Advertisement

Next Generation Sequencing for Next Generation Diagnostics and Therapy

  • Marianna Garonzi
  • Cesare Centomo
  • Massimo Delledonne
Chapter

Abstract

DNA sequencing technologies are evolving at a prodigious rate. First-generation approaches have now been largely replaced by second-generation technologies (still known as “next generation sequencing” (NGS) even though they are now current and commonplace), and third-generation technologies (sometimes called “next-next generation sequencing”) are starting to arrive. This has led to global boom in whole genome or exome sequencing, boosting the discovery of sequence variants associated with disease that will eventually be translated into new diagnostic, prognostic, and therapeutic targets for individual patients in “precision medicine.” Acknowledgement of disease predisposition and specific therapeutic behavior for each individual addresses a more preventive approach. Adoption of such novel means represents an anticipation-relevant outcome as it can affect our healthcare on many different levels, ranging from a simple lifestyle adjustment to a well-defined clinical guideline. In this chapter we summarize current and emerging sequencing technologies for clinical applications, and some of the challenges that lie ahead.

Keywords

Precision medicine Genomics Sequencing technologies 

References

  1. 1.
    Nadin, M.: The anticipatory profile. An attempt to describe anticipation as process. In: Nadin, M. (ed.) Anticipation (special issue of the International Journal of General Systems), vol. 41, no. 1, pp. 43–75. Taylor and Francis, London (2012). http://www.tandfonline.com/doi/abs/10.1080/03081079.2011.622093, doi: 10.1080/03081079.2011.622093
  2. 2.
    Lander, E.S., Heaford, A., Sheridan, A., Linton, L.M., Birren, B., Subramanian, A., Coulson, A., Nusbaum, C., Zody, M.C., Dunham, A., Baldwin, J., et al.: Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001)CrossRefGoogle Scholar
  3. 3.
    Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., Gocayne, J.D., et al.: The sequence of the human genome. Science 291, 1304–1351 (2001)CrossRefGoogle Scholar
  4. 4.
    Liang, F., Holt, I., Pertea, G., Karamycheva, S., Salzberg, S.L., Quackenbush, J.: Gene index analysis of the human genome estimates approximately 120,000 genes. Nat. Genet. 25, 239–240 (2000)CrossRefGoogle Scholar
  5. 5.
    Clamp, M., Fry, B., Kamal, M., Xie, X., Cuff, J., Lin, M.F., Kellis, M., Lindblad-Toh, K., Lander, E.S.: Distinguishing protein-coding and noncoding genes in the human genome. Proc. Natl. Acad. Sci. U.S.A. 104, 19428–19433 (2007)CrossRefGoogle Scholar
  6. 6.
    Schloss, J.A.: How to get genomes at one ten-thousandth the cost. Nat. Biotechnol. 26, 1113–1115 (2008)CrossRefGoogle Scholar
  7. 7.
    Margulies, M., Egholm, M., Altman, W.E., Attiya, S., Bader, J.S., Bemben, L.A., Berka, J., Braverman, M.S., Chen, Y.-J., Chen, Z., Dewell, S.B., et al.: Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–381 (2005)Google Scholar
  8. 8.
    Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlén, M., Nyrén, P.: Real-time DNA sequencing using detection of pyrophosphate release. Anal. Biochem. 242, 84–89 (1996)CrossRefGoogle Scholar
  9. 9.
    Ronaghi, M., Uhlén, M., Nyrén, P.: A sequencing method based on real-time pyrophosphate. Science 281, 363, 365 (1998)Google Scholar
  10. 10.
    Bentley, D.R., Balasubramanian, S., Swerdlow, H.P., Smith, G.P., Milton, J., Brown, C.G., Hall, K.P., Evers, D.J., Barnes, C.L., Bignell, H.R., Boutell, J.M., et al.: Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008)CrossRefGoogle Scholar
  11. 11.
    Valouev, A., Ichikawa, J., Tonthat, T., Stuart, J., Ranade, S., Peckham, H., Zeng, K., Malek, J.A., Costa, G., McKernan, K., Sidow, A., et al.: A high-resolution, nucleosome position map of C. elegans reveals a lack of universal sequence-dictated positioning. Genome Res. 18, 1051–1063 (2008)Google Scholar
