Target selection
At Dana-Farber's Molecular Biology Core Facilities, lab manager James Lee inspects a mass spectrometer, shown more closely below.
With all the technological and analytical hurdles associated with proteomics, it's natural to ask: Why bother? Why attempt to deal with close to half a million different types of proteins, some of which are present only in infinitesimal amounts, when genes offer a more manageable target? Why invest so much effort on a project whose dimensions can only be described as staggering, when a simpler alternative – genes, again – is at hand?
One answer is offered by Jarrod Marto, PhD, director of the Blais Proteomics Center: "Many cancer drugs target proteins rather than genes. By following the 'protein trail' within tumor cells, we'll be able to identify new entry points for therapies."
A focus on proteins makes sense because they are direct actors in the cancer process. Knowing that a particular gene is "upregulated," or overactive, in cancer cells is important; knowing which proteins are overproduced as a result, and what they do, is equally significant. Genes are not the be-all and end-all of a cell's existence. Occasionally, perfectly normal genes give rise to abnormal proteins. For these reasons and others, genomics and proteomics are considered sister disciplines.
All one needs to do is peer inside the Blais Proteomics Center at Dana-Farber to see how it differs from a standard biomedical laboratory. Rather than being dominated by bays of lab benches, sinks, ventilation hoods, autoclaves, and rows and rows of glassware (though these can be found there), the center is organized around a group of high-powered mass spectrometers and other devices for determining the protein content of cells. Mass spectrometry takes advantage of the fact that each type of protein has a different molecular weight, and these differences can be used to identify which proteins are present, and in what quantities. Because molecular weights can vary by minute amounts – the mass of a handful of atoms, in some cases – 'mass spec' machines have been dubbed "the smallest scales in the world."
The Blais Center's mission, as Marto describes it, is to "track protein dynamics in relation to biological change," that is, to take a "census" of proteins in healthy cells and watch how it shifts as cells become cancerous. The result will be a deeper understanding of the cancer process, and a clearer idea of how it can be arrested, prevented, or reversed. "High-throughput" technology for rapidly analyzing cell samples gives proteomics experts, for the first time, the ability to get a broad enough picture of the protein landscape to draw useful conclusions.
A mass spectrometer
"It lets us zero in on the bad players," the proteins most closely implicated in cancer, Marto says. "It should enable us to dig into particular protein pathways [chains of reacting proteins] and determine where they go astray."
The field should also intensify the hunt for biomarkers, substances in blood or other tissue that are telltale signs of cancer. "Most of the biomarkers that have already been identified are single agents," Marto observes. "Proteomics should give us the ability to identify whole groups of cancer biomarkers, which would lead to better tests for early detection of cancers."
Marto readily recalls his own introduction to proteomics at a conference he attended while a graduate student at Ohio State University in 1994. "The potential of the field is undeniable," he remarks. Following graduate and postdoctoral work, he spent four years in the biotech field, working on improvements to mass spec technology and applications, before joining Dana-Farber last year.
Marto's confidence is shared by his colleague John Quackenbush, PhD, whose team will be developing computational tools for proteomic studies. "In the 'omic' sciences – genomics and proteomics – the challenge is one of scale: how you collect and make sense of data from large numbers of genes and proteins," asserts Quackenbush, who also came to Dana-Farber in 2005. "In addition to high-powered machinery to produce data, you need high-powered mathematics and computer programs to translate the data into a useful form. It's like surveying a much larger universe with a telescope that lets us see smaller and smaller objects."
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