Flow cytometric assessment of monocyte activation markers and circulating endothelial cells in patients with localized or metastatic breast cancer
Bibliographic record
Abstract
BACKGROUND: Monocyte activation in cancer patients may be reflective of anticancer activity. However, studies indicate that recruitment of macrophages can actually promote tumor growth and angiogenesis. Assessment of other microenvironmental cells such as circulating endothelial cells (CECs) may provide additional information regarding disease progression. The objective of this study was to assess monocyte activation and CECs in breast cancer patients and determine the potential clinical relevance during disease progression. METHODS: Patients (n = 41) with localized or metastatic breast cancer who were not currently receiving treatment were eligible for study inclusion. Peripheral blood was collected and analyzed by flow cytometry for monocyte activation (Leuko64 assay kit), and for CECs (CD146(+)CD45(-) phenotype). RESULTS: Metastatic breast cancer patients demonstrated a higher monocyte CD64 index relative to normal donors and localized breast cancer patients (P < 0.05). Furthermore, breast cancer patients had a lower monocyte CD163 index relative to normal donors (P = 0.008). Localized breast cancer patients demonstrated higher levels of CD146(+)CD45(-) cells CECs relative to metastatic breast cancer patients and normal donors. Within the localized breast cancer population, levels of CD146(+)CD45(-) cells increased with disease stage (P < 0.05). CONCLUSIONS: These results suggest that monocyte activation and CECs may play a role in breast cancer progression. We speculate that monocyte activation may reflect a reaction to metastatic cells and/or response to tissue damage caused by metastatic growth in distant organs. Furthermore, the observation that CECs increase with disease stage in localized breast cancer suggests that CECs could be a useful surrogate marker for disease progression in this patient population.
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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 | 0.001 |
| 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".