  12. 12.
    Rothberg, J.M., Hinz, W., Rearick, T.M., Schultz, J., Mileski, W., Davey, M., Leamon, J.H., Johnson, K., Milgrew, M.J., Edwards, M., Hoon, J., et al.: An integrated semiconductor device enabling non-optical genome sequencing. Nature 475, 348–352 (2011)CrossRefGoogle Scholar
  13. 13.
    Wetterstrand, K.A.: DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP). www.genome.gov/sequencingcosts
  14. 14.
    Budworth, H., McMurray, C.T.: A brief history of triplet repeat diseases. Methods Mol. Biol. 1010, 3–17 (2013)CrossRefGoogle Scholar
  15. 15.
    Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., Lese-martin, C., Walsh, T., Yamrom, B., Yoon, S., Krasnitz, A., Kendall, J., Leotta, A., et al.: Strong association of de novo copy number mutations with autism. Science 316, 445–449 (2007)CrossRefGoogle Scholar
  16. 16.
    Stankiewicz, P., Lupski, J.R.: Structural variation in the human genome and its role in disease. Annu. Rev. Med. 61, 437–455 (2010)CrossRefGoogle Scholar
  17. 17.
    Girirajan, S., Campbell, C.D., Eichler, E.E.: Human copy number variation and complex genetic disease. Annu. Rev. Genet. 45, 203–226 (2011)CrossRefGoogle Scholar
  18. 18.
    Rowley, J.D.: A new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and giemsa staining. Nature 243, 290–293 (1973)CrossRefGoogle Scholar
  19. 19.
    Rowley, J.D.: Identification of a translocation with quinacrine fluorescence in a patient with acute leukemia. Ann. génétique 16, 109–112 (1973)Google Scholar
  20. 20.
    Browning, S.R., Browning, B.L.: Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12, 703–714 (2011)CrossRefGoogle Scholar
  21. 21.
    Lee, J.-E., Choi, J.H., Lee, J.H., Lee, M.G.: Gene SNPs and mutations in clinical genetic testing: haplotype-based testing and analysis. Mutat. Res. 573, 195–204 (2005)CrossRefGoogle Scholar
  22. 22.
    Pericak-Vance, M.A.: Complete genomic screen in late-onset familial Alzheimer disease. Evidence for a new locus on chromosome 12. JAMA 278, 1237 (1997)CrossRefGoogle Scholar
  23. 23.
    Blacker, D., Wilcox, M.A., Laird, N.M., Rodes, L., Horvath, S.M., Go, R.C., Perry, R., Watson, B., Bassett, S.S., McInnis, M.G., Albert, M.S., et al.: Alpha-2 macroglobulin is genetically associated with Alzheimer disease. Nat. Genet. 19, 357–360 (1998)Google Scholar
  24. 24.
    Saunders, A.J., Bertram, L., Mullin, K., Sampson, A.J., Latifzai, K., Basu, S., Jones, J., Kinney, D., MacKenzie-Ingano, L., Yu, S., Albert, M.S., et al.: Genetic association of Alzheimer’s disease with multiple polymorphisms in alpha-2-macroglobulin. Hum. Mol. Genet. 12, 2765–2776 (2003)CrossRefGoogle Scholar
  25. 25.
    Bowers, J., Mitchell, J., Beer, E., Buzby, P.R., Causey, M., Efcavitch, J.W., Jarosz, M., Krzymanska-Olejnik, E., Kung, L., Lipson, D., Lowman, G.M., et al.: Virtual terminator nucleotides for next-generation DNA sequencing. Nat. Methods 6, 593–595 (2009)CrossRefGoogle Scholar
  26. 26.
    Harris, T.D., Buzby, P.R., Babcock, H., Beer, E., Bowers, J., Braslavsky, I., Causey, M., Colonell, J., Dimeo, J., Efcavitch, J.W., Giladi, E., et al.: Single-molecule DNA sequencing of a viral genome. Science 320, 106–109 (2008)CrossRefGoogle Scholar
  27. 27.
    Lipson, D., Raz, T., Kieu, A., Jones, D.R., Giladi, E., Thayer, E., Thompson, J.F., Letovsky, S., Milos, P., Causey, M.: Quantification of the yeast transcriptome by single-molecule sequencing. Nat. Biotechnol. 27, 652–658 (2009)CrossRefGoogle Scholar
  28. 28.
    Tessler, L.A., Reifenberger, J.G., Mitra, R.D.: Protein quantification in complex mixtures by solid phase single-molecule counting. Anal. Chem. 81, 7141–7148 (2009)Google Scholar
  29. 29.
    Ozsolak, F., Platt, A.R., Jones, D.R., Reifenberger, J.G., Sass, L.E., McInerney, P., Thompson, J.F., Bowers, J., Jarosz, M., Milos, P.M.: Direct RNA sequencing. Nature 461, 814–818 (2009)CrossRefGoogle Scholar
  30. 30.
