The paper is devoted to the investigation of next-generation sequencing technologies and traditional sequencing methods, as well as a comparison of their utility in clinical practice. Peer-reviewed articles were reviewed and analysed to identify major areas of implementation of sequencing technologies. The findings on their use in the treatment of various forms of cancers and genetic and congenital disorders as well as in drug metabolism are synthesised in the paper.
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The research evidence suggests that next-generation sequencing is associated with greater benefits in comparison to standard methods such as Sanger sequencing. The major potential advantages linked to their use include cost efficiency and speed of genetic and genomic assays. The advanced capabilities of contemporary DNA characterisation tools allow them to contribute to the advancement of personalised medicine. Based on this assumption, recommendations are given to improve individualised healthcare.
The rapid advancement of molecular biology technologies has provoked an increase in the demand for high-quality medical diagnostics in the modern world. At the beginning of the 21st century, it became possible to apply massively parallel sequencing, i.e. determination of the nucleotide composition of the DNA molecule. The new methodology allowed the deepening of the understanding of the structure and functioning of the genome of living organisms (primarily humans, various types of viruses and bacteria) and provided a background for the application of sequencing techniques in clinical practice.
Next-generation sequencing (NGS) became a prerequisite for the development of a new scientific and practical area – molecular medicine, in which the problems of diagnosis, prevention and treatment are solved at the molecular level using the analysis of nucleic acids (DNA, RNA) and products of their expression (proteins). At the same time, modern ideas about the structure of the genome as a whole and separate genes in particular, as well as their functional interactions that define various health conditions, provide a methodological basis for molecular medicine.
NGS technologies have helped to advance traditional sequencing methods: Edman degradation and Sanger sequencing. Edman’s method was developed in the 1950s, and it involves the treatment of studied peptides with a specific set of reagents, resulting in the separation of one amino acid from the N-terminus.
The cyclic repetition of the reaction and its analysis provides information about the chain of amino acids in the peptide. Sanger sequencing, also known as the chain-termination method, was developed in the 1970s and continues to be widely used today. It assists in identifying the nucleotide sequence of DNA through hybridisation of the synthetic oligonucleotide. The major disadvantages of both traditional sequencing techniques are a low level of output and relatively high cost.
In comparison, NGS technologies are mainly automatic and function based on unique combinations of DNA matrixes, visualisation and alignment of DNA sequences (Metzker 2010). In contrast to traditional methods, NGS technologies allow the simultaneous sequencing of thousands of DNA molecules. Thus, they are associated with an increase in the speed of analysis and the amount of data received, as well as a decrease in costs.
In the present paper, the ways in which NGS can be applied in clinical practice will be evaluated. Focusing on the areas of clinical oncology, genetic disease and pharmacogenomics, we will identify implications associated with these new technologies and provide recommendations for their further integration into disease treatment and prevention practices.
Implementation in Clinical Oncology
Depending on the character of the task, NGS technologies allow sequencing either the whole genome or exome. Researchers may also use panels that include only the necessary target genes associated with particular modifications and tumour locations. Thus, it is possible to achieve greater cost efficiency for the analysis while at the same time simplifying the process of interpretation and data management, in addition to increasing productivity (Morozova & Marra 2008). Such genetic tests represent diagnostic and prognostic factors in clinical oncology and play an important role in the selection of treatment tactics for each patient.
NGS technologies are effective in searching for new and rare somatic mutations. Full-genome, full-focus and targeted sequencing can be used to find new genetic aberrations and their related potential therapeutic targets. For instance, full-genome sequencing in patients with a rare form of acute promyelocytic leukaemia made it possible to identify new genetic recombination of PML-RARA that previously could not be detected by standard cytogenetic methods (Welch et al. 2011).
Additionally, Gui et al. (2011) sequenced exomes of nine samples of transitional cell carcinoma of the urinary bladder when searching for somatic mutations associated with this oncological pathology. They then tested samples of eighty-eight patients with this disease for the presence of the identified genetic variants. As a result, the researchers found fifty-five mutations, and forty-nine of them were identified for the first time in patients with bladder cancer.
