Editorial: Hereditary Breast and Ovarian Cancer: Current Concepts of Prevention and Treatment
Bibliographic record
Abstract
Hereditary Breast and Ovarian Cancer: Current Concepts of Prevention and TreatmentBreast cancer is the leading cause of cancer-related death in women worldwide.Whilst ovarian cancer is less common, it remains challenging due to late detection and high mortality (1).Most cases are considered sporadic; however, both tumor types may occur in patients with inherited mutations in cancer susceptibility genes (2).Hereditary breast and ovarian cancer syndrome (HBOC) accounts for 90% of the hereditary neoplasms and is predominantly associated with germline mutations in BRCA1 or BRCA2 genes (3).The mean cumulative risk of breast cancer is 57% in BRCA1 mutation carriers and 49% in BRCA2 mutation carriers, while ovarian cancer risk in women with BRCA1 and BRCA2 mutations is 40% and 18%, respectively.HBOC can also increase the risk, albeit to a lesser extent, for other neoplasms such as prostate or pancreatic cancer and malignant melanoma (3).Since the discovery of the BRCA genes and the development of clinical testing, the health advantages of identifying individuals at risk for HBOC have been well documented leading to the investigation and implementation of genetic counseling and screening, enhanced surveillance as well as surgical and non-surgical risk reduction options.We hope that a collection of review (including systematic review), mini-review, perspective and original research articles in this Research Topic will provide further insight on HBOC, including clinicopathologic features of associated cancers, genetic testing and treatment modalities, and their impact on patient outcomes.The most common and well-characterized genes implicated in HBOC are BRCA1 and BRCA2.Hatano et al. provide a comprehensive summary of the molecular biology of these genes, with a brief history of their discovery and review of the clinical implications of mutations (4).They also discuss the emerging research into the concept of "mutational signatures," representing the characteristic combination of mutation types in somatic cells.Deciphering mutational signatures in cancer provides insight into the mechanisms of cancer progression and this comprehensive genome analysis enables researchers to not only learn the current status of cancer predisposing genes but potentially to predict their future behavior through the understanding of the molecular underpinnings from which they arose.The advancement of molecular techniques and gene sequencing platforms has enabled the discovery of additional genes, beyond BRCA1 and BRCA2, that play a role in the development of hereditary breast and ovarian cancers.In a review of PALB2, a functional protein partner of BRCA2, Wu et al. discuss its function and role in breast cancer (5).Patients with monoallelic PALB2 mutations are susceptible to breast, pancreatic, and ovarian cancer.PALB2 mutation carriers are predisposed to breast cancer with a similar cumulative risk as BRCA2 and having as much as a nine-fold higher than average lifetime risk,
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".