Where Do We Stand in Targeted Therapy Against BRCA1/2 Deficient Cancers?
Bibliographic record
Abstract
Introduction: BRCA1 and BRCA2 are tumour suppressor genes that, when mutated, majorly increase the risk of cancer, particularly breast and ovarian cancers. Cancer patients with BRCA mutations are more likely to have aggressive forms of cancer. Targeted therapy is a key component of treatment for BRCA-deficient cancers. An important focus for targeted therapy is synthetic lethality. Synthetic lethality is the loss of viability from the disruption of two genes, but not from the disruption of either gene alone. The most established targeted therapy for BRCA-deficient cancers is poly (ADP-ribose) polymerase inhibitors (PARPi). This paper aims to summarize advancements in targeted therapy against BRCA-deficient cancers and provide future directions. Methods: Relevant articles were found using the search engines PubMed and Google Scholar. Search terms for relevant articles included “BRCA1”, “BRCA2”, “targeted therapies”, “BRCA-deficient cancer”, and “synthetic lethality”. Results: PARPi is widely used in clinical settings and is the only targeted therapy approved by the FDA for clinical use. PARPi exploits synthetic lethality of the HR pathway in BRCA-deficient cells by trapping PARP at sites of DNA damage, obstructing replication machinery, and generating an accumulation of DSBs, leading to cell death. In addition to PARPi, there has been further research into the use of other synthetic lethal interactors and targeted therapy approaches to target BRCA-deficient cancers, such as RAD52 inhibitors, FANCD2 inhibitors, immunotherapy, FEN1 inhibitors, APE2 inhibitors, PLK1 inhibitors, DNA Damage Response Kinase inhibitors, and RNF168 inhibitors. Discussion and Conclusion: One major limitation of the use of PARPi in clinical settings is the rapid development of resistance. Future steps must be taken to overcome PARPi resistance and improve sensitivity by finding therapies to use alone and with PARPi to create synergistic therapy. In sum, ongoing advancements in BRCA-targeted therapies are occurring, and future steps to improve the efficacy of targeted therapies will improve patient outcomes and quality of life.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".