- MRI Reveals Tumor Patterns
SAN DIEGO, Calif., March 26, 2008 -- A noninvasive magnetic resonance imaging (MRI) method has the potential to determine how tumors will respond to treatments based on their specific molecular properties.
"For the first time, we have shown that the activity of specific molecular programs in these tumors can be determined based on MRI scans alone," said Michael Kuo, MD, assistant professor of interventional radiology at the University of California San Diego School of Medicine. "We were also able to link the MRI with a group of genes that appear to be involved in tumor cell invasion -- a phenotype associated with a reduced rate of patient survival."
Kou and colleagues analyzed more than 2000 genes that had previously been shown to have altered expression in glioblastoma multiforme (GBM) tumors -- considered the most common and lethal type of primary brain tumor. They then mapped the correlations between gene expression and MRI features and identified characteristics associated with overall survival of patients with GBM tumors.
Five distinct MRI features were found to be significantly linked with particular gene expression patterns. For example, one characteristic seen in some images is associated with proliferation of the tumor, and another with growth and formation of new blood vessels within the tumor -- both of which are susceptible to treatment with specific drugs.
These physiological changes seen in the images are caused by genetic programs, or patterns of gene activation within the tumor
(Image courtesy University of Southern California at San Diego)
cells. Some of these programs are tightly associated with drug targets, so when they are detected, they could indicate which patients would respond to a particular anticancer therapy, the researchers saud.
Laboratory work that relies on tissue samples is routinely used to diagnose and guide treatment for GBM. However, the biological activity shown may depend on the portion of the tumor from which the tissue sample is obtained. The researchers said they have shown that MRI could be used to identify differences in gene expression programs within the same tumor.
"Gene expression results in the production of proteins, which largely determine a tumor's characteristics and behavior, Kuo said. "This noninvasive MRI method could, for example, detect which part of a tumor expresses genes related to blood vessel formation and growth or tumor cell invasion. Understanding the genetic activity could prove to be a very strong predictor of survival in patients, and help explain why some patients have better outcomes than others."
Kuo also led a study (Nature Biotechnology, May 2007) correlating computed tomography images of cancerous tissue with gene expression patterns in liver tumors. "In the new study, we were able to take a different imaging technology, MRI, and apply it to a totally different tumor type," he said. The studies open promising avenues for noninvasive diagnoses and classification of cancer, he added.
The study was published online this week by the Proceedings of the National Academy of Science (PNAS). The research was funded in part by the National Institutes of Health.
Contributors to the paper include first author Maximilian Diehn, UCSD Department of Radiology and Department of Radiation Oncology at Stanford University School of Medicine; Christine Nardini and David S. Wang, UCSD Department of Radiology; Susan McGovern and Kenneth Aldape, Department of Neuropathology, University of Texas M.D. Anderson Cancer Center, Houston; Mahesh Jayaraman, Department of Radiology, Brown University; Yu Liang, UCSF Brain Tumor Research Center; and Soonmee Cha, Department of Radiology, UCSF Medical Center.
For more information, visit: www.ucsd.edu
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