Detecting the Presence and Progression of Premalignant Lung Lesions via Airway Gene Expression
Bibliographic record
Abstract
Abstract Purpose: Lung cancer is the leading cause of cancer-related death in the United States. The molecular events preceding the onset of disease are poorly understood, and no effective tools exist to identify smokers with premalignant lesions (PMLs) that will progress to invasive cancer. Prior work identified molecular alterations in the smoke-exposed airway field of injury associated with lung cancer. Here, we focus on an earlier stage in the disease process leveraging the airway field of injury to study PMLs and its utility in lung cancer chemoprevention. Experimental Design: Bronchial epithelial cells from normal appearing bronchial mucosa were profiled by mRNA-Seq from subjects with (n = 50) and without (n = 25) PMLs. Using surrogate variable and gene set enrichment analysis, we identified genes, pathways, and lung cancer–related gene sets differentially expressed between subjects with and without PMLs. A computational pipeline was developed to build and test a chemoprevention-relevant biomarker. Results: We identified 280 genes in the airway field associated with the presence of PMLs. Among the upregulated genes, oxidative phosphorylation was strongly enriched, and IHC and bioenergetics studies confirmed pathway findings in PMLs. The relationship between PMLs and squamous cell carcinomas (SCC) was also confirmed using published lung cancer datasets. The biomarker performed well predicting the presence of PMLs (AUC = 0.92, n = 17), and changes in the biomarker score associated with progression/stability versus regression of PMLs (AUC = 0.75, n = 51). Conclusions: Transcriptomic alterations in the airway field of smokers with PMLs reflect metabolic and early lung SCC alterations and may be leveraged to stratify smokers at high risk for PML progression and monitor outcome in chemoprevention trials. Clin Cancer Res; 23(17); 5091–100. ©2017 AACR.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".