Implementing rapid, robust, cost-effective, patient-centred, routine genetic testing in ovarian cancer patients
Bibliographic record
Abstract
SUMMARY Background: Advances in DNA sequencing have made gene testing fast and affordable, but adaptation of clinical services to capitalise on this for patient benefit has been slow. Ovarian cancer exemplifies limitations of current systems and potential benefits of increased gene testing. Approximately 15% of ovarian cancer patients have a germline mutation in BRCA1 or BRCA2 (collectively termed ‘BRCA’) and this has substantial implications for their personal management and that of their relatives. However, in most countries implementation of BRCA testing in ovarian cancer has been inconsistent and largely unsuccessful. Methods: We developed a mainstream pathway in which BRCA testing was undertaken by cancer team members after 30 minutes online training. Patients with a mutation were sent a genetic appointment with their results. Cascade testing to relatives was performed via standard clinical genetic procedures. Findings: 207 women with ovarian cancer were offered gene testing through the mainstream pathway and all accepted. 33 (16%) had a BRCA mutation. The result informed management of 79% (121/154) women with active disease including 97% (32/33) women with a mutation. All mutation-positive women and ~3.5 relatives per family have been seen in genetics. Patient and clinician feedback was very positive. >95% found the pathway to be simple and effective. The pathway offers considerable reduction in time (~5-fold) and resource requirements (~13-fold) compared to the traditional genetic pathway. We estimate it would deliver £2.6M NHS cost savings per year, and would allow implementation of national testing recommendations with existing infrastructure. Interpretation: Mainstream genetic testing is effective, efficient and patient-centred and offers a mechanism for large-scale implementation of BRCA gene testing in cancer patients. The principles could be applied in many other countries and to many other areas of genomic medicine.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".