Not only does each cell function seem to be affected by many genes, but researchers now think any given function can be the result of many different molecular pathways. Just as some airplanes have more engines than they need, cells have redundancy built in. If a drug interferes with one protein that helps a cancer cell divide, the cell might have recourse to four or five more pathways.
The complexity of these regulatory networks makes it very hard to predict what a drug is going to do, says Lauffenburger. As a result, drug development is “all trial and error”–and extremely inefficient and costly. Researchers test countless compounds to zero in on those with promise. The earlier an ineffective compound can be taken out of the running, the lower the costs of drug development. So in an approach known as systems or network biology, Lauffenburger and others are looking at cells as complex systems and building computer programs that can process large amounts of data about biomolecular interactions. Lauffenburger maps how proteins interact and shows how these interactions influence cell functions, including growth. With such models, he and others hope to predict how drugs will affect cells biochemically and, in turn, how they will affect cell function.
Lauffenburger recently used this approach to predict the effect of a particular drug on epithelial cancer cells, a class of cells that includes those involved in cancers of the colon, breast, cervix, and skin. The drug inhibits one pathway that prevents cell death. This pathway is active in almost all epithelial cancers, so a researcher considering only one pathway at a time would expect the drug to kill multiple kinds of cancer cells. But it turns out that it doesn’t. While the drug increases the death rate of colon cancer cells, it doesn’t increase those of breast or cervical cancer cells, which have several other pathways to fall back on. Lauffenburger’s maps of protein interactions “ascertained how the sum of five pathways works together to govern cell death,” he says.
It’s unclear, however, how broadly applicable Lauffenburger’s models will be. He ticks off the many questions that remain: Will a model of protein interactions in an epithelial cancer cell need many modifications before it can be applied to other kinds of cancer cells? What are the best kinds of measurements to feed into these models? And most important, can the models help researchers uncover why a drug works for some patients and not others? He’s currently pursuing collaborations with the drug companies Pfizer, AstraZeneca, and Merrimack Pharmaceuticals to develop models for use in testing new drugs.
More broadly, the work of Lauffenburger and other network biologists is changing the way biologists look at cancer. “There’s no magic molecule,” Lauffenburger says–no simple, single target to identify that will be the key to treating the disease. But looking at cancer cells as complex systems gone awry, and focusing on what he calls the “actual activities” of their proteins, is leading to a new understanding of how the cells operate.
Today, the only way for doctors to verify that cancer drugs are reaching their targets is to perform magnetic resonance imaging (MRI) scans after weeks of treatment to see if patients’ tumors have shrunk. Sangeeta Bhatia, an associate professor of electrical engineering and computer science in the Division of Health Sciences and Technology and a member of the Koch Institute, is developing multipurpose nanoparticles that she hopes will shorten this process, reduce the side effects of chemotherapy, and make treatment more effective.
Bhatia’s compounds act as precise drug-delivery vehicles and MRI contrast agents; they zero in on tumor blood vessels and, once there, attract more nanoparticles. Recently, she developed a way to make them release their payloads on command when heated by low-frequency electromagnetic waves applied from outside the body.