1932

Abstract

Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types.

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2022-01-24
2024-04-24
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Literature Cited

  1. 1. 
    Saxonov S, Berg P, Brutlag DL. 2006. A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters. PNAS 103:1412–17
    [Google Scholar]
  2. 2. 
    Bird A. 2002. DNA methylation patterns and epigenetic memory. Genes Dev 16:6–21
    [Google Scholar]
  3. 3. 
    Edgar R, Tan PP, Portales-Casamar E, Pavlidis P. 2014. Meta-analysis of human methylomes reveals stably methylated sequences surrounding CpG islands associated with high gene expression. Epigenet. Chromatin 7:28
    [Google Scholar]
  4. 4. 
    Berger SL, Kouzarides T, Shiekhattar R, Shilatifard A. 2009. An operational definition of epigenetics. Genes Dev 23:781–83
    [Google Scholar]
  5. 5. 
    Okano M, Bell DW, Haber DA, Li E. 1999. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99:247–57
    [Google Scholar]
  6. 6. 
    Bestor TH. 1992. Activation of mammalian DNA methyltransferase by cleavage of a Zn binding regulatory domain. EMBO J 11:2611–17
    [Google Scholar]
  7. 7. 
    Pradhan S, Bacolla A, Wells RD, Roberts RJ. 1999. Recombinant human DNA (cytosine-5) methyltransferase. I. Expression, purification, and comparison of de novo and maintenance methylation. J. Biol. Chem. 274:33002–10
    [Google Scholar]
  8. 8. 
    Paziewska A, Dabrowska M, Goryca K, Antoniewicz A, Dobruch J et al. 2014. DNA methylation status is more reliable than gene expression at detecting cancer in prostate biopsy. Br. J. Cancer 111:781–89
    [Google Scholar]
  9. 9. 
    Schwalbe EC, Williamson D, Lindsey JC, Hamilton D, Ryan SL et al. 2013. DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies. Acta Neuropathol 125:359–71
    [Google Scholar]
  10. 10. 
    Vilahur N, Baccarelli AA, Bustamante M, Agramunt S, Byun HM et al. 2013. Storage conditions and stability of global DNA methylation in placental tissue. Epigenomics 5:341–48
    [Google Scholar]
  11. 11. 
    Gama-Sosa MA, Slagel VA, Trewyn RW, Oxenhandler R, Kuo KC et al. 1983. The 5-methylcytosine content of DNA from human tumors. Nucleic Acids Res 11:6883–94
    [Google Scholar]
  12. 12. 
    Esteller M, Silva JM, Dominguez G, Bonilla F, Matias-Guiu X et al. 2000. Promoter hypermethylation and BRCA1 inactivation in sporadic breast and ovarian tumors. J. Natl. Cancer Inst. 92:564–69
    [Google Scholar]
  13. 13. 
    Merlo A, Herman JG, Mao L, Lee DJ, Gabrielson E et al. 1995. 5′ CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers. Nat. Med. 1:686–92
    [Google Scholar]
  14. 14. 
    Yang X, Han H, De Carvalho DD, Lay FD, Jones PA, Liang G. 2014. Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell 26:577–90
    [Google Scholar]
  15. 15. 
    Spainhour JC, Lim HS, Yi SV, Qiu P 2019. Correlation patterns between DNA methylation and gene expression in The Cancer Genome Atlas. Cancer Inform 18:1176935119828776
    [Google Scholar]
  16. 16. 
    Ziller MJ, Gu H, Muller F, Donaghey J, Tsai LT et al. 2013. Charting a dynamic DNA methylation landscape of the human genome. Nature 500:477–81
    [Google Scholar]
  17. 17. 
    Costello JF, Fruhwald MC, Smiraglia DJ, Rush LJ, Robertson GP et al. 2000. Aberrant CpG-island methylation has non-random and tumour-type-specific patterns. Nat. Genet. 24:132–38
    [Google Scholar]
  18. 18. 
    Leon SA, Shapiro B, Sklaroff DM, Yaros MJ. 1977. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res 37:646–50
    [Google Scholar]
  19. 19. 
