Richard Crooks's Website

Clinical Genetics and Personalised Medicine

Clinical Genetics in Personalised Healthcare

Clinical genetics is an exciting field which is set to revolutionise medicine. The development of next generation sequencing (NGS) methodologies (Illumina, 2018) and more recently the development of nanopore sequencing methods (Oxford Nanopore Technologies, 2018) are reducing the cost of genetic sequencing, and turning what was once a cutting edge and laborious research technique into a routine clinical investigation. The advancement of DNA sequencing technology has coincided with the growth of the “big data” industry (Yu and Zeng, 2018), as the volume of sequence data has increased, so too has the sophistication of the algorithms used to process it into clinically meaningful results (Ko et al., 2018).

The information gathered from genetic sequence data describes in high detail the biological basis for a patient’s condition. This information can inform clinical interventions. A mature example of this is in familial cancer syndromes. A number of genes such as the BRCA genes (Cornelisse et al., 1996), MLH1 (Lynch and Lynch, 1994), APC (Ashton-Rickardt et al., 1989) and AP1 (Malkin et al., 1990) have a long established association with hereditary cancers. Patients with pathogenic variants in these genes can be offered certain clinical interventions (such as early screening or prophylactic surgery) to reduce cancer mortality. Another condition that can benefit from this type of genetic testing is familial hypercholesterolaemia (Brown and Goldstein, 1976), which can cause heart attacks, but can be readily treated with statins, which reduce the incidence of cardiac mortality. As the scope of genetic sequencing increases there may be other genetic susceptibilities that may be uncovered, and for which early intervention can be offered.

In these familial syndromes, the pathogenic consequences are directly caused by the loss of a gene function. There are another class of genetic conditions which have a less direct genetic cause and these are known as inborn errors of metabolism (Saudubray and Garcia-Cazorla, 2018). In these conditions, rather than the loss of function directly leading to pathology, the loss of function changes the metabolic processes within the patient, leading to either an excess, or deficiency of a particular biological molecule, which leads to end organ damage. A condition of this sort of is phenylketonuria (PKU) (Parker, 1979), which is routinely tested for in newborns. In PKU, the patient is unable to metabolise phenylalanine into tyrosine, leading to a toxic excess of phenylalanine which cause nervous system damage. This condition can be treated effectively with a low protein diet and amino acid supplementation to prevent the end organ damage.

As the field of clinical genetics evolves, the genetic bases behind more and more inborn errors of metabolism are being discovered. How metabolic pathways work is a complicated field, with the same metabolite being part of multiple metabolic pathways, and with cellular and subcellular differences affecting which pathways are present. Genetics has spawned the field of “post-genomic technologies” (Beattie and Ghazal, 2003), which aim to bridge the gap between the genetic information and the functioning of biological systems by applying the same approaches as genetics. One of these technologies is known as “metabolomics” and studies how metabolic pathways in biological systems behave (Fiehn, 2002, Whitfield et al., 2004). As metabolic pathways are mediated by enzymes, which are in turn gene products, pathogenic variants in such enzymes may cause inborn errors of metabolism. As the metabolome becomes better understood, including data sharing of metabolomics data (Mandal et al., 2018), and the use of genetic testing increases, it may be possible in the future to routinely identify an excess or shortage of a particular metabolite as the underlying cause of a patient’s condition.

Some individual differences in metabolism or wider genetics may not be pathogenic usually, however they may affect how a patient responds to certain drugs. Some drugs can have rare but serious side effects, or unusual behaviour, or reduced effectiveness in certain patients. In the past it would have been impossible to predict these, meaning that all patients would receiving the same general treatments, but some would suffer side effects or reduced drug efficacy.

