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Using Molecular Pathways to Assess Cancer Patients

The first complete map of protein interactions in human cells could lead to better treatment for breast cancer.

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.

Pathways to cancer: Pictured above are two protein networks whose activity is associated with an increased risk of the spread of breast cancer. At top is a protein network associated with cell growth, survival, and division; at bottom, a protein network associated with tumors’ ability to shape surrounding tissue.

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.

The protein-interaction map is then overlaid with a gene-expression profile from a breast-cancer patient’s biopsy. Instead of looking at whether a patient with metastatic breast cancer is making more or less of one protein than a patient with a less aggressive form of the disease, the San Diego researchers were able to highlight protein pairs whose activity changed. They then looked for clusters in the interaction map where the activity level of a group of connected proteins was different in patients whose cancer eventually metastasized than in patients whose cancer did not. “Once you find the hot spots, you extract them from the hairball, and you have networks that correlate with metastasis,” says Ideker.

Ideker’s group discovered changes in the average activity of networks associated with the hallmarks of cancer, including metabolism, cell growth and division, and cell mobility. The researchers found that these changes in the activity of networks were better at predicting risk of metastasis than was analysis of gene-expression profiles alone. Looking at networks of proteins, “you see key changes you can’t see looking at individual genes,” says Collins. “Proteins rarely act individually.”

Ideker says that he is currently in discussion with several companies about how to develop the network approach into a commercial test. However, he cautions that because his group tested its approach on preexisting gene-expression databases, it needs further testing in breast-cancer patients.

Ideker says that his group is already applying the protein network to other diseases, with promising early results. Collins agrees that the approach is generalizable, and he says that it may allow for early detection of diseases besides cancer.

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