Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa188.
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.
癌症的多组学生物分子特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，肿瘤活检包含多种类型的细胞，不仅限于癌细胞，还包括肿瘤微环境细胞和相邻的正常细胞。这种异质性是一个主要的混杂因素，阻碍了使用多组学生物特征进行稳健和可重复的生物信息学分析以识别生物标志物。此外，多年来，异质性本身因其在某些癌症类型中的显著预后价值而受到关注，因此为治疗干预提供了另一个有前途的途径。已经提出了许多从肿瘤样本的高通量分子谱中揭示这种异质性的计算方法，但大多数方法都依赖于单个组学层的数据。由于细胞的异质性广泛分布在多组学生物层中，基于单个层的方法只能部分地描述细胞的异质混合物。为了帮助促进同时考虑多个多组学生物特征的方法的进一步发展，我们基于三个不同的组学层：基因组、表观基因组和转录组，撰写了一篇全面综述，以描述肿瘤异质性的不同方法。因此，这篇综述对于分析由许多大型联盟产生的多组学生物特征可能是有用的。联系信息：sunkim.bioinfo@snu.ac.kr。
