PO.TB06.01 · 肿瘤生物学

结直肠癌放疗诱导表观遗传重组的生物信息学与实验整合分析

An integrated bioinformatic and experimental analysis of radiation-induced epigenetic reorganization in colorectal cancer

编号 7373 展板 10 时间 4/22 09:00–12:00 区域 Section 26 主讲 Megan Tandar, No Degree
分会场 Biological Mechanisms of Tumor and Normal Tissue Response and Clinical Studies
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作者与单位 Authors & Affiliations

Megan Tandar, Allison Pittman, Christine E. Eyler

Duke University, Durham, NC

摘要 Abstract

中文摘要
结直肠癌是美国癌症相关死亡的第二大原因。对放射治疗的抵抗(即放疗抵抗)常在治疗过程中形成,导致转移和死亡。目前的研究忽视了治疗过程中发生的动态表观遗传变化,限制了有效治疗方法的开发。 我的项目旨在跨多个体外结直肠癌模型对这些放疗诱导的表观遗传改变进行图谱绘制和探究。为在不同的结直肠癌亚型中研究这一现象,我将高通量测序分析与湿实验室CRISPR筛选相整合,以识别受放疗影响的表观遗传区域,并在具有生物学相关性的模型中进行验证。 我对放疗处理的结直肠癌细胞系进行了RNA测序(RNA-Seq)、基因集富集分析(GSEA)和转座酶可及性染色质测序(ATAC-Seq),以表征放疗诱导的表观遗传变化。我对HCT116、SW480、MDST8和RKO细胞系施加了两种放疗方案,以模拟具有临床相关性的放疗疗程:(1)单次剂量处理,0、2或5 Gy;(2)分次剂量处理,每日给予0或2 Gy,连续五天。为验证这些生物信息学分析,我在HCT116和MDST8两种结直肠癌细胞系中采用了双重全基因组CRISPR敲除筛选,以实验方式识别放疗诱导的表观遗传改变,在0 Gy对照和2 Gy多次剂量照射条件下筛选了1.8亿个细胞。 RNA-Seq揭示在所有四种细胞系中放疗后转录变化一致。经5 Gy照射的RKO细胞系显示出基因组中某些区域随放疗剂量增加而基因表达升高,提示存在潜在的放疗诱导表观遗传调控机制。在多次放疗剂量实验中,火山图分析揭示了免疫相关基因(如HCT116细胞及类似细胞系中的IFIT1)表达升高。我进行了额外的GSEA分析,证实放疗后免疫感应通路发生改变,提示结直肠癌中免疫应答、放疗与表观遗传调控之间存在潜在联系。我目前正在分析CRISPR和ATAC-Seq数据,但预期各数据集之间放疗改变的表观遗传区域会存在重叠。 我们旨在将CRISPR筛选数据与RNA-Seq、GSEA和ATAC-Seq分析相整合。我期望这些结果有助于对关键的放疗诱导结直肠表观遗传改变进行新的表征,并为研究结直肠癌放疗抵抗提供一个稳健的框架。最终,我们希望利用这些结果开发个性化、具有时间敏感性的治疗方法,以改善患者预后和生活质量。
查看英文原文 English abstract
Colorectal cancer is the second-leading cause of cancer-related deaths in the US. Resistance to radiation therapy - known as radioresistance - often develops during treatment, leading to metastases and death. Current research overlooks the dynamic epigenetic changes that occur during therapy, limiting the development of effective therapeutics. My project aims to profile and interrogate these radiation-induced epigenetic alterations across multiple in vitro colorectal cancer models. To study this phenomenon across diverse colorectal cancer subtypes, I integrated high-throughput sequencing analyses with wet-lab CRISPR screens to identify radiotherapy-affected epigenetic regions and validate them in biologically relevant models. I performed RNA Sequencing (RNA-Seq), Gene Set Enrichment Analysis (GSEA), and Assay for Transposase-Accessible Chromatin Sequencing (ATAC-Seq) on radiation-treated colorectal cancer cell lines to characterize radiation-induced epigenetic changes. I administered two courses of radiation treatments on HCT116, SW480, MDST8, and RKO lines to mimic clinically relevant radiation courses: (1) a single-dose treatment of 0, 2, or 5 Gy, and (2) a fractionated-dose treatment of 0 or 2 Gy administered daily for five consecutive days. To validate these bioinformatic analyses, I employed dual whole-genome-wide CRISPR knockout screens in both HCT116 and MDST8 colorectal cancer cell lines to experimentally identify radiation-induced epigenetic alterations, screening 180 million cells under 0 Gy control and 2 Gy multi-dose irradiation conditions. RNA-Seq revealed consistent post-radiation transcriptional changes in all four cell lines. The 5 Gy-irradiated RKO lines showed regions of the genome that had increases in gene expression with increasing radiation treatment, suggesting a potential radiation-induced epigenetic regulatory mechanism. In multi-radiation dose experiments, volcano plot analysis revealed increased expression of immune-related genes such as IFIT1 in HCT116 cells and similar lines. I ran additional GSEA analyses, which confirmed alterations in immune-sensing pathways following radiation treatment and suggest a potential link among immune response, radiation treatment, and epigenetic regulation in colorectal cancer. I am currently analyzing CRISPR and ATAC-Seq data, but anticipate overlap in radiation-altered epigenetic regions between datasets. We aim to integrate CRISPR-screen data with RNA-Seq, GSEA, and ATAC-Seq analyses. I expect these results to contribute to the novel characterization of key radiation-induced colorectal epigenetic alterations and provide a robust framework for studying radioresistance in colorectal cancer. Ultimately, we hope to use these results to develop personalized, time-sensitive therapies that may improve patient outcomes and quality of life.
利益披露 Disclosure
M. Tandar, None.. A. Pittman, None.. C. E. Eyler, None.

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