Skip to main content

Advertisement

SpringerLink
Log in

High-resolution melting effectively pre-screens for TP53 mutations before direct sequencing in patients with diffuse glioma

  • Research Article
  • Published:
Human Cell Aims and scope Submit manuscript

Abstract

TP53 mutations are important molecular markers in diffuse astrocytic tumors and medulloblastomas. We examined the efficacy of a pre-screening method for high-resolution melting (HRM) analysis of TP53 mutation before direct sequencing using samples from patients with diffuse glioma. Surgical samples from 64 diffuse gliomas were classified based on the 2016 World Health Organization (WHO) histopathological grading system and the cIMPACT-NOW (consortium to inform molecular and practical approaches to CNS tumor taxonomy-not official WHO) update. TP53 mutations from exon 5 to exon 8 were assessed by direct sequencing. The results of HRM and p53 immunohistochemistry (IHC) analysis were compared by recording the sensitivity, specificity, and false negative and false positive rates. Direct sequencing detected TP53 mutations in 18 of 64 samples (28.1%): diffuse astrocytoma, IDH-mutant (n = 3); diffuse astrocytoma, IDH-wild type (n = 1); anaplastic astrocytoma, IDH-mutant (n = 3); anaplastic astrocytoma, IDH-wild type (n = 4); and glioblastoma, IDH-wild type (n = 7). A total of 22 mutations was detected in the 18 samples; 4 samples exhibited duplicate missense mutations. Sensitivity and specificity were 0.96 and 0.96, respectively, for HRM analysis; they were 0.89 and 0.52, respectively, for p53 IHC. Overall accuracy was 0.98 for HRM and 0.63 for IHC. HRM analysis is a good pre-screening method for the detection of TP53 mutation before direct sequencing.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Komori T. The 2016 WHO classification of tumours of the central nervous system: the major points of revision. Neurol Med Chir Tokyo. 2017;57(7):301–11. https://doi.org/10.2176/nmc.ra.2017-0010.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131(6):803–20. https://doi.org/10.1007/s00401-016-1545-1.

    Article  PubMed  Google Scholar 

  3. Von Deimling A, Perry A, Louis D, Ohgaki H, Wiestler O, Cavenee W. WHO classification of tumours of the central nervous system. Lyon: IARC; 2016.

    Google Scholar 

  4. Cambruzzi E. Medulloblastoma, WNT-activated/SHH-activated: clinical impact of molecular analysis and histogenetic evaluation. Childs Nerv Syst. 2018;34(5):809–15. https://doi.org/10.1007/s00381-018-3765-2.

    Article  PubMed  Google Scholar 

  5. Murnyak B, Hortobagyi T. Immunohistochemical correlates of TP53 somatic mutations in cancer. Oncotarget. 2016;7(40):64910–20. https://doi.org/10.18632/oncotarget.11912.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bouaoun L, Sonkin D, Ardin M, Hollstein M, Byrnes G, Zavadil J, et al. TP53 variations in human cancers: new lessons from the IARC TP53 database and genomics data. Hum Mutat. 2016;37(9):865–76. https://doi.org/10.1002/humu.23035.

    Article  CAS  PubMed  Google Scholar 

  7. Salnikova LE. Clinicopathologic characteristics of brain tumors are associated with the presence and patterns of TP53 mutations: evidence from the IARC TP53 Database. Neuromol Med. 2014;16(2):431–47. https://doi.org/10.1007/s12017-014-8290-1.

    Article  CAS  Google Scholar 

  8. Chan AK, Mao Y, Ng HK. TP53 and histone H3.3 mutations in triple-negative lower-grade gliomas. N Engl J Med. 2016;375(22):2206–8. https://doi.org/10.1056/nejmc1610144.

    Article  PubMed  Google Scholar 

  9. Nie E, Jin X, Wu W, Yu T, Zhou X, Zhi T, et al. BACH1 promotes temozolomide resistance in glioblastoma through antagonizing the function of p53. Sci Rep. 2016;6:39743. https://doi.org/10.1038/srep39743.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ohgaki H, Dessen P, Jourde B, Horstmann S, Nishikawa T, Di Patre PL, et al. Genetic pathways to glioblastoma: a population-based study. Cancer Res. 2004;64(19):6892–9. https://doi.org/10.1158/0008-5472.CAN-04-1337.

    Article  CAS  PubMed  Google Scholar 

  11. Ohgaki H, Kleihues P. Genetic profile of astrocytic and oligodendroglial gliomas. Brain Tumor Pathol. 2011;28(3):177–83. https://doi.org/10.1007/s10014-011-0029-1.

    Article  CAS  PubMed  Google Scholar 

  12. Okamoto Y, Di Patre PL, Burkhard C, Horstmann S, Jourde B, Fahey M, et al. Population-based study on incidence, survival rates, and genetic alterations of low-grade diffuse astrocytomas and oligodendrogliomas. Acta Neuropathol. 2004;108(1):49–56. https://doi.org/10.1007/s00401-004-0861-z.

