Pharma Strategy Blog

Commentary on Pharma & Biotech Oncology / Hematology New Product Development

Scientists have created a gene profiling test that may someday help reveal which people with early lung cancer are likely to suffer a relapse and would benefit most from chemotherapy.  Separately, other researchers found that several new tumour profiling tests for breast cancer, including two already in general use, are similar in accuracy and should allow many women to avoid unnecessary chemo.

The lung cancer test needs far more testing,  but is potentially breakthrough research building on years of work to develop personalized cancer treatments.  Its accuracy so far (about 80 percent) is better than what we have, but inevitably, it’s not as good as we would like.

Both studies were reported in yesterday’s New England Journal of Medicine.  Treatment guidelines for cancer have been relatively crude, i.e. based on a tumour’s size, whether it has spread, and other specific tumour characteristics.

As a result, many women with early breast cancer get chemo even though the vast majority would do fine without it.  It is the opposite with early-stage lung cancer:  even though about a third of patients will get worse and die, few get chemo because doctors can’t tell which ones will benefit, and the treatment itself carries risks.  Chemotherapy can damage the liver, heart and other organs, and in some cases can kill.
Doctors hope that growing knowledge about the genes fueling these cancers will lead to better ways to tell who really needs chemo.  The newly emerging tumour profile tests are tools to let them do that.
To develop the lung cancer test, Duke University researchers examined 198 tumour samples and analyzed 133 genes whose activity correlated with how aggressive the cancer was.  Essentially, they created a fingerprint unique to the individual patient (that) predicts survival chances.  Patients were scored as having a low or a high risk of recurrence based on the test, and results were compared to what actually happened to them.

The test was 93 percent accurate on the half of patients whose tumour samples came from Duke and 75 percent accurate on the rest.  Current best tests to estimate risk based on tissue characteristics are about 60 percent accurate,

A larger study of 1,200 lung cancer patients will start in early 2007 to further evaluate the Duke test.  After surgery to remove the initial tumour, patients will get chemotherapy or not, depending on their test score, and then will be followed for a few years to see how they do.

In the other study, researchers at the University of North Carolina at Chapel Hill compared how five gene profiling tests performed at predicting outcomes of 295 breast cancer patients.  Although the tests used different sets of genes, four were remarkably similar in accuracy and better than tests used now that are based on tumour characteristics.

They agreed 80 percent of the time, indicating they all are ultimately tracking the same biological processes affecting tumour growth.  Two of the four tests have been widely available for two years, and two large international studies have been launched to establish their ultimate accuracy and usefulness.
The tests can be automated and standardized for wide use, eliminating the variability that exists when pathologists have to evaluate the appearance of tumour cells under a microscope.  Such tests in the future could potentially allow 30 percent to 50 percent of women to skip chemotherapy for breast cancer based on their gene profile.

Further information:

New England Journal of Medicine
American Cancer Society

One Response to “Gene profiling in cancer”

