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Personalised medicine, genomics, big data – behind the buzzwords

Personalized medicine, genomics, and big data all promise to revolutionize healthcare, but what do they mean?  NHS England is exploring ways to take these advances to the front line of clinical practice as well as contributing to research.

Personalized or precision medicine

For most conditions, we have guidelines based on the best current evidence from NICE (National Institute of Clinical Excellence.) In each case the same ‘best’ treatment is given to all patients. If this doesn’t work, then we switch to the 2nd line treatment.  Personalized medicine is about identifying subtypes of patients and giving them the treatment most likely to be effective rather than having a blanket ‘trial-and-error’ approach.

And it’s not new! For many years the guidelines for high blood pressure have advised starting black people of African or Caribbean origin (of any age) as well as people over 55 on Calcium Channel Blockers as a 1st line medication. However, ACE Inhibitors are the 1st line medication for non-black people under 55.

So why the hype now? Because due to technological advances in genomics and big data, people can apply this approach much more widely. And there are large potential gains: it is estimated that for the top 10 highest grossing drugs in the US, only between 1 in 25 people and 1 in 4 people benefit.  We might be able to save money and reduce the harms due to medication side effects by only giving the treatment to the group in whom it is effective. Medicine just became data, and data just became medicine.

Genomics

Genomics or genetics? Genetics is the study of single genes or small groups of genes. Genomics looks at the entire genetic code of an organism – including genes with no known function.

What’s new?  When we first sequenced the whole human genome it took 13 years and cost about £2 billion. Now it costs less than £1000 and takes only a few days. This has opened up many new avenues for research – including the 100, 000 Genomes Project which aims to sequence 100, 000 whole sets of human genetic code. This includes genomes from cancer cells (where defects cause uncontrolled growth) as well as from people with rare diseases and their relatives.  It also aims to collect other information like blood pressure, medications and co-existing illnesses.  By analysing thousands of genomes, the hope is that links can be found between specific genes and responses to treatments. These can be used to identify subtypes of people and treatment can be tailored to these groups to maximize efficacy and minimize harmful side effects.   One example of this is Abacavir, which is the first-line treatment for HIV.  About 1 in 17 people have an allergic reaction to it, which can be fatal in the worst cases.  Now before being started on the treatment, patients can be tested for a gene linked to high risk of allergy.  Alternative drugs could be used thus reducing complications from medication side effects.

Big data

Current technology means that it is now easier than ever to generate large amounts of data.  This can come from genome sequencing, electronic patient records, step counters or symptom reporting via smartphone.  Conventional solutions for data storage, security and analysis are not sufficient for such a large quantity of information, so people have had to develop new “big data” methods to explore this complex but potentially rich resource.  One challenge is that the data often comes from a variety of sources with no standardized format and new techniques have to be applied to find valid links within the mass of genomic and non-genomic information.  In particular, big data could aid research into rare diseases (where it is difficult to recruit enough patients for conventional studies) or into treatments where the benefit to the individual is small (but may have significant gains on a population level).

In summary

Advances in computing and genomics have allowed us to generate large amounts of data.  Researchers in the scientific community as well as public health bodies (e.g. NICE and NHS England) are harnessing this “big data” to bring more targeted, personalised treatment into mainstream clinical practice.  The data age of medicine has begun.

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Disclaimer

Any opinions above are the author’s alone and may not represent those of his/her affiliations. Any comment is based on the best available evidence at the time of writing.  All data is based on externally validated studies unless expressed otherwise. Novel data is representative of the sample surveyed. An online recommendation is no substitute for seeing your own doctor and should not be taken as medical advice.

Sources and Further Reading

Image courtesy of Pixabay

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