    Article  PubMed  Google Scholar 

  13. Sarma PP, Dutta D, Mirza Z, Saikia KK, Baishya BK. Point mutations in the DNA binding domain of p53 contribute to glioma progression and poor prognosis. Mol Biol Mosk. 2017;51(2):334–41. https://doi.org/10.7868/S0026898417020185.

    Article  CAS  PubMed  Google Scholar 

  14. Peraud A, Kreth FW, Wiestler OD, Kleihues P, Reulen HJ. Prognostic impact of TP53 mutations and P53 protein overexpression in supratentorial WHO grade II astrocytomas and oligoastrocytomas. Clin Cancer Res. 2002;8(5):1117–24.

    CAS  PubMed  Google Scholar 

  15. Hermisson M, Klumpp A, Wick W, Wischhusen J, Nagel G, Roos W, et al. O6-methylguanine DNA methyltransferase and p53 status predict temozolomide sensitivity in human malignant glioma cells. J Neurochem. 2006;96(3):766–76. https://doi.org/10.1111/j.1471-4159.2005.03583.x.

    Article  CAS  PubMed  Google Scholar 

  16. Wang X, Chen JX, Liu JP, You C, Liu YH, Mao Q. Gain of function of mutant TP53 in glioblastoma: prognosis and response to temozolomide. Ann Surg Oncol. 2014;21(4):1337–44. https://doi.org/10.1245/s10434-013-3380-0.

    Article  PubMed  Google Scholar 

  17. Wang X, Chen JX, Liu YH, You C, Mao Q. Mutant TP53 enhances the resistance of glioblastoma cells to temozolomide by up-regulating O(6)-methylguanine DNA-methyltransferase. Neurol Sci. 2013;34(8):1421–8. https://doi.org/10.1007/s10072-012-1257-9.

    Article  PubMed  Google Scholar 

  18. Zhang C, Wang X, Hao S, Su Z, Zhang P, Li Y, et al. Analysis of treatment tolerance and factors associated with overall survival in elderly patients with glioblastoma. World Neurosurg. 2016;95:77–84. https://doi.org/10.1016/j.wneu.2016.07.079.

    Article  CAS  PubMed  Google Scholar 

  19. Newcomb EW, Madonia WJ, Pisharody S, Lang FF, Koslow M, Miller DC. A correlative study of p53 protein alteration and p53 gene mutation in glioblastoma multiforme. Brain Pathol. 1993;3(3):229–35.

    Article  CAS  PubMed  Google Scholar 

  20. Takami H, Yoshida A, Fukushima S, Arita H, Matsushita Y, Nakamura T, et al. Revisiting TP53 mutations and immunohistochemistry–a comparative study in 157 diffuse gliomas. Brain Pathol. 2015;25(3):256–65. https://doi.org/10.1111/bpa.12173.

    Article  CAS  PubMed  Google Scholar 

  21. Krypuy M, Ahmed AA, Etemadmoghadam D, Hyland SJ, Australian Ovarian Cancer Study G, DeFazio A, et al. High resolution melting for mutation scanning of TP53 exons 5-8. BMC Cancer. 2007;7:168. https://doi.org/10.1186/1471-2407-7-168.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Louis DN, Giannini C, Capper D, Paulus W, Figarella-Branger D, Lopes MB, et al. cIMPACT-NOW update 2: diagnostic clarifications for diffuse midline glioma, H3 K27M-mutant and diffuse astrocytoma/anaplastic astrocytoma, IDH-mutant. Acta Neuropathol. 2018;135(4):639–42. https://doi.org/10.1007/s00401-018-1826-y.

    Article  PubMed  Google Scholar 

  23. Louis DN, Wesseling P, Paulus W, Giannini C, Batchelor TT, Cairncross JG, et al. cIMPACT-NOW update 1: not otherwise specified (NOS) and not elsewhere classified (NEC). Acta Neuropathol. 2018;135(3):481–4. https://doi.org/10.1007/s00401-018-1808-0.

