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Data from A Versatile ES Cell–Based Melanoma Mouse Modeling Platform

Authors :
Florian A. Karreth
Jose G. Gonzalez
Arianna Nenci
Jordan Reff
Koji Nakamura
Neel Jasani
Xiaonan Xu
Olga Vera
Ilah Bok
Publication Year :
2023
Publisher :
American Association for Cancer Research (AACR), 2023.

Abstract

The cumbersome and time-consuming process of generating new mouse strains and multiallelic experimental animals often hinders the use of genetically engineered mouse models (GEMM) in cancer research. Here, we describe the development and validation of an embryonic stem cell (ESC)-GEMM platform for rapid modeling of melanoma in mice. The platform incorporates 12 clinically relevant genotypes composed of combinations of four driver alleles (LSL-BrafV600E, LSL-NrasQ61R, PtenFlox, and Cdkn2aFlox) and regulatory alleles to spatiotemporally control the perturbation of genes of interest. The ESCs produce high-contribution chimeras, which recapitulate the melanoma phenotypes of conventionally bred mice. Using the ESC-GEMM platform to modulate Pten expression in melanocytes in vivo, we highlighted the utility and advantages of gene depletion by CRISPR-Cas9, RNAi, or conditional knockout for melanoma modeling. Moreover, complementary genetic methods demonstrated the impact of Pten restoration on the prevention and maintenance of Pten-deficient melanomas. Finally, we showed that chimera-derived melanoma cell lines retain regulatory allele competency and are a powerful resource to complement ESC-GEMM chimera experiments in vitro and in syngeneic grafts in vivo. Thus, when combined with sophisticated genetic tools, the ESC-GEMM platform enables rapid, high-throughput, and versatile studies aimed at addressing outstanding questions in melanoma biology.Significance: This study presents a high-throughput and versatile ES cell-based mouse modeling platform that can be combined with state-of-the-art genetic tools to address unanswered questions in melanoma in vivo.See related commentary by Thorkelsson et al., p. 655

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........b69329a2c6a959ca5e4a985a0443ea22
Full Text :
https://doi.org/10.1158/0008-5472.c.6511762.v1