A CRISPR Path to Finding Vulnerabilities and Solving Drug Resistance: Targeting the Diverse Cancer Landscape and Its Ecosystem

Benjamin McLean, Aji Istadi, Teleri Clack, Mezzalina Vankan, Daniel Schramek, G. Gregory Neely, Marina Pajic
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Abstract

Cancer is the second leading cause of death globally, with therapeutic resistance being a major cause of treatment failure in the clinic. The dynamic signaling that occurs between tumor cells and the diverse cells of the surrounding tumor microenvironment actively promotes disease progression and therapeutic resistance. Improving the understanding of how tumors evolve following therapy and the molecular mechanisms underpinning de novo or acquired resistance is thus critical for the identification of new targets and for the subsequent development of more effective combination regimens. Simultaneously targeting multiple hallmark capabilities of cancer to circumvent adaptive or evasive resistance may lead to significantly improved treatment response in the clinic. Here, the latest applications of functional genomics tools, such as clustered regularly interspaced short palindromic repeats (CRISPR) editing, to characterize the dynamic cancer resistance mechanisms, from improving the understanding of resistance to classical chemotherapeutics, to deciphering unique mechanisms that regulate tumor responses to new targeted agents and immunotherapies, are discussed. Potential avenues of future research in combating therapeutic resistance, the contribution of tumor–stroma signaling in this setting, and how advanced functional genomics tools can help streamline the identification of key molecular determinants of drug response are explored.

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寻找脆弱性和解决耐药性的CRISPR路径:针对不同的癌症景观及其生态系统
癌症是全球第二大死亡原因,治疗耐药性是临床治疗失败的主要原因。肿瘤细胞与周围肿瘤微环境的多种细胞之间发生的动态信号传导积极促进疾病进展和治疗耐药性。因此,提高对肿瘤在治疗后如何演变以及支持新生或获得性耐药的分子机制的理解对于确定新的靶点和随后开发更有效的联合治疗方案至关重要。同时针对癌症的多个标志能力来规避适应性或逃避性抵抗可能会显著改善临床治疗反应。本文讨论了功能基因组学工具的最新应用,如聚类规则间隔短回文重复序列(CRISPR)编辑,以表征动态癌症耐药机制,从提高对经典化疗药物耐药的理解,到破译调节肿瘤对新靶向药物和免疫疗法反应的独特机制。未来研究的潜在途径是对抗治疗耐药性,肿瘤基质信号在这种情况下的贡献,以及先进的功能基因组学工具如何帮助简化识别药物反应的关键分子决定因素。
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