Precision Medicine

What is Precision or Personalized Medicine?

Although therapies are efficient in treating diseases, some of the treatments can be more efficient in some patients, while for others, the response is mitigated. Precision medicine or personalized medicine is an innovative therapeutic approach that focuses on tailoring therapies based on the patient’s genetic content or other molecular or cellular analysis [1].

To inform and ensure the specificity of the therapy for the patient, diagnostic molecular profiling of the patient at the genomic, proteomic, epigenomic, and metabolic levels, is required.

Molecular Profiling for Precision Medicine

Pathological differences between each patient can be assessed using “omics” technologies that investigate and provide data on the genome, transcriptome, epigenome, proteome, and metabolome of the patient. These approaches permit a specific targeting of the disease to obtain maximum efficiency in treating the patient.

Genome and transcriptome technologies such as DNA and RNA sequencing and gene expression analysis will inform on any potential alterations in genes (e.g., mutations or amplifications) or their RNA expression levels. The most known technological platform that is used for these purposes is a next-generation sequencing (NGS).

The epigenome is associated with environmental and behavioral factors that affect the expression and activity of genes, independently of alterations in DNA sequences (genotype). These modifications can be transmitted to daughter cells and are influenced by several factors, including age, the environment, lifestyles, and disease states.

These factors can modify DNA through mechanisms involving cytosine methylation and hydroxymethylation or via modifications of histones that regulate the level of DNA availability for transcriptional events [3].

They can also promote or prevent the translation of proteins that are involved in the different physiological and pathological processes through mechanisms involving other types of RNAs and non-coding RNAs (ncRNAs), including microRNAs (miRNAs), small interfering RNA (siRNAs), Piwi-interacting RNA (piRNAs), Circular RNAs (circRNAs), and long non-coding RNA (LncRNAs) [4].

To analyze the epigenome, technologies such as chromatin immunoprecipitation sequencing (ChIP-seq) for histone modification analysis and identification of transcription factor binding sites, Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), and Hi-C for chromatin structure analysis, are used [5]. Additionally, next-generation sequencing (NGS) is also used for non-coding RNAs (ncRNAs).

Although proteins are generated by ribosomes through RNAs (messenger RNAs (mRNAs)), not all mRNAs generate proteins (proteome) as this may depend on physiological, pathological, and environmental (Epigenetics) factors. Therefore, analyzing the overall level of protein composition, structure, and activity is known as proteomics.

For this purpose, technologies such as mass spectrometry, are used to identify proteins based on their mass to charge ratio. Mass spectrometry is also used to determine the levels of expression and modifications of proteins (e.g., enzymatic phosphorylation) that regulate their activity and degradation [6].

Metabolites are small molecules that act as intermediates or end products of cellular metabolism or derive from various other external sources such as diet, microbes, or xenobiotic sources.

To measure and compare large numbers of metabolites present in biological samples, analytic profiling techniques known as metabolomics, are used. metabolites are quantified using a combination of liquid or gas chromatography and mass spectrometry and/or nuclear magnetic resonance spectrometry [7].

Bioinformatics data analysis

Although complete profiling of the genome, transcriptome, epigenome, proteome, and metabolome of the patient is obtained, a large amount of data is also generated, and therefore, requires analysis using bioinformatics approaches. These approaches rely on computational models (algorithms based) to interrogate, identify, and score pathways with differential activity in genes, proteins, or metabolites.

For instance, artificial neural networks (ANN) are computing systems that were inspired by the brain’s biological neural networks. They are used to analyze large amounts of data and detect patterns in the data generated through patient molecular profiling using omics technologies.

The results of these analyses can inform the type of treatment to be used and predict the outcome of the therapy for the patient [8].

Disadvantages of Precision Medicine

Precision medicine has a few potential disadvantages. First, it could lead to increased healthcare costs if only those who can afford the tests and treatments receive them. Second, there is the potential for misuse or misinterpretation of data, which could lead to incorrect diagnoses or treatment plans. Third, precision medicine could exacerbate health disparities if only wealthy patients can afford the newest and most expensive treatments. Finally, there is always the possibility that new treatments will be found to be unsafe or ineffective.


Although precision medicine is an extremely efficient approach to precisely inform the treatment that a patient should receive to ensure maximum therapeutic success, the cost of using molecular profiling to generate data may not be accessible to everybody.

Therefore, developing affordable omics technologies could popularize precision medicine for all patients.


[1] Ashley, E.A., 2016. Towards precision medicine. Nature Reviews Genetics17(9), pp.507-522.

[2] Karahalil, B., 2016. Overview of systems biology and omics technologies. Current medicinal chemistry23(37), pp.4221-4230.

[3] Mattick, J.S., Amaral, P.P., Dinger, M.E., Mercer, T.R. and Mehler, M.F., 2009. RNA regulation of epigenetic processes. Bioessays31(1), pp.51-59.

[4] Palazzo, A.F. and Lee, E.S., 2015. Non-coding RNA: what is functional and what is junk?. Frontiers in genetics6, p.2.

[5] Asada, K., Kaneko, S., Takasawa, K., Machino, H., Takahashi, S., Shinkai, N., Shimoyama, R., Komatsu, M. and Hamamoto, R., 2021. Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Frontiers in Oncology11, p.1725.

[6] Aslam, B., Basit, M., Nisar, M.A., Khurshid, M. and Rasool, M.H., 2017. Proteomics: technologies and their applications. Journal of chromatographic science55(2), pp.182-196.

[7] Burgess, K., Rankin, N. and Weidt, S., 2014. Metabolomics. Handbook of pharmacogenomics and stratified medicine, pp.181-205.

[8] Cartwright, H.M., 2008. Artificial neural networks in biology and chemistry—the evolution of a new analytical tool. Artificial Neural Networks, pp.1-13.

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