Tracking down gene fusions
Stanford researchers use single-cell statistical modeling method to go after cancer
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STANFORD, Calif.—Stanford University researchers are looking to track down gene fusions that can drive cancers proliferation and uncover fusions in individual tumor cells. To do that, they have turned to a single-cell statistical modeling method, and a member of the team presented their method during the Bioinformatics and Artificial Intelligence minisymposium at the AACR Virtual Annual Meeting II in June.
In the session, Dr. Roozbeh Dehghannasin, a postdoctoral researcher at the Stanford School of Medicine, described the statistical model designed to detect gene fusions at a single-cell level, explaining that many gene fusions are known to drive cancer development, but limitations in current technologies have hindered fully understanding their functions.
The model developed by the Stanford team, which is called SICILIAN, can be integrated into conventional splice alignment tools to detect different RNA variants, such as splice junctions, gene fusions and circular RNA.
Dehghannasin said that the team found that SICILIAN detected gene fusions with similar sensitivity but greater specificity than an existing gene fusion detection tool called STAR-Fusion.
