Menyhárt Otília, Győrffy Balázs
Semmelweis University, Department of Bioinformatics and 2 Department of Pediatrics, H-1094 Budapest, Hungary.
Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2., H-1117 Budapest, Hungary.
Comput Struct Biotechnol J. 2021 Jan 22;19:949-960. doi: 10.1016/j.csbj.2021.01.009. eCollection 2021.
While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.
虽然具有成本效益的高通量技术提供了越来越多的数据，但对单层数据的分析很少能提供因果关系。跨不同细胞功能水平的多组学数据整合策略，包括基因组、表观基因组、转录组、蛋白质组、代谢组和微生物组，为理解诸如癌症等复杂疾病的潜在生物学机制提供了无与伦比的机会。我们回顾了一些最常用的数据整合方法，并概述了多组学在哪些研究领域能显著增进我们对恶性转化过程和结果的理解。我们讨论了为揭示癌症亚型、疾病机制而开发的算法框架，以及识别驱动基因组改变的方法，并考虑多组学在肿瘤分类、诊断和预后评估中的重要性。我们全面总结了每种组学策略在临床背景下的最新进展，并讨论了其临床应用面临的主要挑战。尽管多组学数据整合具有无与伦比的优势，但它进入日常临床实践的速度却很慢。一个主要障碍是不同组学方法的成熟度参差不齐，以及与数据处理能力相比，生成大量数据的速度越来越快。推进样本处理和分析流程标准化的倡议，以及对专家进行数据分析和解释的多学科培训，对于促进理论研究成果的可转化性至关重要。
