Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo

 

Unsupported browser: Your browser does not meet modern web standards. See how it scores »

Researchers at the University of California, San Diego, have created a map of all known protein networks in human cells and shown that it can be used to better assess whether a patient’s breast cancer will spread. Their work, though in its early stages, could lead to better diagnostic tests that spare patients toxic treatments, such as chemotherapy, if they are unnecessary. The researchers also expect that their approach will be widely applicable to other diseases, including other cancers and diabetes.

The current standard for assessing how to treat breast-cancer tumors involves clinical diagnosis and gene-expression profiling tests. But it has been difficult to predict how aggressive a cancer will be using this method.

“We decided to look at breast cancer because it’s a very difficult problem in terms of prognosis,” says Trey Ideker, the bioengineer who led the construction of the protein map.

He notes that even the best diagnostic chips for the disease have only between 60 and 70 percent accuracy. “Maybe the reason why it’s hard to predict the course of metastasis is that it’s never caused by the same gene or set of genes,” says Ideker. (Metastasis is the spread of a cancer from its original site throughout the body–in the case of breast cancer, often to the lungs or bones.)

“Individual cancers are pretty unique” in terms of which genes contribute to disease, says Julie Gralow, an oncologist and associate professor of medicine at the University of Washington. Metastasis in one patient might be caused by gene A, while in two other patients it’s caused by gene B or C.

“There are many routes to cancer,” says Ideker. “Maybe the rule is that genes A, B, and C are in the same pathway. The main idea is that we shouldn’t be looking for individual genes but at whole processes with multiple genes and proteins tied together in networks.”

Finding these pathways or networks in cancer and other diseases is not a new idea. For years, researchers have speculated that detecting changes in molecular pathways–not just in individual genes–could provide more accurate diagnoses and better predictions of the course of complex diseases such as cancer. But James Collins, a biomedical engineer at Boston University, notes that “Ideker is the first to figure out how to do it.”

Ideker’s group pooled decades of research about proteins “to get huge wiring diagrams” that map out how all proteins in the human cell physically interact with each other. The map connects 11,203 proteins (which are visually represented by spheres) through 57,235 interactions (which are represented by lines drawn between the spheres). Ideker likens the tangled network to a hairball.

1 comment. Share your thoughts »

Credit: Trey Ideker, UCSD

Tagged: Biomedicine, cancer, disease, tumor, molecular biology, toxicity

Reprints and Permissions | Send feedback to the editor

From the Archives

Close

Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me