First-ever Genomic Test Predicts Which Lung Cancer Patients Need Chemotherapy To Live
Main Category: Lung CancerAlso Included In: Cancer / Oncology; Radiology / Nuclear Medicine
Article Date: 14 Aug 2006 - 10:00 PDT
Duke University Medical Center scientists have developed the first-ever genomic test to predict which patients with early-stage lung cancer will need chemotherapy to live and which patients can avoid the toxic regimen of drugs.
The test has the potential to save thousands of lives each year by recommending chemotherapy for patients who are currently advised against it, said the test's developers at Duke's Institute for Genome Sciences & Policy.
The test's promising results have initiated a landmark multi-center clinical trial, to be led by Duke investigators next year. Patients with early-stage non-small cell lung cancer, the most common and fatal form of cancer, will receive the genomic test and its results will determine their treatment.
The new test, called the Lung Metagene Predictor, scans thousands of genes to identify patterns of gene activity in individual tumors that indicate a patient is likely to suffer a recurrence of disease. Recurrent tumors are typically fatal, so identifying at-risk patients is critical to properly treating them, said the Duke researchers.
"Using the unique genomic signatures from each tumor, our new test predicted with up to 90 percent accuracy which early-stage lung cancer patients would suffer a recurrence of their cancer and which patients would not," said Anil Potti, M.D., an assistant professor of medicine and lead author of the study. "We now have a tool that can be used to move these high-risk patients from the 'no chemotherapy' group into the aggressive treatment group."
The researchers will publish their findings in the August, 2006, issue of the New England Journal of Medicine. The research was funded by the National Institutes of Health.
The genomic test can theoretically apply to any cancer, but the Duke team focused its effort on lung cancer because the survival rate is just 15 percent. Lung cancer now kills more Americans each year than breast, prostate and colorectal cancers combined. But toxic chemotherapy drugs are prescribed only to patients with relatively large and aggressive tumors.
Early-stage patients - those with small, stationary tumors - are considered at low risk of recurrence. Hence, they only receive surgery but not chemotherapy. The dilemma, said Potti, is that a third or more of early-stage patients who appear to be at low risk will experience a recurrent tumor.
"Until now, there simply has been no way to identify the 30 percent to 40 percent of early-stage lung cancer patients who would experience a recurrence," Potti said. "Now, with our test, we can say with confidence that we can identify this group of patients so they can be treated accordingly."
The upcoming trial is the first to use a genomic test to select treatment options for individual lung cancer patients, said David Harpole, M.D., a professor of thoracic surgery at Duke and principal investigator of the upcoming clinical trial. The trial, to begin within six months, will enroll more than 1,000 patients at multiple centers in the United States and Canada.
"If we can use the test to increase patient survival by even 5 percent, we would save 10,000 lives a year," Harpole said.
The Duke researchers developed the test by analyzing the activity of genes from early-stage lung cancer patients whose disease outcomes were known. The Duke scientists then validated the genomic test in 129 patients by comparing the test's predictions with the patient's actual outcomes. The test predicted their risk of recurrence with 90 percent accuracy, the study showed.
If proven to be effective in the clinical trial, the test will replace the current method of assessing risk, which is imprecise and provides only a broad estimate of a patient's risk, said Joseph Nevins, Ph.D., a professor of molecular genetics at Duke and senior author of the study being reported.
Physicians now assign each patient to a clinical "stage" based on the size of the patient's tumor, whether it has invaded lymph nodes and whether it has spread to other organs. They use this staging method to prescribe the best treatment options. But staging parameters are general, at best, and do not accurately define who should receive chemotherapy, Nevins said.
"Instead of placing all patients with small tumors in the same early-stage category, as physicians currently would do, we can now assess their risk based on the tumor's genomic profile," Nevins said. "The current system of 'staging' lung cancer tumors will eventually become obsolete."
To employ the test, physicians take a sample of the tumor as it is removed during surgery. They extract its "messenger RNA," which represents the activity of thousands of genes in the tumor. Messenger RNA translates a gene's DNA code into proteins that run the cell's activities. Hence, it is a barometer of a gene's activity level inside the cell.
Scientists label the messenger RNA with fluorescent tags. The fluorescent RNA is then placed on a tiny glass slide, called a gene chip. There, it binds to its complementary DNA sequence on the gene chip.
When scanned with special light, the fluorescent RNA emits a telltale luminescence that demonstrates how much RNA is present on the chip - and thus which genes are most active in a given tumor. The physicians then use a rigorous statistical analysis to assess the relative risk of large grouping of genes, called metagenes, which have similar characteristics.
The test generates a risk "number" for each patient. If their risk exceeds 50 percent, the patient is advised to get chemotherapy.
