Polymorphisms in<i>XRCC1</i>,<i>XRCC3</i>, and<i>CCND1</i>and Survival After Treatment for Metastatic Breast Cancer
Bibliographic record
Abstract
PURPOSE: Single nucleotide polymorphisms (SNPs) in DNA repair and cell cycle control genes may alter protein function and therefore the efficacy of DNA damaging chemotherapy. We retrospectively evaluated the association of SNPs in DNA repair genes, XRCC1-01 (Arg399Gln) and XRCC3-01 (Thr241Met), and a cell cycle control gene, CCND1-02 (A870G), with progression-free survival (PFS) and breast cancer specific survival (BCSS) in patients with metastatic breast cancer (MBC). PATIENTS AND METHODS: SNPs in 95 patients with MBC enrolled onto one of five prospective clinical trials of high-dose chemotherapy and autologous stem-cell transplantation were evaluated using genotyping assays. RESULTS: For XRCC1-01, the hazard ratio (HR) for BCSS was 2.8 (95% CI, 1.60 to 5.00) and the HR for PFS was 2.0 (95%CI, 1.12 to 3.43). For XRCC3-01, the HR for BCSS was 2.0 (95%CI, 1.12 to 3.70) and the HR for PFS was 2.0 (95%CI, 1.09 to 3.59). For CCND1-02, the HR for BCSS was 1.8 (95%CI, 1.12 to 2.78) and the HR for PFS was 1.8 (95%CI, 1.15 to 2.85). Patients carrying one variant genotype (HR, 1.7; 95%CI, 1.07 to 2.82) or combinations of any two variant genotypes (HR, 4.7; 95% CI, 2.41 to 8.94) had significantly poorer BCSS compared with patients carrying zero variants. In multivariable analysis, XRCC1-01, presence of liver metastases, and bone metastases independently predicted BCSS. Combinations of any two variant genotypes were stronger independent predictors of BCSS and PFS than the presence of liver or bone metastases. CONCLUSION: XRCC1-01, XRCC3-01, and CCND1-01 may be predictive of survival outcome in patients with MBC treated with DNA damaging 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 | 0.001 | 0.002 |
| 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".