Expression profiling of non-small cell lung carcinoma identifies metastatic genotypes based on lymph node tumor burden.

OBJECTIVE: This study hypothesized that non-small cell lung carcinoma cells from primary tumors isolated by laser capture microdissection would exhibit gene expression profiles associated with graded lymph node metastatic cell burden.
METHODS: Non-small cell lung carcinoma tumors (n = 15) were classified on the basis of nodal metastatic cell burden by 2 methods, obtaining 3 groups: no metastasis, micrometastasis, and overt metastasis. We then performed microarray analysis on microdissected primary tumor cells and identified gene expression profiles associated with graded nodal tumor burden using a correlation-based selection algorithm coupled with cross-validation analysis. Hierarchical clustering showed the regrouping of tumor specimens; the classification inference was assessed with Fisher's exact test. We verified data for certain genes by using another independent assay.
RESULTS: The 15 specimens clustered into 3 groups: cluster A predominated in specimens with overt nodal metastasis; cluster B had more specimens with nodal micrometastases; and cluster C included only specimens without nodal metastases. Cluster assignment was based on a validated 75-gene discriminatory subset. Notably, genes not previously associated with positive non-small cell lung carcinoma lymph node status were encountered in the profiling analysis.
CONCLUSIONS: Microdissection, combined with microarray analysis, is a potentially powerful method to characterize the molecular profile of tumor cells. The 75-gene expression profiles representative of clusters A and B may define genotypes prone to metastasize. Overall, the 3 groups of tumor specimens clustered separately, suggesting that this approach may identify graded metastatic propensity. Further, genes singled out in clustering may yield insights into underlying metastatic mechanisms and may represent new therapeutic targets.