Atul Butte and Colleagues Receive Comparative Systems Genetics of Cancer Grant

Research to be performed under a grant shared by Atul Butte’s lab and two other laboratories will give new insight into the molecular mechanisms underlying lung cancer. The Comparative Systems Genetics of Cancer grant will combine molecular genetics and integrated bioinformatics to take a system-wide look at lung cancer in hopes of better understanding lung cancer and finding new treatment targets.

“Lung Cancer is one of the most common and lethal cancers afflicting humans, yet the underlying molecular mechanisms of the disease remain poorly understood,” Joel Dudley, a researcher in Butte’s lab, says. Researchers sharing this three million dollar grant will use innovative systems biology to study this disease, which kills 1.4 million people annually and is the leading cause of cancer related deaths worldwide (1).

Dr. Alejandro Sweet-Cordero and Dr. Julien Sage, cancer researchers at Stanford University, will participate in the study by researching genetic mouse models of lung cancer, and the Butte Lab will integrate this data with genome-scale molecular measurements from humans, most of which is available in the public domain.

By first looking at the effects of knocking out three genes in mice known to be significantly involved in lung cancer in humans – p53, RB and KRAS – researchers hope to gain a better understanding of the different types of lung cancer tumors. Looking at mouse models with these specific oncogenes turned on or off, Sweet-Coredero and Sage will be able to identify changes in the gene networks that are operating in the tumor cells.

The gene expression data generated from these gene knockout mice will hopefully illuminate the different causal networks involved in small cell and non-small cell lung cancer. By “walking back up the chain of events,” they can learn what happens in downstream pathways, uncovering the molecular links between these genes and the disease phenotypes.

Sweet-Coredero attempted similar research as a post-doc in 2005, yielding some of the first mouse-human comparative research to successfully identify gene expression profiles exclusive to oncogenes. Research on this grant will use improved mouse modeling and gene expression profiling techniques, as mouse and human data is layered into existing gene networks in order to map the gene expression data.

“We’re integrating human data from the public domain with mouse model data, which will help inform the human models,” Dudley explains.

According to Julien Sage the advantage of using mouse models extends beyond identifying key molecules and networks. “We can also use the mice in pre-clinical studies, to validate these targets. This could really identify novel targets to treat the patient, or even to help better diagnose their disease and its evolution.”

To do this, the researchers will use tools and methods borrowed from the artificial intelligence and business domains to build causal event networks. This team is among the first to apply this method to medical research.

By linking mouse models of lung cancer, known human genomic phenomena and cancer phenotypes seen in humans, researchers hope to gain a better understanding of the mechanisms behind this deadly disease as a whole. “This is a great example of Stanford providing a collaborative environment for us to work together,” Sweet-Cordero says.

The Comparative Systems Genetics of Cancer grant is funded by the National Institutes of Health and reviewed by the National Cancer Institutes.

(1) WHO Fact Sheet No 297. (July 2008). “Cancer.” http://www.who.int/mediacentre/factsheets/fs297/en/index.html

by Shauna Kanel