Clinical study results published at the annual meeting of the American Society of Clinical Oncology (ASCO) show that a new laboratory test has accurately identified patients who would benefit from treatment with the molecularly-targeted anti-cancer therapies gefitinib (Iressa) and erlotinib (Tarceva).
The new test, called EGFRx (TM), predicted accurately for the survival of patients treated with the targeted drugs. The finding is important because the EGFRx (TM) test, which can also be applied to many emerging targeted cancer drugs, could help solve the growing problem of knowing which patients should receive costly, new treatments that can have harmful side-effects and which work for some but not all cancer patients who receive them. (The test can discriminate between the activity of different targeted drugs and identify situations in which it is advantageous to combine the targeted drugs with other types of cancer drugs
The new test relies upon what is called "Whole Cell Profiling" in which living tumor 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 combined 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.
The whole cell profiling method makes the statistically significant association between prospectively reported test results and patient survival. Using the EGFRx (TM) assay and the whole cell profiling method, can correlate test results which are obtained in the lab and reported to physicians prior to patient treatment, with significantly longer or shorter overall patient survival depending upon whether the drug was found to be effective or ineffective at killing the patient's tumor cells in the laboratory.
Patients prospectively identified as favorable candidates averaged 485 days of life after treatment with the targeted therapy drugs. In contrast, patients identified as unfavorable candidates for the drugs averaged 75 days survival after receiving the drugs. This compares to 76 days average survival among patients identified as unfavorable candidates and who did not receive a targeted therapy drug. Survival among patients identified as unfavorable candidates was similar regardless of whether or not they received the targeted drugs.
Over the past few years, researchers have put enormous efforts into genetic profiling as a way of predicting patient response to targeted therapies. However, not gene-based test as been described that can discriminate differing levels of anti-tumor activity occurring among different targeted therapy drugs. Nor can a gene-based test identify situations in which it is advantageous to combine a targeted drug with other types of cancer drugs.
There is a growing array of targeted drugs to choose from. Most patients today are treated not with a targeted therapy drug alone but rather with a combination of chemotherapy drugs. Therefore, the existing DNA and RNA tests do not reflect the way cancer medicine actually is practiced today.
These so-called "smart drugs" focus their effects on specific, identifiable processes occurring within cancer cells. The new drugs are highly promising in that they sometimes provide benefit to patients who have failed traditional therapies. However, they do not work for everyone, they often have unwanted side effects, and they are all extremely expensive. 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.
The EGFRx assay 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. Not only is it an important predictive test, 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.
Journal of Clinical Oncology, 2006 ASCO Annual Meeting Proceedings Part I. Vol 24, No. 18S (June 20 Supplement), 2006: 17117
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