Tumor Mutational Burden by Whole-Genome Sequencing in Resected NSCLC of Never Smokers
Bibliographic record
Abstract
BACKGROUND: Data are scarce about tumor mutational burden (TMB) as a biomarker in never smokers with non-small cell lung cancer (NSCLC). METHODS: TMB was assessed by whole-genome sequencing (WGS) and compared with in silico reduced whole-exome sequencing (WES) and targeted commercial next-generation sequencing (NGS) gene panels in 92 paired tumor-normal samples from never smokers who underwent NSCLC resection with curative intent. Analyses were performed to test for association with survival after surgery and to identify the optimal prognostic TMB cutoff. RESULTS: Tumors of never smokers with NSCLC had low TMB scores (median 1.57 mutations/Mb; range, 0.13-17.94). A TMB cutoff of 1.70 mutations/Mb was associated with a 5-year overall survival of 58% in the high-TMB (42% of cases) compared with 86% in low-TMB patients (Wald P = 0.0029). TMB scores from WGS and WES were highly correlated (Spearman ρ = 0.93, P < 2.2e-16). TMB scores from NGS panels demonstrated high intraindividual fluctuations and identified high-TMB patients with 65% concordance in average compared with WGS. CONCLUSIONS: In resected NSCLC of never smokers, high TMB was associated with worse prognosis. WES provided a good estimate of TMB while targeted NGS panels seem to lack adequate depth and resolution in the setting of low mutation burden. IMPACT: TMB is a prognostic indicator of survival in resected NSCLC from individuals who never smoked. In this setting of low mutation counts, TMB can be accurately measured by WGS or WES, but not NGS panels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".