Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia, Canada.
PLoS Comput Biol. 2019 Feb 22;15(2):e1006799. doi: 10.1371/journal.pcbi.1006799. eCollection 2019 Feb.
Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies.
基因突变特征反映了内源性和外源性的突变过程，为肿瘤病因学提供了深入的了解，有助于预后和生物学分层，并为治疗提供潜在的靶点。我们提出了一种新的机器学习形式主义，用于改进特征推断，基于多模态相关主题模型（MMCTM），可以同时从来自癌症基因组测序数据的单核苷酸和结构变异计数中推断特征。我们在两种激素驱动、DNA 修复缺陷的癌症（乳腺癌和卵巢癌）中举例说明了我们方法的实用性（总共 755 个样本）。我们展示了如何在突变模式的内部和之间引入相关结构，可以提高特征发现的准确性，特别是在数据稀疏的情况下。我们的研究强调了整合多种突变模式进行特征发现和患者分层的重要性，并提供了一个统计建模框架，用于为未来的研究纳入其他感兴趣的特征。
