The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr

中文翻译：

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对肿瘤异质性的多组学表征：统计和机器学习方法的全面回顾。

癌症的多组学分子表征为我们理解癌症生物学和治疗策略开辟了新的视野。然而，肿瘤活检包括多种类型的细胞，不仅限于癌细胞，还包括肿瘤微环境细胞和邻近的正常细胞。这种异质性是一个主要的混杂因素，它阻碍了使用多组学特征进行生物标志物识别的稳健且可重复的生物信息学分析。此外，多年来，异质性本身因其在某些癌症类型中的重要预后价值而得到认可，从而为治疗干预提供了另一种有希望的途径。已经提出了许多计算方法来从肿瘤样本的高通量分子谱中解开这种异质性，但它们中的大多数依赖于来自单个组学层的数据。由于细胞的异质性广泛分布在多个组学层中，基于单个层的方法只能部分表征细胞的异质混合物。为了帮助进一步开发同步解释多个多组学特征的方法，我们撰写了一篇综合综述，对基于三个不同组学层（基因组、表观基因组和转录组）表征肿瘤异质性的各种方法进行了全面回顾。因此，该综述可用于分析许多大型财团产生的多组学特征。基于单个层的方法只能部分表征细胞的异质混合物。为了帮助进一步开发同步解释多个多组学特征的方法，我们撰写了一篇综合综述，对基于三个不同组学层（基因组、表观基因组和转录组）表征肿瘤异质性的各种方法进行了全面回顾。因此，该综述可用于分析许多大型财团产生的多组学特征。基于单个层的方法只能部分表征细胞的异质混合物。为了帮助进一步开发同步解释多个多组学特征的方法，我们撰写了一篇综合综述，对基于三个不同组学层（基因组、表观基因组和转录组）表征肿瘤异质性的各种方法进行了全面回顾。因此，该综述可用于分析许多大型财团产生的多组学特征。联系方式： sunkim.bioinfo@snu.ac.kr