Somatic genome editing with the RCAS/TVA-CRISPR/Cas9 system for precision tumor modeling

Oldrini, B.; Curiel-Garcia, A.; Marques, C.; Matia, V.; Uluckan, O.; Torres-Ruiz, R.; Rodriguez-Perales, S.; Huse, J.; Squatrito, M.

doi:10.1101/162669

bioRxiv · 10.1101/162669

Somatic genome editing with the RCAS/TVA-CRISPR/Cas9 system for precision tumor modeling

Oldrini, B.·Curiel-Garcia, A.·Marques, C.·Matia, V.·Uluckan, O.·Torres-Ruiz, R.·Rodriguez-Perales, S.·Huse, J.·Squatrito, M.

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Abstract

It has been gradually established that the vast majority of human tumors are extraordinarily heterogeneous at a genetic level. To accurately recapitulate this complexity, it is now evident that in vivo animal models of cancers will require to recreate not just a handful of simple genetic alterations, but possibly dozens and increasingly intricate. Here, we have combined the RCAS/TVA system with the CRISPR/Cas9 genome editing tools for precise modeling of human tumors. We show that somatic deletion in neural stem cells (NSCs) of a variety of known tumor suppressor genes (Trp53, Cdkn2a and Pten), in combination with the expression of an oncogene driver, leads to high-grade glioma formation. Moreover, by simultaneous delivery of pairs of guide RNAs (gRNAs) we generated different gene fusions, either by chromosomal deletion (Bcan-Ntrk1) or by chromosomal translocation (Myb-Qk), and we show that they have transforming potential in vitro and in vivo. Lastly, using homology-directed-repair (HDR), we also produced tumors carrying the Braf V600E mutation, frequently identified in a variety of subtypes of gliomas. In summary, we have developed an extremely powerful and versatile mouse model for in vivo somatic genome editing, that will elicit the generation of more accurate cancer models particularly appropriate for pre-clinical testing.

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Oldrini, B., Curiel-Garcia, A., Marques, C., Matia, V., Uluckan, O., Torres-Ruiz, R., Rodriguez-Perales, S., Huse, J., Squatrito, M.. 2017-08-14. Somatic genome editing with the RCAS/TVA-CRISPR/Cas9 system for precision tumor modeling. https://doi.org/10.1101/162669

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