Chemotherapy Gets Personal
A gene-screening method can predict an individual’s response to cancer drugs.
A new approach to personalized medicine examines the activity of a whole group of genes to predict a patient’s sensitivity to drugs. The results of the study, published by researchers at MIT in the journal Genes and Development last week, may help better match cancer patients with the most effective chemotherapeutic drugs while also keeping side effects on healthy cells to a minimum. The technique could also help determine an individual’s sensitivity to environmental toxins, such as those contained in cigarette smoke.
Two approaches that are commonly used to determine how a patient’s tumor will react to chemotherapy include laboratory tests of the tumor cells’ responsiveness to the drug, and genetic screenings for mutations or activity levels of genes that are known to influence drug sensitivity.
But Leona Samson, director of MIT’s Center for Environmental Health Sciences and senior author of the current study, believes that her new technique may offer a quicker and more accurate diagnostic approach.
“Directly looking at the biological response of cells to drugs is a delicate and time-consuming process,” she says of the first approach. The second method–looking for changes in specific gene, works well for certain cancers, she says, such as glioblastoma–the brain tumor that Ted Kennedy is battling–but the accuracy is limited to around 60 percent because other genes will also affect the tumor’s sensitivity.
In glioblastoma, specific markers that reveal the expression of MGMT, a DNA-repair enzyme that makes tumors resistant to the DNA-damaging drug MNNG (N-methyl-N’-nitro-N nitrosoguanidine). MNNG can damage and mutate healthy cells by introducing errors into their DNA. Drugs like MNNG are used in chemotherapy because it’s especially toxic to cancer cells, which often have reduced DNA-repair abilities, but the compound and its close relatives are also found in tobacco smoke, exhaust fumes, and some foods.
To overcome this limitation, Samson and her colleagues used microarray analysis to discover if looking at many genes in parallel could improve the prediction. They took 24 blood-derived cell lines from healthy and unrelated people with diverse genetic backgrounds and tested how well they grew when exposed to MNNG.
Next, the researchers analyzed the activity of 20,000 genes in each cell line by measuring the number of gene transcripts on DNA microarray chips. Finally, they chose the four most sensitive and the four least sensitive cell lines and used them to train a computer algorithm to identify which of the 20,000 genes are most relevant to drug sensitivity. These genes showed different expression levels in the sensitive versus resistant cell lines, and their activity either gradually increased or decreased with the sensitivity of the cells.
This exercise yielded a set of 48 genes that markedly change their activity depending on their sensitivity to MNNG. When Samson’s team used this group of genes to predict the sensitivity of the remaining 16 cell lines, the algorithm anticipated whether or not their growth would be inhibited by the drug with an accuracy of 94 percent.
“This is incredibly encouraging for a clinical application,” says Samson, who hopes that her approach will quickly be extended to map the relationships between gene activity and sensitivity to other drugs.
Joanna Peak of Cancer Research UK says that the research highlights “that cancer treatment should no longer follow a ‘one size fits all approach’. The accuracy of 48 genes is impressively good, but I would like to see this tested on a much larger panel of hundreds of patients”.
The results for MNNG alone have several benefits, explains Samson. The predictive power of the 48 genes, combined with the relative ease and speed at which transcriptional profiles can be compiled for individual patients, make it a very fast and reliable diagnostic tool. But the 48 genes offer further insights into the mechanism of cancer itself that may one day lead to better treatments. For example, when the researchers analyzed the molecular pathways in which the genes participate, they discovered that most of the pathways can be placed in a highly connected network of genes that are already known to affect cancer.
Learning more about the roles of these genes could lead to a better understanding of how the disease can be attacked. Some of the genes could become direct therapeutic targets if reducing or enhancing their activity leads to increased drug sensitivity.
William Phelps of the American Cancer Society says that taking a baseline profile of healthy cells, as the current study does, will be particularly useful for determining a patient’s response to therapy before treatment is started.
Samson hopes that the approach will be explored in clinical trials and that tests will go beyond predicting the sensitivity of cancer cells. “I’d love to see this being used to look at normal tissues, not just at tumors,” she says. The method could help doctors predict and better protect patients from drug side effects, and it could help medical professionals advise individuals who may be exposed to such compounds in the environment–heavy smokers, for example.