Confirmation of germline variants identified by tumor testing: A population-based study.
Bibliographic record
Abstract
e13021 Background: Multi-gene panel tumour testing (TT) has been available in British Columbia since mid-2016 for metastatic non-small cell lung cancer (NSCLC), colorectal cancer (CRC), melanoma (MEL), low-grade glioma (LGG), and gastro-intestinal stromal tumours (GIST). TT can detect somatic driver mutations and potential pathogenic germline variants (pPGVs) associated with hereditary cancer susceptibility. We reviewed the frequency of pPGVs identified by TT and examined referral rates to the Hereditary Cancer Program (HCP) for confirmatory germline testing (GT) and therapeutic implications of PGV findings. Methods: All patients (pts) undergoing TT testing from October 1, 2016 to December 31, 2018 were identified. Diagnosis, age, gender, family history and treatment data were obtained. TT was performed by next-generation sequencing for all/selected regions of the following genes: AKT1, ALK, BRAF, BRCA1, BRCA2, CCND1, CCND3, CIC, EGFR, ERBB2, ERBB3, FUBP1, HRAS, IDH1, IDH2, KIT, KRAS, MAP2K1, MET, NRAS, PDGFRA, PIK3CA, PTEN, ROS1, SDHA, SDHB, SDHC, SDHD. Results: Among 2937 TTs, pPGVs were identified in 83 pts (2.8%) [Table 1]. 50 pts (57%) were referred to HCP, 41 had germline testing, and 14 PGV were confirmed. PGVs were most commonly identified in BRCA1/2 and SDHA and these findings did not influence oncologic treatments. Conclusions: TT detected pPGVs in 2.8% of unselected pts with metastatic cancers. Among 41 pts undergoing germline testing, 34% who would not have otherwise met testing criteria, had a confirmed PGV. Referral rates were low due to lack of patient and clinician awareness and poor health status. Although PGV findings did not directly impact treatment, TT identified 14 new families with hereditary cancer who can benefit from early detection and screening. Future directions include expansion of TT to include additional hereditary cancer susceptibility genes and development of digital tools for pts and clinicians. [Table: see text]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".