    Clark SJ, Harrison J, Paul CL, Frommer M 1994. High sensitivity mapping of methylated cytosines. Nucleic Acids Res 22:2990–97
    [Google Scholar]
  20. 20. 
    Frommer M, McDonald LE, Millar DS, Collis CM, Watt F et al. 1992. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. PNAS 89:1827–31
    [Google Scholar]
  21. 21. 
    Hayatsu H, Wataya Y, Kai K, Iida S 1970. Reaction of sodium bisulfite with uracil, cytosine, and their derivatives. Biochemistry 9:2858–65
    [Google Scholar]
  22. 22. 
    Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB. 1996. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. PNAS 93:9821–26
    [Google Scholar]
  23. 23. 
    Gonzalgo ML, Jones PA. 1997. Rapid quantitation of methylation differences at specific sites using methylation-sensitive single nucleotide primer extension (Ms-SNuPE). Nucleic Acids Res 25:2529–31
    [Google Scholar]
  24. 24. 
    Dupont JM, Tost J, Jammes H, Gut IG 2004. De novo quantitative bisulfite sequencing using the pyrosequencing technology. Anal. Biochem. 333:119–27
    [Google Scholar]
  25. 25. 
    Hashimoto K, Kokubun S, Itoi E, Roach HI 2007. Improved quantification of DNA methylation using methylation-sensitive restriction enzymes and real-time PCR. Epigenetics 2:86–91
    [Google Scholar]
  26. 26. 
    Zhou L, Ng HK, Drautz-Moses DI, Schuster SC, Beck S et al. 2019. Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing. Sci. Rep. 9:10383
    [Google Scholar]
  27. 27. 
    Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L et al. 2009. Genome-wide DNA methylation profiling using Infinium® assay. Epigenomics 1:177–200
    [Google Scholar]
  28. 28. 
    Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW et al. 2006. High-throughput DNA methylation profiling using universal bead arrays. Genome Res 16:383–93
    [Google Scholar]
  29. 29. 
    Moran S, Arribas C, Esteller M 2016. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics 8:389–99
    [Google Scholar]
  30. 30. 
    Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA et al. 2011. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6:692–702
    [Google Scholar]
  31. 31. 
    Hovestadt V, Remke M, Kool M, Pietsch T, Northcott PA et al. 2013. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol 125:913–16First study to identify medulloblastoma molecular groups by DNA methylation.
    [Google Scholar]
  32. 32. 
    Du P, Zhang X, Huang CC, Jafari N, Kibbe WA et al. 2010. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 11:587
    [Google Scholar]
  33. 33. 
    Li W, Cerise JE, Yang Y, Han H 2017. Application of t-SNE to human genetic data. J. Bioinform. Comput. Biol. 15:1750017
    [Google Scholar]
  34. 34. 
    Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH et al. 2018. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37:38–44
    [Google Scholar]
  35. 35. 
    Capper D, Stichel D, Sahm F, Jones DTW, Schrimpf D et al. 2018. Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience. Acta Neuropathol 136:181–210
    [Google Scholar]
  36. 36. 
    Braczynski AK, Capper D, Jones DTW, Schittenhelm J, Stichel D et al. 2020. High density DNA methylation array is a reliable alternative for PCR-based analysis of the MGMT promoter methylation status in glioblastoma. Pathol. Res. Pract. 216:152728
    [Google Scholar]
  37. 37. 
    Stichel D, Schrimpf D, Sievers P, Reinhardt A, Suwala AK et al. 2020. Accurate calling of KIAA1549-BRAF fusions from DNA of human brain tumours using methylation array-based copy number and gene panel sequencing data. Neuropathol. Appl. Neurobiol. 47:406–14
    [Google Scholar]
  38. 38. 
    Breiman L. 2001. Random forests. Mach. Learn. 45:5–32
    [Google Scholar]
  39. 39. 
    Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D et al. 2018. DNA methylation-based classification of central nervous system tumours. Nature 555:469–74CNS DNA methylation classifier description. This is the first large-scale DNA methylation cancer classifier.
