Skip to main content
SpringerLink
Log in

Molecular Diagnostic Methods

  • Chapter
  • First Online:
Practical Oncologic Molecular Pathology

Part of the book series: Practical Anatomic Pathology ((PAP))

  • 1858 Accesses

Abstract

Molecular diagnostic testing provides actionable clinical information in many types of cancer. In this chapter, the principles of molecular testing including pre-analytical sample considerations, nucleic acid extraction, and assessment of DNA and RNA qualities are outlined. Molecular test methods including PCR, qPCR, Sanger sequencing, DNA methylation, RT-PCR, and next-generation sequencing are discussed. Questions about the benefits of single-gene, microsatellite, and signal amplification testing are considered as alternatives to multiplex, NGS, and non-molecular tests. The common NGS library preparation methods and sequencing platforms are outlined, including considerations for using this technique in molecular diagnostics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vandeven N, Nghiem P. Pathogen-driven cancers and emerging immune therapeutic strategies. Cancer Immunol Res. 2014;2(1):9–14. Available from: https://cancerimmunolres.aacrjournals.org/content/2/1/9.full. https://doi.org/10.1158/2326-6066.CIR-13-0179.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Medeiros F, Rigl T, Anderson G, Becker S, Halling K. Tissue handling for genome-wide expression analysis: a review of the issues, evidence, and opportunities. Arch Pathol Lab Med. 2007;131(12):1805–16. Available from https://meridian.allenpress.com/aplm/article/131/12/1805/63021. https://doi.org/10.1043/1543-2165(2007)131[1805:THFGEA]2.0.CO;2.

    Article  CAS  PubMed  Google Scholar 

  3. Wang Y, Zheng H, Chen J, Zhong X, Wang Y, Wang Z, et al. The impact of different preservation conditions and freezing-thawing cycles on quality of RNA, DNA, and proteins in cancer tissue. Biopreserv Biobank. 2015;13(5):335–47. Available from https://www.liebertpub.com/doi/10.1089/bio.2015.0029. https://doi.org/10.1089/bio.2015.0029.

    Article  CAS  PubMed  Google Scholar 

  4. Bass BP, Engel KB, Greytak SR, Moore HM. 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. 2014;138(11):1520–30. Available from: https://meridian.allenpress.com/aplm/article/138/11/1520/128727. https://doi.org/10.5858/arpa.2013-0691-RA.

    Article  PubMed  Google Scholar 

  5. Wickham CL, Boyce M, Joyner MV, Sarsfield P, Wilkins BS, Jones DB, Ellard S. Amplification of PCR products in excess of 600 base pairs using DNA extracted from decalcified, paraffin wax embedded bone marrow trephine biopsies. Mol Pathol. 2000;53(1):19–23. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1186897. https://doi.org/10.1136/mp.53.1.19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sarsfield P, Wickham CL, Joyner MV, Ellard S, Jones DB, Wilkins BS. Formic acid decalcification of bone marrow trephines degrades DNA: alternative use of ETA allows the amplification and sequencing of relatively long PCR products. Mol Pathol. 2000;53(6):336. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1186990. https://doi.org/10.1136/mp.53.6.336.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tsongalis G, Al-Turkmani MR, Suriawinata M, Babcock MJ, Mitchell K, Ding Y, et al. Comparison of tissue molecular biomarker testing turnaround times and concordance between standard of care and the biocartis idylla platform in patients with colorectal cancer. Am J Clin Pathol. 2020;154(2):266–76. Available from: https://academic.oup.com/ajcp/article/154/2/266/5856048. https://doi.org/10.1093/ajcp/aqaa044.

    Article  CAS  PubMed  Google Scholar 

  8. Chester N, Marshak DR. Dimethyl sulfoxide-mediated primer tm reduction: a method for analyzing the role of renaturation temperature in the polymerase chain reaction. Anal Biochem. 1993;209(2):284–90. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0003269783711218?via%3Dihub. https://doi.org/10.1006/abio.1993.1121.

