Living organisms experience millions of signals transferred every second between cells, tissues, organs, and external environmental stimuli. Fine-tuned responses at various degrees and scales within the human body are central to the homeostatic mechanism that copes with potentially harmful environmental perturbations, including pathogens, smoking, and drugs, and interacts with the genetic background arising from spontaneous somatic mutations and numerous germline variants. Thus, a holistic view of homeostatic mechanisms through the study of genomic and epigenetic aberrations is needed to understand the core of cancer biology and the pathophysiological features of cancer during oncogenesis and tumor progression.
A multi-omics study is a data-driven scientific investigation that analyzes a range of high-dimensional datasets at multiple levels and scales to reveal the complexity of cells and their environment. Such type of study can provide novel frameworks to untangle biological phenomena or models to test certain hypotheses using various datasets. In cancer research, a paradigm shift toward multi-omics approaches has been achieved with the recent development of high-throughput technologies in genomics and transcriptomics, increasing effort in large-scale research collaboration, and advancement of computational algorithms (Basu et al., 2013; Berns and Bernards, 2012; Cancer Genome Atlas Network, 2012b; Gentles and Gallahan, 2011; Whitehurst et al., 2007). Together with advances in genomics and transcriptomics, proteomics is emerging as a prominent field to elucidate the dynamics of gene activity. Large-scale proteomic research, such as that promoted by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), has uncovered the ubiquitous link of biomolecules to the environment and disease status (Gillette et al., 2020; Krug et al., 2020; Mertins et al., 2016; Mun et al., 2019; Zhang et al., 2016). Such a transition has extensively deepened our knowledge on the function of driver genes and proteins and has provided a comprehensive understanding of the signaling networks occurring between cells, tissues, organs, and the entire organism. Multi-omics approaches have been applied to numerous clinical studies for better identification of clinical subtypes or drug resistance, prediction of effective combination therapies, and identification of predictive biomarkers to increase the response rate to targeted treatments.
In this review, we introduce the concept of multi-omics approaches in cancer research and provide useful resources for this. We focus on some of the clinical and basic science studies that have benefited from the use of a multi-omics approach to uncover novel concepts and properties. We also discuss some of the challenges connected to multi-omics approaches and how this relatively young field of study can have a positive impact on cancer research.
