Molecular Fingerprinting of Lung Cancer

Brief Summary

Detailed Description

OBJECTIVES: * Develop statistical and computational methods for modeling the relationships between multiple variable protein and RNA expression data and clinical endpoints using both supervised and unsupervised classification and pattern recognition approaches. * Determine protein and RNA expression fingerprints on completely resected non-small cell lung cancer without prior chemotherapy. * Correlate protein and RNA expression fingerprints with T-stage and nodal involvement at the time of surgery, and collect outcome data to allow correlation with recurrence (local and/or distant) and survival. OUTLINE: This is a multicenter study. Any excess tissues removed from surgery and would otherwise be discarded (tissues not used for diagnosis and/or treatment decision making) are obtained for this study. Tissue are analyzed for molecular features that predict biologic behavior. Quantitation of RNA, gene expression profiles, and protein expression patterns are assessed by matrix-assisted laser desorption/ionization time of flight mass spectroscopy and microarray analysis. Medical records are reviewed to obtain information about results of tests associated with cancer diagnosis. Further progress in cancer treatment and tumor behavior after surgery are followed via record review.

Primary Outcomes

Secondary Outcomes

• Development of statistical and computational methods to model relationship of data and clinical outcomes(after laboratory gene analysis work is completed)
• Correlation of protein and RNA expression fingerprints with recurrence (local and/or distant) and survival(after collection of designated samples)