Abstract A07: Alterations in G2/M phase associated transcriptional networks highlight lung cancer predisposition in COPD patients
Bibliographic record
Abstract
Abstract Background: Patients with chronic obstructive pulmonary disease (COPD) are at increased risk of developing lung cancer. COPD, clinically defined by reduced lung function measurements, is characterized by chronic airway inflammation, remodeling and loss as well as destruction of alveoli (emphysema). While this disease is an important lung cancer risk factor independent of smoking, the molecular progression from COPD to lung cancer tumourigenesis is relatively understudied. Method: We first analyzed small-airway epithelial gene expression profiles obtained from bronchial brushings from 127 COPD and 140 non-COPD ever-smoker patients. We performed weighted gene correlation network analysis (WGCNA) on these gene expression profiles to discover deregulated gene modules (“metagenes”) associated with reduced lung function (Forced Expiratory Volume at 1 second, FEV-1)—a clinical measure of COPD severity most robustly negatively correlated with lung cancer risk. We then assessed the preservation of these modules in two non-small cell lung cancer (NSCLC) tumor/normal data sets (lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), n= 887 tumors total) to examine the molecular overlap between COPD and lung cancer. Airway and tumor patient cohorts were matched for age, gender, tumor stage, and smoking status. Result: We discovered 10 distinct small-airway gene expression modules, two of which were significantly negatively correlated (p < 0.05) with patient FEV-1. One of these FEV-1 modules was the top overall module preserved in both NSCLC subtypes. This lung cancer-FEV-1 module contained 31 genes solely enriched for two related mitotic functions—G2/M phase transition (BH-p = 0.02) and mitotic roles of polo-like kinase (BH-p = 0.001, n=31). Of these, 28 genes were significantly overexpressed in both LUAD and LUSC, and mapped to a highly clustered sub-network of 23 proteins with 465 known and in silico-predicted protein-protein interactions. When tumors enriched for this lung cancer-FEV-1 gene signature were further examined, we observed a significant co-occurrence of DNA-level alterations in DNA damage-associated checkpoints, specifically mutated TP53. Conclusion: Coordinated gene expression changes associated with COPD severity measures in small airways and preserved in NSCLC tumors are enriched for G2/M phase transition genes. These genes are further disrupted in tumors, where co-occurring mutations to gatekeeper genes are present. Progression of mitosis during abnormal aneuploidy in lung tissues of COPD patients may confer increased risk of oncogenic transformation in this population, and may underlie the molecular progression from COPD to lung cancer. Citation Format: Erin A. Marshall, Emily A. Vucic, Victor D. Martinez, Raymond T. Ng, Wan L. Lam. Alterations in G2/M phase associated transcriptional networks highlight lung cancer predisposition in COPD patients [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr A07.
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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 | 0.001 |
| 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) | 0.003 | 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".