Circulating endothelial-cell kinetics and viability predict survival in breast cancer patients receiving metronomic chemotherapy
Bibliographic record
Abstract
Antiangiogenic agents and therapeutic strategies have entered the clinical oncology arena. The classical tumor size measurements defined to monitor efficacy of chemotherapy, however, might not be appropriate for these newer therapeutics. We previously found that circulating endothelial cells (CECs) were increased in number and more viable in cancer patients compared with control subjects. We investigated the correlation between CEC kinetics and clinical outcome in patients with advanced breast cancer receiving metronomic chemotherapy, a therapeutic strategy associated with antiangiogenic activity and anticancer efficacy. CEC number and viability were measured by flow cytometry in patients and in preclinical models. CECs were decreased in patients for whom no overall clinical benefit (defined as a clinical response or a stable disease) was observed compared with those who had a clinical benefit (P = .015). This difference was due to an increased fraction of apoptotic CECs in patients with a clinical benefit. Univariate and multivariate analyses indicated that CEC values greater than 11/microL were associated with a longer progression-free survival (P = .001) and an improved overall survival (P = .005). Preclinical models indicated that the source of apoptotic CECs was most likely the tumor vasculature. CEC kinetics and viability are very promising as predictors of clinical response in patients undergoing metronomic chemotherapy.
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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 | 0.000 |
| 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) | 0.001 | 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".