Abstract A27: Airway molecular alterations associated with premalignant lesion progression and lung cancer development
Bibliographic record
Abstract
Abstract Recently, the National Lung Screening Trial reported a 20% reduction in lung cancer mortality and lung cancer chemoprevention trials targeting the arachidonic acid pathway have demonstrated decreases in lung cancer associated markers. These studies highlight possibilities for future reductions in lung cancer mortality through early detection and chemoprevention. Our group has previously identified smoking- and lung cancer-specific gene expression alterations in cytologically normal airway epithelial cells that can serve as a clinically-relevant biomarker for the early detection of lung cancer. Here, in an effort that could lead to markers of lung cancer risk, we identify changes in these cells associated with regression of premalignant airway lesions. Airway epithelial cells were collected via bronchoscopy from patients with bronchial dysplasia at baseline, on-treatment, and post-treatment with green tea extract (GTE) or placebo ranging from 2 to 6 months (n=27 patients, n=63 samples). RNA from the samples was processed and hybridized to Affymetrix Human Gene 1.0 ST microarrays. A linear mixed effect model was used to identify a gene expression signature predictive of subsequent dysplasia regression. Further a paired t-test was used to identify genes associated with dysplasia regression over time. Using gene set enrichment analysis (GSEA) we identified that the baseline dysplasia regression signature was enriched (FDR<0.05) among genes whose altered expression was associated with: dysplasia regression over time; the presence or future development of lung cancer; and human bronchial biopsies at successive morphological stages of lung squamous carcinogenesis. The genes associated with dysplasia regression were validated small cohorts of independent samples from chemoprevention trials testing Sulindac, Myo-inositol, and GTE. Analysis of the Connectivity Map identified compounds that reverse the dysplasia regression signature in vitro and are therefore candidate chemoprevention agents. Our studies suggest that the airway “field of injury” is modulated by bronchial premalignant lesions. The molecular signatures identified may be important tools for stratifying high-risk smokers for chemoprevention trials, as surrogate endpoints of efficacy in these trials, and for identification of novel molecular targets for chemoprevention. In addition, the molecular signature of regression of airway dysplasia may have additional utility as a biomarker predictive of the presence of or future lung cancer development. Citation Format: Jennifer Ebel Beane, Kahkeshan Hijazi, Katrina Steiling, Gang Liu, Sherry Zhang, Stephen Lam, Marc Lenburg. Airway molecular alterations associated with premalignant lesion progression and lung cancer development. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A27.
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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 | 0.001 | 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".