Researchers studied 149 people with low grade gliomas, a slow-growing type of brain tumor, who were treated with temozolomide chemotherapy for up to 30 months.
The study found temozolomide reduced the size of brain tumors in 53 percent of the study's participants and stabilized the size of brain tumors in 37 percent of the study's participants. However, in 10 percent of the group, the size of the brain tumors increased by more than 25 percent.
Genetic testing was also performed in 86 of the participants. In 42 percent of the participants, the gene 1p/19q was missing. Those missing the gene were more likely to respond well to the drug. They also had more months without the tumor developing than those with the gene and were less likely to die during the study.
"Our findings are consistent with previous smaller studies showing temozolomide as a primary treatment is effective and tolerable, and an added benefit is the discovery that the loss of chromosome 1p/19q predicts how well a person is going to respond to the treatment," said study author Khe Hoang-Xuan, MD, PhD, with the Pitie-Salpetriere Hospital in Paris, France.
However, Hoang-Xuan says comparing temozolomide to radiotherapy, which is the standard treatment for such brain tumors, isn't easy and he says that's why continued research into comparing the two treatments is needed.
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Kuo first conceived of the project in 2001 while he was a radiology resident at Stanford University. "Radiology,while making great technological advances towards capturing more and more detailed information in a non-invasive manner,seemed to be largely unaware to a fundamental shift in medicine towards genomic, personalized medicine," he said. At the time, Stanford Medical School was center to ground-breaking studies of DNA microarrays, lab tools that can screen thousands of genes at a time, developed by Stanford biochemistry professor Patrick Brown, MD, Ph.D. Microarrays were proving to be extremely useful for identifying groups of genes and their patterns in diseases such as cancer, enabling scientists to compare them with normal tissue activity.
Genomics expert Howard Chang, M.D., assistant professor of dermatology at Stanford, and the paper's lead author, Eran Segal, Ph.D., joined the project in 2004. Chang had been using the gene activity patterns of microarrays to predict cancer outcome. Segal developed algorithms during his doctoral studies at Stanford that played a critical role in the analysis of the massive amounts of data encompassed in the study.
"When we looked at noninvasive images, there were a lot of different patterns that had no known meaning," said Chang. "We thought that maybe we could come up with a way to systematically connect the gene activity seen with microarrays to imaging patterns, enabling us to translate images to gene patterns, and ultimately to the outcome of the disease process."
"Clearly, we are very far from clinical applications of these tools that we developed," said Segal, who is now a computational biologist at the Weizmann Institute of Science in Rehovot, Israel. "But the fact that we saw strong connections between the imaging features and the molecular gene activity data suggests that this could be a promising and fruitful research direction."
Such use of non-invasive imaging to determine unique molecular characteristics of disease could lead to more individualized diagnosis and treatment of patients, according to the researchers.
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