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Reporting Clinical Genomic Assay Results and the Role of the Pathologist

  • Janina A. Longtine
Chapter

Abstract

High-throughput sequencing is a disruptive technology that will significantly impact pathologists’ role in analyzing, interpreting, and reporting clinical laboratory results. Laboratory personnel must develop procedures and policies for producing and managing the vast amount of data, integrating bioinformatics analysis and filters, and annotating variants to create clear, informative, and timely reports. Professional societies and organizations are developing recommendations and guidelines. This chapter delineates current challenges to generating high-throughput sequencing clinical reports and emerging solutions.

Keywords

Autism Spectrum Disorder Acute Myeloid Leukemia Germline Variant Molecular Stratification Normal Karyotype Acute Myeloid Leukemia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Watson MS, Cutting GR, Desnick RJ, Driscoll DA, Klinger K, Mennuti M, Palomaki GE, Popovich BW, Pratt VM, Rohlfs EM, Strom CM, Richards CS, Witt DR, Grody WW. Cystic fibrosis population carrier screening: 2004 revision of American College of Medical Genetics mutation panel. Genet Med. 2004;6(6):387–91.PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, Tsalenko A, Sampas Teekakirikul P, Kelly MA, Rehm HL, Lakdawala NK, Funke BH. Inherited cardiomyopathies: Molecular genetics and clinical genetic testing in the postgenomic era. J Mol Diagn. 2013;15(2):1–13. http://dx.doi.org/10.1016/j.jmoldx.2012.09.002.
  3. 3.
    Cardarella S, Ortiz TM, Joshi VA, Butaney M, Jackman DM, Kwiatkowski DJ, Yeap BY, Janne PA, Lindeman NI, Johnson BA. The introduction of systemic genomic testing for patients with non-small-cell lung cancer. J Thorac Oncol. 2012;7(12):1767–74. doi: 10.1097/JTO.0b013e3182745bcb.PubMedCrossRefGoogle Scholar
  4. 4.
    Sequist LV, Heist RS, Shaw AT, Fidias P, Rosovsky R, Temel JS, Lennes IT, Digumarthy S, Waltman BA, Bast E, Tammireddy S, Morrissey L, Muzikansky A, Goldberg SB, Gainor J, Channick CL, Wain JC, Gaissert H, Donahue DM, Muniappan A, Wright C, Willers H, Mathisen DJ, Ellisen LW, Mino-Kenudson M, Lanuti M, Borger DR, Iafrate AJ, Engelman JA, Dias-Santagata D. Implementing multiplexed genotyping of non-small cell lung cancers into routine clinical practice. Ann Oncol. 2011;22(12):2616–24. doi: 10.1093/annonc/mdr489. Epub 2011 Nov 9.PubMedCrossRefPubMedCentralGoogle Scholar
  5. 5.
    Gulley ML, Braziel RM, Halling HC, Hsi ED, Kant JA, Nikiforova MN, Nowak JA, Ogino S, Oliveira A, Polesky HF, Silverman L, Tubbs RR, Van Deerlin VM, Vance GH, Versalovic J, Molecular Pathology Resource Committee, College of American Pathologists. Clinical laboratory reports in molecular pathology. Arch Pathol Lab Med. 2007;131(6):852–63.PubMedGoogle Scholar
  6. 6.
    Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–9. doi: 10.1038/nmeth0410-248.PubMedCrossRefPubMedCentralGoogle Scholar
  7. 7.
    Richards CS, Bale S, Bellissimo DB, Das S, Grody WW, Hedge MR, Lyon E, Ward BE, Molecular Subcommittee of the ACMG Laboratory Quality Assurance Committee. ACMG recommendations for standards for interpretation and reporting of sequence variations: Revisions 2007. Genet Med. 2008;10(4):294–300. doi: 10.1097/GIM.0b013e31816b5cae.PubMedCrossRefGoogle Scholar
  8. 8.
    Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40(Web Server issue):W452–257. doi: 10.1093/nar/gks539. Epub 2012 Jun 11 Accessed February 10, 2013.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Drmanac R. The advent of personal genomic sequencing. Genet Med. 2011;13(3):188–90. doi: 10.1097/GIM.0b013e31820f16e6.PubMedCrossRefGoogle Scholar
  10. 10.
    Li Y, Vinckenbosch N, Tian G, Huerta-Sanchez E, Jiang T, Jiang H, Albrechtsen A, Andersen G, Cao H, Korneliussen T, Grarup N, Guo Y, Hellman I, Jin X, Li Q, Liu J, Liu X, Sparsø T, Tang M, Wu H, Wu R, Yu C, Zheng H, Astrup A, Bolund L, Holmkvist J, Jørgensen T, Kristiansen K, Schmitz O, Schwartz TW, Zhang X, Li R, Yang H, Wang J, Hansen T, Pedersen O, Nielsen R, Wang J. Resequencing of 200 human exomes identifies an excess of low-frequency non-synonymous coding variants. Nat Genet. 2010;42(11):969–72. doi: 10.1038/ng.680. Epub 2010 Oct 3.PubMedCrossRefGoogle Scholar
