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Comparative Study
. 2011 Jan 15;117(2):353-60.
doi: 10.1002/cncr.25592. Epub 2010 Sep 13.

A transcriptional network signature characterizes lung cancer subtypes

Hsun-Hsien Chang  1 , Jonathan M Dreyfuss, Marco F Ramoni
Affiliations
Comparative Study

A transcriptional network signature characterizes lung cancer subtypes

Hsun-Hsien Chang et al. Cancer. .

Abstract

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.

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Figures

Figure 1
Figure 1
The Bayesian network model encoding the dependence relation among the subtype variable and genes is shown. For each gene, its likelihood of dependence on the subtype variable or another gene were evaluated, and then its parent node was determined by the highest likelihood. The subtype variable’s first tier child nodes, which are colored in green, are under its Markov blanket and assemble a signature to discriminate between adenocarcinoma (AC) and squamous cell carcinoma (SCC).
Figure 2
Figure 2
The adenocarcinoma (AC)-squamous cell carcinoma (SCC) discriminative surface in the use of KRT6A, KRT6B, and KRT6C as a signature is shown. The classification accuracy achieved by this signature was 90.2%, accounting for 95% of the accuracy of the entire 25-gene signature. Simulating the possible expression levels of the 3 genes generated a nonlinear discriminative surface, in which the region below it belonged to AC, and the region above belonged to SCC
Figure 3
Figure 3
The adenocarcinoma (AC)-squamous cell carcinoma (SCC) discriminative surface generated by the comparative genomic hybridization data is shown. The discriminative surface is a saddle, in which the region below it belongs to AC, and the region above belongs to SCC. This surface can recognize the lung cancer samples with 83.9% accuracy.
See this image and copyright information in PMC

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