NSCLC driver mutations in the Quebec population: Epidemiologic and clinical evaluation.
Bibliographic record
Abstract
e22159 Background: Recent advances in lung cancer treatment embrace the recognition of molecular pathways implicated in its pathogenicity, paving the path to personalized therapies. We conducted a retrospective analysis to characterize the molecular features of the population treated for non-squamous non-small cell lung cancer (NS-NSCLC) in the province of Quebec. Methods: 622 patients with NS-NSCLC and adequate tumor blocks, treated at the CHUM between 2006 and 2008, were included. All samples were tested for ALK translocations (by IHC and FISH), EGFR classical exon 19 and 21 mutations by PCR (fragment analysis and qPCR) and for KRAS codon 12 and 13 mutations by mismatch PCR-RFLP. Molecular features were matched to demographic characteristics and clinical outcomes. Results: So far, complete results are available for 153 patients. Considering the amount of tumor tissue available, this population is largely represented by patients with local or loco-regional disease (n= 140, 91.5%). A minority of patients (10.3%) was never or light smokers (< 10 pack-yrs). Only 2 patients (1.3%) were of Asian descent. The following table depicts the outcomes of this cohort of patients segregated according to mutation status and extent of disease. Conclusions: ALK rearrangements were not identified in this unselected NS-NSCLC population characterized by localized disease and strong smoking history. ALK translocation prevalence in different populations is likely to be largely influenced by its tumor stage distribution, tobacco exposure and the use of selection criteria for molecular testing. An expanded cohort of patients will be presented at the meeting. [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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".