Impact of Gene Polymorphisms on Clinical Outcome for Stage IV Melanoma Patients Treated with Biochemotherapy: An Exploratory Study
Bibliographic record
Abstract
PURPOSE: Biochemotherapy can achieve high response rates in advanced melanoma, but the factors that influence regression and survival remain unknown. The present exploratory study tested the hypothesis that cytokine gene polymorphisms predict clinical outcome in stage IV melanoma patients treated with biochemotherapy. EXPERIMENTAL DESIGN: Ninety patients with stage IV melanoma were treated with biochemotherapy, including cisplatin, vinblastine, and dacarbazine combined with interleukin (IL)-2 and IFN-alpha either with or without tamoxifen. Cytokine gene polymorphisms for IFN-gamma (+874A-->T) and IL-10 (-1082G-->A) were assessed. X-ray repair cross-complementing gene 1 (XRCC1; Arg399Gln), xeroderma pigmentosum complementary group D (XPD; Lys751Gln), and excision repair cross-complementing gene 1 (ERCC1; codon 118) DNA repair polymorphisms were also determined. RESULTS: IFN-gamma (+874A-->T) gene polymorphism was statistically significantly associated with response (P = 0.001), progression-free survival (P = 0.0012), and overall survival (P < 0.001), whereas the IL-10 polymorphism was marginally associated with response (P = 0.03) and overall survival (P = 0.065). Multivariate analysis revealed that IFN-gamma (+874A-->T) independently predicted overall survival (P = 0.003). The ERCC1 polymorphism was weakly associated with overall survival (P = 0.045). Combining polymorphisms for IFN-gamma, IL-10, and ERCC1 stratified patients into four distinct groups with significantly different clinical outcome (P < 0.001), so that patients with more "favorable" polymorphisms had a better outcome. CONCLUSIONS: Cytokine gene polymorphisms predicted clinical outcome for advanced melanoma patients who received biochemotherapy. The combined effects of multiple genetic polymorphisms may provide more accurate prognostic information. Additional independent studies are needed to confirm these pilot findings.
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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) | 0.002 | 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".