Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy.
Bioinformatics. 2024 May 2;40(5). doi: 10.1093/bioinformatics/btae320.
Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients.
We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics.
MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE.
突变特征是解析癌症发展背后遗传改变的关键组成部分，已成为了解肿瘤发生过程中基因组变化的宝贵资源。因此，采用精确和准确的方法提取突变特征对于确保可靠地识别潜在模式并将其有效地应用于癌症患者的新诊断、预后和治疗策略至关重要。
我们提出了 MUSE-XAE，这是一种使用可解释自动编码器从癌症基因组中提取突变特征的新方法。我们的方法采用了一种混合架构，包括一个可以捕获特征之间非线性相互作用的非线性编码器，以及一个确保主动特征可解释性的线性解码器。我们在合成和真实癌症数据集上评估并比较了 MUSE-XAE 和其他可用工具，结果表明它在恢复突变特征谱方面具有卓越的精度和灵敏度性能。MUSE-XAE 通过增强对真实世界环境中主要肿瘤类型和亚型的分类，提取出高度有区别的突变特征谱。这种方法可以促进该领域的进一步研究，神经网络在推进我们对癌症基因组学的理解方面发挥着关键作用。
MUSE-XAE 软件可在 https://github.com/compbiomed-unito/MUSE-XAE 上免费获得。
