Sporadic Epithelial Ovarian Cancer: Clinical Relevance of BRCA1 Inhibition in the DNA Damage and Repair Pathway
Bibliographic record
Abstract
Among the most promising pathways for molecular targets in sporadic epithelial ovarian cancer (SEOC) are those involving the BRCA1 protein. Because somatic mutations in BRCA1 are rare in SEOC, it was originally postulated that BRCA1 plays a limited role in the pathogenesis of this disease. However, inactivation of BRCA1 through various mechanisms is a relatively frequent event in ovarian cancer. This is important because BRCA1 is involved in the cellular response to DNA damage and repair and has an essential role in the maintenance of genomic stability. The BRCA1 tumor suppressor protein is known to interact with genes and proteins known collectively as the BRCA1 pathway, and defects in this pathway are believed to be a driving force for cancer progression. As a result, there is compelling evidence to suggest that the dysfunction of BRCA1 may be a central mechanism in all ovarian carcinogenesis, and this has clinical and molecular significance beyond the management of patients with hereditary ovarian cancer. The aim of this review is to evaluate the evidence for BRCA1 dysfunction in SEOC and to link this dysfunction to a defective DNA repair pathway and ultimately the promotion of genomic instability and tumorigenesis. Furthermore, we advocate the continued need to study BRCA1 and its pathway by prospectively correlating clinicopathologic data with molecular aberrations. This will determine whether BRCA1 has relevance as a predictive and prognostic marker in SEOC and whether aberrations in the BRCA1 pathway warrant further study as potential therapeutic targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".