A Training-Testing Approach to the Molecular Classification of Resected Non-Small Cell Lung Cancer

Noboru Yamagata, Yu Shyr, Kiyoshi Yanagisawa, Mary Edgerton, Thao P. Dang, Adriana Gonzalez, Sorena Nadaf, Paul Larsen, John R. Roberts, Jonathan C. Nesbitt, Roy Jensen, Shawn Levy, Jason H. Moore, John D. Minna, David P. Carbone

Research output: Contribution to journalArticlepeer-review

117 Scopus citations


Purpose: RNA expression patterns associated with non-small cell lung cancer subclassification have been reported, but there are substantial differences in the key genes and clinical features of these subsets casting doubt on their biological significance. Experimental Design: In this study, we used a training-testing approach to test the reliability of cDNA microarray-based classifications of resected human non-small cell lung cancers (NSCLCs) analyzed by cDNA microarray. Results: Groups of genes were identified that were able to differentiate primary tumors from normal lung and lung metastases, as well as identify known histological subgroups of NSCLCs. Groups of genes were identified to discriminate sample clusters. A blinded confirmatory set of tumors was correctly classified by using these patterns. Some histologically diagnosed large cell tumors were clearly classified by expression profile analysis as being either adenocarcinoma or squamous cell carcinoma, indicating that this group of tumors may not be genetically homogeneous. High α-actinin-4 expression was identified as highly correlated with poor prognosis. Conclusions: These results demonstrate that gene expression profiling can identify molecular classes of resected NSCLCs that correctly classifies a blinded test cohort, and correlates with and supplements standard histological evaluation.

Original languageEnglish (US)
Pages (from-to)4695-4704
Number of pages10
JournalClinical Cancer Research
Issue number13
StatePublished - Oct 15 2003

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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