    Article  CAS  PubMed  Google Scholar 

  9. Taylor SC, Laperriere G, Germain H. Droplet digital PCR versus qPCR for expression analysis with low abundant targets: from variable nonsense to publication quality data. Sci Rep. 2017;7:2409. Available from: https://www.nature.com/articles/s41598-017-02217-x. https://doi.org/10.1038/s41598-017-02217-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Majumdar N, Banerjee S, Pallas M, Wessel T, Hegerich P. Poisson plus quantification for digital PCR systems. Sci Rep. 2017:9617. Available from: https://www.nature.com/articles/s41598-017-09183-4. https://doi.org/10.1038/s41598-017-09183-4.

  11. Andrikovics H, Orfi Z, Meggyesi N, Bors A, Varg L, Kovy P, et al. Current trends in applications of circulatory microchimerism detection in transplantation. Int J Mol Sci. 2019;20(18):4450. Available from: https://www.mdpi.com/1422-0067/20/18/4450/htm. https://doi.org/10.3390/ijms20184450.

    Article  CAS  PubMed Central  Google Scholar 

  12. Sanchez R, Ayala R, Martinez-Lopez J. Minimal residual disease monitoring with next-generation sequencing methodologies in hematological malignancies. Int J Mol Sci. 2019;20(11):2832. Available from: https://www.mdpi.com/1422-0067/20/11/2832. https://doi.org/10.3390/ijms20112832.

    Article  CAS  PubMed Central  Google Scholar 

  13. Blais J, Lavoie SB, Giroux S, Bussieres J, Lindsay C, Dionne J, et al. Risk of misdiagnosis due to allele dropout and false-positive PCR artifacts in molecular diagnostics: analysis of 30769 genotypes. J Mol Diagn. 2015;17(5):505–14. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1525157815001208?showall%3Dtrue%26via%3Dihub. https://doi.org/10.1016/j.jmoldx.2015.04.004.

    Article  CAS  PubMed  Google Scholar 

  14. Sasaki A, Kim B, Murphy KE, Matthews SG. Impact of ex vivo sample handling on DNA methylation profiles in human cord blood and neonatal dried blood spots. Front Genet. 2020;11:224. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106936/. https://doi.org/10.3389/fgene.2020.00224.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Groen K, Lea RA, Maltby VE, Scott RJ, Lechner-Scott J. Letter to the editor: blood processing and sample storage have negligible effects on methylation. Clin Epigenetics. 2018;10:22. Available from: https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-018-0455-6. https://doi.org/10.1186/s13148-018-0455-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Moran B, Das S, Smeets D, Peutman G, Klinger R, Fender B, et al. Assessment of concordance between fresh-frozen and formalin-fixed paraffin embedded tumor DNA methylation using a targeted sequencing approach. Oncotarget. 2017;8(29):48126–37. Available from: https://www.oncotarget.com/article/18296/text. https://doi.org/10.18632/oncotarget.18296.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kurdyukov S, Bullock M. DNA methylation analysis: choosing the right method. Biology. 5(1):3. Available from: https://www.mdpi.com/2079-7737/5/1/3/htm. https://doi.org/10.3390/biology5010003.

  18. Gerard GF, Potter RJ, Smith MD, Rosenthal K, Dhariwal G, Lee J, et al. The role of template-primer in protection of reverse transcriptase from thermal inactivation. Nucleic Acids Res. 2002;30(14):3118–29. Available from: https://academic.oup.com/nar/article/30/14/3118/2904298. https://doi.org/10.1093/nar/gkf417.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yun JW, Yang L, Park HY, Lee CW, Cha H, Shin HT, et al. Dysregulation of cancer genes by recurrent intergenic fusions. Genome Biol. 2020;21(1):166. Available from: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02076-2. https://doi.org/10.1186/s13059-020-02076-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ozsolak F, Milos PM. RNA sequencing: advances, challenges, and opportunities. Nat Rev Genet. 2011;12(2):87–98. Available from: https://www.nature.com/articles/nrg2934. https://doi.org/10.1038/nrg2934.