"This new genomic test is a clear example of personalized medicine, where we use the unique molecular characteristics of each patient's tumor to guide treatment decisions," said Geoffrey Ginsburg, M.D., Ph.D., a professor of medicine and co-author of the study.
Eventually, physicians will use genomic tests not only to predict patient outcomes, but also to select the individual drugs that will best match a tumor's molecular makeup, Ginsburg said.
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Other collaborators on the study being reported include, from Duke, Sayan Mukherjee, Holly Dressman, Andrea Bild, Rebecca Petersen, Jason Koontz, Michael Kelley and Mike West; Robert Kratzke of the University of Minnesota; and Mark Watson of Washington University in St. Louis.
Contact: Marla Vacek Broadfoot
Duke University Medical Center
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Lung Metagene Predictor
posted by Gregory D. Pawelski on 6 Apr 2007 at 6:39 pmThe new genomic test - Lung Metagene Predictor - is supposed to tell physicians which lung cancer patients will benefit from chemotherapy and which ones do not need to be unnecessarily exposed to toxic chemotherapy cocktails.
The test doesn't predict which patients will benefit from chemotherapy (i.e. which patients are chemosensitive). Rather, it's like the Oncotype Dx test, which identifies patients who are unlikely to have a recurrence if treated with surgery alone. If you aren't going to have a recurrence, you don't need chemotherapy.
The test doesn't do anything to indicate if chemotherapy would or would not be helpful for those patients at higher risk for recurrence, much less which chemotherapy would be most likely to be helpful. Also, the test has a 10% false reassurance rate (10% of the good prognosis patients none the less recurred).
A genomic test can help to find out if a cancer patient will benefit from chemotherapy or not, and if they do, Whole Cell Profiling can help see what treatments have the best opportunity of being successful. 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.
Whole Cell Profiling (via Cell Function Analysis) measures the response of the tumor cells to drug exposure. Following this exposure, they measure both cell metabolism and cell morphology. The integrated effect of the drugs on the whole cell, resulting in a cellular response to the drug, measuring the interaction of the entire genome. No matter which genes are being affected, Whole Cell Profiling is measuring them through the surrogate of measuring if the cell is alive or dead.
For example, the epidermal growth factor receptor (EGFR) is a protein on the surface of a cell. EGFR inhibiting drugs certainly do target specific genes, but even knowing what genes the drugs target doesn't tell you the whole story. Both Iressa and Tarceva target EGFR protein-tyrosine kinases. But all the EGFR mutation or amplificaton studies can tell us is whether or not the cells are potentially susceptible to this mechanism of attack.
It doesn't tell you if Iressa is better or worse than Tarceva or other drugs which may target this. There are differences. The drugs have to get inside the cells in order to target anything. So, in different tumors, either Iressa or Tarceva might get in better or worse than the other. And the drugs may also be inactivated at different rates, also contributing to sensitivity versus resistance.
One of the most promising new approaches that may deal with early detection of cancer is called Proteomics (Protein Expression Analysis), the study of proteins in the cells, tissues and body fluids. Even before a tumor can be felt, some researchers have found, the tumor begins secreting a distinctive pattern, or fingerprint of proteins. Here, you go beyond genes (DNA, the Genomic Analysis or structure of the human genome) and beyond Gene Expression (the measure of RNA content, like Her2/neu in breast cancer) to measure the actual proteins themselves.
Genomic Analysis is only important insofar as it influences Gene Expression Analysis, which is only important insofar as it influences Protein Expression Analysis (Proteonomics), which is only important insofar as it influences Protein Function Analysis (are proteins active or inactive), which is only important insofar as it influences Cell Function Analysis (cell culture testing), which is only important insofar as it influences Disease Analysis (doing something to treat the patient and then making a measurement on the patient with CT/PET scanning), in that order. There is an inverse hierachy between relevance and ease of measurement.
There are many pathways to altered cellular (forest) function (hence all the different "trees" which correlate in different situations). It serves to validate Whole Cell Profiling. The forest is looked at, and not the trees. Whole Cell Profiling measures what happens at the end (the effects on the forest), rather than the status of the individual trees. Cancer is a complex disease and needs to be attacked on many fronts. The best thing to do is to combine these different tests in ways which make the most sense. The future of cancer therapy will be personalized treatments for individual patients, and will require a combination of novel diagnostics and therapeutics.
Improving cancer patient diagnosis and treatment through a combination of cellular and gene-based testing will offer predictive insight into the nature of an individual's particular cancer and enable oncologists to prescribe treatment more in keeping with the heterogeneity of the disease. The biologies are very different and the response to given drugs is very different.
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