Multi-Omics in Cancer Subtyping
Impact on Prognosis
Advances in Diagnosis
Technological and Analytical Challenges
Conclusion
References
Multi-omics describes the integration of multiple high-throughput screening technologies based on genomics, proteomics, transcriptomics, single-cell transcriptomics, metabolomics, and more, which all hold a significant role in studying human diseases.1
With rapidly evolving technology, researchers can use these for an in-depth analysis of the pathogenesis of diseases, including cancer.1,2 This can aid in discovering cancer subtypes and disease mechanisms, developing approaches for identifying driver genomic alterations and being involved in diagnostics and deciphering prognostics.2
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Multi-Omics in Cancer Subtyping
Understanding the pathogenesis of disease involves the investigation of malignant transformations that have developed through molecular alterations at several levels.2
Single-level omics approaches, such as genomics and transcriptomics, have led to the identification of several driver genes in cancer. However, it may lack the ability to establish causal relationships between molecular alterations and phenotypic manifestations, which would be significant in cancer research.2,3
Integrating multidisciplinary information enables a more holistic and systematic approach to understanding biological interactions in order to investigate altered cellular functions that drive complex diseases and cancers.2
Multi-omics analysis improves the clustering of samples into categories that are more biologically meaningful, providing a better comprehension of prognostic and predictive phenotypes, investigates cellular responses to therapies, and aids with translational research using integrative models.2
A challenge for cancer research includes tumor heterogeneity, which is an obstacle that prevents a total understanding of tumor biology. An example includes a wide variation within the clinical evolution for patients that have the same tumor type but demonstrate differences in tumor cell progression or mutation.3
These differences prevent the development of effective and efficient treatments, with some therapies working on some patients but not others.3
While the classification of cancers is based on histotype and site of origin, this system has evolved and become more complex with genomics and molecular features that better represent clinical evolution.
The use of omics can individualize tumor types, with the aim of using a range of bioinformatic tools that have been developed to allow for more precise cancer subtyping and refine sample classification.3
The implication of more precise cancer subtyping is significant for cancer medicine, enabling heterogeneity to be understood more fully within the same tumor type and allowing for the development of better therapies that are specific to a patient’s molecular alterations for a more personalized medicine approach.3
What is Multiomics?
Impact on Prognosis
Identifying biomarkers that are associated with prognosis and treatment sensitivity, or treatment resistance is critical for risk-group classification as well as informing therapeutic decision-making.3
Treatment-specific biomarkers can also aid in developing therapies that are tailored to the biological characteristics of specific tumors, in line with personalized medicine.3
Multi-omics data can improve prognosis accuracy by identifying novel biomarkers in oncology. An example is the jNMF tool, which enables biomarkers to be discovered by predicting drug response through pathway signature analysis.
Novel connections identified between tumor biology and drug response demonstrated an association between efficacy of BRAF inhibitors and BRAF/MITF overexpression in breast cancer.3
Another multi-omics tool that can be used for prognosis accuracy is Lemon-Tree. It was able to run a series of tasks and led to the discovery of biomarkers associated with glioblastoma genes by assessing gene amplification and copy-loss levels.
The results of this analysis demonstrated that genes that have copy number alteration of EGFR, a glioblastoma oncogene, tumor suppressors CDKN2A and PTEN, as well as KRIT1 and PAOX, were associated with having a worse cancer prognosis.3
Advances in Diagnosis
Multi-omics tools may also be significant when uncovering novel diagnostic markers to allow for early and accurate cancer diagnoses.2
Integrating transcriptomics and metabolomics in multi-omics can enable further understanding of tumor pathogenesis. For example, comprehensive analysis using these tools led to the identification of five metabolites, including bilirubin, as candidate biomarkers for cervical cancer, which may be beneficial when used in early screening and diagnostic purposes.2
Early cancer detection is a crucial component for ensuring timely cancer treatment as well as preventing cancer-related mortality.
Joint detection of various biomarkers and integration of multiple methods, such as combining protein-DNA mutations or RNA expression and genome alterations, can be useful for detecting biomarkers of early-stage cancers while also improving the sensitivity of detection of liquid biopsy-based diagnosis.2
Multiomics News and Research
Technological and Analytical Challenges
While multi-omics research holds significant potential in cancer research and medicine, there are many challenges in this area, including the slow translation of these technologies into accessible tools for daily clinical use.2
Additionally, the uneven maturity of various omics approaches is an obstacle for its progression for clinical application, with genomics being the closest to routine diagnostics.
This is then followed by metabolomics due to metabolite screening being already adopted within clinical laboratories, such as routine screening of inborn errors of metabolism, drug monitoring, and microbiomics.2
However, other omics, such as epigenomics, transcriptomics, and proteomics are still behind in their translation into daily clinical routine.2
Furthermore, for most cancer types, not all omics data types are generated or accessible. The representation of different tumor types in multi-omics investigations is also uneven, as within 24 multi-omics studies, breast and prostrate cancer have been reported to be overrepresented; however, rare cancer types such as glioblastomas have become increasingly investigated.2
Other challenges include technical obstacles when streamlining sample processing for each omics platform, limited accessibility of available patient data, lack of gold standard sample processing workflows that are unified, and post-processing data analysis protocols.2
The gap between data generation and interpretation is also a concern, with technological development increasing the scope and complexity of data. Additionally, understanding diverse layers of multi-omics outcomes requires significant infrastructure and computational power to convert them into predictive computational models.2
Conclusion
Multi-omics holds significant potential for cancer subtyping, prognosis, and diagnosis, with the integration of various datasets enabling more comprehensive knowledge of the pathogenesis of cancer and tumor heterogeneity.2.3
This can lead to more complex but accurate cancer subtyping, the discovery of novel biomarkers that may aid in solving treatment resistance due to molecular alterations, and early cancer diagnoses and better prognoses.2,3
However, the challenges in this area with translating multi-omics technologies into more accessible tools for daily medical routine requires ongoing research, which may one day enable a more personalized approach to medicine and cancer treatment.2
References
- Chen C, Wang J, Pan D, et al. Applications of multi-omics analysis in human diseases. MedComm. 2023;4(4):e315. doi:https://doi.org/10.1002/mco2.315
- Menyhárt, O, Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Computational and Structural Biotechnology Journal. 2021;19:949-960. doi:https://doi.org/10.1016/j.csbj.2021.01.009
- Raufaste-Cazavieille V, Santiago R, Droit A. Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology. Frontiers in Molecular Biosciences. 2022;9. doi:https://doi.org/10.3389/fmolb.2022.962743
Further Reading
Azenta opens the doors to its new genomics laboratory in Oxford, UK