    [Google Scholar]
  40. 40. 
    Koelsche C, Schrimpf D, Stichel D, Sill M, Sahm F et al. 2021. Sarcoma classification by DNA methylation profiling. Nat. Commun. 12:498Sarcoma DNA methylation classifier description. This is the second large-scale DNA methylation cancer classifier.
    [Google Scholar]
  41. 41. 
    Priesterbach-Ackley LP, Boldt HB, Petersen JK, Bervoets N, Scheie D et al. 2020. Brain tumour diagnostics using a DNA methylation-based classifier as a diagnostic support tool. Neuropathol. Appl. Neurobiol. 46:478–92
    [Google Scholar]
  42. 42. 
    Shen SY, Burgener JM, Bratman SV, De Carvalho DD. 2019. Preparation of cfMeDIP-seq libraries for methylome profiling of plasma cell-free DNA. Nat. Protoc. 14:2749–80
    [Google Scholar]
  43. 43. 
    Schumacher A, Kapranov P, Kaminsky Z, Flanagan J, Assadzadeh A et al. 2006. Microarray-based DNA methylation profiling: technology and applications. Nucleic Acids Res 34:528–42
    [Google Scholar]
  44. 44. 
    Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV et al. 2020. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31:745–59
    [Google Scholar]
  45. 45. 
    Nunes SP, Moreira-Barbosa C, Salta S, Palma de Sousa S, Pousa I et al. 2018. Cell-free DNA methylation of selected genes allows for early detection of the major cancers in women. Cancers 10:357
    [Google Scholar]
  46. 46. 
    Constancio V, Nunes SP, Moreira-Barbosa C, Freitas R, Oliveira J et al. 2019. Early detection of the major male cancer types in blood-based liquid biopsies using a DNA methylation panel. Clin. Epigenet. 11:175
    [Google Scholar]
  47. 47. 
    Kang S, Li Q, Chen Q, Zhou Y, Park S et al. 2017. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol 18:53
    [Google Scholar]
  48. 48. 
    Nassiri F, Chakravarthy A, Feng S, Shen SY, Nejad R et al. 2020. Detection and discrimination of intracranial tumors using plasma cell-free DNA methylomes. Nat. Med. 26:1044–47
    [Google Scholar]
  49. 49. 
    Pegg AE, Dolan ME, Moschel RC. 1995. Structure, function, and inhibition of O6-alkylguanine-DNA alkyltransferase. Prog. Nucleic Acid Res. Mol. Biol. 51:167–223
    [Google Scholar]
  50. 50. 
    Watts GS, Pieper RO, Costello JF, Peng YM, Dalton WS, Futscher BW 1997. Methylation of discrete regions of the O6-methylguanine DNA methyltransferase (MGMT) CpG island is associated with heterochromatinization of the MGMT transcription start site and silencing of the gene. Mol. Cell. Biol. 17:5612–19
    [Google Scholar]
  51. 51. 
    Esteller M, Garcia-Foncillas J, Andion E, Goodman SN, Hidalgo OF et al. 2000. Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N. Engl. J. Med. 343:1350–54
    [Google Scholar]
  52. 52. 
    Hegi ME, Diserens AC, Godard S, Dietrich PY, Regli L et al. 2004. Clinical trial substantiates the predictive value of O-6-methylguanine-DNA methyltransferase promoter methylation in glioblastoma patients treated with temozolomide. Clin. Cancer Res. 10:1871–74
    [Google Scholar]
  53. 53. 
    Paz MF, Yaya-Tur R, Rojas-Marcos I, Reynes G, Pollan M et al. 2004. CpG island hypermethylation of the DNA repair enzyme methyltransferase predicts response to temozolomide in primary gliomas. Clin. Cancer Res. 10:4933–38
    [Google Scholar]
  54. 54. 
    Wick W, Meisner C, Hentschel B, Platten M, Schilling A et al. 2013. Prognostic or predictive value of MGMT promoter methylation in gliomas depends on IDH1 mutation. Neurology 81:1515–22
    [Google Scholar]
  55. 55. 
    Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K et al. 2010. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17:510–22First study to describe G-CIMP, IDH-mutant gliomas.
    [Google Scholar]
  56. 56. 
    Turcan S, Rohle D, Goenka A, Walsh LA, Fang F et al. 2012. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483:479–83
    [Google Scholar]
  57. 57. 
    Sturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT et al. 2012. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22:425–37
    [Google Scholar]
  58. 58. 
    Hartmann C, Hentschel B, Wick W, Capper D, Felsberg J et al. 2010. Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol 120:707–18
    [Google Scholar]
  59. 59. 
    Wijnenga MMJ, Dubbink HJ, French PJ, Synhaeve NE, Dinjens WNM et al. 2017. Molecular and clinical heterogeneity of adult diffuse low-grade IDH wild-type gliomas: Assessment of TERT promoter mutation and chromosome 7 and 10 copy number status allows superior prognostic stratification. Acta Neuropathol 134:957–59
    [Google Scholar]
  60. 60. 
    Brat DJ, Aldape K, Colman H, Holland EC, Louis DN et al. 2018. cIMPACT-NOW update 3: recommended diagnostic criteria for “Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV. .” Acta Neuropathol 136:805–10
    [Google Scholar]
  61. 61. 
    Weller M, Weber RG, Willscher E, Riehmer V, Hentschel B et al. 2015. Molecular classification of diffuse cerebral WHO grade II/III gliomas using genome- and transcriptome-wide profiling improves stratification of prognostically distinct patient groups. Acta Neuropathol 129:679–93
    [Google Scholar]
  62. 62. 
    Thomas C, Sill M, Ruland V, Witten A, Hartung S et al. 2016. Methylation profiling of choroid plexus tumors reveals 3 clinically distinct subgroups. Neuro Oncol 18:790–96
    [Google Scholar]
  63. 63. 
    Deng MY, Sill M, Chiang J, Schittenhelm J, Ebinger M et al. 2018. Molecularly defined diffuse leptomeningeal glioneuronal tumor (DLGNT) comprises two subgroups with distinct clinical and genetic features. Acta Neuropathol 136:239–53
    [Google Scholar]
  64. 64. 
    Banan R, Stichel D, Bleck A, Hong B, Lehmann U et al. 2020. Infratentorial IDH-mutant astrocytoma is a distinct subtype. Acta Neuropathol 140:569–81
    [Google Scholar]
  65. 65. 
    Hou Y, Pinheiro J, Sahm F, Reuss DE, Schrimpf D et al. 2019. Papillary glioneuronal tumor (PGNT) exhibits a characteristic methylation profile and fusions involving PRKCA. Acta Neuropathol 137:837–46
    [Google Scholar]
  66. 66. 
    Pfaff E, Aichmuller C, Sill M, Stichel D, Snuderl M et al. 2020. Molecular subgrouping of primary pineal parenchymal tumors reveals distinct subtypes correlated with clinical parameters and genetic alterations. Acta Neuropathol 139:243–57
    [Google Scholar]
  67. 67. 
    Olar A, Wani KM, Wilson CD, Zadeh G, DeMonte F et al. 2017. Global epigenetic profiling identifies methylation subgroups associated with recurrence-free survival in meningioma. Acta Neuropathol 133:431–44
    [Google Scholar]
  68. 68. 
    Sahm F, Schrimpf D, Stichel D, Jones DTW, Hielscher T et al. 2017. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol 18:682–94
    [Google Scholar]
  69. 69. 
    Nassiri F, Mamatjan Y, Suppiah S, Badhiwala JH, Mansouri S et al. 2019. DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management. Neuro Oncol 21:901–10
    [Google Scholar]
  70. 70. 
    Sievers P, Sill M, Blume C, Tauziede-Espariat A, Schrimpf D et al. 2021. Clear cell meningiomas are defined by a highly distinct DNA methylation profile and mutations in SMARCE1. Acta Neuropathol 141:281–90
    [Google Scholar]
  71. 71. 
