Establishment of perineural invasion models and analysis of gene expression revealed an invariant chain (CD74) as a possible molecule involved in perineural invasion in pancreatic cancer
Authors: Koide Norimasa, Taketo Yamada, Rie Shibata, Taisuke Mori, Mariko Fukuma, Ken Yamazaki, Michiie Sakamoto
Journal: Cancer Research (2006)

Proc Amer Assoc Cancer Res, Volume 47, 2006

1832 

Lung cancer is the leading cause of cancer death in North America. Squamous cell (SqC) carcinoma is a distinct subtype of lung cancer that comprises about 35% of total cases. The developmental stages of this subtype have been well-characterized by histology. However, the genetic changes that occur through these stages are not fully understood. In this study, we examined if the application of a combination of statistical methods could result in consistent signatures of expressed genes, predictive of commitment to more malignant phenotypes. We generated 31 serial analysis of gene expression (SAGE) profiles from several of the developmental stages of squamous cell lung carcinoma (bronchial epithelium from 11 former smokers and 9 from current smokers; 1 squamous metaplasia; 5 carcinoma in situ (cis); and 5 invasive carcinoma). The bronchial epithelium and metaplasia samples (21) were obtained by brushing with a 3mm teflon brush. The areas brushed were assessed (i.e. without abnormality, metaplasia) using combined autofluorescent and white light bronchoscopy. These profiles have been sequenced to an average depth of >100,000 tags. We quality-filtered this dataset to focus on 17,986 potentially informative SAGE tags. An unsupervised clustering analysis reveals that the majority of gene expression changes occur with a transition to squamous metaplasia. Profiles from former smokers cluster tightly, while those from current smokers show increased heterogeneity. There are no clear boundaries between metaplasia, CIS, and invasive stages. We developed a supervised classification protocol that is able to identify gene signatures that can predict these developmental stages. We compare the performance of several statistical models that have been previously described for identifying significant class-specific gene expression changes. This analysis has produced optimal classifiers that contain between 8-33 transcripts, and have cross-validation performances between 96.2-100%. There is a substantial contribution from genes involved in cell cycle control and energy metabolism. Finally, we further compared the above results with Monte Carlo simulations using gene expression significance values calculated from the best-performing statistical models. These simulations were designed to identify known pathways that show substantial stage-specific changes. This analysis suggests step-wise changes to the cell cycle, energy metabolism pathways, and in signalling controlled by extracellular matrix-receptor interactions. These findings are applicable to the identification of diagnostic markers, particularly those can differentiate malignant stage. Work is ongoing to validate these results using publicly available microarray data from lung tumours. This work was supported by funding from Genome Canada, Genome BC, and the Michael Smith Foundation for Health Research.