Genome-wide copy number analyses of samples from LACE-Bio project identify novel prognostic and predictive markers in early stage non-small cell lung cancer
Bibliographic record
Abstract
Background: Adjuvant chemotherapy (ACT) provides modest benefit in resected non-small cell lung cancer (NSCLC) patients. Genome-wide studies have identified gene copy number aberrations (CNA), but their prognostic implication is unknown. Methods: DNA from 1,013 FFPE tumor samples from three pivotal multicenter randomized trials (ACT vs. control) in the LACE-Bio consortium (median follow-up: 5.2 years) was successfully extracted, profiled using a molecular inversion probe SNP assay, normalized relative to a pool of normal tissues and segmented. Minimally recurrent regions were identified. P values were adjusted to control the false discovery rate (Q values). Results: A total of 976 samples successfully profiled, 414 (42%) adenocarcinoma (ADC), 430 (44%) squamous cell carcinoma (SCC) and 132 (14%) other NSCLC; 710 (73%) males. We identified 431 recurrent regions, with on average 51 gains and 43 losses; 253 regions (59%) were ≤3 Mb. Most frequent gains (up to 48%) were on chr1, 3q, 5p, 6p, 8q, 22q; most frequent losses (up to 40%) on chr3p, 8p, 9p. CNA frequency of 195 regions was significantly different (Q≤0.05) between ADC and SCC. Fourteen regions (7p11–12, 9p21, 18q12, and 19p11–13) were associated with disease-free survival (DFS) (univariate P≤0.005, Q<0.142), with poorer DFS for losses of regions including CDKN2A/B [hazard ratio (HR) for 2-fold lower CN: 1.5 (95% CI: 1.2–1.9), P<0.001, Q=0.020] and STK11 [HR =2.4 (1.3–4.3), P=0.005, Q=0.15]. Chromosomal instability was associated with poorer DFS (HR =1.5, P=0.015), OS (HR =1.2, P=0.189) and lung-cancer specific survival (HR =1.7, P=0.003). Conclusions: These large-scale genome-wide analyses of gene CNA provide new candidate prognostic markers for stage I–III 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 | 0.001 |
| 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) | 0.002 | 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".