p16 Expression and Breast Cancer Risk in Women with Atypical Hyperplasia.
Bibliographic record
Abstract
Abstract Background: p16 is a nuclear protein encoded by the p16INK4a gene that regulates the G1-S cell cycle checkpoint. Its overexpression could reflect normal proliferative arrest, but also the evolution of carcinogenic processes that have overcome the p16 block, with progression from hyperplasic lesions to invasive breast cancer. Our aim with this study was to correlate p16 expression, both individually and in combination with expression of COX-2 and Ki67, with subsequent risk of breast cancer among women with atypical hyperplasia. Methods: p16 expression was assessed by immunohistochemical assays in archival paraffin-embedded, formalin-fixed sections from 233 women with atypia whose biopsy specimens were obtained via surgical excision at the Mayo Clinic from January 1, 1967, through December 31, 1991. p16 expression was scored as percent of cells positive and intensity of staining. Risk factor information and follow-up for breast cancer events were obtained via study questionnaire and the medical records. Standardized incidence ratios (SIRs) were used to compare observed numbers of breast cancer events to population-based expected counts, both at 10 years and over the entire course of follow-up. Results: Forty-seven patients (20%) developed breast cancer with a mean follow-up of 14.3 (SD=6.96) years. The overall risk of developing breast cancer was not modified by increasing p16 overexpression (low percent of staining with RR=3.89, 95% CI=2.72 to 5.56; high percent of staining with RR=3.91, 95% CI=2.43 to 6.28; similar results for intensity of staining). However, overexpression of both p16 and COX2 conveyed a stronger risk of breast cancer (SIR=4.01, 95% CI 2.28 to 7.07) as compared to dual low expression (SIR 1.78, 95% CI 0.67-4.74). Moreover, overexpression of both Ki67 and p16 was related to a higher 10-year breast cancer risk (SIR 6.25, 95% CI 2.35-16.6) as compared to low expression of both markers (SIR 0.87, 95% CI 0.22-3.49). Conclusions: although single expression of p16 does not stratify breast cancer risk, its overexpression in concert with biomarkers that reflect other carcinogenic processes (e.g. Ki67 and COX2) could help to define risk of tumor development. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 910.
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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 | 0.002 |
| 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".