Using Genes to Predict Responsiveness to Cancer Drugs

Personalizing cancer treatments has long been heralded as a way to improve survivor rates and limit detrimental side effects. This is largely due to a particular patient’s level of sensitivity to the drugs prescribed. In an effort to better predict this sensitivity, researchers at MIT went digging through our DNA.

Presently, there are two tactics used for predicting the effectiveness of a particular cancer treatment. The first is done by performing laboratory tests on the tumor cells. The second involves screening for genetic mutations and gene activity levels related to drug sensitivity.

In an effort to provide more accurate and faster genetic information, the team at MIT used microarray analysis to simultaneously look at 20,000 genes in our DNA. Then, through the use of a computer algorithm, the genes most related to drug sensitivity were identified. In total, 48 genes were found to significantly effect a patient’s receptiveness to chemotherapy.

The research indicates that these 48 genes can be used to better predict the effectiveness of cancer treatments on specific patients. The process also benefits from being remarkably fast and easy to obtain. More importantly, the procedure improves the accuracy of sensitivity to 94 percent. Current practices are estimated to offer an accuracy of about 60 percent.

These results were taken by studying the blood samples of 24 diverse cancer patients. The team at MIT now hopes to expand the research to include hundreds of participants. Eventually, the procedure may lead to clinical trials.