Causative Genes of Homologous Recombination Deficiency (HRD)-Related Breast Cancer and Specific Strategies at Present
Bibliographic record
Abstract
Recently, homologous recombination deficiency (HRD) has become a new target for hereditary cancers. Molecular-based approaches for hereditary cancers in the clinical setting have been reviewed. In particular, the efficacy of the PARP inhibitor has been considered by several clinical trials for various kinds of hereditary cancers. This indicates that the PARP inhibitor can be effective for any kind of BRCA mutated cancers, regardless of the organ-specific cancer. Homologous recombination deficiency (HRD) has become a new target for hereditary cancers, indicating the necessity to confirm the status of HRD-related genes. ARID1A, ATM, ATRX, PALB2, BARD1, RAD51C and CHEK2 are known as HRD-related genes for which simultaneous examination as part of panel testing is more suitable. Both surgical and medical oncologists should learn the basis of genetics including HRD. An understanding of the basic mechanism of homologous repair recombination (HRR) in BRCA-related breast cancer is mandatory for all surgical or medical oncologists because PARP inhibitors may be effective for these cancers and a specific strategy of screening for non-cancers exists. The clinical behavior of each gene should be clarified based on a large-scale database in the future, or, in other words, on real-world data. Firstly, HRD-related genes should be examined when the hereditary nature of a cancer is placed in doubt after an examination of the relevant family history. Alternatively, HRD score examination is a solution by which to identify HRD-related genes at the first step. If lifetime risk is estimated at over 20%, an annual breast MRI is necessary for high-risk screening. However, there are limited data to show its benefit compared with BRCA. Therefore, a large-scale database, including clinical information and a long-term follow-up should be established, after which a periodical assessment is mandatory. The clinical behavior of each gene should be clarified based on a large-scale database, or, in other words, real-world data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".