Targeted cancer drugs—those that selectively kill cancer cells—have been a revelation in cancer treatment, giving years of healthy life to some lucky patients. But unfortunately, only a subset of patients responds to the various therapies, and even those who do respond eventually grow resistant.

Now scientists are starting to figure out how to make those drugs work more effectively in a larger number of patients, in part by better understanding the complex inner workings of the cancer cell. In two studies focusing on a promising class of drugs for lung cancer called EGFR tyrosine kinase inhibitors, researchers have pinpointed new drug targets that could enhance the drugs’ activity. They hope the findings will enable a new approach to personalized cancer care, suggesting specific combinations of drugs that will be most effective for an individual’s cancer.

“We have hundreds of drugs now in development and cannot test them in all possible combinations,” says Philip Sharp, Institute Professor at the Koch Institute for Integrative Cancer Research at MIT. “To personalize cancer care, we must interpret changes in cellular networks or mutations to predict the correct drug combination to use. [These] results indicate that this is beginning to become possible.”

EGFR inhibitors work in about 10 to 40 percent of lung cancer patients, depending on ethnicity, gender, and smoking history. People whose cancers have mutations in the gene for EGFR are also more likely to respond to the drugs, but these mutations aren’t as predictive as scientists had hoped. Not everyone who does well on the drug has the mutation—depending on the study, about 10 to 60 percent of responders don’t have it—and not everyone with the mutation responds.

Starting with a line of cancer cells known to be resistant to EGFR inhibitors, Charles Sawyers and collaborators at Sloan Kettering silenced a selection of cancer-related genes one by one. They found they could make the cells sensitive to the drug by inhibiting different genes in a molecular pathway called NF-Kappa Beta, which regulates cell division and death.

The findings held true in lung cancer patients; among patients with the EGFR mutation, those with higher activity in this pathway fared much worse when given the drug than those with lower activity. The results suggest that EGFR inhibitors would be more effective in some patients if given along with drugs that inhibit the NF-Kappa Beta pathway.

Doug Lauffenburger and colleagues at MIT took a converse approach. Rather than trying to figure out why some patients with EGFR mutations don’t response to EGFR inhibitors, Lauffenburger wanted to know why the drug worked in so many people without the mutation.

His team began with two sets of cancer cells, those that are resistant to the drug and others that are sensitive to it. The team measured various biochemical and biophysical properties of the cells, including an analysis of how molecules move around the cell, how well receptors bind to molecules called growth factors, and the activation of molecular pathways downstream from the EGFR receptor.

Using those measurements, the team constructed a model of each cell type, looking for the major differences between the two. They found multiple variables, but one stood out; cells that were sensitive to the drug had a slower uptake of receptors into the cell. While it’s not exactly clear how this makes the cells respond to the drug, Lauffenburger says, the location of the receptor when it binds its target effects which downstream chemical pathways are activated.

The findings, if they are replicated in patients, point toward a new way to determine which patients would respond to EGFR inhibitors. Researchers are now trying to figure out how to devise a screening test that would work well in patients. (Assessing the behavior of a particular receptor in a patient’s cancer cells is too difficult for a test to be used routinely in patients.)

In addition, Lauffenburger’s team found they could make cancer cells sensitive to the drug by adding another drug that inhibits one of the downstream pathways implicated in the model. This class of drugs, called MEK inhibitors, is currently in clinical trials for different kinds of cancer.

“Both studies have generated new insights into combinational therapy that may enhance targeted therapies,” says Dan Gallahan, deputy director of the National Cancer Institute’s Division of Cancer Biology. “The results underline the complexity of the disease, but also show that if we critically understand what’s going on, we can figure out how to intervene.” Clinical studies are needed to confirm the findings, he adds, but “these studies lay the scientific groundwork to do that.”