Noninvasive Imaging Decodes The Genetic Activity of Tumors

By correlating images of cancerous liver tissue with gene expression patterns, a research team has developed tools that may some day allow clinicians to view a computed tomography (CT) image of a tumor and decipher its genetic activity. The study was designed to help clinicians obtain the molecular details of a specific tumor or disease without having to do an invasive biopsy procedure.

According to researchers from the Stanford University School of Medicine and the University of California, San Diego (UCSD) School of Medicine (USA), the study represents the convergence of two developing fields of medical research: the mapping of the human genome and advances in diagnostic imaging.

The researchers systematically compared features from CT images of liver tumors with gene expression patterns obtained from surgery and tissue biopsies. Once they located the genomic correlates of the features identified by CT imaging, the researchers found that the two very different aspects of studying cancer--how the tumor looks in a CT scan and how it behaves on a molecular level--had a very strong connection.

The study's senior author, Michael Kuo, M.D., assistant professor of interventional radiology at UCSD, commented, "We studied what the various genes were doing and the biological activity they were involved in such as angiogenesis or cell growth. We also looked at how the genes contributed to a particular phenotype in the liver tumor seen on the CT scans, for example, the presence of characteristics vessels, or the tumor's texture and other important diagnostic imaging traits,” said Dr. Kuo.

This new approach would avoid the pain and risk of infection and bleeding from a biopsy and would not destroy tissue, so the same site could be tested again and again.

The research process sought to reveal the relationship between genetic activity patterns in liver tumors and the tumor's appearance on CT scans, and provides a simple means of translation. The scientists first began with about 135 basic tumor descriptors, and then narrowed down the multitude of traits to the 28 most significant diagnostic descriptors, matching those imaging features with a vast stockpile of microarray data generated from human liver cancer samples.

Microarrays are 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 "We found a rich association between the images and the gene expression,” Dr. Kuo said, adding that out of approximately 7,000 genes in the tumors, the research team was able to consistently associate imaging traits with 75% of the genes.

The study was published May 21, 2007, in the advance online issue of the journal Nature Biotechnology.


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Stanford University School of Medicine
University of California, San Diego