该研究综述了通过DNA甲基化分析揭示乳腺癌细胞系和组织间的关系，以及从不同角度推断肿瘤克隆进化的计算方法。提出了一种基于DNA甲基化的乳腺癌组织中细胞亚克隆群体动力学建模策略。通过模拟和真实细胞系的数据分析，该方法在估计细胞亚群组成和比例方面表现出稳定性能。此外，该策略应用于癌症基因组图谱的乳腺癌患者中，识别了不同分子表型的细胞亚群。讨论了这一策略在临床乳腺癌研究中的当前和潜在应用，强调了基于DNA甲基化的肿瘤内在异质性识别的重要性。
摘要：
Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlas's identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.
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