With genetics it is possible to identity when particular patients may respond differently to drugs, allowing for stratified medicine (Academy of Medical Sciences 2016). Stratified medicine involves identifying specific drugs that would best treat patients with as few side effects as possible. It is particularly used in cancer treatment, since cancer is a genetic disease characterised by mutations in the cancer cells causing aberrant growth, thus there are likely to be biological differences between the patient’s cells and their cancer, and between cancers in different patients. Selective targeting of oestrogen (Riggs and Hartmann, 2003) and HER2 (Le et al., 2005) receptors is used in the treatment of breast cancer, and as cell surface receptors these susceptibilities can be detected via immunohistochemistry. However the monoclonal antibody Cetuximab (trade name Erbitux) is a chemotherapy drug which can be used for tumours (currently approved by NICE for some colorectal cancers (NICE, 2017)) which have a wildtype KRAS gene. This is the first cancer drug approved where a genetic test is used to identify patients who will respond to it. Personalised medicine in this way isn’t just drugs, the use of genetic testing to predict radiosensitivity has also been investigated (Bibault and Tinhofer, 2017). As cancers become better classified, and the range of treatments available increases, it is likely there will be more examples of this sort of stratified medicine being used in the future. Acute myeloid leukaemia (AML) is a heterogeneous disease which is particularly amenable to a personalised medicine approach (Hussaini et al., 2018), as the condition has a large and variable number of mutations and these affect how the disease will respond to treatment. Recently identification of particular genes and mutations within has allowed for stratification of AML, allowing more accurate prediction of patients who will benefit from treating residual disease (Bullinger et al., 2017), and these findings are also likely to improve the development of anti-leukaemia drugs, which are particularly needed as leukaemias are not solid tumours that can be readily targeted with surgery or radiotherapy.

It is not just cancers which benefit from stratified medicine. The first example of a genetic basis behind different drug effects was in 1956 with the antimalarial drug primaquine (Alving et al., 1956). A more recently discovered example is the commonly used painkiller codeine. Codeine is metabolised into the active form morphine, which has the analgesic effects. As this metabolism is mediated by the action of the Cytochrome P450 2D6 enzyme (CYP2D6), there exist 4 distinct populations with respect to codeine metabolism (Dean et al., 2012). There is a normal population who respond to codeine as expected. There are also intermediate and poor metabolizers who do not efficiently metabolise codeine into the active morphine, whereas an ultra-rapid metabolizer population rapidly produce highly elevated morphine concentrations and can suffer toxic effects as a result. This variable response to codeine can be detected by genetic testing of the CYP2D6 gene, allowing for more appropriate treatments to be identified.

Like with inborn errors of metabolism, as the metabolism of drugs and the genes involved becomes better understood it is likely that other genetic tests that can predict drug efficacy and adverse reactions will be available in the future.

Fully realising the potential of personalised medicine will have significant advantages for patients and for the healthcare system. For the patient, personalised medicine allows for more effective treatments and better clinical outcomes, and for some conditions treatments which treat the underlying cause of the condition rather than the symptoms and end organ damage. For healthcare providers, personalised medicine allows more efficient healthcare expenditure. Money spent on treatments that are ineffective is money that cannot be spent on effective treatments, so being able to identify patient subgroups who will and will not benefit from treatment allows better allocation of healthcare resources.

The challenges to realising personalised medicine are the tests need to be cheap enough to provide a cost benefit to conducting them, and rapid enough to make a clinical difference to the course of the disease. As well as the technical developments in genetic sequencing, it is also necessary to elucidate gene function and interactions as highlighted, through post genomic technologies to understand how the genetic sequence affects the behaviour of biological systems. Developments in these two areas will see personalised medicine change from a theoretical concept to a routine part of healthcare.

The increased use of genetic testing in medicine also raises ethical challenges (Mégarbané, 2011). It has been long noted that genetic testing can potentially open a Pandora’s box of information of great importance to the patient, but which the patient would not wish to know (Weil et al., 1993). Genetic tests, particularly those that analyse large amounts of the genome (such as array CGH, clinical exome sequencing, or whole genome sequencing) can be broad in scope and test for more than just the condition being investigated. These incidental findings may be of interest to the patient, however the patient may not be aware that they could receive these results and be upset about any adverse findings. Futher more it may have insurance and social implications if genetic findings indicate a risk of a life limiting condition. The risk of these incidental findings need to be explained to patients, although the framework for how best to do this is unclear (Leppert et al., 2018). Unlike other healthcare tests which are in the main limited to the patient who has them, genetic tests produce results which have implications for family members, since genetic conditions are heritable. Some recent cases have highlighted where a clinician’s duty of confidentiality to a patient with a genetic condition is contradictory to the best interests of relatives (Lucassen and Gilbar, 2018), who may be impacted by the result of that genetic test. As well as medical issues there are also social implications with genetic testing, a false paternity finding in a genetic test may have implications a couple’s risk of having further affected children (Lucassen and Parker, 2001), however whether such a finding should be automatically disclosed to the father is debated (Tozzo et al., 2014). There are also issues for civil liberties, as DNA profiles are used in law enforcement purposes to help police track serious criminals, there have been recent controversial cases where police have tracked criminals by comparing DNA profiles retrieved from crime scenes to genetic profiles produced for genealogical purposes (Chang and Queally, 2018). While the results of tests for single genes are unlikely to be useful for such law enforcement purposes, the fact that such an investigation was carried out using data collected from people who hadn’t consented for it to be used in this way damages public confidence in all uses of genetic testing. The number of such controversial genetic testing cases is likely to increase as genetic testing becomes more widespread (Parker and Lucassen, 2018), as society and legislators should be ready to discuss the competing interests of different parties in genetic testing.


