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Personalised medicine

Personalised healthcare is the use of information about an individual to provide the best possible healthcare for that person. This information may include physical measurements such as blood pressure or weight, biochemical measurements such as blood glucose, presence of a specific protein within blood/body tissue, or the interrogation of that person's genetic code. These measurements are often referred to as biomarkers, a term that is commonly used when referring to levels of proteins that indicate disease status.

These individual measurements may be used in a number of ways, including:
Diagnosis and treatment:
Genetic analysis may enable the diagnosis of a disease or a disease sub-type. This information can facilitate rapid and appropriate treatment for individuals based on their own genomes, or in the case of cancer, the genome of their cancer cells.

For example, where diseases do not appear as a well-defined set of symptoms and signs it may be appropriate for a genetic test to be used to diagnose the presence of a disease, an approach that is already used in muscular dystrophy. Some diseases may feature a complex set of genetic markers that help to define the nature of the disease and best treatment, for example some inherited cardiovascular conditions and eye diseases.

In cancer care (oncology), it is now more common to classify a disease by its molecular characteristics than morphology. Overall treatment strategies can be planned more effectively. For example, for tumours that have been identified as fast-growing, clinicians may choose an aggressive treatment first. Or, for a less aggressive disease, the clinician may choose to start therapy with a less aggressive treatment with a more favourable side-effect profile. The choice of drug can also be tailored to the molecular characteristics of the disease. These techniques are currently best developed in non-solid cancers, such as example Acute Myeloid Leukemia, where mutations in the genes FTL3 and KIT provide useful information to the clinician, or Acute Lymphoblastic Leukemia, where understanding of specific genes relating to receptors guides treatment strategy.

The cost of sequencing an exome or full genome is decreasing, towards a similar cost to an MRI scan. As a consequence, the use of large-scale genetic analyses to inform treatment protocols is increasingly being used in oncology and other areas. In this way a large number of genetic indicators in that patient's cancer can be examined. 

The number of available clinical tests that measure protein biomarkers is also increasing; these tests may monitor progression of a disease, whether a drug is being well tolerated by a patient, or a number of other clinical scenarios.

In some cases it may be possible to use a patient's genotype to predict their response to a drug treatment. In this way a patient's treatment can be tailored for the best possible efficacy and lowest risk of side effects. For example, warfarin is an effective anti-clotting medicine, but difficult to manage as patients respond differently to different dosages. Studies to determine the genetic roots of the warfarin response have resulted in new genetic tests that allow physicians to tailor the dose to the patient, minimising dangerous side effects and maximising the therapeutic efficacy of the drug.

Many available treatments are already stratified so that they are only prescribed to patients most likely to respond to that treatment. These are often used with a 'companion diagnostic', a protein or DNA-based test to determine whether that patient is suitable for the treatment. For example the breast cancer drug trastuzumab (Herceptin), has the best efficacy in the 20% of patients whose tumour cells show high expression levels of the HER2 gene.

Another cancer drug, Imatinib (Glivec) is used for chronic myelogenous leukaemia. However it is most effective in patients who have the 'Philadelphia chromosome', a genetic abnormality created when part of chromosome 9 wrongly attaches to chromosome 22 during cell division.

Although an emerging field, personalised medicine is still in its infancy. Cheaper and faster genome-sequencing technology will facilitate the development of personalised medicines in two ways. Firstly, a more complete understanding of the genotype–phenotype relationship will allow researchers to understand the biochemical pathways of disease in more depth. This, and the stratification of patients into subtypes, is expected to support the development of new drugs and early markers for disease detection. Cheaper and faster sequencing will also allow companies developing new drug treatments to understand more fully the relationship between genotype and drug response, increasing therapeutic efficacy and decreasing the risk of adverse events.

Identification of genetic risk factors for disease may have clinical utility in early screening or prevention programmes.

Despite major advances in recent years, research into the relationship between genomic variation and disease risk is still in its infancy. Of the diseases with some genetic influence, few are 'Mendelian' – inherited and attributed to a variation at a specific locus or point in the genome. In most cases, genetic contribution to a disease is complex and these elements are likely to interact with additional environmental factors. In the case of common diseases such as cardiovascular disease or diabetes, genetic factors are frequently outweighed by lifestyle factors.

However, there are examples where genetic characteristics can provide important guidance on a risk of disease. For example, the COGS study is evaluating the use of genetic information incorporated into risk screening for breast cancer, prostate cancer and ovarian cancer.

Currently, most work of genetic-testing services is focused on families where there appears to be risk of inherited disease and the relevant genetic markers can predict the disease with a high level of confidence. This is done in the context of a genetic-counselling programme with informed consent of the family. In these circumstances the result of the test will be actionable in some way, whether using prophylactic treatment or a screening programme or other life decisions important to that patient.  For example, families with a history of breast or ovarian cancer may be tested.  Women with certain mutations in the BRCA1 gene have a higher risk of developing breast, ovarian, and possibly colon cancers. Or, testing for the APC gene may be indicated where families have a history of bowel cancers.

In 2013, the use of DNA information for disease prevention is mainly focused on these patients who have a family history of disease and undergo genetic testing as part of a counselled, targeted screening process. Commercial services have been established that offer interpreted Single Nucleotide Polymorphism (SNP) information direct to consumers. However, these services are not yet commonly used by healthcare professionals; the power of SNPs to ascertain disease risk is varied and these services are not regulated for clinical use. As the cost of DNA sequencing falls over time, the possibility of exome sequencing (the 'coding regions' of the genome) or full genome sequencing in the clinical setting for disease risk/screening is increasingly affordable. As further understanding of predictive functions of the genome increases, the utility of increasing targeted screening or using broader screening will be further debated. 

Use of the GridION™ system in personalised healthcare
The GridION system is an electronic analysis platform that can be tailored for the analysis of DNA, RNA, protein and other analytes. This novel technology has applications across personalised healthcare. This may include the analysis of a patient's DNA, discovery and validation of new protein biomarkers, or an electronic diagnostic test for discovered biomarkers.