  11. 11.
    Adams DR, Sincan M, Fuentes Fajardo K, Mullikin JC, Pierson TM, Toro C, Boerkoel CF, Tifft CJ, Gahl WA, Markello TC. Analysis of DNA sequence variants detected by high-throughput sequencing. Hum Mutat. 2012;33(4):599–608. doi: 10.1002/humu.22035. Epub 2012 Feb 28.PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NS, Cooper DN. The human gene mutation database: 2008 update. Genet Med. 2009;1(1):13. doi: 10.1186/gm13.Google Scholar
  13. 13.
    Stenson PD, Ball EV, Mort M, Phillips AD, Shaw K, Cooper DN. The human gene mutation database (HGMD) and its exploitation in the fields of personalized genomics and molecular evolution. Curr Protoc Bioinformatics. 2012;Chapter 1:Unit1.13. doi:10.1002/0471250953.bi0113s39.Google Scholar
  14. 14.
    Won HH, Kim HJ, Lee KA, Kim JW. Cataloging coding sequence variation in human genome databases. PLoS One. 2008;3(10):e3575. doi: 10.1371/journal.pone.0003575. Epub 2008 Oct 30.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Tong MY, Cassa CA, Kohane IS. Automated validation of genetic variants from large databases: ensuring that variant references refer to the same genomic locations. Bioinformatics. 2011;27(6):891–3. doi: 10.1093/bioinformatics/btr029. Epub 2011 Jan 22.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Howard HJ, Horaitis O, Cotton RG, Vhinen M, Dalgleish R, Robinson P, Brooks AJ, Axton M, Hoffmann R, Tuffery-Giraud S. The Human variome project (HVP) 2009 Forum “Towards Establishing Standards”. Hum Mutat. 2010;31(3):366–7. doi: 10.1002/humu.21175.PubMedCrossRefGoogle Scholar
  17. 17.
    Biesecker LG. Opportunities and challenges for the integration of massively parallel genomic sequencing into clinical practice: lessons from the ClinSeq project. Genet Med. 2012;14(4):393–8. doi: 10.1038/gim.2011.78. Epub 2012 Feb 16.PubMedCrossRefPubMedCentralGoogle Scholar
  18. 18.
    Aronson SJ, Clark EH, Varugheese M, Baxter S, Babb LJ, Rehm HJ. Communicating new knowledge on previously reported genetic variants. Genet Med. 2012;14(8):713–9. doi: 10.1038/gim.2012.19.CrossRefPubMedCentralGoogle Scholar
  19. 19.
    Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J, Vardiman J, editors. WHO classification of tumours of haematopoietic and lymphoid tissues. Lyon: International Agency for Research on Cancer; 2008.Google Scholar
  20. 20.
    Estey EH. Acute Myeloid leukemia: 2013 update on risk-stratification and management. Am J Hematol. 2013;88(4):318–27. doi: 10.1002/ajh.23404.PubMedCrossRefGoogle Scholar
  21. 21.
    Lynch TJ, Bell DS, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, Harris PL, Haserlat SM, Supko JG, Haluska FG, Louis DN, Christiani DC, Settleman J, Haber DA. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 2004;350(21):2129–39. Epub 2004 Apr 29.PubMedCrossRefGoogle Scholar
  22. 22.
    Paez JG, Jänne PA, Lee JC, Tracy S, Greulich H, Gabriel S, Herman P, Kaye FJ, Lindeman N, Boggon TJ, Naoki K, Sasaki H, Fujii Y, Eck MJ, Sellers WR, Johnson BE, Meyerson M. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science. 2004;304(5676):1497–500. Epub 2004 Apr 29.PubMedCrossRefGoogle Scholar
  23. 23.
    Pao W, Miller V, Zakowski M, Doherty J, Politi K, Sarkaria I, Singh B, Heelan R, Rusch V, Fulton L, Mardis E, Kupfer D, Wilson R, Kris M, Varmus H. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A. 2004;101(36):13306–11. Epub 2004 Aug 25.PubMedCrossRefPubMedCentralGoogle Scholar
  24. 24.
    Dias-Santagata D, Akhavanfard S, David SS, Vernovsky K, Kuhlmann G, Boisvert SL, Stubbs H, McDermott U, Settleman J, Kwak EL, Clark JW, Isakoff SJ, Sequist LV, Engelman JA, Lynch TJ, Haber DA, Louis DN, Ellisen LW, Borger DR, Iafrate AJ. Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine. EMBO Mol Med. 2010;2(5):146–58. doi: 10.1002/emmm.201000070.PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    MacConaill LE, Campbell CD, Kehoe SM, Bass AJ, Hatton C, Niu L, Davis M, Yao K, Hanna M, Mondal C, Luongo L, Emery CM, Baker AC, Philips J, Goff DJ, Fiorentino M, Rubin MA, Polyak K, Chan J, Wang Y, Fletcher JA, Santagata S, Corso G, Roviello F, Shivdasani R, Kieran MW, Ligon KL, Stiles CD, Hahn WC, Meyerson ML, Garraway LA. Profiling critical cancer gene mutations in clinical tumor samples. PLoS One. 2009;4(11):e7887. doi: 10.1371/journal.pone.0007887.PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Pao W, Kris MG, Iafrate AJ, Ladanyi M, Jänne PA, Wistuba II, Miake-Lye R, Herbst RS, Carbone DP, Johnson BE, Lynch TJ. Integration of molecular profiling into the lung cancer clinic. Clin Cancer Res. 2009;15(17):5317–22. doi: 10.1158/1078-0432.CCR-09-0913. Epub 2009 Aug 25.PubMedCrossRefGoogle Scholar
