在癌症中发现治疗点,使用染色质可访问性和转录组数据
Andre Neil Forbes1, Duo Xu1, Sandra Cohen2
1Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA.
Cell systems
|September 5, 2024
概括
本研究介绍了一种使用ATAC-seq和RNA-seq数据的计算方法,以发现各种癌症的新药标. 该方法确定了关键的转录因子,以克服难以治疗的瘤中治疗阻力.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 癌症研究 癌症研究
背景情况:
- 许多癌症类型缺乏有效的向疗法.
- 耐治疗性仍然是一个重大挑战,即使有可用的有针对性的选择.
- 高通量测序技术的进步,如ATAC-seq和RNA-seq,为患者组织提供了新的分子洞察力.
研究的目的:
- 开发一种基于瘤血统的计算策略,用于识别基于瘤血统的新药标.
- 为精确瘤学利用集成的ATAC-seq和RNA-seq数据.
- 解决预后不佳的癌症的治疗脆弱性.
主要方法:
- 通过机器学习,为371名患者在22种癌症类型中构建基因调节网络.
- 整合三维基因组数据,特别是增强者与促进者的联系.
- 识别这些网络中的关键转录因子 (TF) 作为潜在的治疗点或相互作用体.
主要成果:
- 从患者基因组数据成功构建了全面的基因调控网络.
- 确定了关键的转录因子及其相互作用蛋白质作为潜在的治疗点.
- 对神经内分泌,肝脏和脏癌症的四种有前途的药物标候选人进行了验证.
结论:
- 开发的计算方法有效地识别了瘤谱系特定的药物标.
- 这种方法有望为治疗选择有限的癌症发现新的治疗策略.
- 经过验证的目标为克服耐药性和改善挑战性癌症类型的结果提供了潜在的途径.
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