Plasma Cytokine Levels and Cytokine Genetic Polymorphisms in Patients with Metastatic Breast Cancer Receiving High-Dose Chemotherapy
Bibliographic record
Abstract
Differences in the baseline levels of serum cytokines or in single-nucleotide polymorphisms (SNPs) in cytokine genes may be useful to predict outcomes for patients being treated for metastatic breast cancer. We have measured the plasma levels and characterized individual SNPs for IL-1RA, IL-1β, IL-2, IL-6 and TNFα in 130 patients with metastatic breast cancer treated with high-dose chemotherapy. Patients were treated with high-dose cyclophosphamide (Group 1, 74 patients) or high-dose paclitaxel-containing regimens (Group 2, 56 patients). A high plasma level of IL-1RA and a SNP in the IL-1RA gene indicated a better prognosis for patients in Group 1 (but not Group 2). However, the level of plasma IL-1RA did not correlate with the SNP genotype. A high plasma level of IL-6 or TNFα indicated a poorer outcome for patients in Group 1 although the SNP genotypes for the IL-6 and TNFα SNPs were not associated with differences in outcome. The plasma levels of IL-1β and IL-2 and the genotype of the IL-1β SNPs did not indicate differences in outcome. Although, individually, plasma levels of cytokine or “risk” SNP genotypes may not indicate outcome, in combination there was an increased trend to predict outcome for patients treated with high-dose cyclophosphamide but not high-dose paclitaxel. These results suggest that the immune cytokines may be useful as prognostic biomarkers in the treatment of patients with metastatic breast cancer treated with different types of 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 | 0.001 |
| 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".