  27. 27.
    Su Z, Dias-Santagata D, Duke M, Hutchinson K, Lin YL, Borger DR, Chung CH, Massion PP, Vnencak-Jones CL, Iafrate AJ, Pao W. A platform for rapid detection of multiple oncogenic mutations with relevance to targeted therapy in non-small-cell lung cancer. J Mol Diagn. 2011;13(1):74–84. doi: 10.1016/j.jmoldx.2010.11.010. Epub 2010 Dec 23.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Cheng L, Alexander RE, MacLennan GT, Cummings OW, Montironi R, Lopez-Beltran A, Cramer HM, Davidson DD, Zhang S. Molecular pathology of lung cancer: key to personalized medicine. Mod Pathol. 2012;25(3):347–69. doi: 10.1038/modpathol.2011.215. Epub 2012 Jan 27. Review.PubMedCrossRefGoogle Scholar
  29. 29.
    Oxnard GR, Janne P. Power in numbers: meta-analysis to identify inhibitor-sensitive tumor genotypes. Clin Cancer Res. 2013;19(7):1634–6. doi: 10.1158/1078-0432.CCR-13-0169. Epub 2013 Feb 12.PubMedCrossRefGoogle Scholar
  30. 30.
    Flaherty KT, Puzanov I, Kim KB, Ribas A, McArthur GA, Sosman JA, O’Dwyer PJ, Lee RJ, Grippo JF, Nolop K, Chapman PB. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med. 2010;363(9):809–19. doi: 10.1056/NEJMoa1002011.PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    Kopetz S, Desai J, Chan E, Hecht P, O’Dwyer J, Lee RJ, Nolop KB, Saltz L. PLX4032 in metastatic colorectal cancer patients with mutant BRAF tumors. J Clin Oncol (meeting abstracts). 2010;28:15_suppl 3534.Google Scholar
  32. 32.
    Corcoran RB, Ebi H, Turke AB, Coffee EM, Nishino M, Cogdill AP, Brown RD, Della Pelle P, Dias-Santagata D, Hung KE, Flaherty KT, Piris A, Wargo JA, Settleman J, Mino-Kenudson M, Engelman JA. EGFR-mediated re-activation of MAPK signaling contributes to insensitivity of BRAF mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer Discov. 2012;2(3):227–35. doi: 10.1158/2159-8290.CD-11-0341. Epub 2012 Jan 16.PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin D, Beijersbergen RL, Bardelli A, Bernards R. Unresponsiveness of colon cancer to BRAF (V600E) inhibition through feedback activation of EGFR. Nature. 2012;483(7387):100–3. doi: 10.1038/nature10868.PubMedCrossRefGoogle Scholar
  34. 34.
    Yeh P, Chen H, Andrews J, Naser R, Pao W, Horn L. DNA-mutation Inventory to refine and enhance cancer treatment (DIRECT): a catalogue of clinically relevant cancer mutations to enable genome-directed cancer therapy. Clin Cancer Res. 2013;19:1894–901.PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    ACMG Board of Directors. Points to consider in the clinical application of genomic sequencing. Genet Med. 2012;14(8):759–61. doi: 10.1038/gim.2012.74.CrossRefGoogle Scholar
  36. 36.
    Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, McGuire A, Nussbaum RL, O’Daniel JM, Ormand KE, Rehm HL, Watson MS, Williams MS, Biesecker LG. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. http://www.acmg.net/docs/ACMG_Releases_Highly-Anticipated_Recommendations_on_Incidental_Findings_in_Clinical_Exome_and_Genome_Sequencing.pdf (2013). Accessed 15 Apr 2013.
  37. 37.
    Schrijver I, Nazneen A, Farkas DH, Furtado M, Ferreira Gonzalez A, Grenier T, Grody W, Hambuch T, Kalman L, Kant JA, Klein RD, Leonard DGB, Lubin IM, Mao R, Nagan N, Pratt VM, Sobel ME, Voelkerding K, Gibson JS. Opportunities and challenges associated with clinical diagnostic genome sequencing. A report of the Association for Molecular Pathology. J Mol Diagn. 2012;14:525–40. doi: 10.1016/j.jmoldx.2012.04.006. Epub 2012 Aug 20.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Janina A. Longtine
    • 1
  1. 1.Department of PathologyThe Mount Sinai Medical CenterNew YorkUSA

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