Incidental findings from cancer next generation sequencing panels
Bibliographic record
Abstract
Next-generation sequencing (NGS) technologies have facilitated multi-gene panel (MGP) testing to detect germline DNA variants in hereditary cancer patients. This sensitive technique can uncover unexpected, non-germline incidental findings indicative of mosaicism, clonal hematopoiesis (CH), or hematologic malignancies. A retrospective chart review was conducted to identify cases of incidental findings from NGS-MGP testing. Inclusion criteria included: 1) multiple pathogenic variants in the same patient; 2) pathogenic variants at a low allele fraction; and/or 3) the presence of pathogenic variants not consistent with family history. Secondary tissue analysis, complete blood count (CBC) and medical record review were conducted to further delineate the etiology of the pathogenic variants. Of 6060 NGS-MGP tests, 24 cases fulfilling our inclusion criteria were identified. Pathogenic variants were detected in TP53, ATM, CHEK2, BRCA1 and APC. 18/24 (75.0%) patients were classified as CH, 3/24 (12.5%) as mosaic, 2/24 (8.3%) related to a hematologic malignancy, and 1/24 (4.2%) as true germline. We describe a case-specific workflow to identify and interpret the nature of incidental findings on NGS-MGP. This workflow will provide oncology and genetic clinics a practical guide for the management and counselling of patients with unexpected NGS-MGP findings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".