    Article  CAS  PubMed  Google Scholar 

  21. Chasseriau J, Rivet J, Bilan F, Chomel JC, Guilhot F, Bourmeyster N, Kitzis A. Characterization of the different BCR-ABL transcripts with a single multiplex RT-PCR. J Mol Diagn. 2004;6(4):343–7. Available from: https://www.sciencedirect.com/science/article/pii/S1525157810605302?via%3Dihub. https://doi.org/10.1016/S1525-1578(10)60530-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Torres F, Ivanova-Dragoeva A, Pereira M, Veiga J, Rodrigues AS, Sousa AB, et al. An e6a2 BCR-ABL fusion transcript in a CML patient having an iliac chloroma at initial presentation. Leuk Lymphoma. 2007;48(5):1034–7. Available from: https://www.tandfonline.com/doi/abs/10.1080/10428190701216402. https://doi.org/10.1080/10428190701216402.

    Article  CAS  PubMed  Google Scholar 

  23. Piedimonte M, Ottone T, Alfonso V, Ferrari A, Conte E, Divona M, et al. A rare BCR-ABL1 transcript in Philadelphia-positive acute myeloid leukemia: case report and literature review. BMC Cancer. 2019;19(1):50. Available from: https://bmccancer.biomedcentral.com/articles/10.1186/s12885-019-5265-5. https://doi.org/10.1186/s12885-019-5265-5.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gabert J, Beillard E, van der Velden V, Grimwade D, Pallisgaard N, Barbany G, et al. Standardization and quality control studies of ‘real-time’ quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia – a Europe against cancer program. Leukemia. 2003;17:2318–57. Available from: https://www.nature.com/articles/2403135. https://doi.org/10.1038/sj.leu.2403135.

    Article  CAS  PubMed  Google Scholar 

  25. Marchiò C, Scaltriti M, Ladanyi M, Iafrate AJ, Bibeau F, Dietel M, et al. ESMO recommendations on the standard methods to detect NTRK fusions in daily practice and clinical research. Ann Oncol. 2019;30(9):1417–27. Available from: https://www.sciencedirect.com/science/article/pii/S0923753419459929?via%3Dihub. https://doi.org/10.1093/annonc/mdz204.

    Article  PubMed  Google Scholar 

  26. Teixidó C, Giménez-Capitán A, Molina-Vila MA, Peg V, Karachaliou N, Rodríguez-Capote A, et al. RNA analysis as a tool to determine clinically relevant gene fusions and splice variants. Arch Pathol Lab Med. 2018;142(4):474–9. Available from: https://meridian.allenpress.com/aplm/article/142/4/474/194239/RNA-Analysis-as-a-Tool-to-Determine-Clinically. https://doi.org/10.5858/arpa.2017-0134-RA.

    Article  PubMed  Google Scholar 

  27. Bruno R, Fontanini G. Next generation sequencing for gene fusion analysis in lung cancer: a literature review. Diagnostics (Basel). 2020;10(8):521. Available from: https://www.mdpi.com/2075-4418/10/8/521. https://doi.org/10.3390/diagnostics10080521.

    Article  CAS  Google Scholar 

  28. Racanelli D, Brenca M, Baldazzi D, Goeman F, Casini B, De Angelis B, et al. Next-generation sequencing approaches for the identification of pathognomonic fusion transcripts in sarcomas: the experience of the Italian ACC sarcoma working group. Front Oncol. 2020;10:944. Available from: https://www.frontiersin.org/articles/10.3389/fonc.2020.00489/full. https://doi.org/10.3389/fonc.2020.00489.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zheng J. Oncogenic chromosomal translocations and human cancer. Oncol Rep. 2013;30(5):2011–9. Available from: https://www.spandidos-publications.com/10.3892/or.2013.2677. https://doi.org/10.3892/or.2013.2677.

