Clinical Utility of Genomic Sequencing for Hereditary Cancer Syndromes: An Observational Cohort Study
Bibliographic record
Abstract
PURPOSE Genomic sequencing (GS) is increasingly used to improve diagnoses and inform targeted therapies. GS can also be used to identify the 10% of cancer patients with an underlying hereditary cancer syndrome (HCS), who can benefit from surveillance and preventive surgery that reduce morbidity/mortality. However, the evidence on clinical utility of GS for HCS is limited: we aimed to fill this gap by assessing yield of all cancer results and associated recommendations for patients undergoing GS for HCS. MATERIALS AND METHODS An observational chart review and survey were conducted for cancer patients with previous uninformative cancer gene panel results, who received GS as part of the Incidental Genomics Trial (ClinicalTrials.gov identifier: NCT03597165 ). Descriptive statistics were used to describe demographics and clinical history. Proportions were calculated to compare frequencies of result types and recommendations made and followed. RESULTS A total of 276 patients were eligible and included. Participants were mostly female (n = 240), European (n = 158), and with breast cancer history (n = 168). Yield: 25 patients (9.1%) received ≥1 pathogenic/likely pathogenic variant, 246 (89%) received ≥1 variant of uncertain significance (VUS), and 27 (10%) were negative. Most pathogenic variants (20/26) were in low/moderate cancer risk genes. The mean number of VUS was 2.7/patient and higher in non-Europeans versus Europeans (3.5 v 2.5, P < .05). Recommendations: Pathogenic variants triggered 100 recommendations in 21/25 patients; most were for genetic counseling, communication to relatives, and cascade testing. CONCLUSION GS provided a modest increase in utility after first-tier cancer gene panels, at the cost of a high frequency of uncertain results. Furthermore, most positives were low/moderate cancer risk results that did not have corresponding evidence-based, management guidelines.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".