CRISPR Technology Identifies Cancer-Causing Genes

Researchers have identified specific gene combinations that can cause the brain cancer glioblastoma, using their newly developed modified CRISPR technology that can also pinpoint triggers and primary drivers of other types cancers. Thus the new technology could help improve diagnostics, as well as in finding more specific targets for developing more effective drugs.

Scientists have become adept at identifying mutations present in a variety of cancers in patients, but it is still particularly challenging to identify genes or combinations of genes that are directly causing progression of the disease. For instance, more than 223 individual genes have been linked to glioblastoma, a difficult-to-treat brain cancer with a median survival rate of only 1-1.5 years. Thousands of combinations of those genes could act in concert to cause the disease in an individual patient.

“The human cancer genome is now mapped and thousands of new mutations were associated with cancer, but it has been difficult to prove which ones or their combinations actually cause cancer,” said study co-corresponding author Sidi Chen, assistant professor at Yale University (New Haven, CT, USA), “We [could] also use this information to determine which existing drugs are most likely to have therapeutic value for individual patients, a step towards personalized cancer therapy.’’

The Yale team and the team of co-corresponding author Randall J. Platt at ETH Zurich (Zurich, Switzerland) developed an improved application of CRISPR gene editing and screening technology – “AAV-mediated direct in vivo CRISPR screen” – to search for primary drivers of glioblastoma in living mice. They assessed impacts of mutations from over 1500 genetic combinations and found multiple combinations that could cause this cancer. They also found 2 mutations that could make tumors resistant to chemotherapy — information that could help doctors tailor existing treatments for individual patients. Primary funding for the research was provided by Yale System Biology Institute and the National Institutes of Health.

The study, by Chow RD et al, was published August 14, 2017, in the journal Nature Neuroscience.

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ETH Zurich