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Analyses of all the protein-encoding genes in a tumor have revealed mutations in genes that never would have been implicated in cancer, says HHMI investigator Bert Vogelstein of the Johns Hopkins University School of Medicine. In 2009, Vogelstein and his colleagues used exome sequencing to discover a mutation in a gene called IDH1 in brain tumors.
“This was a gene that was thought to be involved in basic metabolism and no one would have thought to check whether it was mutated in cancer,” says Vogelstein. Since then, scientists have found the same mutation in other cancers including leukemia. The discovery has led to a new area of research, he says, to understand how cancer cells alter their metabolism to survive.
Today, more than 25 cancer types have been subjected to exome sequencing, in many cases revealing surprises (see Bulletin May 2011, A Crowd in the Kitchen)—or at least newfound genes. In July 2011, Vogelstein and HHMI investigator Todd R. Golub of the Dana-Farber Cancer Institute separately published data online in Science on the exomes of head and neck cancers. They revealed a handful of mutations that could help drive the development of new therapeutics for the cancers.
Focus on the Drivers
Hearing the way researchers praise exome sequencing for expediting their work, you’d think it was the solve-all technique. But it has its limits. After all, it provides only what it advertises: the sequences of exomes. For scientists, interpreting those sequences still requires old-fashioned elbow grease.
“In cancer sequences it’s often difficult to distinguish the wheat from the chaff,” says Vogelstein. The wheat, in cancer genetics, includes those mutations that drive cells to become cancerous or encourage a tumor’s growth. The chaff is the mutations that just happen to also be present—called passenger mutations.
And it’s not just a problem in cancer genomics. Every researcher who uses exome sequencing is faced with a pile of data to sort through. Sequencing is the easy part; you prepare a sample of DNA and feed it into a lab machine. “The interpretation is the hard part,” says Walsh. “If you have a big family to study, it will be easier. But interpreting the hard cases is still hard.”
Then there’s that other 99 percent of the genome. If researchers can’t find a disease-causing gene in the exome, is it because that gene is in the regulatory part of the genome, or because they just haven’t pinpointed the right mutation in the exome?
“It’s one of the really pressing questions,” says Golub. “How much are we missing? We’re beginning to see cancer types that appear to have particularly low mutation rates, based on the exome. It could be that you don’t need many mutations [to cause the cancer]. But it also could be that you need mutations in the other 99 percent of the genome.”
Eventually, researchers will use whole genome sequencing the way they use exome sequencing today. It’s a matter of waiting for the cost to drop, they all say. Today, sequencing a whole genome costs five to 10 times more than sequencing an exome. And the costs of storing and processing whole genome data are as much as a hundred times higher—and dropping more slowly— than whole genome sequencing costs, which are now below $5,000 per genome, and quickly approaching $1,000 per genome. But the initial costs of sequencers, which can be used for either exome or whole genome sequencing, are also part of the equation.
“Exome sequencing, while it’s amazing, is really just a bridge until the price drops further and we can do whole genome sequencing,” says Gleeson.
“If you were told you could sequence 1 percent of the genome and you asked yourself what’s the most important 1 percent of the genome to sequence, you’d say it’s probably the 1 percent that makes proteins,” says Golub. “Which isn’t to say that nothing else is important. But it’s a good place to start.”