Molecular Classification and Prediction of Survival in Non-Small-Cell Lung Cancer

BACKGROUND: Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer. The aim of this study was to evaluate the feasibility of integrating expression profiling into routine clinical work-up by including minute bronchoscopic biopsies and develop a robust prognostic gene expression signature METHODS: Tissue samples from a series of 41 chemotherapy-naïve non-small cell lung cancer patients and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using a highly sensitive oligonucleotide array platform (Novachip ; 34'207 transcripts). Gene expression signatures were analyzed by correlation with histological and clinical parameters and validated on independent published datasets and immunohistochemistry. RESULTS: Tumor tissue classification based on the gene expression results was strongly dependent on the proportion of tumor cells present in the biopsies and showed an overall sensitivity of 80% and specificity of 89%. For prognostication we developed a metagene consisting of 13 genes, which was validated on 4 independent published datasets. The robustness of this metagene has been demonstrated by a virtual independence from tumor cells present in the biopsies. Furthermore, vascular endothelial growth factor-beta, one of the key prognostic genes was validated by immunohistochemistry on 508 independent tumor samples. CONCLUSIONS: The proposed strategy of integrating functional genomics into routine clinical work-up allows molecular tumor classification and prediction of survival in patients with non-small cell lung cancer of all stages and is suitable for an integration in the daily clinical practice. Keywords: Gene expression profiling for disease state analysis in lung cancer patients 56 lung biopsies, 4 different Phenotypes: NSCLC-squa., NSCLC-NOS, NSCLC-Adeno, Ctr.-Infl.