Identification of network motifs in lung disease

1. Identification of network motifs in lung disease Cecily Swinburne Mentor: Carol J. Bult Ph.D. Summer 2007

2. Goals • Use genome wide gene expression data to study lung biology • Identify pathways that are characteristic of both normal lung development and lung disease (cancer) • Use normal mouse lung development as a framework for identifying potential lung cancer biomarkers in humans

3. Significance • Signature pathways could provide insights into the pathology of these diseases • Provide possible targets for diagnostics or treatments

4. Background: Lung Cancer • Leading cause of cancer deaths in the world • Approximately 160,000 deaths annually in the US • Contributing factors: Smoking (90% of cases), 2nd hand smoke, asbestos and other inhaled carcinogens • Two Types • Small Cell: rapidly spreading, almost only in smokers • Non-Small Cell: 75% of cases, more slowly progressing and easier to treat than small cell • Little progress has been made in developing new, more effective diagnostic and treatment procedures

5. Background: Development and Cancer • “Cancer has been called a "developmental disorder" (Dean, 1998) because it involves a disruption of the normal developmental program for cells, in terms of both differentiation and proliferation. It follows that some of the molecular players involved in controlling development might be implicated in causing cancer.” (http://www.ucalgary.ca/UofC/eduweb/virtualembryo/dev_cancer.html) Dean, M. 1998. Cancer as a complex developmental disorder - Nineteenth Cornelius P. Rhoads Memorial Lecture. Cancer Research 58: 5633-5636.

6. <mgm.duke.edu> <www.imbb.forth.gr> Transcriptional Profiling with Microarrays • Provides genome wide gene expression data by measuring the presence of messenger RNA in a sample • Many different platforms, the two most common are spotted arrays and Affymetrix arrays

7. Lung Development Data Sets • Analyze two mouse lung development time series • Bonner et al. (2003) – inbred A/J mice e14.5, e17.5, birth, 1w, 2w, and 4w • Jackson Laboratory/Children’s Hospital (Boston) – inbred C57Bl/6J mice e11.5, e13.5, e14.5, e16.5 and 5days • Developmental overlap time series Bonner: 14.5e, 17.5e, 1w Jax: 14.5e, 16.5e, 5days Bonner A.E., Lemon W.J., & You M. (2003): Gene expression signatures identify novel regulatory pathways during murine lung development: implications for lung tumorigenesis. Journal of Medical Genetics40, 408-417.

8. Analysis of time series using Short Time Series Expression Miner (STEM) (http://www.cs.cmu.edu/~jernst/stem/) Bonner et al. (2003) JAX/Boston (unpublished)

9. Enrichment of Biological Processes Represented in STEM gene clusters Combined list of genes with upward trend of expression over development (Bonner et al.) Cell Adhesion Anatomical Structure System Development Vasculature Development Blood Vessel Development Angiogenesis Visual Annotation Display (VLAD) (http://proto.informatics.jax.org/prototypes/vlad-1.02/)

10. Enrichment of Biological Processes Represented in STEM gene clusters Combined list of genes with downward trend of expression over development (JAX/Boston) Cell Cycle Cell Division Mitotic Cell Cycle DNA Repair RNA Processing Mitosis Regulation of transcription

11. Construct Pathways in IPA • Use Ingenuity Pathways Analysis (IPA) (www.ingenuity.com) to construct pathways based on the genes involved in angiogenesis and cell adhesion Cell Adhesion Angiogenesis

12. Analysis of Human Lung Cancer Data Set • Downloaded microarray data from Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) • Dehan and Kaminski (GSE1987) 16 Squamous Cell Carcinoma 7 Adenocarcinoma 9 Normal lung tissue samples

13. Generate Top Hits List • Log2 normalize data • Calculate the analysis of variance using Microarray analysis of variance (MAANOVA) • Identify a top-hits list of genes that are significantly up or down regulated • R Statistical Software (www.r-project.org)

14. VLAD analysis of Human Cancer Genes Up Regulated M Phase Cell cycle process Mitosis Cell division Cell cycle checkpoint DNA replication Cellular metabolic process Chromosome segregation Down Regulated Anatomical structure development System Development Response to Wounding Biological Adhesion Cell Adhesion Cell Communication Cell Morphogenesis Angiogenesis Blood Vessel Morphogenesis Anti-parallels lung development results Up in Development Angiogenesis and Cell Adhesion Down in development Cell cycle

15. Overlap human lung cancer genes onto angiogenesis and adhesion pathways in IPA Adhesion pathway overlapped with cancer expression Angiogenesis pathway overlapped with cancer expression

16. Cell Adhesion Pathway • The same overlap was done with a breast cancer top hits list and just 3 genes overlapped in each pathway

17. Results of overlap 23 genes from the angiogenesis / adhesion pathways overlapped with both Adenocarcinoma and Squamous gene lists Of those: 6 – mouse lung phenotype in MGI 14 –previously associated with cancer 5 –previously identified as potential cancer biomarker • The expression of these 23 genes in lung development and in cancer suggest that they are important to the pathology of lung cancer. • They could serve as potential biomarkers for diagnosis or prognosis of lung cancer.

18. Summary • Evaluating human cancer genes in the context of normal lung development identifies subsets of biological networks that are likely to be important to disease processes

19. Further Work • Follow up on candidate biomarkers identified in this study • Perform same comparisons on genes that were down regulated in lung development • Compare lung tumor data for mouse to mouse lung development • Expand approach to other types of cancer and to other lung diseases such as pulmonary fibrosis

20. Acknowledgements • Carol J. Bult Ph.D. • Benjamin L. King, M.S. • Jon Geiger • Randy O’Rouke • The Jackson Laboratory Summer Student Program • Jane D. Weinberger Endowed Scholarship Fund • The Horace W. Goldsmith Foundation