Omics is an increasingly important field of science that has already achieved a lot in the few short decades it has existed. While omics first appeared in scientific literature in 1987, it was not until the completion of the Human Genome Project in 2003 that the field gained momentum. The "post-genomic era" inspired many omics areas of research, including proteomics, transcriptomics, genomics, metabolomics, lipidomics, and epigenomics, revolutionizing modern healthcare.
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Why omics is important in healthcare
Omics has already taught us a lot about a wide range of diseases. Omics methodologies have been leveraged in a wide body of research to study the complex interactions between genetic and environmental factors to help uncover the nature of pathogenesis of several serious diseases such as cancer, cardiovascular disease, diabetes, and more.
In particular, the omics approach has become particularly popular in cancer studies. For example, proteomics methodologies have been used to identify numerous potential biomarkers of cancer as well as protein expression patterns that can be used to generate key insights regarding tumor prognosis, prediction, and disease classification, as well as identify patients likely to respond to particular treatment options.
Other disease areas that have benefited include diabetes. Metabolomics methodologies have been used to identify metabolic signatures related to type II diabetes, including specific patterns of lactates, amino acids, long-chain fatty acids, and glycolytic intermediates.
Additionally, omics have facilitated great advances in our knowledge of neurological and neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. Metabolomics studies have identified biomarkers of disease onset and progression, identified novel mechanisms of disease progression, and assessed treatment prognosis and outcome.
As the omics field matures, the technologies and techniques available have become more sophisticated, and researchers have become specialized. This is now facilitating the advent of precision medicine, which is set to revolutionize the future of healthcare.
Opening the door to precision medicine
Precision medicine, also known as personalized medicine, is an emerging approach that considers a person's individual differences when deciding on patient treatment plans. Not all patients will respond in the same way to the same treatment, particularly in cancer. Individual differences, often rooted in genetics, can make a person more likely or less likely to benefit from a particular therapeutic approach. We already know that certain gene mutations play a key role in how patients respond to certain cancer treatments. Precision medicine uses information about the patient to make informed predictions about how they will likely respond to a given treatment option. This means that the appropriate treatment can be selected early without the risk of trialing ineffective medicines, which may waste time and allow the disease to progress.
Omics facilitates the evolution of precision medicine because it helps to elucidate the link between genotype and phenotype. In other words, it reveals how genetics influence disease initiation and progression and how the disease will likely respond to various treatment options.
At the birth of omics, scientists embarked on genome-wide association studies (GWAS), which searched for the link between genomic variants and phenotypes of interest. After years of research, 1,355 publications have been included in the National Human Genome Research Institute (NHGRI) GWAS catalog, reporting the relationship of 7,226 Single Nucleotide Polymorphisms (SNPs) with 710 complex traits. These traits vary greatly, covering diseases such as cancer, type I and II diabetes, and Crohn's disease to common traits such as height and BMI. This data has proven invaluable in expanding our knowledge of disease loci, translating to better disease risk prediction and treatments.
Following this, the focus moved to whole genome sequencing (WGS) and whole exome sequencing (WES) due to the increasing affordability of these methodologies and the difficulties of GWAS studies in studying more complex diseases. WGS and WES studies have allowed for single-base analysis that has led to furthering our knowledge of the genetic basis of disease to an unprecedented level.
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Cancer research, in particular, has benefited from WGS technologies. A wide body of research has led to many cancer genomes being sequenced. Examples of notable work include the International Cancer Genome Consortium and the Cancer Genome Atlas. DNA from different kinds of cancer, such as breast, chronic lymphocytic leukemia, hepatocellular carcinoma, pediatric glioblastoma, ovarian cancer, melanoma, small-cell lung cancer, and Sonic-Hedgehog medulloblastoma, has been sequenced. These studies have revealed that almost all tumors are unique and have a distinct mutation that is the driver of the disease.
This information has been particularly useful to pharmacogenomics, which uses this data collected by omics studies to understand the individual differences between patients to better predict how they will respond to certain treatments. This is precision medicine. Omics data can be leveraged to gain insight into which drugs and doses are most appropriate for each patient, helping to increase treatment efficacy and reduce side effects.
Precision medicine has the potential to change the face of healthcare, and omics will play a vital role in seeing it reach its full potential.
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Sources:
- Bhinderwala, F. et al. (2019) "Metabolomics analyses from tissues in parkinson's disease," Methods in Molecular Biology, pp. 217–257. Available at: https://doi.org/10.1007/978-1-4939-9488-5_19.
- Chen, R. and Snyder, M. (2012) "Promise of personalized omics to precision medicine," WIREs Systems Biology and Medicine, 5(1), pp. 73–82. Available at: https://doi.org/10.1002/wsbm.1198.
- Huo, Z. et al. (2020) "Brain and blood metabolome for alzheimer's dementia: Findings from a targeted Metabolomics analysis," Neurobiology of Aging, 86, pp. 123–133. Available at: https://doi.org/10.1016/j.neurobiolaging.2019.10.014.