Investigation of Leukocyte Telomere Length and Genetic Variants in Chromosome 5p15.33 as Prognostic Markers in Lung Cancer
Bibliographic record
Abstract
Abstract Background: Lung cancer remains the leading cause of cancer mortality with relatively few prognostic biomarkers. We investigated associations with overall survival for telomere length (TL) and genetic variation in chromosome 5p15.33, an established telomere maintenance locus. Methods: Leukocyte TL was measured after diagnosis in 807 patients with non–small cell lung cancer (NSCLC) from the Princess Margaret Cancer Center in Toronto and assessed prospectively in 767 NSCLC cases from the Copenhagen City Heart Study and the Copenhagen General Population Study. Associations with all-cause mortality were tested for 723 variants in 5p15.33, genotyped in 4,672 NSCLC cases. Results: Short telomeres (≤10th percentile) were associated with poor prognosis for adenocarcinoma in both populations: TL measured 6 months after diagnosis [HR = 1.65; 95% confidence intervals (CI), 1.04–2.64] and for those diagnosed within 5 years after blood sampling (HR = 2.42; 95% CI, 1.37–4.28). Short TL was associated with mortality in never smokers with NSCLC (HR = 10.29; 95% CI, 1.86–56.86) and adenocarcinoma (HR = 11.31; 95% CI, 1.96–65.24). Analyses in 5p15.33 identified statistically significant prognostic associations for rs56266421-G in LPCAT1 (HR = 1.86; 95% CI, 1.38–2.52; P = 4.5 × 10−5) in stage I–IIIA NSCLC, and for the SLC6A3 gene with OS in females with NSCLC (P = 1.6 × 10−3). Conclusions: Our findings support the potential clinical utility of TL, particularly for adenocarcinoma patients, while associations in chromosome 5p15.33 warrant further exploration. Impact: This is the largest lung cancer study of leukocyte TL and OS, and the first to examine the impact of the timing of TL measurement. Our findings suggest that extremely short telomeres are indicative of poor prognosis in NSCLC.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".