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J Thorac Cardiovasc Surg 2004;127:1332-1342
© 2004 The American Association for Thoracic Surgery

General thoracic surgery

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

^a Division of Cardiovascular and Thoracic Surgery, University of Minnesota Medical School, Minneapolis, Minn, USA
^b Department of Pathology, University of Minnesota Medical School, Minneapolis, Minn, USA
^c Division of Surgical Sciences, Statistics, Department of Surgery, University of Minnesota Medical School, Minneapolis, Minn, USA
^d Division of Hematology, Oncology, and Transplant, University of Minnesota Medical School, and the Research Service, Veterans Affairs Medical Center, Minneapolis, Minn, USA

Received for publication May 2, 2003; revisions received October 30, 2003; accepted for publication November 10, 2003.

^* Address for reprints: Michael A. Maddaus, MD, Section of General Thoracic Surgery, Division of Cardiovascular and Thoracic Surgery, MMC 207, 420 Delaware Street SE, Minneapolis, MN 55455, USA
madda001{at}tc.umn.edu

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.

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