p53 Alterations and Protein Accumulation in Benign Breast Tissue and Breast Cancer Risk: A Cohort Study
Bibliographic record
Abstract
Disruption of p53 gene function seems to have a pivotal role in carcinogenesis. p53 gene changes occur before the development of breast cancer and therefore might influence breast cancer risk. We investigated the association between p53 protein accumulation and p53 mutations detected in benign breast tissue and risk of subsequent breast cancer. We conducted a case-control study nested within the cohort of 4,888 women in the Canadian National Breast Screening Study who were diagnosed with biopsy-confirmed benign breast disease during active follow-up. Cases were women with benign breast disease who subsequently developed breast cancer; five controls were matched to each case. p53 protein accumulation was assessed immunohistochemically using sections of paraffin-embedded benign breast tissue from 104 cases and 385 controls; for 82 of these cases and 327 of the controls, DNA was successfully extracted from the breast tissue for p53 gene analysis using PCR-single-strand conformation polymorphism/direct sequencing. p53 protein accumulation was associated with a 2-fold increase in risk of progression to breast cancer [adjusted odds ratio (OR), 2.16; 95% confidence interval (95% CI), 1.08-4.30], whereas p53 nucleotide changes overall were not associated with altered risk (adjusted OR, 1.22; 95% CI, 0.68-2.19); those with both p53 immunopositivity and a p53 nucleotide change had an OR (95% CI) of 3.20 (1.21-8.50). Nonpolymorphic intronic changes were associated with a 2.8-fold increase in risk (OR, 2.84; 95% CI, 1.09-7.41). The results of this study suggest that p53 protein accumulation and nonpolymorphic intronic changes in p53 are associated with increased risk of progression to breast cancer in women with benign breast disease.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".