Uncovering molecular features driving lung adenocarcinoma heterogeneity in patients who formerly smoked
Bibliographic record
Abstract
BACKGROUND: An increasing proportion of lung adenocarcinoma (LUAD) occurs in patients even after they have stopped smoking. Here, we aimed to determine whether tobacco smoking induced changes across LUADs from patients who formerly smoked correspond to different biological and clinical factors. METHODS: Random forest models (RFs) were trained utilizing a smoking associated signature developed from differentially expressed genes between LUAD patients who had never smoked (NS) or currently smoked (CS) from TCGA (n = 193) and BCCA (n = 69) cohorts. The RFs were subsequently applied to 299 and 131 formerly smoking patients from TCGA and MSKCC cohorts, respectively. FS were RF-classified as either CS-like or NS-like and associations with patient characteristics, biological features, and clinical outcomes were determined. RESULTS: We elucidated a 123 gene signature that robustly classified NS and CS in both RNA-seq (AUC = 0.85) and microarray (AUC = 0.92) validation test sets. The RF classified 213 patients who had formerly smoked as CS-like and 86 as NS-like from the TCGA cohort. CS-like and NS-like status in formerly smoking patients correlated poorly with patient characteristics but had substantially different biological features including tumor mutational burden, number of mutations, mutagenic signatures and immune cell populations. NS-like formerly smoking patients had 17.5 months and 18.6 months longer overall survival than CS-like patients from the TCGA and MSKCC cohorts, respectively. CONCLUSIONS: Patients who had formerly smoked with LUAD harbor heterogeneous tumor biology. These patients can be divided by smoking induced gene expression to inform prognosis and underlying biological characteristics for treatment selection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".