Multi-omics: Opportunities, Challenges and Applications in Biomarker Discovery

Biomarker discovery holds immense potential to revolutionize healthcare by providing insights into disease mechanisms, enabling early diagnosis, and guiding personalized treatment strategies. Traditional approaches often focus on single-omics data, which can provide a limited understanding of complex biological systems. In contrast, multi-omics integrates various omics data, ensuring a detailed and holistic view of human biology.

In this blog, we discuss the potential of multi-omics in biomarker discovery as well as the challenges faced in this domain.

Understanding Omics Data in Biomarker Discovery

Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism(s). Omics fields include genomics (study of genomes), proteomics (study of proteins), transcriptomics (study of RNA transcripts), and metabolomics (study of metabolites), among others. By comparing omics data of healthy and diseased states, researchers can identify the specific molecules which are consistently different. These molecules can then be validated as biomarkers, which can be used for diagnosis, prognosis, or monitoring of diseases.

Some examples of biomarkers identified using a single-omics approach include:

Breast Cancer gene 1(BRCA1) and Breast Cancer gene 2 (BRCA2) are genes identified through genomic studies as critical biomarkers for breast and ovarian cancer. These genes play a vital role in deoxyribose nucleic acid (DNA) repair, and their mutations can lead to genomic instability and cancer development. Individuals carrying these mutations have a significantly higher lifetime risk of developing these cancers compared to the general population. This discovery has led to the development of targeted screening programs, such as regular mammograms and MRIs, along with preventive measures including prophylactic surgeries (e.g., mastectomy and oophorectomy) and chemoprevention. Additionally, the identification of BRCA1 and BRCA2 mutations has guided personalized treatment strategies, which are particularly effective against cancers associated with these mutations.

Prostate-Specific Antigen (PSA) is a protein produced by the prostate gland, and its levels in the blood can be measured using proteomic techniques. Elevated PSA levels are used as a biomarker for prostate cancer screening. This discovery has led to widespread use of PSA testing in the early detection and monitoring of prostate cancer.

Glycated Hemoglobin (HbA1c) is a metabolite used as a biomarker to monitor long-term blood glucose levels in individuals with diabetes. Metabolomic studies identified HbA1c as a reliable indicator of average blood sugar levels over the past two to three months, which is crucial for diabetes management and diagnosis.

Potential of Multi-omics Approach in Uncovering Complex Biomarker Signatures

Biological processes are governed by intricate interactions among various molecular entities. A single omics approach provides a limited view of the biological system by focusing on a single aspect while ignoring others. However, multi-omics as an integrative approach combines data from various omics fields,offers a comprehensive perspective, and enhances the potential to discover complex biomarker signatures.

For example, alterations in amyloid precursor protein (APP) processing and tau protein phosphorylation were identified as critical biomarkers with implications for the diagnosis and treatment strategies of Alzheimer's disease. This was achieved through a multi-omics approach, including genomics, transcriptomics, proteomics, and metabolomics, by finding that specific genetic variants, changes in RNA expression, protein modifications, and metabolic disturbances were associated with the disease.

Some of the main advantages of a multi-omics approach include:

Holistic View: By integrating data from multiple omics layers, researchers can gain a comprehensive understanding of biological systems and disease mechanisms. This holistic view can reveal interactions and regulatory mechanisms that single-omics studies might overlook.
Complex Biomarker Signatures: Diseases often result from complex interactions among genes, proteins, metabolites, and other molecules. Multi-omics approaches can identify composite biomarker signatures that reflect these intricate networks and also provide more accurate and reliable biomarkers for diagnosis, prognosis, and treatment response.
Improved Sensitivity and Specificity: Combining different types of omics data can enhance the sensitivity and specificity of biomarker detection. For instance, a biomarker signature based on integrated transcriptomic and proteomic data might be more predictive of a disease state than the one based solely on gene expression.
Personalized Medicine: Multi-omics approaches facilitate the development of personalized medicine strategies by considering the unique molecular profiles of individual patients. This can lead to more tailored and effective treatments based on an individual's specific biomarker signature.
Identification of Novel Targets: Integrative multi-omics analyses can uncover novel molecular targets for therapeutic intervention. By understanding the interconnected pathways and networks involved in disease,researchers can identify new points of intervention that might not be apparent from single-omics studies.

