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                                            BIOINFORMATICS COLLOQUIUM
					        College of Science
                                              George Mason University
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			    Microarray Studies in Lung Cancer:  Beyond Classification

                                                  Robert Stearman

                                 University of Colorado Health Sciences Center


Abstract:
Human adenocarcinoma (AC) is the most frequently diagnosed human lung cancer, and its absolute 
incidence is increasing dramatically. One animal model for lung AC is the A/J mouse after
urethane injection, which resembles human AC with similar histological appearance and molecular 
changes. We collected the gene expression profiles of human and murine normal and AC lung tissues, 
and compared the two species' datasets after aligning ~7,500 orthologous genes. A list of 409 gene 
classifiers (p-value<0.0001) common to both species (joint classifiers) was generated which showed 
significant, positive correlation in expression levels between the two species. A number of 
previously reported expression changes in the published literature were recapitulated in both species, 
such as changes in glycolytic enzymes and cell cycle proteins. These results demonstrate that the A/J 
mouse-urethane model reflects significant molecular details of human lung AC, and comparison of 
orthologous gene expression changes can provide novel insights into lung carcinogenesis.

As an extension to this work, microarray datasets were compiled from age-matched, untreated A/J mouse 
lung tissues to evaluate the ability of this high throughput methodology to identify transcriptional 
effects in the tumor micro-environment. We identifed 39 "field effect" genes which are up-regulated 
in adjacent tissues to lung tumors and are generally expressed in immune cell types. Correctly 
identifying patients with cancerous versus benign lung nodules is currently the biggest challenge in 
deciding treatment. As a proof of principle for surrogate tissue biomarkers, we collected microarray 
datasets from murine bronchioavelolar lavage samples and could correctly identify tumor-bearing 
animals using the field effect gene classifier. Alternative approaches to biomarker development 
in surrogate tissues will be discussed.