Academy of Medical Sciences. (2016) Stratified, personalised or P4 medicine: a new direction for placing the patient at the centre of healthcare and health education (Technical report). [Online]. London: Academy of Medical Sciences. [Accessed: 2018-04-27] Available from:

Alving, A. S., Carson, P. E., Flanagan, C. L. and Ickes, C. E. (1956) Enzymatic deficiency in primaquine-sensitive erythrocytes. Science. 124: 484-5. PubMed: 13360274

Ashton-Rickardt, P. G., Dunlop, M. G., Nakamura, Y., Morris, R. G., Purdie, C. A., Steel, C. M., Evans, H. J., Bird, C. C. and Wyllie, A. H. (1989) High frequency of APC loss in sporadic colorectal carcinoma due to breaks clustered in 5q21-22. Oncogene. 4: 1169-74. PubMed: 2797819

Beattie, J. and Ghazal, P. (2003) Post-genomic technologies--thinking beyond the hype. Drug Discov Today. 15: 909-10. PubMed: 14554146

Bibault, J. E. and Tinhofer, I. (2017) The role of Next-Generation Sequencing in tumoral radiosensitivity prediction. Clin Transl Radiat Oncol. 3: 16-20. PubMed: 29658008 - DOI: 10.1016/j.ctro.2017.03.002 - PMC: PMC5893518

Brown, M. S. and Goldstein, J. L. (1976) Receptor-mediated control of cholesterol metabolism. Science. 191: 150-4. PubMed: 174194

Bullinger, L., Döhner, K. and Döhner, H. (2017) Genomics of Acute Myeloid Leukemia Diagnosis and Pathways. J Clin Oncol. 35: 934-946. PubMed: 28297624 - DOI: 10.1200/JCO.2016.71.2208

Chang, C. and Queally, J. (2018) From Golden State Killer to Grim Sleeper, DNA helping break serial killer mysteries from 1970s and 1980s. Los Angeles Times [Online]. 2018-04-29 [Date accessed: 2018-04-30]. Available from:

Cornelisse, C. J., Cornelis, R. S. and Devilee, P. (1996) Genes responsible for familial breast cancer. Pathol Res Pract. 192: 684-93. PubMed: 8880869 - DOI: 10.1016/S0344-0338(96)80090-2

Dean, L. (2012) Codeine Therapy and CYP2D6 Genotype in Pratt, V., McLeod, H., Rubinstein, W., Dean, L., Kattman, B. and Malheiro, A. (eds) Medical Genetics Summaries Bethesda (MD): National Center for Biotechnology Information (US) [Online] Available From: PubMed: 28520350 - Bookshelf: NBK100662

Fiehn, O. (2002) Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol. 48: 155-71. PubMed: 11860207

Hussaini, M. O., Mirza, A. S., Komrokji, R., Lancet, J., Padron, E. and Song, J. (2018) Genetic Landscape of Acute Myeloid Leukemia Interrogated by Next-generation Sequencing: A Large Cancer Center Experience. Cancer Genomics Proteomics. 15: 121-126. PubMed: 29496691 - DOI: 10.21873/cgp.20070 - PMC: PMC5892606

Illumina (2018) Introduction to NGS [Online] [Accessed 2018-04-25] Available from:

Ko, G., Kim, P. G., Yoon, J., Han, G., Park, S. J., Song, W. and Lee, B. (2018) Closha: bioinformatics workflow system for the analysis of massive sequencing data. BMC Bioinformatics. 19: 43. PubMed: 29504905 - DOI: 10.1186/s12859-018-2019-3 - PMC: PMC5836837

