Bahari Farideh, Ahangari Cohan Reza, Montazeri Hesam
Department of Nanobiotechnology, New Technologies Research Group, Pasteur Institute of Iran, Tehran, Iran.
Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
NPJ Precis Oncol. 2025 Mar 29;9(1):92. doi: 10.1038/s41698-025-00871-3.
Mutational burden tests are essential for detecting signals of positive selection in cancer driver discovery by comparing observed mutation rates with background mutation rates (BMRs). However, accurate BMR estimation is challenging due to the diversity of mutational processes across genomes, complicating driver discovery efforts. Existing methods rely on various genomic regions and features for BMR estimation but lack a model that integrates both intergenic intervals and functional genomic elements on a comprehensive set of genomic features. Here, we introduce eMET (element-specific Mutation Estimator with boosted Trees), which employs 1372 (epi)genomic features from intergenic data and fine-tunes it with element-specific data through transfer learning. Applied to PCAWG somatic mutations, eMET significantly improves BMR accuracy and has potential to enhance driver discovery. Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.
通过将观察到的突变率与背景突变率（BMR）进行比较，突变负担测试对于在癌症驱动因素发现中检测正选择信号至关重要。然而，由于整个基因组中突变过程的多样性，准确估计BMR具有挑战性，这使得驱动因素发现工作变得复杂。现有方法依靠各种基因组区域和特征来估计BMR，但缺乏一个在一整套基因组特征上整合基因间间隔和功能基因组元件的模型。在这里，我们引入了eMET（带增强树的元素特异性突变估计器），它利用来自基因间数据的1372个（表观）基因组特征，并通过迁移学习用元素特异性数据对其进行微调。应用于PCAWG体细胞突变时，eMET显著提高了BMR的准确性，并具有增强驱动因素发现的潜力。此外，我们对BMR估计进行了广泛分析，研究了不同的机器学习模型、基因组区间策略、特征类别和降维技术。