    Northcott PA, Hielscher T, Dubuc A, Mack S, Shih D et al. 2011. Pediatric and adult sonic hedgehog medulloblastomas are clinically and molecularly distinct. Acta Neuropathol 122:231–40
    [Google Scholar]
  72. 72. 
    Taylor MD, Northcott PA, Korshunov A, Remke M, Cho YJ et al. 2012. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol 123:465–72
    [Google Scholar]
  73. 73. 
    Pietsch T, Schmidt R, Remke M, Korshunov A, Hovestadt V et al. 2014. Prognostic significance of clinical, histopathological, and molecular characteristics of medulloblastomas in the prospective HIT2000 multicenter clinical trial cohort. Acta Neuropathol 128:137–49
    [Google Scholar]
  74. 74. 
    Schwalbe EC, Hicks D, Rafiee G, Bashton M, Gohlke H et al. 2017. Minimal methylation classifier (MIMIC): a novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures. Sci. Rep. 7:13421
    [Google Scholar]
  75. 75. 
    Ramaswamy V, Remke M, Bouffet E, Bailey S, Clifford SC et al. 2016. Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathol 131:821–31
    [Google Scholar]
  76. 76. 
    Korshunov A, Sahm F, Zheludkova O, Golanov A, Stichel D et al. 2019. DNA methylation profiling is a method of choice for molecular verification of pediatric WNT-activated medulloblastomas. Neuro Oncol 21:214–21
    [Google Scholar]
  77. 77. 
    Schwalbe EC, Lindsey JC, Nakjang S, Crosier S, Smith AJ et al. 2017. Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: a cohort study. Lancet Oncol 18:958–71
    [Google Scholar]
  78. 78. 
    Johann PD, Erkek S, Zapatka M, Kerl K, Buchhalter I et al. 2016. Atypical teratoid/rhabdoid tumors are comprised of three epigenetic subgroups with distinct enhancer landscapes. Cancer Cell 29:379–93First comprehensive description of ATRT epigenetic groups.
    [Google Scholar]
  79. 79. 
    Johann PD, Hovestadt V, Thomas C, Jeibmann A, Hess K et al. 2017. Cribriform neuroepithelial tumor: molecular characterization of a SMARCB1-deficient non-rhabdoid tumor with favorable long-term outcome. Brain Pathol 27:411–18
    [Google Scholar]
  80. 80. 
    Thomas C, Wefers A, Bens S, Nemes K, Agaimy A et al. 2020. Desmoplastic myxoid tumor, SMARCB1-mutant: clinical, histopathological and molecular characterization of a pineal region tumor encountered in adolescents and adults. Acta Neuropathol 139:277–86
    [Google Scholar]
  81. 81. 
    Korshunov A, Jakobiec FA, Eberhart CG, Hovestadt V, Capper D et al. 2015. Comparative integrated molecular analysis of intraocular medulloepitheliomas and central nervous system embryonal tumors with multilayered rosettes confirms that they are distinct nosologic entities. Neuropathology 35:538–44
    [Google Scholar]
  82. 82. 
    Sturm D, Orr BA, Toprak UH, Hovestadt V, Jones DTW et al. 2016. New brain tumor entities emerge from molecular classification of CNS-PNETs. Cell 164:1060–72Showed that many histological CNS-PNETs have DNA methylation patterns of a variety of non-PNET tumors. Identified new molecular classes of CNS-PNETs.
    [Google Scholar]
  83. 83. 
    Witt H, Mack SC, Ryzhova M, Bender S, Sill M et al. 2011. Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma. Cancer Cell 20:143–57
    [Google Scholar]
  84. 84. 
    Mack SC, Witt H, Piro RM, Gu L, Zuyderduyn S et al. 2014. Epigenomic alterations define lethal CIMP-positive ependymomas of infancy. Nature 506:445–50
    [Google Scholar]
  85. 85. 
