A transcriptional network signature characterizes lung cancer subtypes.
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.
Chang
Hsun-Hsien
HH
Children's Hospital Informatics Program, Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts 02115, USA. hsun-hsien.chang@childrens.harvard.edu
Dreyfuss
Jonathan M
JM
Ramoni
Marco F
MF
D000230:Adenocarcinoma / Q000235:genetics*; D001499:Bayes Theorem; D002294:Carcinoma, Squamous Cell / Q000235:genetics*; D002881:Chromosomes, Human, Pair 12; D053263:Gene Regulatory Networks; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification* / Q000235:genetics*; D049490:Systems Biology / Q000379:methods