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NGS in medicine; what does the future hold?

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Next generation sequencing (NGS) is the high-throughput methodology that enables rapid sequencing of the base pairs in RNA or DNA samples. Supporting a broad range of applications, including gene expression profiling, chromosome counting, detection of epigenetic changes, and molecular analysis, NGS is driving discovery and revolutionising the future of personalised medicine.

This methodology continues to make waves throughout healthcare, offering patients treatment based on their disease-driving molecular alterations – so what can we expect to see developing throughout 2020?

Precision medicine

A revolutionary approach to patient care, precision medicine uses advanced medical tools, including genetic and molecular testing, and big data analytics to help doctors better predict which treatment and prevention strategies will work best for individual patients. It continues to work towards one overall aim – to replace the ‘one-size-fits-all’ model, where therapies and medications are developed to treat the ‘average’ person, with an approach that cares to each patient’s unique biology.

Precision medicine in oncology

While NGS has been tested across multiple health care settings, its use is most advanced in oncology, with doctors sequencing their patients’ tumours to match them to therapies designed to target the genetic alterations driving the tumour’s growth.

Several studies show the use of NGS in identifying mutations in cancer patients that can be targeted with an existing targeted therapy – the Genomics Evidence Neoplasia Information Exchange (GENIE), which is an international data-sharing consortium, estimated that 30% of tumours sequenced in the GENIE consortium could be targeted. There have also been advancements in developing drugs that target these tumour-driving mutations.

The number of cancer drugs approved between 2010-2018 was higher than the previous twenty years, as the understanding of genetic changes in specific cancers improves, along with the development of drugs targeting specific pathways. This has been especially beneficial for the detection of drug sensitivity and resistance in lung cancer. We expect to see this innovation continue to develop, as more targeted drugs and mutations are discovered.

Emerging biomarkers in diabetes, and autoimmune and inflammatory diseases

A mix of circulating molecules such as cell-free DNA, cell-free RNA (including microRNAs), circulating tumour cells and extracellular vesicles (more specifically exosomes) have been explored as biomarkers that can have the potential to identify early stages of disease.

Genetic and epigenetic alterations, including DNA methylation and altered miRNA expression might be contributing to several autoimmune diseases, cancer, transplantation and infectious diseases. For example, a recent study of rheumatoid arthritis has identified epigenetic factors involved in RA, and compared these results with those obtained from osteoarthritis patients, as osteoarthritis is not autoimmune – and found that there were changes in novel key genes. The study showed that several genes were identified as risk factors contributing to RA and other autoimmune diseases. Likewise, changes in miRNA levels in blood and other body fluids have been linked to a variety of autoimmune diseases including type 1 diabetes, insulin resistance, metabolic diseases, multiple sclerosis, sjögren’s syndrome and psoriasis. As research in this area continues, we again expect to see an increase in precision medicine, and tailored approaches to improve patient outcomes.

Further discoveries around the genomics of Parkinson’s disease

While the International Parkinson Disease Genomics Consortium (IPDGC) has now been in existence for 10 years, the consortium has grown considerably and furthered the knowledge around the genomics of Parkinson’s. As an example, we now know that brain tissue generally, and nigral neurons specifically, are critical in the disease process, contrasting to what has been observed in Alzheimer’s, in which immune cells are a key effector of genetic risk.

Furthermore, using the large IPDGC genetic datasets, multiple advanced analyses can be performed to predict disease, assign more ‘function’ or biology, and identify potential disease-relevant pathways.

As medical innovation continues to advance, we expect to see further development in the areas we’ve touched upon, as well as the identification of further gene mutations and the development of precision drugs which will benefit the medical industry immeasurably.

 


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