Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

Abstract

The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from "The Cancer Genome Atlas (TCGA)" consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.

Figure 1

Pyramid of complexity. The pyramid represents the flow of information from genome (top) to transcriptome (middle), to proteome (bottom). The complexity increases from genome to proteome (indicated by down arrow). The complexity of transcriptome is largely mediated by temporal dynamics and alternative splicing. In contrast, spatiotemporal dynamics and posttranslational modifications (PTMs) are mainly responsible for high proteome complexity. Examples of PTMs include phosphorylation (P) and acetylation (Ac).

Figure 2

Schematic diagram representing the basic steps of NGS and mass-spectrometry. NGS (left) can be used for both genomic DNA and RNA-sequencing. Mass-spectrometry based proteomics (right) are typically used to identify and quantify large amount of proteins from cells, tissues, and body fluids. ¹Primary cells and tissues from patients can be mixed with labelled proteins, typically extracted from cell lines cultured in presence of stable isotopically labelled amino acids. This method is called Super-SILAC. Proteomics can also be done without any labelling steps. This method is known as label free-quantification (LFQ). ²Peptides obtained after tryptic digestion can also be labelled chemically by methods known as “Tandem Mass Tag (TMT)” or “Isobaric tags for relative and absolute quantitation (iTRAQ)”.

Figure 3

Proteomics and phosphoproteomics driven identification of the aberrant regulation of signalling pathways leading to poor patient survival. (a) Aberrant PDGFRB signalling pathway induces angiogenesis in patients and results in poor survival. Phosphorylated and unphosphorylated forms of proteins are indicated by blue and orange color. The directions of arrows indicate the regulation—up and down arrows indicate upregulation and downregulation, respectively. Colors of the arrows indicate the phosphoform or the total protein detected by (phospho)proteomics experiments. Blue and red arrows indicate phospho- and total protein, respectively. (b) Integrin-linked kinase pathway induces cell mobility and invasion, leading to poor patient survival. Two signalling pathways, MAPK (green) and PI3K (purple), are highlighted. Both these pathways were found to be upregulated in cancer patients with poor survival.

Figure 4

Application of NGS and mass-spectrometry (LC-MS/MS) based OMICS techniques in cancer research. Genomics, epigenomics, and transcriptomics are based on NGS techniques; whereas proteomics and metabolomics are driven by mass-spectrometric (LC-MS/MS) technique. The principal application of genomics, epigenomics, and transcriptomics is screening of genome-wide somatic mutations, identification of altered epigenomic landscape, and exploring differential RNA expression, respectively. The major application of proteomics/metabolomics is identification of differentially regulated proteins/phosphoproteins/metabolites. The integration of NGS-based techniques can identify the concordance or discordance between copy number alterations (CNAs), promoter/gene-body methylation, and RNA levels. Integration of NGS and LC-MS/MS based techniques may result in the correlation analysis between CNAs, promoter/gene-body methylation, and mRNA levels with protein/metabolite levels.