Association of Metabolic, Inflammatory, and Tumor Markers With Circulating Tumor Cells in Metastatic Breast Cancer
Bibliographic record
Abstract
Abstract Background Circulating tumor cells (CTCs) are associated with worse prognosis in metastatic breast cancer (BC). We evaluated the association of metabolic, inflammatory, and tumor markers with CTCs in women with metastatic BC before commencing a new systemic therapy. Methods Ninety-six patients with newly diagnosed or progressing metastatic BC without current diabetes or use of anti-inflammatory agents were recruited from four Ontario hospitals. Women provided fasting blood for measurement of metabolic, inflammatory, and tumor markers and CTCs. CTCs were assayed within 72 hours of collection using CellSearch. Other blood was frozen at –80°C, and assays were performed in a single batch. Associations between CTC counts with study factors were evaluated using Spearman correlation, and the chi-square or Fisher exact test. All statistical tests were two-sided and P value ≤ .05 was considered statistically significant. Results The median age was 60.5 years; 90.6% were postmenopausal. The cohort included hormone receptor–positive (87.5%), HER2–positive (15.6%), and triple-negative (10.4%) BCs. Patients were starting firstline (35.5%), second-line (26.0%), or third-or-later-line therapy (38.5%). CTC counts (per 7.5 mL of blood) ranged from 0 to 1238 (median 2); an elevated CTC count, defined as five or more CTCs, was detected in 42 (43.8%) patients. Those with liver metastases (vs not) more frequently had an elevated CTC count (59.0% vs 33.3%, P = .02). CTCs were significantly associated with C-reactive protein (R = .22, P = .02), interleukin (IL)-6 (R = .25, P = .01), IL-8 (R = .38, P = .0001), plasminogen activator inhibitor 1 (R = .31, P = .001), carcinoembryonic antigen (R = .31, P = .002), and cancer antigen 15-3 (R = .40, P = .0001) and inversely associated with body mass index (R = –.23, P = .02) and leptin (R = –.26, P = .01). Conclusions CTC counts were positively associated with tumor and inflammatory markers and inversely associated with some metabolic markers, potentially reflecting tumor burden and cachexia.
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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 | 0.001 |
| 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) | 0.002 | 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".