Challenges in Biomarker Discovery Using Multi-omics

Multi-omics approaches hold great promise for biomarker discovery and personalized medicine but they also present significant challenges that need to be addressed.Some of these challenges are listed below:

1. Data Integration and Standardization

a. Heterogeneous Data: Different omics platforms generate diverse types of data, such as genomic sequences, RNA expression levels, protein abundances, and metabolite concentrations. Integrating these heterogeneous data types into a cohesive analysis framework is complex.

b. Lack of Standardization: There is a lack of standardized protocol for data generation, processing, and analysis across different omics technologies. This can lead to inconsistencies and difficulties in comparing and integrating data from different studies.

2. Data Scale and Computational Power

Multi-omics studies often produce vast amounts of data, requiring substantial computational resources for storage, processing, and analysis. For example, genomics data measures entire genomes, encompassing hundreds of gigabytes to terabytes due to its high dimensionality and single-base resolution. In contrast, proteomics data involves analyzing the entire set of proteins, which includes mass spectrometry measurements and generates tens of gigabytes per experiment.

3. Availability and Accessibility of Multi-Omics Datasets

Vast amounts of biological multi-omics data are generated globally, holding great potential for reuse. However, as this data is scattered across various sources, its efficient use becomes difficult.Therefore,effective data-sharing policies and infrastructures are needed to facilitate collaboration and data reuse.

4. Interpretation and Validation

a. Complex Biological Interactions: The interplay among different molecular layers, such as DNA, RNA, proteins, and metabolites, involves complex regulatory networks and pathways. Scientists often lack expertise across all omics fields, making it challenging to interpret multi-omics data comprehensively. Utilizing specialized software tools can significantly ease this process by providing user-friendly interfaces and advanced analytical capabilities.

b. Validation of Findings: Identified biomarkers need to be validated through independent studies and functional assays to confirm their relevance and utility. This process is resource-intensive and time-consuming, often requiring extensive experimental and clinical validation.

5. Computational and Statistical Challenges

Sophisticated algorithms and models are needed to uncover meaningful patterns and relationships across the different omics data. Multi-omics data often exhibits complex, non-linear relationships between different molecular entities that standard linear models cannot capture. Understanding and modeling correlations between different omics layers (e.g., gene expression and protein levels) is essential but challenging.

Mitigating Challenges to Uncover Biomarkers- Elucidata’s Polly

Elucidata offers comprehensive solutions and services to address the challenges of biomarker discovery through Polly.

Feature Selection and Subsetting

Polly's comprehensive metadata annotations facilitate the efficient deduction of important features under study such as genes, proteins, or metabolites affecting disease progression. It enables prioritization of subsetted features through different machine learning (ML) algorithms and helps to optimize biomarker classification using clinical metadata information.

Quality Check

Polly performs 50+ quality checks through the harmonization process to ensure reproducibility and reusability of the data.

Analysis Ready Datasets

It performs complex network analysis to segregate biomarkers according to their function (prognostic, diagnostic, predictive) which can consequently help with classification accuracy and clinical relevance.

Scalable Processing

Polly's scalable cloud computing infrastructure allows companies to efficiently process millions of samples across various modalities while ensuring cost optimization.

Fast-track Validation and Clinical Translation

One can accelerate the validation of identified biomarkers using the ML-ready public datasets on Polly.These datasets can further be leveraged to validate the credibility of detected biomarkers against published studies on related biomarkers. The biomedical researchers can thus benefit from Elucidata's development of tailored biomarker discovery pipelines designed to meet specific research objectives and experimental designs. It also ensures efficient and accurate biomarker identification.

Expert Support and Consulting

Receive personalized support and consulting services from Elucidata's team of bioinformatics experts who can guide researchers throughout the biomarker discovery process-ranging from experimental design to data analysis and interpretation.

Elucidata's Polly has emerged as a one-stop solution that facilitates harmonizing large scale data, comprehensive metadata annotations, and validation services. Such an integrated approach empowers researchers to efficiently and accurately identify clinically relevant biomarkers.