The findings is important because the EGFRx test, which can also be applied to many emerging targeted cancer drugs, could help to solve the problem of knowing which patients can tolerate costly, new treatments and their harmful side-effects.
Today's treatments for cancer, namely the molecularly-targeted anti-cancer therapies gefitinib (Iressa, AstraZeneca) and erlotinib (Tarceva, Genentech), are a vast improvement on treatments of ten years ago.
However, the therapies do not work for everyone, at least not as effectively and a test to determine the efficacy of these drugs in a patient could be the first crucial step in personalising treatment to the individual.
Additonally, most patients today are treated not with a targeted therapy drug alone but with a combination of chemotherapy drugs. Therefore, existing DNA and RNA tests do not reflect the way cancer medicine is practiced today.
The EGFRx assay, developed by The Weisenthal Cancer Group relies upon a technique known as "Whole Cell Profiling" in which living tumour cells are removed from an individual cancer patient and exposed in the laboratory to the new drugs.
A variety of metabolic and apoptotic measurements are then used to determine if a specific drug was successful at killing the patient's cancer cells.
The whole cell profiling method differs from other tests in that it assesses the activity of a drug upon combined effect of all cellular processes, using several metabolic and morphologic endpoints.
Other tests, such as those which identify DNA or RNA sequences or expression of individual proteins often examine only one component of a much larger, interactive process.
"Over the past few years, researchers have put enormous efforts into genetic profiling as a way of predicting patient response to targeted therapies. However, no gene-based test as been described that can discriminate differing levels of anti-tumour activity occurring among different targeted therapy drugs," said Larry Weisenthal, a medical oncologist and developer of the EGFRxTM assay.
He continued: "Nor can an available gene-based test identify situations in which it is advantageous to combine a targeted drug with other types of cancer drugs. So far, only whole profiling has demonstrated this critical ability."
Using the EGFRx assay and the whole cell profiling method, Weisenthal's group correlated test results, which were obtained by his lab.
Patients prospectively identified by Weisenthal as favourable candidates averaged 485 days of life after treatment with the targeted therapy drugs.
In contrast, patients identified as unfavourable candidates for the drugs averaged 75 days survival after receiving the drugs.
This compares to 76 days average survival among patients identified as unfavourable candidates and who did not receive a targeted therapy drug.
Survival among patients identified by Weisenthal as unfavourable candidates was therefore similar regardless of whether or not they received the targeted drugs.
"Not only is this an important predictive test that is available today," said Weisenthal, "but it is also a unique tool that can help to identify newer and better drugs, evaluate promising drug combinations, and serve as a 'gold standard' correlative model with which to develop new DNA, RNA, and protein-based tests that better predict for drug activity."
Several new targeted drugs have been introduced during the last few years. These "smart drugs" focus their effects on specific, identifiable processes occurring within cancer cells.
Whilst the drugs are highly promising in that they provide an alternative to patients who have failed traditional therapies.
In addition, they are expensive, costing patients and insurance carriers $5,000 to $7,000 (€3900 - €5500) or more per month of treatment. Patients, physicians, insurance carriers, and the FDA are all calling for the discovery of predictive tests that allow for rational and cost-effective use of these drugs.
Weisenthal believes that his cell profiling approach, holds the key to solving some of the problems confronting a healthcare system that is seeking ways to best allocate available resources while accomplishing the critical task of matching individual patients with the treatments most likely to benefit them.