使用单细胞转录组的个性化瘤组合疗法优化
Chen Tang1, Shaliu Fu1,2, Xuan Jin1
1Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
Genome medicine
|December 2, 2023
概括
这项研究介绍了comboSC,这是一种使用单细胞转录组来预测最佳个性化癌症药物组合的计算工具. 它分析免疫微环境以确定协同疗法,加速个性化癌症治疗.
科学领域:
- 计算生物学 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:
- 个性化癌症治疗依赖于精确的瘤和免疫微环境的特征.
- 目前的方法,如体外分析或批量转录组,忽略了瘤异质性和体内免疫微环境.
- 单细胞转录组对于在个性化癌症治疗中克服这些局限性至关重要.
研究的目的:
- 介绍comboSC,一个计算概念验证,用于优化个性化癌症组合疗法.
- 通过使用单细胞RNA测序来评估他们的免疫微环境来分层患者样本.
- 为了确定协同作用的药物组合和免疫疗法配对,以实现个性化的临床使用.
主要方法:
- comboSC使用单细胞RNA测序数据来定量评估免疫微环境.
- 它集成了体外细胞响应数据,以确定协同作用的药物组合.
- 双分割图形优化用于对临床使用的药物组合进行优先排序.
主要成果:
- 组合SC被应用于来自15种癌症类型的119个单细胞转录组数据集.
- 预测的药物组合使用文献,临床试验数据,细胞系干扰和体内样本进行了验证.
- 这项研究证明了comboSC用于预测有效的癌症药物组合的可行性.
结论:
- comboSC是一个可行的计算原型,用于预测个性化癌症药物组合.
- 它通过减少查时间和节省临床时间来加速个性化瘤治疗.
- 一个Web服务器和源代码可供临床和研究用户使用.
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