Identification of network motifs in lung disease

Cecily Swinburne

Mentor: Carol J. Bult Ph.D.

Summer 2007

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

Signature pathways could provide insights into the pathology of these diseases

Provide possible targets for diagnostics or treatments

Significance

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

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.

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

<mgm.duke.edu> <www.imbb.forth.gr>

Lung Development Data Sets

Analyze two mouse lung development time series

Bonner et al. (2003) – inbred A/J micee14.5, 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 Genetics 40, 408-417.

Analysis of time series using Short Time Series Expression Miner (STEM) (http://www.cs.cmu.edu/~jernst/stem/)

Bonner Profiles Jackson Lab Profiles

15 13 12 11 0 13 8 12 15 2 7 3

Number of Genes

87 75 58 49 43 312 349 196 141 431 320 139

Bonner et al. (2003) JAX/Boston (unpublished)

Enrichment of Biological Processes Represented in STEM gene clusters

Cell Adhesion

Anatomical Structure

System Development

Vasculature Development

Blood Vessel Development

Angiogenesis

Combined list of genes with upward trend of expression over development (Bonner et al.)

Visual Annotation Display (VLAD)(http://proto.informatics.jax.org/prototypes/vlad-1.02/)

Cell Cycle

Cell Division

Mitotic Cell Cycle

DNA Repair

RNA Processing

Mitosis

Regulation of transcription

Enrichment of Biological Processes Represented in STEM gene clusters

Combined list of genes with downward trend of expression over development (JAX/Boston)

Construct Pathways in IPAUse Ingenuity Pathways Analysis (IPA) (www.ingenuity.com) to construct pathways based on the genes involved in angiogenesis and cell adhesion

Cell Adhesion Angiogenesis

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

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)

VLAD analysis of Human Cancer GenesUp 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

Overlap human lung cancer genes onto angiogenesis and adhesion pathways in IPA

Adhesion pathway overlapped with cancer expression

Angiogenesis pathway overlapped with cancer expression

Cell Adhesion Pathway

The same overlap was done with a breast cancer top hits list and just 3 genes overlapped in each pathway

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.

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

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

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

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