Cancer hallmarks typically incorporate complex phenotypic and molecular changes including uncontrolled and sustained proliferation, evading growth suppressors, resisting cell death, replicative immortality, angiogenesis, and metastasis. Attaining these hallmarks requires a series of alterations at the molecular and cellular level. Understanding the molecular basis of changes of intra-cellular phenotypes involves alterations in genes (DNA sequences), RNA transcripts, protein levels and metabolic profiles. Different OMICS approaches have been developed to untangle the complexity of biological systems in different dimensions (e.g., contiguous DNA sequences, RNA expression, and protein levels).
The different OMICS parameters vary greatly in their complexity which is largely driven by the spatial and/or temporal dynamics and chemical modifications (Figure 1). The flow of information from DNA to RNA and protein result in an exponential increase in the complexity.
Deciphering the mechanisms of cancer progression needs a systematic point of view. Compared to single omics research, multi-omics can provide investigators with a greater understanding of the flow of information, from the origin of the cause of disease to the functional consequences.
What is multi-omics?
“OMICS” technologies are characterized by high-throughput interfaces which facilitate the investigation of the genome, epigenome, transcriptome, proteome, and metabolome in a globally-unbiased manner.
- Genomics is the most mature of the omics arena. In medical research, genomics focuses on identifying genetic variants associated with disease, response to treatment or prognosis. The GWAS study has identified thousands of genetic variants associated with complex diseases.
- Epigenomics focuses on genome-wide characterization of reversible modification of DNA or DNA-associated proteins.
- Transcriptomics examines RNA levels genome-wide both qualitatively (which transcripts, novel splice sites, RNA editing sites) and quantitively (how much of each transcript is expressed).
- Proteomics is used to quantify peptide/protein abundance, modification and interaction. The analysis of proteins has been revolutionized by MS-based methods.
- Metabolomics simultaneously quantifies multiple small molecule types, such as amino acids, fatty acids, carbohydrates or other products of cellular metabolic functions.
- Microbiomes focus on a study of all the microorganisms of a given bio-community altogether. Accumulating evidence supports that the microbiome affects the therapeutic efficacy of cancer immunotherapy.
The NeoGenomics approach for provision of multi-omics analyses
NeoGenomics Pharma Services provides informative, state-of-the-art molecular technology, ranging from whole genome sequencing, whole exome sequencing, whole transcriptome sequencing, HLA typing, and tissue-based solutions including the proprietary MultiOmyx™ high-order multiplexing methodology and Akoya Vectra Polaris low-order multiplexing. Additionally, NeoGenomics’ Pharma Services has launched NanoString’s GeoMx Digital Spatial Profiler (DSP) platform to provide spatial and molecular profiling technologies by generating digital whole transcriptomes and profiling data for hundreds of validated proteins analytes. Combining these complementary technologies/platforms provides pharma clients the benefit of a single-sourced strategy that can ultimately improve the quality of customized and directed patient treatment.
Currently, genomic studies contribute the vast majority of precision medicine-based data, e.g. DNA sequencing is already being used to identify genetic variants that drive specific cancers. Although each individual molecular layer can be profiled, these measurements are restricted to the functional roles each respective omics domain plays in a biological system. What remains a challenging task is the integration of multi-omics data with clinical response/outcomes information into patient-centric models.
In summary, the multi-Omics approach is opening up an amazing opportunity to capture the whole picture of biological systems in a hypothesis-free and unbiased mode and paving the way for the next-generation of diagnostics, involving the discovery of more complex biomarker “signatures” that can predict the individual disease risk with greater precision leading to the development of more efficient therapeutics.