High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo.
High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo.
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DOI：
10.1101/cshperspect.a041382
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发表时间：
2023-06
影响因子：
5.4
通讯作者：
Yuning J. Tang;Emily G. Shuldiner;S. Karmakar;M. Winslow
中科院分区：
文献类型：
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作者：
Yuning J. Tang;Emily G. Shuldiner;S. Karmakar;M. Winslow
癌症中大量的基因组和分子变化对揭示肿瘤发生的机制和确定治疗靶点构成了巨大的挑战。在基因工程小鼠模型中的高通量功能基因组方法允许快速和系统地研究癌症驱动基因。在这篇综述中，我们讨论了利用自体肿瘤模型对体内重要的功能基因进行多重研究的基本概念和工具。此外，我们强调了该领域的新技术进步，未来研究的潜在机会，并概述了将多重遗传扰动与详细的分子分析相结合的愿景，以促进我们对癌症的遗传和分子基础的理解。
The vast number of genomic and molecular alterations in cancer pose a substantial challenge to uncovering the mechanisms of tumorigenesis and identifying therapeutic targets. High-throughput functional genomic methods in genetically engineered mouse models allow for rapid and systematic investigation of cancer driver genes. In this review, we discuss the basic concepts and tools for multiplexed investigation of functionally important cancer genes in vivo using autochthonous cancer models. Furthermore, we highlight emerging technical advances in the field, potential opportunities for future investigation, and outline a vision for integrating multiplexed genetic perturbations with detailed molecular analyses to advance our understanding of the genetic and molecular basis of cancer.