It looks like the researchers at Duke University’s Institute for Genome Sciences & Policy (IGSP) have taken a lesson from internet social networks. They developed a new method that essentially does for the genetic pathways underlying cancer what social networking websites can do for people: It finds the connections among them. The research team reported its findings in PLoS Computational Biology on Feb. 15, 2008.

“Our major innovation is the use of gene sets in modeling tumor progression rather than single genes,” said Sayan Mukherjee, an IGSP investigator and assistant professor in the department of statistical science at Duke. Their goal is to find out why cancer becomes metastatic and to find out why and how certain sets of genes work together to allow uncontrolled growth of cancer cells or blood vessels. Their modeling method also enabled them to characterize gene networks as they evolve over the course of tumor progression, from normal tissue to the beginnings of cancer and on to that cancer’s spread, or metastasis. Their hope is that they could give these maps to a clinician and be able to tell them ‘This is what it looks like.’”

That intuitive understanding of how each pathway fits into the broader context might allow physicians to more strategically select drug targets. For instance, they might target a pathway that serves as a kind of central hub, holding the rest of the network together. Or, Mukherjee said, if the genes in a particular cancer type were found to fall into isolated clusters, they might develop a combination treatment designed to hit each one.

The networks of genes in prostate cancer and melanoma that came out of their analysis fit well with earlier findings. “It was a surprise that we were able to recapitulate a lot of what was known,” while making these broader connections, he added.

Their method can now be applied to develop models of other cancers and Mukherjee said his team is already working to unravel the pathway connections underlying colon cancer. It could also be applied in other realms, such as the study of embryonic development, he added.

Collaborators on the study include Elena Edelman and Justin Guinney, both graduate students in the Computational Biology and Bioinformatics Program, Jen-Tsan Chi, assistant professor of molecular genetics and microbiology and Phillip Febbo, assistant professor in the department of medicine. All of the researchers are at the IGSP at Duke. The work was supported by the Damon Runyon Cancer Research Foundation, the National Science Foundation and the National Institutes of Health.

It’s an interesting way to look at the problem, genes forming social networks. Maybe they will even find out how they instant (chemically) message each other.

Read more at http://www.dukenews.duke.edu.