An update on genetic risk assessment and prevention: the role of genetic testing panels in breast cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it