Next-generation sequencing of non-small cell lung cancer at a Quebec health care cancer centre
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the leading cause of cancer death in both men and women. Quebec has the highest lung cancer mortality out of all provinces in Canada, believed to be caused by higher smoking rates. Molecular testing for lung cancer is standard of care due to the discovery of actionable driver mutations that can be targeted with tyrosine kinase inhibitors. To date, no detailed molecular testing characterization of Quebec patients with lung cancer using next generation sequencing (NGS) has been performed. MATERIALS AND METHODS: The aim of this study was to describe the genomic landscape of patients with lung cancer (n = 997) who underwent NGS molecular testing at a tertiary care center in Quebec and to correlate it with clinical and pathology variables. RESULTS: Compared to 10 other NGS studies found through a structured search strategy, our cohort had a higher prevalence of KRAS mutations (39.2%) compared to most geographical locations. Additionally, we observed a significant positive association between decreasing age and a higher proportion of KRAS G12C mutations. CONCLUSION: Overall, it remains important to assess institutional rates of actionable driver mutations to help guide governing bodies, fuel clinical trials and create benchmarks for expected rates as quality metrics.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".