p53 Missense Mutations in Microdissected High-Grade Ductal Carcinoma In Situ of the Breast
Bibliographic record
Abstract
BACKGROUND: To understand the role of sporadic mutations in the tumor suppressor gene p53 (also known as TP53) in the pathogenesis of breast cancer, it is important to identify at which histologic stage such mutations first occur. We previously showed that a p53 mutation present in invasive breast cancer was found in all surrounding areas of ductal carcinoma in situ (DCIS) but not in areas of hyperplasia or normal breast epithelium. In the present investigation, we studied patients with DCIS, but without invasive breast cancer, to determine the spectrum of DCIS types that can harbor a p53 mutation. METHODS: Formalin-fixed, paraffin-embedded tissues from 94 patients with DCIS were evaluated histologically for the predominant cellular architectural pattern, degree of necrosis, and nuclear grade. Each specimen was also assigned an overall histologic grade (with the use of the Van Nuys Prognostic Index pathologic classification). Tissue specimens were stained immunohistochemically with an anti-p53 antibody. Positively stained tissue areas were analyzed for the presence of p53 mutations by single-strand conformation polymorphism and direct sequencing. All statistical tests were two-sided. RESULTS: DCIS from 10 of 94 patients were found to contain p53 missense mutations. All 10 were of a solid or a comedo histologic pattern and contained cells of nuclear grade 2 or 3 (i.e., more abnormal nuclei). The frequency of p53 missense mutations was statistically significantly different among the three overall histologic grade categories (zero [0%] of 49 with low-grade DCIS, one [4.35%] of 23 with intermediate-grade DCIS, and nine [40.9%] of 22 with high-grade DCIS; df = 2 and P<.0001). CONCLUSION: The DCIS types in patients in this series are representative of clinically detected DCIS. Our finding that p53 mutations can occur before the development of invasive breast cancer, particularly in DCIS of high histologic grade, has potentially important implications for prevention and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".