A transcriptional network signature characterizes lung cancer subtypes

• Published in: Cancer Vol. 117; no. 2; pp. 353 - 360
• Main Authors: Chang, Hsun‐Hsien, Dreyfuss, Jonathan M., Ramoni, Marco F.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 15.01.2011; Wiley-Blackwell
• Subjects: Adenocarcinoma - genetics; Bayes Theorem; Biological and medical sciences; Carcinoma, Squamous Cell - genetics; Chromosomes, Human, Pair 12; disease diagnosis; gene expression; Gene Regulatory Networks; Humans; lung cancer; Lung Neoplasms - classification; Lung Neoplasms - genetics; Medical sciences; Pneumology; Systems Biology - methods; transcriptional networks; Tumors; Tumors of the respiratory system and mediastinum; Lung disease; Transcription; Respiratory disease; Lung cancer; Malignant tumor; Gene expression; Cancerology; Network; transcriptional networks; Bronchus disease; disease diagnosis; Diagnosis; Subtype; Cancer
• ISSN: 0008-543X; 1097-0142
• DOI: 10.1002/cncr.25592

BACKGROUND: Transcriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC). METHODS: A transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas. The authors tested its classification accuracy in 7 independent cohorts, for a total of 422 subjects of Caucasian, African, and Asian descent. RESULTS: The model for distinguishing AC from SCC was a 25‐gene network signature. Its performance on the 7 independent cohorts achieved 95.2% classification accuracy. Even more surprisingly, 95% of this accuracy was explained by the interplay of 3 genes (KRT6A, KRT6B, KRT6C) on a narrow cytoband of chromosome 12. The role of this chromosomal region in distinguishing AC and SCC was further confirmed by the analysis of another group of 28 independent subjects assayed by DNA copy number changes. The copy number variations of bands 12q12, 12q13, and 12q12‐13 discriminated these samples with 84% accuracy. CONCLUSIONS: These results suggest the existence of a robust signature localized in a relatively small area of the genome, and show the clinical potential of reverse engineering transcriptional networks from molecular profiles. Cancer 2011. © 2010 American Cancer Society. A transcriptional network model derived from gene expression microarrays identified a narrow cytoband on chromosome 12 able to characterize lung cancer subtypes. The signature genes were also confirmed by a comparative genomic hybridization analysis.