Clinico-pathological characteristics of BRCA1- and BRCA2-related breast cancer
Bibliographic record
Abstract
Approximately 2% to 5% of all breast cancers are hereditary, meaning that the cancer predisposition is carried as a monogenic trait. Several highly penetrant breast cancer predisposing genes have been identified. These discoveries will permit a refined description of breast cancer occurring as part of the different genetic syndromes. We reviewed the medical literature on the clinico-pathological features of breast cancer associated with the major breast cancer susceptibility genes BRCA1 and BRCA2. BRCA1-associated breast cancers are more frequently ductal invasive, high-grade carcinomas with an important lymphocytic infiltration. They are aneuploid, estrogen and progesterone receptors negative, and p53 positive. BRCA2-related breast cancers tend to be higher-grade tumors than are non-hereditary cases, although this association is less strong then for BRCA1 cases. These tumors exhibit substantially less tubule formation, but mitotic count and cellular pleomorphism do not differ significantly from those of sporadic cases. The overall pattern of the identified pathological characteristics suggests a carcinogenic pathway in BRCA1- and BRCA2-related breast cancers different from that found in sporadic cases. The probability of finding a BRCA1/2 germ-line mutation is partly determined by these characteristics. In addition, these features will likely influence the behavior of BRCA1/2-related breast cancer.
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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 | 0.002 | 0.001 |
| 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".