A Cohort Study of p53 Mutations and Protein Accumulation in Benign Breast Tissue and Subsequent Breast Cancer Risk
Bibliographic record
Abstract
Mutations in the p53 tumor suppressor gene and accumulation of its protein in breast tissue are thought to play a role in breast carcinogenesis. However, few studies have prospectively investigated the association of p53 immunopositivity and/or p53 alterations in women with benign breast disease in relation to the subsequent risk of invasive breast cancer. We carried out a case-control study nested within a large cohort of women biopsied for benign breast disease in order to address this question. After exclusions, 491 breast cancer cases and 471 controls were available for analysis. Unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI). Neither p53 immunopositivity nor genetic alterations in p53 (either missense mutations or polymorphisms) was associated with altered risk of subsequent breast cancer. However, the combination of both p53 immunopositivity and any p53 nucleotide change was associated with an approximate 5-fold nonsignificant increase in risk (adjusted OR 4.79, 95% CI 0.28-82.31) but the confidence intervals were extremely wide. Our findings raise the possibility that the combination of p53 protein accumulation and the presence of genetic alterations may identify a group at increased risk of breast cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".