Le, X. F., Pruefer, F. and Bast Jr, R. C. (2005) HER2-targeting antibodies modulate the cyclin-dependent kinase inhibitor p27Kip1 via multiple signaling pathways. Cell Cycle. 4: 87-95. PubMed: 15611642 - DOI: 10.4161/cc.4.1.1360

Leppert, K., Bisordi, K., Nieto, J., Maloney, K., Guan, Y., Dixon, S. and Egense, A. (2018) Genetic Counselors' Experience with and Opinions on the Management of Newborn Screening Incidental Carrier Findings. J Genet Couns. 27: 1328-1340. PubMed: 29687313 - DOI: 10.1007/s10897-018-0258-0 - PMC: PMC6209045

Lucassen, A. and Gilbar, R. (2018) Alerting relatives about heritable risks: the limits of confidentiality. BMJ. 361: k1409. PubMed: 29622529 - PMC: PMC5885756 - DOI: 10.1136/bmj.k1409

Lucassen, A. and Parker, M. (2001) Revealing false paternity: some ethical considerations. Lancet. 357: 1033-5. PubMed: 11293609 - DOI: 10.1016/S0140-6736(00)04240-9

Lynch, H. T. and Lynch J. F. (1994) 25 years of HNPCC. Anticancer Res. 14: 1617-24. PubMed: 7979196

Malkin, D., Li, F. P., Strong, L. C., Fraumeni Jr, J. F., Nelson, C. E., Kim, D. H., Kassel, J., Gryka, M. A., Bischoff, F. Z. and Tainsky, M. A. (1990) Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Science. 250: 1233-8. PubMed: 1978757

Mandal, R., Chamot, D. and Wishart, D. S. (2018) The role of the Human Metabolome Database in inborn errors of metabolism. J Inherit Metab Dis. 41: 329-336. PubMed: 29663269 - DOI: 10.1007/s10545-018-0137-8

Mégarbané, A. (2011) [Is good genetic counseling possible with good ethical principals?]. J Med Liban. 59: 23-6. PubMed: 21675020

NICE (2017) Cetuximab and panitumumab for previously untreated metastatic colorectal cancer [TA439]. [Online]. London: National Institute for Health and Care Excellence. [Accessed 2018-04-27]. Available from:

Oxford Nanopore Technologies (2018) DNA: Nanopore Sequencing [Online] [Accessed 2018-04-26] Available from:

Parker, C. E. (1979) Diseases of phenylalanine metabolism. West J Med. 131: 285-97. PubMed: 388868 - PMC: PMC1271823

Parker, M. and Lucassen, A. (2018) Using a genetic test result in the care of family members: how does the duty of confidentiality apply? Eur J Hum Genet. 26: 955-959. PubMed: 29700390 - DOI: 10.1038/s41431-018-0138-y - PMC: PMC6018806

Riggs, B. L. and Hartmann, L. C. (2003) Selective estrogen-receptor modulators -- mechanisms of action and application to clinical practice. N Engl J Med. 348: 618-29. PubMed: 12584371 - DOI: 10.1056/NEJMra022219

Saudubray, J. M. and Garcia-Cazorla, À. À. (2018) Inborn Errors of Metabolism Overview: Pathophysiology, Manifestations, Evaluation, and Management. Pediatr Clin North Am. 65: 179-208. PubMed: 29502909 - DOI: 10.1016/j.pcl.2017.11.002

Tozzo, P., Caenazzo, L. and Parker, M. J. (2014) Discovering misattributed paternity in genetic counselling: different ethical perspectives in two countries. J Med Ethics. 40: 177-81. PubMed: 23443210 - DOI: 10.1136/medethics-2012-101062

Weil, J. and MacKay, C. R.; Pacific Medical Center (San Francisco). Bioethics Committee. (1993) Howard: paternity and Pandora's box. Camb Q Healthc Ethics. 2: 229-37. PubMed: 11643206

Whitfield, P. D., German, A. J. and Noble, P. J. (2004) Metabolomics: an emerging post-genomic tool for nutrition. Br J Nutr. 92: 549-55. PubMed: 15522124

Yu, X. T. and Zeng, T. (2018) Integrative Analysis of Omics Big Data. Methods Mol Biol. 1754: 109-135. PubMed: 29536440 - DOI: 10.1007/978-1-4939-7717-8_7

Back to Biology Index
Back to Homepage