    Article  CAS  PubMed  Google Scholar 

  30. Espinet B, Bellosillo B, Melero C, Vela MC, Pedro C, Salido M, et al. FISH is better than BIOMED-2 PCR to detect IgH / BCL 2 translocation in follicular lymphoma at diagnosis using paraffin-embedded tissue sections. Leuk Res. 2008;32(5):737–42. Available from: https://www.clinicalkey.com/#!/content/journal/1-s2.0-S0145212607003517. https://doi.org/10.1016/j.leukres.2007.09.010.

    Article  CAS  PubMed  Google Scholar 

  31. Szankasi P, Bolia A, Liew M, Schumacher JA, Gee EPS, Matynia AP, et al. Comprehensive detection of chromosomal translocations in lymphoproliferative disorders by massively parallel sequencing. J Hematop. 2019;12:121–33. Available from: https://link.springer.com/article/10.1007/s12308-019-00360-0#citeas. https://doi.org/10.1007/s12308-019-00360-0.

    Article  Google Scholar 

  32. Selvi N, Kosova B, Hekimgil M, Gunduz C, Kaymaz BT, Karaca E, et al. Molecular evaluation of t(14;18)(bcl-2/IgH) translocation in follicular lymphoma at diagnosis using paraffin-embedded tissue sections. Turk J Haematol. 2012;29(2):126–34. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3986950/. https://doi.org/10.5505/tjh.2012.93898.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bonneville R, Krook MA, Chen H, Smith A, Samorodnitsky E, Wing MR, et al. Detection of microsatellite instability biomarkers via next-generation sequencing. Methods Mol Biol. 2020;2055:119–32. Available from: https://link.springer.com/protocol/10.1007%2F978-1-4939-9773-2_5. https://doi.org/10.1007/978-1-4939-9773-2_5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Levy SE, Myers RM. Advancements in next generation sequencing. Annu Rev Genom Hum G. 2016;17:95–115. Available from: https://www.annualreviews.org/doi/full/10.1146/annurev-genom-083115-022413. https://doi.org/10.1146/annurev-genom-083115-022413.

    Article  CAS  Google Scholar 

  35. Pfeiffer F, Grober C, Blank M, Handler K, Beyer M, Schultze JL, Mayer G. Systematic evaluation of error rates and causes in short samples in next-generation sequencing. Sci Rep. 2018;8:10950. Available from: https://www.nature.com/articles/s41598-018-29325-6. https://doi.org/10.1038/s41598-018-29325-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Song L, Huang W, Kang J, Huang Y, Ren H, Ding K. Comparison of error correction algorithms for ion torrent PGM data: application to hepatitis B virus. Sci Rep. 2017;7:8106. Available from: https://www.nature.com/articles/s41598-017-08139-y. https://doi.org/10.1038/s41598-017-08139-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kwong JC, McCallum N, Sintchenko V, Howden BP. Whole genome sequencing in clinical and public health microbiology. Pathology. 2015;47(3):199–210. Available from: https://www.clinicalkey.com/#!/content/journal/1-s2.0-S003130251630126X. https://doi.org/10.1097/PAT.0000000000000235.

    Article  CAS  PubMed  Google Scholar 

  38. Do H, Dobrovic A. Sequence artifacts in DNA from formalin-fixed tissues: causes and strategies for minimization. Clin Chem. 2015;61(1):64–71. Available from: https://academic.oup.com/clinchem/article/61/1/64/5611545. https://doi.org/10.1373/clinchem.2014.223040.

    Article  CAS  PubMed  Google Scholar 

  39. Haile S, Corbett RD, Bilobram S, Bye MH, Kirk H, Pandoh P, et al. Sources of erroneous sequences and artifact chimeric reads in next-generation sequencing of genomic DNA from formalin-fixed paraffin-embedded samples. Nucleic Acids Res. 2018;47(2):e12. Available from: https://academic.oup.com/nar/article/47/2/e12/5173669. https://doi.org/10.1093/nar/gky1142.

