiting poor response to therapy showed significantly higher CD34/CD309 values than those with a good response (p < 0.001). The delineation of response based on CD34/CD309 expression was established with a cutoff ≤ 0.9 for percentage (yielding 100% sensitivity and 94.1% specificity) and ≤ 12.5 for absolute value (also with 100% sensitivity and 94.1% specificity). These results underscore the potential of EPC population levels, as quantified by CD34/CD309, to serve as a predictive biomarker for immunomodulatory treatment in MM patients undergoing Proteasome Inhibitor and Dexamethasone therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".