    Korlach, J., Marks, P.J., Cicero, R.L., Gray, J.J., Murphy, D.L., Roitman, D.B., Pham, T.T., Otto, G.A., Foquet, M., Turner, S.W.: Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nanostructures. Proc. Natl. Acad. Sci. U.S.A. 105, 1176–1181 (2008)CrossRefGoogle Scholar
  31. 31.
    Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso, P., Rank, D., Baybayan, P., Bettman, B., Bibillo, A., et al.: Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009)CrossRefGoogle Scholar
  32. 32.
    Quail, M., Smith, M.E., Coupland, P., Otto, T.D., Harris, S.R., Connor, T.R., Bertoni, A., Swerdlow, H.P., Gu, Y.: A tale of three next generation sequencing platforms: comparison of Ion torrent, pacific biosciences and illumina MiSeq sequencers. BMC Genom. 13, 1 (2012)CrossRefGoogle Scholar
  33. 33.
    Branton, D., Deamer, D.W., Marziali, A., Bayley, H., Benner, S.A., Butler, T., Di Ventra, M., Garaj, S., Hibbs, A., Huang, X., Jovanovich, S.B., et al.: The potential and challenges of nanopore sequencing. Nat. Biotechnol. 26, 1146–1153 (2008)CrossRefGoogle Scholar
  34. 34.
    Mikheyev, A.S., Tin, M.M.Y.: A first look at the Oxford Nanopore MinION sequencer. Mol. Ecol. Resour. 14, 1097–1102 (2014)CrossRefGoogle Scholar
  35. 35.
    Jain, M., Fiddes, I.T., Miga, K.H., Olsen, H.E., Paten, B., Akeson, M.: Improved data analysis for the MinION nanopore sequencer. Nat. Methods 12, 351–356 (2015)CrossRefGoogle Scholar
  36. 36.
    Amberger, J.S., Bocchini, C.A., Schiettecatte, F., Scott, A.F., Hamosh, A.: OMIM.org: Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015)CrossRefGoogle Scholar
  37. 37.
    Landrum, M.J., Lee, J.M., Riley, G.R., Jang, W., Rubinstein, W.S., Church, D.M., Maglott, D.R.: ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014)CrossRefGoogle Scholar
  38. 38.
    Cooper, D.: The human gene mutation database. Nucleic Acids Res. 26, 285–287 (1998)CrossRefGoogle Scholar
  39. 39.
    Kaplun, A., Hogan, J.D., Schacherer, F., Peter, A.P., Krishna, S., Braun, B.R., Nambudiry, R., Nitu, M.G., Mallelwar, R., Albayrak, A.: PGMD: a comprehensive manually curated pharmacogenomic database. Pharmacogenomics J. 1–5 (2015)Google Scholar
  40. 40.
    Hewett, M., Oliver, D.E., Rubin, D.L., Easton, K.L., Stuart, J.M., Altman, R.B., Klein, T.E.: PharmGKB: the pharmacogenetics knowledge Base. Nucleic Acids Res. 30, 163–165 (2002)CrossRefGoogle Scholar
  41. 41.
    Bamford, S., Dawson, E., Forbes, S., Clements, J., Pettett, R., Dogan, A., Flanagan, A., Teague, J., Futreal, P.A., Stratton, M.R., Wooster, R.: The COSMIC (catalogue of somatic mutations in cancer) database and website. Br. J. Cancer 2, 355–358 (2004)Google Scholar
  42. 42.
    Abecasis, G.R., Auton, A., Brooks, L.D., DePristo, M.A., Durbin, R.M., Handsaker, R.E., Kang, H.M., Marth, G.T., McVean, G.A.: An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012)Google Scholar
  43. 43.
    Ng, P.C.: SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003)CrossRefGoogle Scholar
  44. 44.
    Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., Sunyaev, S.R.: A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010)Google Scholar
  45. 45.
    Schwarz, J.M., Rödelsperger, C., Schuelke, M., Seelow, D.: MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010)CrossRefGoogle Scholar
  46. 46.
    Liu, X., Jian, X., Boerwinkle, E.: dbNSFP: A lightweight database of human nonsynonymous SNPs and their functional predictions. Hum. Mutat. 32, 894–899 (2011)CrossRefGoogle Scholar
  47. 47.
    Liu, X., Jian, X., Boerwinkle, E.: dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum. Mutat. 34, 2393–2402 (2013)Google Scholar
  48. 48.
    Nadin. M.: Anticipation and the brain. In: Nadin, M. (ed.): Anticipation and Medicine, pp. 135–162. Springer, Cham (2016)Google Scholar
  49. 49.
    Sudmant, P.H., Rausch, T., Gardner, E.J., Handsaker, R.E., Abyzov, A., Huddleston, J., Zhang, Y., Ye, K., Jun, G., Fritz, M.H.-Y., Konkel, M.K., et al.: An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015)CrossRefGoogle Scholar