    Pajtler KW, Witt H, Sill M, Jones DT, Hovestadt V et al. 2015. Molecular classification of ependymal tumors across all CNS compartments, histopathological grades, and age groups. Cancer Cell 27:728–43First large-scale study to describe ependymal tumor DNA methylation classes, some with a particularly poor prognosis.
    [Google Scholar]
  86. 86. 
    Gessi M, Giagnacovo M, Modena P, Elefante G, Gianno F et al. 2019. Role of immunohistochemistry in the identification of supratentorial C11ORF95-RELA fused ependymoma in routine neuropathology. Am. J. Surg. Pathol. 43:56–63
    [Google Scholar]
  87. 87. 
    Panwalkar P, Clark J, Ramaswamy V, Hawes D, Yang F et al. 2017. Immunohistochemical analysis of H3K27me3 demonstrates global reduction in group-A childhood posterior fossa ependymoma and is a powerful predictor of outcome. Acta Neuropathol 134:705–14
    [Google Scholar]
  88. 88. 
    Jaunmuktane Z, Capper D, Jones DTW, Schrimpf D, Sill M et al. 2019. Methylation array profiling of adult brain tumours: diagnostic outcomes in a large, single centre. Acta Neuropathol. Commun. 7:24
    [Google Scholar]
  89. 89. 
    Sievers P, Appay R, Schrimpf D, Stichel D, Reuss DE et al. 2019. Rosette-forming glioneuronal tumors share a distinct DNA methylation profile and mutations in FGFR1, with recurrent co-mutation of PIK3CA and NF1. Acta Neuropathol 138:497–504
    [Google Scholar]
  90. 90. 
    Nakano K, Takahashi S. 2018. Translocation-related sarcomas. Int. J. Mol. Sci. 19:3784
    [Google Scholar]
  91. 91. 
    Renner M, Wolf T, Meyer H, Hartmann W, Penzel R et al. 2013. Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas. Genome Biol 14:r137
    [Google Scholar]
  92. 92. 
    Seki M, Nishimura R, Yoshida K, Shimamura T, Shiraishi Y et al. 2015. Integrated genetic and epigenetic analysis defines novel molecular subgroups in rhabdomyosarcoma. Nat. Commun. 6:7557
    [Google Scholar]
  93. 93. 
    Koelsche C, Mynarek M, Schrimpf D, Bertero L, Serrano J et al. 2018. Primary intracranial spindle cell sarcoma with rhabdomyosarcoma-like features share a highly distinct methylation profile and DICER1 mutations. Acta Neuropathol 136:327–37The authors used DNA methylation to identify a distinct group of DICER1-mutated cancers found in different anatomical sites. Some of these patients had germline DICER1 mutations.
    [Google Scholar]
  94. 94. 
    Weidema ME, van de Geer E, Koelsche C, Desar IME, Kemmeren P et al. 2020. DNA methylation profiling identifies distinct clusters in angiosarcomas. Clin. Cancer Res 26:93–100
    [Google Scholar]
  95. 95. 
    Kommoss FKF, Stichel D, Schrimpf D, Kriegsmann M, Tessier-Cloutier B et al. 2020. DNA methylation-based profiling of uterine neoplasms: a novel tool to improve gynecologic cancer diagnostics. J. Cancer Res. Clin. Oncol. 146:97–104
    [Google Scholar]
  96. 96. 
    Wu SP, Cooper BT, Bu F, Bowman CJ, Killian JK et al. 2017. DNA methylation-based classifier for accurate molecular diagnosis of bone sarcomas. JCO Precis. Oncol. 2017:PO.17.00031
    [Google Scholar]
  97. 97. 
    Koelsche C, Kriegsmann M, Kommoss FKF, Stichel D, Kriegsmann K et al. 2019. DNA methylation profiling distinguishes Ewing-like sarcoma with EWSR1NFATc2 fusion from Ewing sarcoma. J. Cancer Res. Clin. Oncol. 145:1273–81
    [Google Scholar]
  98. 98. 
    Koelsche C, Hartmann W, Schrimpf D, Stichel D, Jabar S et al. 2018. Array-based DNA-methylation profiling in sarcomas with small blue round cell histology provides valuable diagnostic information. Mod. Pathol. 31:1246–56
    [Google Scholar]
  99. 99. 