    Article  CAS  PubMed Central  Google Scholar 

  40. Kircher M, Heyn P, Kelso J. Addressing challenges in the production and analysis of illumina sequencing data. BMC Genomics. 2011;12:382. Available from: https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-12-382. https://doi.org/10.1186/1471-2164-12-382.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ma X, Shao Y, Tian L, Flasch DA, Mulder HL, Edmonson MN, et al. Analysis of error profiles in deep next-generation sequencing data. Genome Biol. 2019;20:50. Available from: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1659-6. https://doi.org/10.1186/s13059-019-1659-6.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Schirmer M, D’Amore R, Ijaz UZ, Quince C. Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data. BMC Bioinf. 2016;17:125. Available from: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0976-y. https://doi.org/10.1186/s12859-016-0976-y.

    Article  CAS  Google Scholar 

  43. Abnizova I, Boekhorst R, Orlov YL. Computational errors and biases in short read next generation sequencing. J Proteomics Bioinform. 2017;10(1):1–17. Available from: https://www.longdom.org/open-access/computational-errors-and-biases-in-short-read-next-generationsequencing-jpb-1000420.pdf. https://doi.org/10.4172/jpb.1000420.

    Article  Google Scholar 

  44. Jennings LJ, Arcila ME, Corless C, Kamel-Reid S, Lubin IM, Pfeifer J, et al. Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diag. 2017;19(3):341–65. Available from: https://www.jmdjournal.org/article/S1525-1578(17)30025-9/fulltext. https://doi.org/10.1016/j.jmoldx.2017.01.011.

    Article  Google Scholar 

  45. Peng M, Chen C, Hulbert A, Brock MV, Yu F. Non-blood circulating tumor DNA detection in cancer. Oncotarget. 2017;8(40):69162–73. Available from: https://www.oncotarget.com/article/19942/text/. https://doi.org/10.18632/oncotarget.19942.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Di Meo A, Bartlett J, Cheng Y, Pasic MD, Yousef GM. Liquid biopsy: a step towards precision medicine in urologic malignancies. Mol Cancer. 2017;16:80. Available from: https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-017-0644-5. https://doi.org/10.1186/s12943-017-0644-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jahangiri L, Hurst T. Assessing the concordance of genomic alterations between circulating-free DNA and tumour tissue in cancer patients. Cancers. 2019;11(12):1938. Available from: https://www.mdpi.com/2072-6694/11/12/1938. https://doi.org/10.3390/cancers11121938.

    Article  CAS  PubMed Central  Google Scholar 

  48. Lyman SD, Jacobsen SEW. C-kit ligand and Flt3 ligand: stem/progenitor cell factors with overlapping yet distinct activities. Blood. 1998;91(4):1101–34. Available from: https://ashpublications.org/blood/article/91/4/1101/139529/c-kit-Ligand-and-Flt3-Ligand-Stem-Progenitor-Cell. https://doi.org/10.1182/blood.V91.4.1101.

    Article  CAS  PubMed  Google Scholar 

  49. Small D, Levenstein M, Kim E, Carow C, Amin S, Rockwell P, et al. STK-1, the human homolog of Flk-2/Flt-3, is selectively expressed in CD34+ human bone marrow cells and is involved in the proliferation of early progenitor/stem cells. Proc Natl Acad Sci U S A. 1994;91(2):459–63. Available from: https://www.pnas.org/content/91/2/459. https://doi.org/10.1073/pnas.91.2.459.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mackarehtschian K, Hardin JD, Moore KA, Boast S, Goff SP, Lemischka IR. Targeted disruption of the Flk2/Flt3 gene leads to deficiencies in primitive hematopoietic progenitors. Immunity. 1995;3(1):147–61. Available from: https://www.cell.com/immunity/pdf/1074-7613(95)90167-1.pdf. https://doi.org/10.1016/1074-7613(95)90167-1.