The application of new methods and diagnostic capabilities in the field of oncological diseases has led to the finding that each tumour has inherent characteristics that differentiate it from the cells of healthy tissues. Even when they have one localisation, cancerous tumours are heterogeneous and react to drug therapy in different ways. The identified differences have prompted the development of medicines directly acting on a molecular target in a tumour cell without causing serious damage to other organs and tissues of the patient (Cagan & Van Allen 2015).
The peculiarity of this targeted therapy refers to the fact that each specific drug is effective only in tumours with certain molecular characteristics. The application of new drugs with a precisely targeted focus of influence on molecular mechanisms requires mandatory identification of genetic impairments. Without conducting such a study, the outcome of therapy will be in question.
The Illumina approach to the diagnosis of cancer implies a precise identification of early detection markers in populations and individuals at risk, along with the identification of developmental markers and disease prediction markers (Serratì et al. 2016). This allows designation and evaluation of the effectiveness of conducted therapy. The implementation of TruSight Cancer facilitates the development of solutions to the problems of predisposition to oncological disorders as well as their prevention (Serratì et al. 2016).
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The tool supports sequencing of ninety-four oncogenes and examines their mutations responsible for a predisposition to various types of cancer. Along with this, the sequencing of DNA from cancer cells helps to identify mutations in the tumour and determine the effectiveness of therapy. The TruSight Tumor and TruSeq Amplicon-Cancer Panel allow differentiating cancer genes (solid tumours of the lungs, large intestine, stomach, ovary, melanoma) (Serratì et al. 2016).
These tools can analyse about forty-eight known oncogenes such as ABL1, BRAF, EGFR, KRAS, NRAS, PIK3C, JAK2 and others (Serratì et al. 2016). These instruments are associated with many benefits, e.g. the ability to work with different types of samples, including paraffin blocks, and a high level of sensitivity. The tools can precisely identify mutations in small subpopulations of cancer cells in the sample at the limit of detection below 5% (Illumina 2015).
Nowadays, Sanger’s method, once considered to be the gold standard for genetic diagnostics, is still commonly used in the analysis of genes associated with hereditary oncologic diseases. Nevertheless, researchers have observed that it is applicable only to searching for the known, most widespread mutations (e.g. KRAS, BRAF) and cannot be used for sequencing of the whole of genes (Feliubadaló et al. 2017). NGS technologies, in contrast, assist in the identification of rare genetic variations and allow the simultaneous testing of a large number of genes associated with the presence of clinically significant mutations in a relatively short time.
For instance, by using NGS, researchers found germinal mutations in patients with clinical features of a hereditary disorder that could not be identified via routine methods of molecular diagnostics. When studying 300 families at high risk of breast cancer development, Walsh et al. (2006) found in fifty-two individuals clinically significant genetic variants that had been previously unknown. Additionally, in their work, Ozcelik et al. (2012) showed the advantages of NGS in comparison to Sanger sequencing, providing evidence that the implementation of NGS is more cost-efficient in BRCA testing by examining twelve patients with hereditary breast cancer and verifying the results obtained through Sanger sequencing.
Diagnostics of Genetic and Other Diseases
Congenital disorders constitute a significant part of all hereditary pathologies. Despite a high level of medical-biological knowledge in this area, many genetic illnesses are associated with difficulties in timely diagnosis and effective treatment. They frequently lead to substantial disruption in the quality of the patient’s life, development of disabilities and premature death. Nowadays, NGS technologies may provide solutions to these problems.
Researchers state that the TruSight One Sequencing Panel kit allows sequencing 4813 genes at once and can determine mutations associated with multiple rare genetic disorders described in the International Human Gene Mutation Database (Daoud et al. 2016). Moreover, the system can be applied for diagnostics of drug tolerance, which allows evaluating its effectiveness and justifies the use in each case.
NGS proved to be efficient in diagnostics of cardiomyopathies, which constitute ‘a relatively small group of related but clinically distinguishable primary diseases of the heart muscle, such as hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) and left ventricular non-compaction cardiomyopathy (LVNC)’ (Waldmüller et al. 2015, p. 309). The most frequently affected disease genes are MYH7 and MYBPC3.
