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Researchers followed 532 multiple myeloma patients for seven years after blood stem cell transplant to create a genetic profile to chart the severity of the disease. The team determined that the activity of as few as 17 genes could mean the difference between high or low risk for a poor prognosis. They present their data today at the American Association for Cancer Research’s second International Conference on Molecular Diagnostics in Cancer Therapeutic Development, in Atlanta, Ga.
“There are enormous differences between how different people fare with multiple myeloma. While most do very well others have a highly aggressive form of the disease and this is not recognized well with current prognostic variables,” said lead researcher John D. Shaughnessy, Jr., Ph.D., a professor of medicine at the Myeloma Institute for Research and Therapy. “If we can categorize a patient’s risk early, we can better guide that patient toward therapies that might be more effective for them based on the genetic profile of the disease.”
Multiple myeloma is a cancer affecting the blood plasma cells in bone marrow that produce antibodies. Nearly 14,600 new cases of multiple myeloma occur each year in the United States. The disease is most often treated through the use of high dose chemotherapy and peripheral blood–derived stem cell support. While multiple myeloma often responds well to initial treatment, it often becomes drug resistant and patients are prone to relapse.
According to the researchers, survival varies greatly between low-risk and high-risk patients. “At 24 months, about 90 percent of low-risk patients will be alive, whereas about 50 percent of the high-risk patients have succumbed to the disease,” said Fenghuang Zhan M.D., Ph.D., of the University of Arkansas for Medical Sciences.
To understand the possible molecular mechanisms driving initiation and progression of multiple myeloma, the researchers launched a large-scale, longitudinal study to categorize the differences in gene expression patterns, that is, which genes are activated and inactivated, in relatively indolent versus aggressive disease.
Using purified tumor cells taken from 532 newly diagnosed patients who went on to receive uniform therapy, the researchers screened over 54,000 genes across the human genome for signs that might relate to multiple myeloma survival estimates. About 13 percent of all the patients they studied exhibited a genetic pattern that fit into the high-risk category, a frequency that rose to 76 percent among relapsed patients.
“The observation of an increase in the gene expression risk score among relapsed patients provides evidence that there are likely to be small subsets of high-risk cells even in patients with low risk disease, and that current therapeutics are sub-optimal in that they kill off the low-risk cells, leaving behind cells that exhibit a high-risk genetic profile,” Shaughnessy said. Currently, the researchers have experiments underway to definitively prove this concept.
Initially, the researchers identified 70 genes linked to early cancer-related death, although further analysis narrowed that number to 17. Remarkably, about 30 percent of the genes that predict high risk are found on chromosome 1, enough so that Shaughnessy recognized a trend among the genes, based on where they map on each chromosome in the human genome. The majority of genes that were up-regulated – or over-produced – in high-risk patients mapped to the long arm of chromosome 1, while the majority of genes that were down-regulated – or suppressed – mapped to the short arm of the same chromosome.
“Together these data suggest that defects in chromosome 1 may be directly related to the acquisition of higher risk in patients with multiple myeloma,” Shaughnessy said. “Gene expression profiles have now provided us with signposts that help us risk stratify patients and tailor therapies accordingly.”
“Importantly, these data may provide researchers with key insights into molecular mechanisms driving disease severity which might be the target of future therapies,” Shaughnessy said. -American Association for Cancer Research
Posted September 18th, 2007 by harminka
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 culture 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.
Source: Cell Function Analysis