    Article  CAS  PubMed  Google Scholar 

  51. Patnaik MM. The importance of FLT3 mutational analysis in acute myeloid leukemia. Leuk Lymphoma. 2018;59(10):2273–86. Available from: https://www.tandfonline.com/doi/full/10.1080/10428194.2017.1399312. https://doi.org/10.1080/10428194.2017.1399312.

    Article  CAS  PubMed  Google Scholar 

  52. Schranz K, Hubmann M, Harin E, Vosberg S, Herold T, Metzeler KH, et al. Clonal heterogeneity of FLT3-ITD detected by high-throughput amplicon sequencing correlates with adverse prognosis in acute myeloid leukemia. Oncotarget. 2018;9(53):30128–45. Available from: https://www.oncotarget.com/article/25729/text/. https://doi.org/10.18632/oncotarget.25729.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Annesly CE, Brown P. The biology and targeting of FLT3 in pediatric leukemia. Front Oncol. 2014;4:263. Available from: https://www.frontiersin.org/articles/10.3389/fonc.2014.00263/full. https://doi.org/10.3389/fonc.2014.00263.

    Article  Google Scholar 

  54. Horiike S, Yokota S, Nakao M, Iwai T, Sasai Y, Kaneko H, et al. Tandem duplications of the FLT3 receptor gene are associated with leukemic transformation of myelodysplasia. Leukemia. 1997;11:1442–6. Available from: https://www.nature.com/articles/2400770. https://doi.org/10.1038/sj.leu.2400770.

    Article  CAS  PubMed  Google Scholar 

  55. Yokota S, Kiyoi H, Nakao M, Iwai T, Misawa S, Okuda T, et al. Internal tandem duplication of the FLT3 gene is preferentially seen in acute myeloid leukemia and myelodysplastic syndrome among various hematological malignancies. A study on a large series of patients and cell lines. Leukemia. 1997;11(10):1605–9. Available from: https://www.nature.com/articles/2400812. https://doi.org/10.1038/sj.leu.2400812.

    Article  CAS  PubMed  Google Scholar 

  56. Xu F, Taki T, Yang HW, Hanada R, Hongo T, Ohnishi H, et al. Tandem duplication of the FLT3 gene is found in acute lymphoblastic leukaemia as well as acute myeloid Leukaemia but not in myelodysplastic syndrome or juvenile chronic myelogenous leukaemia in children. Br J Hematol. 1999;105(1):155–62. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2141.1999.01284.x. https://doi.org/10.1111/j.1365-2141.1999.01284.x.

    Article  CAS  Google Scholar 

  57. Thiede C, Steudel C, Mohr B, Schaich M, Schäkel U, Platzbecker U, et al. Analysis of FLT3-activating mutations in 979 patients with acute myelogenous leukemia: association with FAB subtypes and identification of subgroups with poor prognosis. Blood. 2002;99(12):4326–35. Available from: https://ashpublications.org/blood/article/99/12/4326/105972/Analysis-of-FLT3-activating-mutations-in-979. https://doi.org/10.1182/blood.V99.12.4326.

    Article  CAS  PubMed  Google Scholar 

  58. Nakao M, Yokota S, Iwai T, Kaneko H, Horiike S, Kashima K, et al. Internal tandem duplication of the Flt3 gene found in acute myeloid leukemia. Leukemia. 1996;10(12):1911–8. Available from: https://pubmed.ncbi.nlm.nih.gov/8946930/

    CAS  PubMed  Google Scholar 

  59. Schnittger S, Schoch C, Dugas M, Kern W, Staib P, Wuchter C, et al. Analysis of FLT3 length mutations in 1003 patients with acute myeloid leukemia: correlation to Cytogenetics, FAB subtype, and prognosis in the AMLCG study and usefulness as a marker for the detection of minimal residual disease. Blood. 2002;100(1):59–66. Available from: https://ashpublications.org/blood/article/100/1/59/133972/Analysis-of-FLT3-length-mutations-in-1003-patients. https://doi.org/10.1182/blood.V100.1.59.

