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Radiologists have seen some of these connections anecdotally but haven’t had the data to back them up, says Patrick Wen, a neuro-oncologist at the Dana-Farber Cancer Institute, in Boston. For example, Wen says, oncologists have suspected the connection between tumor appearance and response to therapies targeting tumor blood vessels demonstrated in Kuo’s study. If further data back up this result, it is one of many that would prove therapeutically useful. Wen says that the results are a very important first step “towards using imaging to tailor treatment without having to take tissue.”

“Gene-expression patterns result in anatomical changes you can see in these images,” says Webster Cavanee, director of the Ludwig Institute for Cancer Research at the University of California, San Diego. Cavanee, who was not involved in Kuo’s research, believes that the connection between imaging and genetics will hold in other cancers, and perhaps other diseases. Last year, Kuo published a paper connecting characteristics of liver tumors in CT scans with gene-expression patterns. This suggests that the connection between anatomical changes and gene expression will hold up across imaging and tumor types alike.

Cavanee and Wen agree that if Kuo’s work holds up in larger studies, it could have a major clinical impact. “An imaging protocol could be powerful,” says Cavanee, because medical imaging is already part of standard cancer care. “The images are already there. It’s a matter of layering information on something that already exists without adding cost–you’re just adding precision.”

Another advantage of using images for molecular profiling rather than biopsies and microarrays is the global view that this affords. “In general, a biopsy does represent the whole tumor,” says Kuo. But some tumors, particularly glioblastomas, may be heterogeneous–part of a tumor might be more vulnerable or resistant to particular drugs than others–and a biopsy only gives information about one region. Microarrays do give much more specific information, but this level of detail might not be needed: MRI scans might be good enough to rapidly get patients on a drug that’s likely to work. And to get a biopsy, “you need to go in physically and get tissue,” which carries risks, says Kuo.

Kuo’s group also identified a particularly malignant version of glioblastoma and connected its MRI scans and genetic profile with poor patient outcomes. “Instead of saying all patients with [glioblastoma] tumors are the same, we can sort them into two subtypes based on outcome,” says Kuo. His work suggests that these patients should be identified and treated more aggressively, while patients with less malignant cancers can be spared the side effects of aggressive treatment.

Wen describes Kuo’s work as an important first step, but a small one. Kuo’s team is currently working to validate its results in a larger group of glioblastoma patients. “Our data suggest we’re going in the right direction,” he says.

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Credit: Michael Kuo, University of California, San Diego

Tagged: Biomedicine, cancer, personalized medicine, medical imaging

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