Clinical implications of omics and systems medicine: focus on predictive and individualized treatment
Author:
- 19 August 2015
- journal article
- review article
- Published by Wiley in Journal of Internal Medicine
- Vol. 279 (3) , 229-240
Abstract
Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High‐throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole‐genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide a brief introduction to systems medicine and discuss how it may contribute to the clinical implementation of individualized treatment, using clinically relevant examples.Keywords
This publication has 59 references indexed in Scilit:
- Systematic identification of proteins that elicit drug side effectsMolecular Systems Biology, 2013
- Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphismsGenome Biology, 2012
- Network modeling of the transcriptional effects of copy number aberrations in glioblastomaMolecular Systems Biology, 2011
- A CD8+ T cell transcription signature predicts prognosis in autoimmune diseaseNature Medicine, 2010
- A systems approach to prion diseaseMolecular Systems Biology, 2009
- Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue SyndromeBMC Systems Biology, 2008
- Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseasesGenome Biology, 2008
- Network‐based classification of breast cancer metastasisMolecular Systems Biology, 2007
- Network modeling links breast cancer susceptibility and centrosome dysfunctionNature Genetics, 2007
- Network-Based Analysis of Affected Biological Processes in Type 2 Diabetes ModelsPLOS Genetics, 2007