At the same time, over forty other genes have been identified as linked to the familial type of HCM, one of the leading causes of sudden death in youth (Waldmüller et al. 2015). Researchers observe that Illumina’s TruSight Cardiomyopathy Enrichment Panel allows sequencing of forty-six genes associated with various forms of the disease. Precise and prompt differential diagnosis of hereditary types of cardiomyopathies helps in prescribing a comprehensive treatment aimed at the prevention and mitigation of severe consequences for the patient.
Nowadays, the diagnostics of Autism Spectrum Disorder (ASD) in children pose significant issues in the clinical practice − in many cases, healthcare practitioners can confuse the early symptoms and signs of ASD with some other disorders, e.g. cerebral palsy, sensory or speech disorders, neuropathy and others. Unfortunately, it is difficult to start intervention before the age of three because, as a rule, a person is rarely diagnosed with autism before that time. It leads to loss of time and reduces the chances for normal development of communication, social and motor skills, intellectual abilities, speech, etc. in children.
Although the exact causes of ASD are not known to date, genetic background is one possible contributing factor. NGS technologies may assist in the identification of genes and impairments associated with ASD. According to Ungar (2015), advanced sequencing tools can be potentially beneficial in early diagnosis. However, the implementation of NGS in this particular area of practice may entail additional costs for both families and clinical settings. The researcher suggests that decision-making in the implementation of NGS for autism assessment must necessarily be supported by examining the clinical utility and cost-effectiveness of health technology. Currently, such data is lacking in the literature.
The information obtained through NGS technologies can largely facilitate early diagnosis and prevention of Alzheimer’s disease − one of the most common causes of senile dementia in people over 65 years of age. An essential genetic factor that increases the risk of this disease is the variant of the ε4 allele of the APOE gene (Kim, Basak & Holtzman 2009).
The genetic analysis aimed at finding this allele can help prevent the development of Alzheimer’s disease long before the possible manifestation of clinical symptoms. Recent studies have also identified mutations in other genes (CLU, PICALM, CR1, MS4A, CD2AP, CD33, BIN1 and ABCA7) that contribute to the development of Alzheimer’s disease (Hollingworth et al. 2011). Thus, a standard genetic mutation test of the APOE gene alone may not provide complete clinical and diagnostic information. In this case, full-genome sequencing is preferable and more informative.
Genomic methods are used in pharmacogenomics to understand the effect of the genotype of relevant genes on both the metabolism of drugs in the body and the effects of drugs on gene expression. Pharmacogenomics is a field of research that applies knowledge about specific genetic variations to provide an individual approach to prescribing and dispensing medications. One of the most vivid examples of practice and research in pharmacogenomics is treatment with warfarin (Voora et al. 2005). Oral anticoagulant warfarin is prescribed for long-term treatment and prevention of thromboembolism.
In the United States alone, warfarin is prescribed for more than twenty-one million patients. However, this drug may cause various complications that can manifest differently depending on age, sex, weight, diet, etc. For instance, increasing or lowering the dose may lead to internal bleeding or blockage of the veins (Voora et al. 2005). Studies of the pharmacokinetics and pharmacodynamics of warfarin have made it possible to establish the impact of two genes on its effect on the body. One of these genes, CYP2C9, is responsible for the clearance of the pharmacologically active S-enantiomer of warfarin (Voora et al. 2005).
A tenfold difference in warfarin clearance in individuals with different alleles of this gene has been observed. The second gene in which the variations affect the effective dose of warfarin is VKORC1 (Voora et al. 2005). Combinations of variations in the CYP2C9 and VKORC1 genes, as well as such factors as age and weight, are correlated with an effective and safe dosage of the drug.
Summary and Recommendations
The findings of the literature review revealed that opportunities that appeared due to the advancement of medical genetics fostered a more profound understanding of genetic features and factors linked to various disease processes. During full-genome research, several hundred risk factors for such diseases as cancer, Alzheimer’s disease and others have been identified in recent years. The discovery and study of these factors will allow healthcare providers to understand the nature of illnesses better and create new strategies for their treatment and prevention. Moreover, NGS technologies allowed a better comprehension of individual genetic differences, which can be of great use in personalised medicine.
