Abstract 896: The airway field of injury reflects gene expression changes associated with the presence of lung squamous premalignant lesions
Bibliographic record
Abstract
Abstract Lung squamous cell carcinoma (SCC) arises in the epithelial layer of the bronchial airways and is preceded by the development of premalignant lesions (PMLs). The molecular events involved in the progression of PMLs to lung SCC are not clearly understood as not all PMLs that develop go on to form carcinoma. In addition, the majority of lung cancer chemoprevention agents tested to date are ineffective. Molecular characterization of the airway field of injury in individuals with PMLs could provide novel insights into the earliest molecular events associated with carcinogenesis and identify biomarkers to guide lung cancer detection and chemoprevention. RNA-sequencing was conducted on cytologically normal airway brushings from current and former smokers with (n = 50) and without (n = 25) PMLs as part of the British Columbia Lung Health Study. Linear modeling strategies were used to identify 280 differentialy expressed genes at FDR<0.002 between subjects with and without PMLs. Pathway analysis using GSEA and ROAST revealed enrichment of genes involved in the electron transport chain and oxidative phosphorylation pathways in subjects with PMLs. These findings were validated by measuring the cellular bioenergetics of cultured epithelial cells from biopsies of PMLs and non-lesion areas. Baseline oxygen consumption rates were 2.5 fold higher (p<0.001) and the spare respiratory capacity was 1.5 fold higher (p<0.001) in PML cultures. These data suggest that metabolism-associated gene expression observed in the field of injury of PMLs is correlated with PMLs. In addition, there is a significant concordant enrichment (FDR<0.05) between the signature and gene expression in PMLs adjacent to SCC tumors, in SCC tumors, and in the field of individuals with lung cancer. This concordance led to the development of a 200-gene biomarker that accurately predicts the presence of PMLs (AUC = 0.90 n = 17 independent samples). Importantly, this biomarker was also predictive (AUC -.72) of progression/stability vs. regression of these premalignant lesions in an independent cohort of cytologically normal airway brushings collected as part of the RPCI screening clinic (n = 18). This is the first study to comprehensively profile gene expression changes in airway epithelial cells in the presence of PMLs. A subset of these changes reflects the earliest changes in the process of lung squamous cell carcinogenesis including the genes involved in cellular metabolism. However, the molecular alterations in the field of injury are dynamic as bronchial lesions either progress or regress these changes may be leveraged to monitor efficacy in chemoprevention trials. In addition monitoring molecular changes in high-risk smokers may identify smokers with PMLs that should receive lung cancer screening as well as lay the foundation for personalized lung cancer chemoprevention. Citation Format: Sarah A. Mazzilli, Ania Tassinari, Yaron Gethalter, Gang Lui, Mary Pine, Stephen Lam, Mary Reid, Suso Platero, Marc Lenburg, Avrum Spira, Jennifer Beane. The airway field of injury reflects gene expression changes associated with the presence of lung squamous premalignant lesions. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 896.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".