  50. 50.
    Fu, W., O’Connor, T.D., Jun, G., Kang, H.M., Abecasis, G., Leal, S.M., Gabriel, S., Altshuler, D., Shendure, J., Nickerson, D.A., Bamshad, M.J., et al.: Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2012)CrossRefGoogle Scholar
  51. 51.
    Gudbjartsson, D.F., Helgason, H., Gudjonsson, S.A., Zink, F., Oddson, A., Gylfason, A., Besenbacher, S., Magnusson, G., Halldorsson, B.V., Hjartarson, E., Sigurdsson, G.T., et al.: Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015)CrossRefGoogle Scholar
  52. 52.
    Steinberg, S., Stefansson, H., Jonsson, T., Johannsdottir, H., Ingason, A., Helgason, H., Sulem, P., Magnusson, O.T., Gudjonsson, S.A., Unnsteinsdottir, U., Kong, A., et al.: Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nat. Genet. 47, 445–447 (2015)CrossRefGoogle Scholar
  53. 53.
    Siva, N.: UK gears up to decode 100 000 genomes from NHS patients. Lancet 385, 103–104 (2015)CrossRefGoogle Scholar
  54. 54.
    Sidore, C., Busonero, F., Maschio, A., Porcu, E., Naitza, S., Zoledziewska, M., Mulas, A., Pistis, G., Steri, M., Danjou, F., Kwong, A., et al.: Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281 (2015)CrossRefGoogle Scholar
  55. 55.
    Nadin, M.: Anticipation and dynamics: Rosen’s anticipation in the perspective of time. In: Klir, G. (ed.) Special issue of International Journal of General Systems, vol. 39, no. 1, pp. 3–33. Taylor and Blackwell, London (2010)Google Scholar
  56. 56.
  57. 57.
    Bhattacharjee, A., Sokolsky, T., Wyman, S.K., Reese, M.G., Puffenberger, E., Strauss, K., Morton, H., Parad, R.B., Naylor, E.W.: Development of DNA confirmatory and high-risk diagnostic testing for newborns using targeted next-generation DNA sequencing. Genet. Med. 17, 337–347 (2014)CrossRefGoogle Scholar
  58. 58.
    Saunders, C.J., Miller, N.A., Soden, S.E., Dinwiddie, D.L., Noll, A., Alnadi, N.A., Andraws, N., Patterson, M.L., Krivohlavek, L.A., Fellis, J., Humphray, S., et al.: Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci. Transl. Med. 4, 154ra135 (2012)Google Scholar
  59. 59.
    Roychowdhury, S., Iyer, M.K., Robinson, D.R., Lonigro, R.J., Wu, Y.-M., Cao, X., Kalyana-Sundaram, S., Sam, L., Balbin, O.A., Quist, M.J., Barrette, T., et al.: Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl. Med. 3, 111ra121 (2011)Google Scholar
  60. 60.
    Boycott, K.M., Vanstone, M.R., Bulman, D.E., MacKenzie, A.E.: Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat. Rev. Genet. 14, 681–691 (2013)CrossRefGoogle Scholar
  61. 61.
    Méndez, M., Custodio, A., Provencio, M.: New molecular targeted therapies for advanced non-small-cell lung cancer. J. Thorac. Dis. 3, 30–56 (2011)Google Scholar
  62. 62.
    Kamali, F.: Genetic influences on the response to warfarin. Curr. Opin. Hematol. 13, 357–361 (2006)CrossRefGoogle Scholar
  63. 63.
    Tatarunas, V., Lesauskaite, V., Veikutiene, A., Grybauskas, P., Jakuska, P., Jankauskiene, L., Bartuseviciute, R., Benetis, R.: The effect of CYP2C9, VKORC1 and CYP4F2 polymorphism and of clinical factors on warfarin dosage during initiation and long-term treatment after heart valve surgery. J. Thromb. Thrombolysis 37, 177–185 (2014)CrossRefGoogle Scholar
  64. 64.
    Zhang, J., Tian, L., Zhang, Y., Shen, J.: The influence of VKORC1 gene polymorphism on warfarin maintenance dosage in pediatric patients: a systematic review and meta-analysis. Thromb. Res. (2015)Google Scholar
  65. 65.
    Collins, F.S., Varmus, H.: A New Initiative on Precision Medicine. N. Engl. J. Med. 372, 793–795 (2015)CrossRefGoogle Scholar
  66. 66.
    Nadin, M.: Anticipation—The End Is Where We Start From. Müller Verlag, Basel (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Marianna Garonzi
    • 1
  • Cesare Centomo
    • 1
  • Massimo Delledonne
    • 1
  1. 1.Department of BiotechnologyUniversity of VeronaVeronaItaly

Personalised recommendations