Development of Personalised Medicine
With NGS technologies, personalised medicine can become more effective in both the definition and diagnosis of diseases and prevention of the prescribing of inappropriate drugs. Since the main goal of individualised medicine is to optimise and improve the quality of medical care provided for each patient, personal clinical and genetic information will help to achieve these goals. However, the given approach to healthcare is almost impossible without a multidisciplinary medical system that would unite specialists from different fields along with raising the educational level of practitioners and patients in the area of genetics and its utility in medical practice.
Personalised medicine will also require an integration of the genome data: the transcript, the proteome and the metabolite. By implementing NGS, researchers found about fifteen million variations of genome sequences, including those rare ones that can be recognised only through full-genome sequencing (Heikes et al. 2008). Differences in genomes can have different effects on gene expression. Moreover, at least five hundred disease markers have recently been identified in full-genome studies, and several of these are associated with mRNA and protein expression (Hawkins et al. 2010).
It is also worth mentioning that during the transition from a healthy state to morbidity, there are many points at which the knowledge of genomic information can be applied. Susceptibility to disease and the risk of developing the disease can be measured and quantified when accessing DNA information. Such a method can be cost-effective only if the existing healthcare model becomes more focused on prevention than treatment of disorders.
Full-genome sequencing can be used to identify those types of diseases that cannot be diagnosed by traditional methods. As mentioned earlier, traditional cancer research methods focus only on one gene or signalling cell pathway. However, cancer has a complex nature − several cellular signalling pathways may participate in the developmental process. Therefore, it is necessary to conduct a holistic investigation of all levels of gene expressions and signalling pathways in the context of neoplasm formation. Data collected via full-genome analyses can be a valuable source of information for health providers and pathologists. Also, such information will help to elucidate the complex picture of the onset and evolution of cancer (Welch et al. 2011).
Despite a fairly slow start, in recent years, state-of-the-art NGS technologies and software have made significant progress in medical practice. The evidence shows that clinical specialists have started using genetic information in decision-making related to intervention and prevention programs. Due to the specific characteristics of the disease, the progress of an individualised approach is particularly noticeable in oncology.
In other areas of medicine, the benefits are less pronounced. Moreover, questions about the economic feasibility of NGS technology remain open-ended. However, modern technologies have proved to be faster and more convenient than traditional methods in many clinical research trials. One way or another, the results and optimism of researchers and practitioners make it clear that, with NGS technologies, personalised medicine may have a promising future, which is likely to change the traditional notions of diagnosis, treatment and prediction of disease.
Daoud, H, Luco, SM, Li, R, Bareke, E, Beaulieu, C, Jarinova, O, Carson, N, Nikkel, SM, Graham, GE, Richer, J, Armour, C, Bulman, DE, Chakraborty, P, Geraghty, M, Lines, MA, Lacaze-Masmonteil, T, Majewski, J, Boycott, KM & Dyment, DA 2016, ‘Next-generation sequencing for diagnosis of rare diseases in the neonatal intensive care unit’, Canadian Medical Association Journal, vol. 188, no. 11, pp. E254–E260.
Feliubadaló, L, Tonda, R, Gausachs, M, Trotta, J-R, Castellanos, E, López-Doriga, A, Teulé À, Tornero E, Del Valle J, Gel B, Gut M, Pineda M, González S, Menéndez M, Navarro M, Capellá G, Gut I, Serra E, Brunet J, Beltran S & Lázaro, C 2017, ‘Benchmarking of whole exome sequencing and ad hoc designed panels for genetic testing of hereditary cancer’, Scientific Reports, vol. 7, no. 37984, pp. 1-11.
Gagan, J & Van Allen, EM 2015, ‘Next-generation sequencing to guide cancer therapy’, Genome Medicine, vol. 7, no. 80, pp. 1-10.
