Oncology clinic-based germline genetic testing for exocrine pancreatic cancer enables timely return of results and unveils low uptake of cascade testing
Bibliographic record
Abstract
BACKGROUND: Traditional medical genetics models are unable to meet the growing demand for germline genetic testing (GT) in patients with exocrine pancreatic cancer (PC). This study investigates the impact of an ambulatory oncology clinic-based GT model. METHODS: From 2012 to 2021, patients with PC were prospectively enrolled and considered for GT. Two chronological cohorts were compared: (1) the preuniversal genetic testing (pre-UGT) cohort, which received GT based on clinical criteria or family history; and (2) the post-UGT cohort, where an 86-gene panel was offered to all patients with PC. RESULTS: Of 847 eligible patients, 735 (86.8%) were enrolled (pre-UGT, n=579; post-UGT, n=156). A higher proportion of the post-UGT cohort received prospective GT (97.4% vs 58.5%, p<0.001). The rate of pathogenic germline alterations (PGA) across both cohorts was 9.9%, with 8.0% of PGAs in PC susceptibility genes. The post-UGT cohort had a higher prevalence of overall PGAs (17.2% vs 6.6%, p<0.001) and PGAs in PC susceptibility genes (11.9% vs 6.3%, p<0.001). The median turnaround time from enrolment to GT report was shorter in the post-UGT cohort (13 days vs 42 days, p<0.001). Probands with a PGA disclosed their GT results to 84% of their first-degree relatives (FDRs). However, only 31% of informed FDRs underwent GT, and the number of new cases per index case was 0.52. CONCLUSION: A point-of-care GT model is feasible and expedites access to GT for patients with PC. Strategies to increase the uptake of cascade testing are needed to maximise the clinical impact of an oncology clinic-based GT model.
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 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 | 0.003 | 0.042 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".