Original Research: Biomarker Testing in a Canadian Centre for Patients with Non‑small Cell Lung Cancer: Assessing Residual Risks
Bibliographic record
Abstract
Biomarker testing is critical for guiding treatment decisions and clinical management in patients with non-small cell lung cancer (NSCLC). Although the clinical utility of comprehensive testing for point mutations and gene rearrangements is well established, access to next-generation sequencing (NGS)‑based assays in Ontario has historically been limited due to provincial funding constraints. We conducted a retrospective chart review of 215 patients diagnosed with lung adenocarcinoma over a five-year period (2016-2021) and report the observed biomarker testing practice. Testing primarily comprised polymerase chain reaction (PCR)-based detection of common epidermal growth factor receptor (EGFR) mutations and immunohistochemistry (IHC) for anaplastic lymphoma kinase (ALK) overexpression, with or without confirmatory fluorescence in situ hybridization (FISH), and programmed death-ligand 1 (PD-L1) IHC. IHC for ROS1 overexpression, as a surrogate for ROS1 fusion, was observed in the first quarter of 2020. Routine panel-based NGS testing was implemented in the first quarter of 2021. Noting the differences between PCR- and NGS-based EGFR assessment, risks of “false negative” were estimated based on Bayesian analyses. Given the limited scope of PCR tests in terms of variants detected, the post-test, residual risk of “false negative” EGFR was estimated to range ~1:90 in white, Caucasian patients, to ~1:9 in Asian patients. We observed consistent implementation of EGFR, ALK, and PD-L1 testing during the study period, which was in alignment with 2017 National Comprehensive Cancer Network (NCCN) guideline recommendations. However, the delayed adoption of ROS1 testing and NGS-based profiling, including assays for MET and RET alterations, reflects broader limitations in provincial funding policy and highlights the need for equitable access to comprehensive biomarker testing in Ontario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".