Gui, Y, Guo, G, Huang, Y, Hu, X, Tang, A, Gao, S, Wu, R, Chen, C, Li, X, Zhou, L, He, M, Li, Z, Sun, X, Jia, W, Chen, J, Yang, S, Zhou, F, Zhao, X, Wan, S, Ye, R, Liang, C, Liu, Z, Huang, P, Liu, C, Jiang, H, Wang, Y, Zheng, H, Sun, L, Liu, X, Jiang, Z, Feng, D, Chen, J, Wu, S, Zou, J, Zhang, Z, Yang, R, Zhao, J, Xu, C, Yin, W, Guan, Z, Ye, J, Zhang, H, Li, J, Kristiansen, K, Nickerson, ML, Theodorescu, D, Li, Y, Zhang, X, Li, S, Wang, J, Yang, H, Wang, J & Cai, Z 2011, ‘Frequent mutations of chromatin remodeling genes in transitional cell carcinoma of the bladder’, Nature Genetics, vol. 43, no. 9, pp.875–878.
Hawkins, RD, Hon, GC & Ren, B 2010, ‘Next-generation genomics: an integrative approach’, Nature Reviews Genetics, vol. 11, pp. 476-486.
Heikes, KE, Eddy, DM, Arondekar, B & Schlessinger, L 2008, ‘Diabetes risk calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes’, Diabetes Care, vol. 31, pp. 1040-1045.
Hollingworth, P, Harold, D, Sims, R, Gerrish, A, Lambert, JC, Carrasquillo, MM, Abraham, R, Hamshere, ML, Pahwa, JS, Moskvina, V, Dowzell, K, Jones, N, Stretton, A, Thomas, C, Richards, A, Ivanov, D, Widdowson, C, Chapman, J, Lovestone, S, Powell, J, Proitsi, P, Lupton, MK, Brayne, C, Rubinsztein, DC, Gill, M, Lawlor, B, Lynch, A, Brown, KS, Passmore, PA, Craig, D, McGuinness, B, Todd, S, Holmes, C, Mann, D, Smith, AD, Beaumont, H, Warden, D, Wilcock, G, Love, S, Kehoe, PG, Hooper, NM, Vardy, ERLC, Hardy, J, Mead, S, Fox, NC, Rossor, M, Collinge, J, Maier, W, Jessen, F, Rüther, E, Schürmann, B, Heun, R, Kölsch, H, van den Bussche, H, Heuser, I, Kornhuber, J, Wiltfang, J, Dichgans, M, Frölich, L, Hampel, H, Gallacher, J, Hüll, M, Rujescu, D, Giegling, I, Goate, AM, Kauwe, JSK, Cruchaga, C, Nowotny, P, Morris, JC, Mayo, K, Sleegers, K, Bettens, K, Engelborghs, S, De Deyn, PP, Van Broeckhoven, C, Livingston, G, Bass, NJ, Gurling, H, McQuillin, A, Gwilliam, R, Deloukas, P, Al-Chalabi, A, Shaw, CE, Tsolaki, M, Singleton, AB, Guerreiro, R, Mühleisen, TW, Nöthen, M, Moebus, S, Jöckel, K, Klopp, N, Wichmann, HE, Pankratz, VS, Sando, SB, Aasly, JO, Barcikowska, M, Wszolek, ZK, Dickson, DW, Graff-Radford, NR, Petersen, RC, van Duijn, CM, Breteler, MMB, Ikram, MA, DeStefano, AL, Fitzpatrick, AL, Lopez, O, Launer, LJ, Seshadri, S, Berr, C, Campion, D, Epelbaum, J, Dartigues, JF, Tzourio, C, Alpérovitch, A, Lathrop, M, Feulner, TM, Friedrich, P, Riehle, C, Krawczak, M, Schreiber, S, Mayhaus, M, Nicolhaus, S, Wagenpfeil, S, Steinberg, S, Stefansson, H, Stefansson, K, Snædal, J, Björnsson, S, Jonsson, PV, Chouraki, V, Genier-Boley, B, Hiltunen, M, Soininen, H, Combarros, O, Zelenika, D, Delepine, M, Bullido, MJ, Pasquier, F, Mateo, I, Frank-Garcia, A, Porcellini, E, Hanon, O, Coto, E, Alvarez, V, Bosco, P, Siciliano, G, Mancuso, M, Panza, F, Solfrizzi, V, Nacmias, B, Sorbi, S, Bossù, P, Piccardi, P, Arosio, B, Annoni, G, Seripa, D, Pilotto, A, Scarpini, E, Galimberti, D, Brice, A, Hannequin, D, Licastro, F, Jones, L, Holmans, PA, Jonsson, T, Riemenschneider, M, Morgan, K, Younkin, SG, Owen, MJ, O’Donovan, M, Amouyel, P & Williams, J 2011, ‘Common variants at Abca7, Ms4a6a/ms4a4e, Epha1, Cd33 and Cd2ap are associated with Alzheimer’s sisease’, Nature Genetics, vol. 43, no. 5, pp.429–35.
