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A New Strategy for Choosing Cancer Drugs
In a new approach to devising more-personalized cancer treatments, researchers at the Massachusetts Institute of Technology (MIT) and the Dana-Farber Cancer Institute have developed a novel way to test tumors for drug susceptibility. Using a device that measures the masses of single cells, they can predict whether a particular drug will kill tumor cells based on how it affects their growth rates.
The researchers successfully tested the approach with glioblastoma, an aggressive type of brain cancer, and with acute lymphoblastic leukemia. Their findings were published in Nature Biotechnology.
In recent years, scientists have been trying to identify genetic markers in tumors that suggest susceptibility to targeted cancer drugs. However, useful markers have been found for only a small percentage of cancers so far, and even when there is a predictive test, it is not accurate for all patients with that type of cancer.
The MIT and Dana-Farber researchers took a different approach, inspired in part by a test that has been used for decades to choose antibiotics to treat bacterial infections. The antibiotic susceptibility test involves simply taking bacteria from a patient, exposing them to a range of antibiotics, and observing whether the bacteria grow or die. To translate that approach to cancer, scientists need a way to rapidly measure cell responses to drugs, and to do it with a limited number of cells available.
For the past several years, Dr. Scott Manalis and his colleagues at MIT have been developing a device known as a suspended microchannel resonator (SMR), which can measure cell masses 10 to 100 times more accurately than any other technique. This allows the researchers to calculate precisely the growth rates of single cells over short periods.
In the new study, Manalis’ laboratory worked with researchers at Dana-Farber to determine whether drug susceptibility could be predicted by measuring cancer-cell growth rates after drug exposure. The team analyzed different subtypes of glioblastoma or leukemia cells that were shown to be sensitive or resistant to specific therapies. For glioblastoma, these therapies were small-molecule mouse double minute 2 (MDM2) homolog inhibitors, and for acute lymphocytic leukemia, the drugs were BCR-ABL tyrosine kinase inhibitors. This allowed the researchers to test whether their approach would yield accurate predictions.
After exposing cancer cells to the drug, the researchers waited approximately 15 hours and then measured the cell’s growth rates. Each cell was measured several times during a period of 15 to 20 minutes, giving the researchers enough data to calculate the mass accumulation rate. They found that cells known to be susceptible to a given therapy changed the way they accumulate mass, whereas resistant cells continued their growth as if unaffected.
A major advantage of this technique is that it can be performed with small numbers of cells. In the experiments with leukemia cells, the researchers showed that they could achieve accurate results with a tiny droplet of blood containing approximately 1,000 cancer cells.
Another advantage of the system is the speed at which small changes in cell mass can be measured.
The researchers are using their technique to test cells’ susceptibility and then to isolate the cells and sequence the RNA found in them, revealing which genes are “turned on.”
“Now that we have a way to identify cells that are not responding to a given therapy, we are excited about isolating these cells and analyzing them to understand mechanisms of resistance,” Manalis said.
In a recent paper in Nature Biotechnology, the researchers reported on a higher-throughput version of the SMR device that can perform in one day the same number of measurements that took several months with the device used in the current study. This is an important step toward making the approach suitable for clinical samples, Manalis says.
Source: MIT; October 10, 2016.