Use of a genome-wide haploid genetic screen to identify treatment predicting factors: a proof-of-principle study in pancreatic cancer

吉西他滨 胰腺癌 表观遗传学 癌症 癌症研究 细胞毒性T细胞 医学 组蛋白脱乙酰酶抑制剂 肿瘤科 内科学 组蛋白脱乙酰基酶 生物 生物信息学 遗传学 组蛋白 基因 体外
作者
Yuk Ting,Sarah Leonard,Naheema S. Gordon,Jennifer Anderton,Claire D. James,David Huen,Ciarán Woodman,Daniel H. Palmer
出处
期刊:Oncotarget [Impact Journals, LLC]
卷期号:8 (38): 63635-63645 被引量:5
标识
DOI:10.18632/oncotarget.18879
摘要

The ability to develop a comprehensive panel of treatment predicting factors would significantly improve our ability to stratify patients for cytotoxic or targeted therapies, and prevent patients receiving ineffective treatments. We have investigated if a recently developed genome-wide haploid genetic screen can be used to reveal the critical mediators of response to anticancer therapy. Pancreatic cancer is known to be highly resistant to systemic therapy. Recently epigenetic changes have been shown to be a key determinant in the maintenance of subpopulations of cancer cells with high-level resistance to cytotoxic therapy. We show that in human pancreatic cancer cell lines, treatment with the potent class I histone deacetylase inhibitor, entinostat, synergistically enhances gemcitabine-induced inhibition of cell proliferation and apoptosis. Using a genome-wide haploid genetic screen, we identified deoxycytidine kinase (DCK) as one of the genes with the highest degree of insertional enrichment following treatment with gemcitabine and entinostat; DCK is already known to be the rate-limiting activating enzyme for gemcitabine. Immunoblotting confirmed loss of DCK protein expression in the resistant KBM7 cells. CRISPR/Cas-9 inactivation of DCK in pancreatic cancer cell lines resulted in resistance to gemcitabine alone and in combination with entinostat. We have identified gemcitabine and entinostat as a potential new combination therapy in pancreatic cancer, and in this proof-of-principle study we have demonstrated that a recently developed haploid genetic screen can be used as a novel approach to identify the critical genes that determine treatment response.
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