Illumina 2015, Cancer genomics. Web.
Kim, J, Basak, JM & Holtzman, DM 2009, ‘The role of apolipoprotein E in Alzheimer’s disease’, Neuron, vol. 63, no. 3, p. 287.
Metzker, ML 2010, ‘Sequencing technologies − the next generation’, Nature Reviews Genetics, vo. 11, pp. 31-46.
Morozova, O & Marra, M A 2008, ‘Applications of next-generation sequencing technologies in functional genomics’, Genomics, vol. 92 no. 5, pp. 255–264.
Ozcelik H, Shi X, Chang MC, Tram E, Vlasschaert M, Di Nicola N, Kiselova A, Yee D, Goldman A, Dowar M, Sukhu B, Kandel R & Siminovitch K 2012, ‘Long-range PCR and next-generation sequencing of BRCA1 and BRCA2 in breast cancer’, The Journal of Molecular Diagnostics, vol. 14, no. 5, pp. 467–75.
Serratì, S, De Summa, S, Pilato, B, Petriella, D, Lacalamita, R, Tommasi, S & Pinto, R 2016, ‘Next-generation sequencing: advances and applications in cancer diagnosis’, OncoTargets and Therapy, vol. 9, pp. 7355–7365.
Ungar, WJ 2015, ‘Next generation sequencing and health technology assessment in autism spectrum disorder’, Journal of the Canadian Academy of Child and Adolescent Psychiatry, vol. 24, no. 2, pp. 123–127.
Voora, D, McLeod, HL, Eby, C & Gage, BF 2005, ‘The pharmacogenetics of coumarin therapy’, Pharmacogenomics, vol. 6, pp. 503-513.
Waldmüller, S, Schroeder, C, Sturm, M, Scheffold, T, Imbrich, K, Junker, S, Frische, C, Hofbeck, M, Bauer, P, Bonin, M, Gawaz, M & Gramlich, M 2015, ‘Targeted 46-gene and clinical exome sequencing for mutations causing cardiomyopathies’, Molecular and Cellular Probes, vol. 29, no. 5, pp.308–314.
Walsh T, Casadei S, Coats KH, Swisher E, Stray SM, Higgins J, Roach KC, Mandell J, Lee MK, Ciernikova S, Foretova L, Soucek P, King M 2006, ‘Spectrum of mutations in BRCA1, BRCA2, CHEK2, and TP53 in families at high risk of breast cancer. Journal of the American Medical Association, vol. 295, no. 12, pp. 1379-1388.
Welch, J S, Westervelt, P, Ding, L, Larson, D E, Klco, J M, Kulkarni, S , Wallis, J, Chen, K, Payton, JE, Fulton, RS, Veizer, J, Schmidt, H, Vickery, TL, Heath, S, Watson, MA, Tomasson, MH, Link, DC, Graubert, TA, DiPersio, JF, Mardis, ER, Ley, TJ & Wilson, R K 2011, ‘Use of whole genome sequencing to diagnose a cryptic fusion oncogene’, The Journal of the American Medical Association, vol. 305, no. 15, pp. 1577–1584.