Cancers of the lung and bronchus claim over 160,000 lives per year in the United States alone, which is greater than the combined deaths from colon, breast, and prostate cancer. The major histological subtypes of lung cancer identified by light microscopy are small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC), with the latter including adenocarcinoma, (AD), squamous cell carcinoma (SCC), and large cell lung carcinoma (LCLC).  Despite our best attempts to morphologically classify lung cancer, we remain unable to predict the clinical behavior of this diverse disease, particularly in NSCLC. The ineffectiveness of current classification schemes to predict clinical outcome has drastic implications for the management of patients.  For example, while chemotherapy is effective in some patients with lung cancer, many receive no benefit while unfortunately incurring sigificant side effects due entirely to their treatment.  The primary hypothesis of my translational research is that tumor-specific predictors based on high throughput nucleic acid and protein assays will offer significant advances over the current generation of clinical diagnostics, including the currently ineffective morphologic classification of non-small cell lung cancer.

To accomplish this in the current proposal I aim to: (1) Develop and validate an expression-based classification of lung cancer to using real-time qRT-PCR from formalin-fixed paraffin-embedded (FFPE) tissues (2) Assess the predictive and prognostic significance of molecular classification in NSCLC across a set of clinically meaningful patient outcomes (i.e. disease free survival, recurrence pattern, and response to chemotherapy).