Modeling brain cancer with CRISPR


We are proud to announce that our latest manuscript: “Somatic chromosomal engineering identifies BCAN-NTRK1 as a potent glioma driver and therapeutic target”, has been just published in Nature Communications. This work was the result of a truly collaborative effort  between our lab and the lab ot our neighbors at MSKCC: Robert Benezra and Maurizio Scaltriti. A follow-up to Danilo Maddalo’s work, this project used CRISPR-based somatic genome editing to model a set of rare genomic rearrangements responsible for  brain cancer-associated gene fusions. Peter J. Cook, is the lead author of this paper and is a joint postdoctoral fellow between the Ventura and the Benezra lab, and the project was funded by the Pershing Square Sohn Cancer Foundation and by the Brain Tumor Center at MSKCC.

BCAN_NTRK1_IMAGE.001Peter’s initial goal was to use CRISPR-Cas9 to model a set of uncharacterized, potentially oncogenic chromosomal rearrangements in the brain. Four gene fusions identified from human glioma patient RNA-Seq data were selected and the chromosomal inversions, deletions, and duplications underlying these fusions were modeled in mouse adult neural stem cell primary cultures and directly in the brain using adenovirus-mediated in vivo genome editing. He found that a chromosomal deletion resulting in the fusion between the secreted proteoglycan Bcan and the receptor tyrosine kinase Ntrk1 was capable of  transforming p53-null neuronal stem cells cells, resulting in high grade gliomas by either in-vitro orthotopic stem cell implantation or by direct in-vivo viral induction. Interestingly, we also found that these tumors were  sensitive to a small molecule kinase inhibitor specific to Ntrk1.

figure_3_cook.jpgWe are excited about this work not only because it resulted in a new a clinically relevant genetically-engineered mouse model for human glioma, but also because it provides an experimental pipeline that can be readily adapted to interrogate a wide range of brain cancer associated  mutations of unknown functional significance.


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