  1. Anonymous

    What is the Clinical Relevance of Gene Profiling?
    The Microarray (gene chips) is a device that measures differences in gene sequence, gene expression or protein expression in biological samples. Microarrays may be used to compare gene or protein expression under different conditions, such as cells found in cancer.
    Hence the headlong rush to develop tests to identify molecular predisposing mechansims whose presence still does not guarantee that a drug will be effective for an individual patient. Nor can they, for any patient or even large group of patients, discriminate the potential for clinical activity among different agents of the same class.
    Genetic profiles are able to help doctors determine which patients will probably develop cancer, and those who will most likely relapse. However, it cannot be suitable for specific treatments for individual patients.
    In the new paradigm of requiring a companion diagnostic as a condition for approval of new targeted therapies, the pressure is so great that the companion diagnostics they’ve approved often have been mostly or totally ineffective at identifying clinical responders (durable and otherwise) to the various therapies.
    Cancer cells often have many mutations in many different pathways, so even if one route is shut down by a targeted treatment, the cancer cell may be able to use other routes. Targeting one pathway may not be as effective as targeting multiple pathways in a cancer cell.
    Another challenge is to identify for which patients the targeted treatment will be effective. Tumors can become resistant to a targeted treatment, or the drug no longer works, even if it has previously been effective in shrinking a tumor. Drugs are combined with existing ones to target the tumor more effectively. Most cancers cannot be effectively treated with targeted drugs alone. Understanding “targeted” treatments begins with understanding the cancer cell.
    If you find one or more implicated genes in a patient’s tumor cells, how do you know if they are functional (is the encoded protein actually produced)? If the protein is produced, is it functional? If the protein is functional, how is it interacting with other functional proteins in the cell?
    All cells exist in a state of dynamic tension in which several internal and external forces work with and against each other. Just detecting an amplified or deleted gene won’t tell you anything about protein interactions. Are you sure that you’ve identified every single gene that might influence sensitivity or resistance to a certain class of drug?
    Assuming you resolve all of the preceeding issues, you’ll never be able to distinguish between susceptibility of the cell to different drugs in the same class. Nor can you tell anything about susceptibility to drug combinations. And what about external facts such as drug uptake into the cell?
    Gene profiling tests, important in order to identify new therapeutic targets and thereby to develop useful drugs, are still years away from working successfully in predicting treatment response for individual patients. Perhaps this is because they are performed on dead, preserved cells that were never actually exposed to the drugs whose activity they are trying to assess.
    It will never be as effective as the cell “function” method, which exists today and is not hampered by the problems associated with gene expression tests. That is because they measure the net effect of all processes within the cancer, acting with and against each other in real time, and it tests living cells actually exposed to drugs and drug combinations of interest.
    It would be more advantageous to sort out what’s the best “profile” in terms of which patients benefit from this drug or that drug. Can they be combined? What’s the proper way to work with all the new drugs? If a drug works extremely well for a certain percentage of cancer patients, identify which ones and “personalize” their treatment. If one drug or another is working for some patients then obviously there are others who would also benefit. But, what’s good for the group (population studies) may not be good for the individual.
    Patients would certainly have a better chance of success had their cancer been chemo-sensitive rather than chemo-resistant, where it is more apparent that chemotherapy improves the survival of patients, and where identifying the most effective chemotherapy would be more likely to improve survival above that achieved with “best guess” empiric chemotherapy through clinical trials.
    It may be very important to zero in on different genes and proteins. However, when actually taking the “targeted” drugs, do the drugs even enter the cancer cell? Once entered, does it immediately get metabolized or pumped out, or does it accumulate? In other words, will it work for every patient?
    All the validations of this gene or that protein provides us with a variety of sophisticated techniques to provide new insights into the tumorigenic process, but if the “targeted” drug either won’t “get in” in the first place or if it gets pumped out/extruded or if it gets immediately metabolized inside the cell, it just isn’t going to work.
    To overcome the problems of heterogeneity in cancer and prevent rapid cellular adaptation, oncologists are able to tailor chemotherapy in individual patients. This can be done by testing “live” tumor cells to see if they are susceptible to particular drugs, before giving them to the patient. DNA microarray work will prove to be highly complementary to the parellel breakthrough efforts in targeted therapy through cell function analysis.
    As we enter the era of “personalized” medicine, it is time to take a fresh look at how we evaluate new medicines and treatments for cancer. More emphasis should be put on matching treatment to the patient, through the use of individualized pre-testing.
    Upgrading clinical therapy by using drug sensitivity assays measuring “cell death” of three dimensional microclusters of “live” fresh tumor cell, can improve the situation by allowing more drugs to be considered. The more drug types there are in the selective arsenal, the more likely the system is to prove beneficial.
    Literature Citation: Eur J Clin Invest 37 (suppl. 1):60, 2007
    Source: Cell Function Analysis

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