    Article  CAS  PubMed  Google Scholar 

  60. Hayakawa F, Towatari M, Kiyoi H, Tanimoto M, Kitamura T, Saito H, et al. Tandem-duplicated Flt3 constitutively activates STAT5 and MAP kinase and introduces autonomous cell growth in IL-3-dependent cell lines. Oncogene. 2000;19:624–31. Available from: https://www.nature.com/articles/1203354. https://doi.org/10.1038/sj.onc.1203354.

    Article  CAS  PubMed  Google Scholar 

  61. Kiyoi H, Towatari M, Yokota S, Hamaguchi M, Ohno R, Saito H, et al. Internal tandem duplication of the FLT3 gene is a novel modality of elongation mutation which causes constitutive activation of the product. Leukemia. 1998;12:1333–7. Available from: https://www.nature.com/articles/2401130. https://doi.org/10.1038/sj.leu.2401130.

    Article  CAS  PubMed  Google Scholar 

  62. Fenski R, Flesch K, Serve S, Mizuki M, Oelmann E, Kratz-Albers K, et al. Constitutive activation of FLT3 in acute myeloid leukaemia and its consequences for growth of 32D cells. Br. J. Hematol. 2000;108(2):322–30. Available from: https://onlinelibrary.wiley.com/doi/full/10.1046/j.1365-2141.2000.01831.x. https://doi.org/10.1046/j.1365-2141.2000.01831.x.

    Article  CAS  Google Scholar 

  63. Abu-Duhier FM, Goodeve AC, Wilson GA, Care RS, Peake IR, Reilly JT. Identification of novel FLT-3 Asp835 mutations in adult acute myeloid Leukaemia. Br J Haematol. 2001;113(4):983–8. Available from: https://onlinelibrary.wiley.com/doi/full/10.1046/j.1365-2141.2001.02850.x. https://doi.org/10.1046/j.1365-2141.2001.02850.x.

    Article  CAS  PubMed  Google Scholar 

  64. Yamamoto Y, Kiyoi H, Nakano Y, Suzuki R, Kodera Y, Miyawaki S, et al. Activating mutation of D835 within the activation loop of FLT3 in human hematologic malignancies. Blood. 2001;97(8):2434–9. Available from: https://ashpublications.org/blood/article/97/8/2434/53253/Activating-mutation-of-D835-within-the-activation. https://doi.org/10.1182/blood.V97.8.2434.

    Article  CAS  PubMed  Google Scholar 

  65. Maxson JE, Tyner JW. Genomics of chronic neutrophilic leukemia. Blood. 2017;129(6):715–22. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5301820. https://doi.org/10.1182/blood-2016-10-695981.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Szuber N, Tefferi A. Chronic neutrophilic leukemia: new science and new diagnostic criteria. Blood Cancer J. 2018;8(2):19. Available from: https://www.nature.com/articles/s41408-018-0049-8. https://doi.org/10.1038/s41408-018-0049-8.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Buels R, Yao E, Diesh CM, Hayes RD, Munoz-Torres M, Helt G, et al. JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol. 2016;17:66. Available from: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0924-1. https://doi.org/10.1186/s13059-016-0924-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Laboratory Medicine, Geisinger Health, Danville, PA, USA

    Terrance J. Lynn & Andrew Campbell

Authors
  1. Terrance J. Lynn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Campbell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew Campbell .

Editor information

Editors and Affiliations

  1. Department of Laboratory Medicine, Geisinger Health, Danville, PA, USA

    Yi Ding

  2. Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Linsheng Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lynn, T.J., Campbell, A. (2021). Molecular Diagnostic Methods. In: Ding, Y., Zhang, L. (eds) Practical Oncologic Molecular Pathology. Practical Anatomic Pathology. Springer, Cham. https://doi.org/10.1007/978-3-030-73227-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-73227-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73226-4

  • Online ISBN: 978-3-030-73227-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation