Scientists are discovering more about cancer using a method that finds connections between the genetic pathways involved. Researchers from Duke University’s Institute for Genome Sciences & Policy started with familiar gene sets that cooperate to promote cancer development and used a new statistical technique to scan for any links or dependencies between them. These findings were reported on February 15, 2008 in the open-access journal PLoS Computational Biology, a part of the Public Library of Science.
Sayan Mukherjee, a co-author of the study, highlighted the breakthrough this implies: “Our major innovation is the use of gene sets in modeling tumor progression rather than single genes.” The model permitted characterization of gene networks over time as the cancer progressed: from normal tissue to early cancerous tissue to metastasis or spread.
“We’d like to know why cancer goes metastatic,” said Mukherjee. “Primary cancer can often be dealt with fairly well, so the real goal is to understand what happens when it spreads. My hope is that we could give these maps to a clinician and be able to tell them ‘This is what it looks like.'”
How each genetic pathway fits into the broad picture of cancer could allow physicians to select drug targets more efficiently. For example, one drug could target a pathway that links several pathways together, acting as a kind of central support for the rest of the network. Or, according to Mukherjee, if the genes involved with one particular type of cancer were to instead fall into individual clustered, treatments could be developed to target each one individually.
Additionally, the authors see potential future for this method to develop models of cancers in other areas, such as the study of embryonic development.
The work was supported by the Damon Runyon Cancer Research Foundation, the National Science Foundation and the National Institutes of Health.
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Modeling cancer progression via pathway dependencies
Edelman EJ, Guinney J, Chi JT, Febbo PG, Mukherjee S
PLoS Comput Biol 4(2): e28.
doi:10.1371/journal.pcbi.0040028
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Written by Anna Sophia McKenney