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多变量分片线性回归模型用于预测放射敏感性,使用与全基因组拷贝数变异的关联来预测放射敏感性

Joanna Tobiasz^1,2, Najla Al-Harbi³, Sara Bin Judia³

¹Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland.

Frontiers in oncology

|October 18, 2023

概括

这项研究开发了一种机器学习方法,使用副本数变异 (CNV) 来预测癌细胞的放射敏感性. 该方法确定了放射敏感细胞和放射耐药细胞的特定CNV标记物,有助于个性化放射治疗.

科学领域:

基因组学和生物信息学
辐射瘤学辐射瘤学
癌症生物标志物癌症生物标志物

背景情况:

预测患者对放射治疗的反应对于个性化癌症治疗和辐射风险评估至关重要.
全基因组拷贝数变异 (CNVs) 正被研究为放射敏感性的潜在生物标志物.

研究的目的:

开发一种机器学习方法,使用CNVs来分层放射敏感性.
为了确定与不同程度的对电离辐射敏感度相关的特定CNV.

主要方法:

利用Affymetrix CytoScan HD微阵列分析129个纤维细胞细胞株中的CNV.
通过2 Gy (SF2) 的幸存分数测量了辐射敏感性.
应用了一种动态编程 (DP) 算法,用于断片式多变量线性回归来预测SF2并识别相关的CNV.

主要成果:

该DP算法将细胞菌株细分为放射性敏感 (RS),正常敏感 (NS) 和耐辐射 (RR) 组.
一个5段模型确定了C-3SFBP (MCC基因区域) 作为RS细胞的标记物和C-7IUVU (SLC1A6基因区域) 作为RR细胞的标记物.
发现拷贝数量的减少通常与辐射敏感度的增加有关.

结论:

基于DP的方法有效地缩小了用于辐射敏感性预测的CNV标记.
SF2分区改善了估计和识别不同的标记物,有助于对放射治疗进行剂量调整.
识别的CNV标志物可能通过识别那些从剂量减少或升级中受益的患者来指导个性化放射治疗.

关键词:

在Affymetrix CytoScan HD微阵列中使用.副本编号变化 (CNV)动态编程是动态的编程.线性回归是一种线性回归.辐射基因组学辐射基因组学辐射敏感性辐射敏感性在2 Gy (SF2) 的生存分数.

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