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Building a Cancer Model


Ali Torkamani
Title: Assistant Faculty, Scripps Translational Sciences Institute
Education: PhD, University of California, San Diego, 2008
Recommended by: Eric Topol

It's no surprise that someone who had the foresight to switch from biology to chemistry in order to get a more fundamental understanding of the biology he was really interested in would wind up working at a prestigious research institute.

While some might consider the chemistry major at Stanford to have been an indirect path to a career studying biology, the move not only suited Ali Torkamani, but positioned him for a graduate program that would open up tremendous opportunities.

After graduating from Stanford, Torkamani headed to the University of California, San Diego, where he wound up working in mentor Nicholas Schork's lab. Torkamani had no computational background before joining Schork, a biostatistician, so he taught himself the computer science as he went, he says.

That computational training was key for Torkamani's main project, which was to look at all the protein kinase families and to catalog common mutations in them. That included both strongly inherited mutations commonly shared in the population as well as somatic changes in the kinases, which are important anti-cancer drug targets. With that information, Torkamani "developed a computational model where you could predict based on the protein kinase … attributes [such as] where in the structure of the catalytic core the mutation fell," he says. "You could predict whether or not a mutation was going to impact the function of the protein kinase."

Ultimately, the idea of the project was to get to a model that could "tease out which mutations were the actual drivers versus the passengers," Torkamani adds.

Before Torkamani completed his PhD in 2008, Schork moved his lab to the Scripps Research Institute. Torkamani also made the move, and, when he was finished with his doctoral work, Scripps offered him a job. At Scripps, he says, his focus has shifted away from protein kinases but has stayed with cancer. "Now I've taken more of a systems biology or network type of approach to studying cancer," Torkamani says. His work centers on looking "at the network of genes and proteins as a whole within cancer and [determining] what sort of groups of genes … tend to be more frequently mutated."

Looking ahead

While he doesn't yet have funding for this project, Torkamani would like to model cancer predictions from people's genomes. "I would really be interested in seeing whether or not you could predict tumor evolution based on predisposing mutations," he says. That might involve looking at the variants someone has inherited from his or her parents and then using that to determine what type of cancer he or she is likely to develop, as well as what sorts of mutations would be expected with that particular cancer.

Publications of note

Torkamani was lead author on a paper published in Genome Research this past September called "Identification of rare cancer driver mutations by network reconstruction." In the publication, he and Schork analyzed data from breast, colorectal, and gliobastoma tumors to identify genetic mutations that are implicated in tumor formation. It also demonstrated the ability to separate out driver mutations from passenger ones.

And the Nobel goes to...

Torkamani's focus on cancer makes it easy for him to imagine just what he'd like to win the Nobel for. "It would be nice to be one of the key people that … really expanded our genetic knowledge of cancer … and predicting that transition from a normal cell to a cancer cell before it occurs," he says. "If you can make that prediction, then you would either be able to drive the cancer down an evolutionary dead end where it would be easier to combat the disease, or predict where the tumor will eventually be going [and] what sort of characteristics it would take on that would be deadly to the person."

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