    Article  PubMed  Google Scholar 

  24. Hatae R, Hata N, Yoshimoto K, Kuga D, Akagi Y, Murata H, et al. Precise detection of IDH1/2 and BRAF hotspot mutations in clinical glioma tissues by a differential calculus analysis of high-resolution melting data. PLoS ONE. 2016;11(8):e0160489. https://doi.org/10.1371/journal.pone.0160489.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gonzalez-Bosquet J, Calcei J, Wei JS, Garcia-Closas M, Sherman ME, Hewitt S, et al. Detection of somatic mutations by high-resolution DNA melting (HRM) analysis in multiple cancers. PLoS ONE. 2011;6(1):e14522. https://doi.org/10.1371/journal.pone.0014522.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ihle MA, Fassunke J, Konig K, Grunewald I, Schlaak M, Kreuzberg N, et al. Comparison of high resolution melting analysis, pyrosequencing, next generation sequencing and immunohistochemistry to conventional Sanger sequencing for the detection of p.V600E and non-p.V600E BRAF mutations. BMC Cancer. 2014;14:13. https://doi.org/10.1186/1471-2407-14-13.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Yokogami K, Yamasaki K, Matsumoto F, Yamashita S, Saito K, Tacheva A, et al. Impact of PCR-based molecular analysis in daily diagnosis for the patient with gliomas. Brain Tumor Pathol. 2018. https://doi.org/10.1007/s10014-018-0322-3.

    Article  PubMed  Google Scholar 

  28. Baugh EH, Ke H, Levine AJ, Bonneau RA, Chan CS. Why are there hotspot mutations in the TP53 gene in human cancers? Cell Death Differ. 2018;25(1):154–60. https://doi.org/10.1038/cdd.2017.180.

    Article  CAS  PubMed  Google Scholar 

  29. Soussi T, Kato S, Levy PP, Ishioka C. Reassessment of the TP53 mutation database in human disease by data mining with a library of TP53 missense mutations. Hum Mutat. 2005;25(1):6–17. https://doi.org/10.1002/humu.20114.

    Article  CAS  PubMed  Google Scholar 

  30. Muller PA, Vousden KH. Mutant p53 in cancer: new functions and therapeutic opportunities. Cancer Cell. 2014;25(3):304–17. https://doi.org/10.1016/j.ccr.2014.01.021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kogan S, Carpizo DR. Zinc metallochaperones as mutant p53 reactivators: a new paradigm in cancer therapeutics. Cancers Basel. 2018. https://doi.org/10.3390/cancers10060166.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhao D, Tahaney WM, Mazumdar A, Savage MI, Brown PH. Molecularly targeted therapies for p53-mutant cancers. Cell Mol Life Sci. 2017;74(22):4171–87. https://doi.org/10.1007/s00018-017-2575-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tindall EA, Petersen DC, Woodbridge P, Schipany K, Hayes VM. Assessing high-resolution melt curve analysis for accurate detection of gene variants in complex DNA fragments. Hum Mutat. 2009;30(6):876–83. https://doi.org/10.1002/humu.20919.

    Article  CAS  PubMed  Google Scholar 

  34. Davidson CJ, Zeringer E, Champion KJ, Gauthier M-P, Wang F, Boonyaratanakornkit J, et al. Improving the limit of detection for Sanger sequencing: a comparison of methodologies for KRAS variant detection. Biotechniques. 2012;53(3):182–8. https://doi.org/10.2144/000113913.

    Article  CAS  PubMed  Google Scholar 

  35. Sharma Y, Miladi M, Dukare S, Boulay K, Caudron-Herger M, Gross M, et al. A pan-cancer analysis of synonymous mutations. Nat Commun. 2019;10(1):2569. https://doi.org/10.1038/s41467-019-10489-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Duensing A, Duensing S. Guilt by association? p53 and the development of aneuploidy in cancer. Biochem Biophys Res Commun. 2005;331(3):694–700. https://doi.org/10.1016/j.bbrc.2005.03.157.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by a Grant-in-Aid for Clinical Research from Miyazaki University Hospital. We especially thank Ms. Ayumi Nagatomo and Ms. Akemi Yoshida for technical assistance and Ms. Ursula Petralia for editorial assistance.

Author information

Authors and Affiliations

  1. Division of Clinical Neuroscience, Department of Neurosurgery, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake-cho, Miyazaki, 889-1692, Japan

    Kiyotaka Saito, Kiyotaka Yokogami, Shinji Yamashita, Fumitaka Matsumoto, Asako Mizuguchi & Hideo Takeshima

  2. Department of Diagnostic Pathology, Miyazaki University Hospital, 5200 Kihara, Kiyotake-cho, Miyazaki, 889-1692, Japan

    Kazunari Maekawa & Yuichiro Sato

Authors
  1. Kiyotaka Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kiyotaka Yokogami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kazunari Maekawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuichiro Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shinji Yamashita
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fumitaka Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Asako Mizuguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hideo Takeshima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kiyotaka Saito.

Ethics declarations

Conflict of interest

The authors report no conflict of interest concerning the materials or methods used in this study or the findings reported in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 15 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saito, K., Yokogami, K., Maekawa, K. et al. High-resolution melting effectively pre-screens for TP53 mutations before direct sequencing in patients with diffuse glioma. Human Cell 34, 644–653 (2021). https://doi.org/10.1007/s13577-020-00471-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13577-020-00471-2

Keywords

Search

Navigation