Mutation Test to Predict Response to Anticancer Drugs

A diagnostic test in development is designed to predict the response to anticancer drugs and bring personalized cancer treatment closer to reality.

Researchers have recently identified a set of genetic mutations that correlate with response to the lung cancer drug Iressa (AstraZeneca) and help to explain why the therapy works only in a small proportion of patients. These mutations were discovered in epidermal growth factor receptor (EGFR), the target of Iressa. Patients who acquire one of these mutations do very well on Iressa, but the mutations are quite rare: only 10% of patients show this response.

Iressa is not the only therapy that targets EGFR. There is a need for validated diagnostic assays to test the use of all drugs that target Iressa, according to DxS Ltd. (Manchester, UK). However, there is a complication with the type of testing needed for this application. Because the mutations are found in the tumor, they may constitute only a very small proportion of the total genetic material. Therefore, any test must be very sensitive and able to detect low-level mutations.

DxS recently announced a licensing agreement with AstraZeneca (London, UK; www.astrazeneca.com) to access the company's ARMS technology, which DxS describes as "a very powerful method to detect under-represented mutations. When coupled with DxS's Scorpions detection system, we are able to detect mutations even when they comprise less than 1% of the total genetic material.”

"Our diagnostic test, which will meet the standards of the European IVD regulations, will be useful initially within clinical studies to help assess the clinical utility of the EGFR data,” said Dr. Stephen Little, CEO of DxS. "Subsequently it will be used as a diagnostic to identify good candidates for Iressa and other therapies. This is exactly the type of data that is required to progress the development of personalized medicine.”

DxS's Scorpions technology allows rapid and reliable polymerase chain reaction (PCR)-based genotyping.




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