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The Physicist Who Tackled Cancer

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  • Title: Head of Cancer Genome Analysis, Broad Institute
  • Education: PhD, Weizmann Institute of Science, 2003
  • Recommended by: Alan Guttmacher

Gad Getz was a physicist with biology envy. After earning degrees in physics and mathematics, he realized that he was interested in applying some of these concepts, such as statistical mechanics, to the biology realm.

He started out "doing clustering of protein structure and then gene expression data" and eventually found himself on a path to the Broad Institute, where a postdoc with Todd Golub landed him squarely in the middle of microRNA research, among other projects. By last year, Getz became head of the cancer genome analysis group at the Broad and had established himself as a full-fledged member of the systems biology community.

Today, Getz's goal is to combine as much data as possible to paint a comprehensive picture of cancer. He gathers information from sequence reads, copy number, gene expression, methylation status, genome-wide analysis studies, and more. All of that gets fed into GISTIC, an algorithm Getz started writing as a postdoc to scan through reams of cancer data and pick out the mutation patterns that mark the onset of cancer from all the other complex changes that take place in a tumor cell but do not actually cause cancer. Getz refers to this as "distinguishing the drivers from the passengers" on the road from normal cell to tumor. The algorithm is designed to analyze all the data, looking for mutations occurring at a higher frequency than you'd expect by chance, he says.

But picking out cancer-linked genes is just a stepping-stone to the real goal of figuring out which pathways are implicated in tumorigenesis. Getz suspects that it won't be the individual genes as much as the pathways they belong to that could be the crucial factor in cracking the cancer puzzle.

Getz is a member of the Cancer Genome Atlas consortium, for which he's continuing his past work of "developing these methods that take all of these genomic data and come out with biologically significant events," he says.

Looking ahead

Getz hopes to take his knowledge of cancer analysis and put it to work across the tumor development process. He will use mouse models to study several phases of cancer, sampling tumors over time to find out "which events occurred first and which are later," he says. Eventually, he hopes to gather enough data to begin applying population genetics methods to tumor sample collections, but he believes that will require a breakthrough in single-cell interrogation abilities. "We still don't necessarily have the technology to do all of these things," he says. He adds that integration of large-scale data sets remains a problem; dealing with different modalities of data could become more and more arduous unless this challenge is faced.

Publications of note

Two papers in particular sum up Getz's more impressive endeavors. In "Assessing the significance of chromosomal aberrations in cancer: methodology and application to gli-oma" in PNAS, Getz was a co-lead author describing GISTIC, or Genomic Identification of Significant Targets in Cancer, the algorithm he developed to scan data from tumor cells and generate predictions of which mutations are cancer-inducing instead of caused by the cancer.

In the other paper, Getz was part of the Cancer Genome Atlas project, which published findings in Nature this fall regarding its study into lung adenocarcinoma. Researchers found 26 genes that appear to be highly linked to the onset of this type of lung cancer, a significant increase in the number of genes suspected to play a role in the disease.

And the Nobel goes to …

This question doesn't delay Getz at all. His dream achievement? "Curing cancer," he says.

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