An update on genetic risk assessment and prevention: the role of genetic testing panels in breast cancer
Bibliographic record
Abstract
Introduction: In the past 5 years, multi-gene panels have replaced the practice of BRCA1 and BRCA2 genetic testing in cases of suspected inherited breast cancer susceptibility. A variety of genes have been included on these panels without certainty of their clinical utility. Pertinent current and historical literature was reviewed to provide an up-to-date snapshot of the changing landscape of the use of gene panel tests in the context of breast cancer.Areas covered: Following a recent review of the evidence, 10 genes have been found to have definitive evidence of increased breast cancer risk with variable penetrance. Here, we review the recent changes to the practice of multi-gene panel use in breast cancer diagnoses, including an update on next generation sequencing, alternative models of genetic testing, considerations when ordering these panel tests, and recommendations for management in identified carriers for a variety of genes. A comparison of screening recommendations and carrier frequencies from recent studies is also explored. Lastly, we consider what the future of hereditary oncologic genetic testing holds.Expert opinion: The transition to multi-gene panels in breast cancer patients has improved the likelihood of capturing a rare variant in a well-established gene associated with hereditary breast cancer (e.g. BRCA1 and BRCA2, TP53). There is also an increase in the likelihood of uncovering an uncertain result. This could be in the form of a variant of uncertain significance, or a pathogenic variant in a gene with questionable breast cancer risk-association. Concurrently, a changing landscape of who orders genetic tests will improve access to genetic testing. This pervasiveness of genetic testing must be accompanied with increased genetic literacy in all health-care providers, and access to support from genetics professionals for management of patients and at-risk family members.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".