Multiplex Analysis of Serum Cytokines in Melanoma Patients Treated with Interferon-α2b
Bibliographic record
Abstract
PURPOSE: Interferon (IFN)-alpha2b is the only Food and Drug Administration-approved treatment for operable high-risk melanoma that has been shown to significantly and durably prolong relapse-free survival (RFS) of patients with stage IIB-III melanoma. Development of reliable serum assays may contribute to the development of methods for earlier detection of melanoma and the selection of patients who may be most susceptible to current available interventions with IFNalpha. EXPERIMENTAL DESIGN: A powerful high-throughput xMAP multiplex immunobead assay technology (Luminex Corp., Austin, TX) was used to simultaneously test 29 cytokines, chemokines, angiogenic as well as growth factors, and soluble receptors in the sera of 179 patients with high-risk melanoma and 378 healthy individuals. RESULTS: Serum concentrations of interleukin (IL)-1alpha, IL-1beta, IL-6, IL-8, IL-12p40, IL-13, granulocyte colony-stimulating factor, monocyte chemoattractant protein 1 (MCP-1), macrophage inflammatory protein (MIP)-1alpha, MIP-1beta, IFNalpha, tumor necrosis factor (TNF)-alpha, epidermal growth factor, vascular endothelial growth factor (VEGF), and TNF receptor II were found to be significantly higher in patients with resected high-risk melanoma compared with healthy controls. Bayesian Network algorithm classification of the data offered 90% sensitivity at 98% specificity with 96.5% of melanoma patients distinguished from healthy individuals. IFN-alpha2b therapy resulted in a significant decrease of serum levels of immunosuppressive and tumor angiogenic/growth stimulatory factors (VEGF, epidermal growth factor, and hepatocyte growth factor) and increased levels of antiangiogenic IFN-gamma inducible protein 10 (IP-10) and IFN-alpha. Pretreatment levels of proinflammatory cytokines IL-1beta, IL-1alpha, IL-6, TNF-alpha, and chemokines MIP-1alpha and MIP-1beta were found to be significantly higher in the serum of patients with longer RFS values of 1 to 5 and >5 years when compared with patients with shorter RFS of <1 year. CONCLUSION: These data show that multiplexed analysis of serum biomarkers is useful for the evaluation of prognostic markers of clinical outcome and potential predictive markers of response to IFN-alpha2b in patients with high-risk operable melanoma.
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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".