    Lee JC, Villanueva-Meyer JE, Ferris SP, Cham EM, Zucker J et al. 2020. Clinicopathologic and molecular features of intracranial desmoplastic small round cell tumors. Brain Pathol 30:213–25
    [Google Scholar]
  100. 100. 
    Bernthal NM, Putnam A, Jones KB, Viskochil D, Randall RL. 2014. The effect of surgical margins on outcomes for low grade MPNSTs and atypical neurofibroma. J. Surg. Oncol. 110:813–16
    [Google Scholar]
  101. 101. 
    Rohrich M, Koelsche C, Schrimpf D, Capper D, Sahm F et al. 2016. Methylation-based classification of benign and malignant peripheral nerve sheath tumors. Acta Neuropathol 131:877–87
    [Google Scholar]
  102. 102. 
    Goel A, Nagasaka T, Arnold CN, Inoue T, Hamilton C et al. 2007. The CpG island methylator phenotype and chromosomal instability are inversely correlated in sporadic colorectal cancer. Gastroenterology 132:127–38
    [Google Scholar]
  103. 103. 
    Muzny DM, Bainbridge MN, Chang K, Dinh HH, Drummond JA et al. 2012. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487:330–37
    [Google Scholar]
  104. 104. 
    Hinoue T, Weisenberger DJ, Lange CP, Shen H, Byun HM et al. 2012. Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res 22:271–82
    [Google Scholar]
  105. 105. 
    Toyota M, Ahuja N, Ohe-Toyota M, Herman JG, Baylin SB, Issa JP. 1999. CpG island methylator phenotype in colorectal cancer. PNAS 96:8681–86
    [Google Scholar]
  106. 106. 
    Weisenberger DJ, Siegmund KD, Campan M, Young J, Long TI et al. 2006. CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nat. Genet. 38:787–93
    [Google Scholar]
  107. 107. 
    Kane MF, Loda M, Gaida GM, Lipman J, Mishra R et al. 1997. Methylation of the hMLH1 promoter correlates with lack of expression of hMLH1 in sporadic colon tumors and mismatch repair-defective human tumor cell lines. Cancer Res 57:808–11
    [Google Scholar]
  108. 108. 
    Bettstetter M, Dechant S, Ruemmele P, Grabowski M, Keller G et al. 2007. Distinction of hereditary nonpolyposis colorectal cancer and sporadic microsatellite-unstable colorectal cancer through quantification of MLH1 methylation by real-time PCR. Clin. Cancer Res. 13:3221–28
    [Google Scholar]
  109. 109. 
    Adar T, Rodgers LH, Shannon KM, Yoshida M, Ma T et al. 2017. A tailored approach to BRAF and MLH1 methylation testing in a universal screening program for Lynch syndrome. Mod. Pathol. 30:440–47
    [Google Scholar]
  110. 110. 
    Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y et al. 2013. Integrated genomic characterization of endometrial carcinoma. Nature 497:67–73
    [Google Scholar]
  111. 111. 
    Esteller M, Levine R, Baylin SB, Ellenson LH, Herman JG. 1998. MLH1 promoter hypermethylation is associated with the microsatellite instability phenotype in sporadic endometrial carcinomas. Oncogene 17:2413–17
    [Google Scholar]
  112. 112. 
    Simpkins SB, Bocker T, Swisher EM, Mutch DG, Gersell DJ et al. 1999. MLH1 promoter methylation and gene silencing is the primary cause of microsatellite instability in sporadic endometrial cancers. Hum. Mol. Genet. 8:661–66
    [Google Scholar]
  113. 113. 
    Creighton CJ, Morgan M, Gunaratne PH, Wheeler DA, Gibbs RA et al. 2013. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499:43–49
    [Google Scholar]
  114. 114. 
    Ricketts CJ, De Cubas AA, Fan H, Smith CC, Lang M et al. 2018. The Cancer Genome Atlas comprehensive molecular characterization of renal cell carcinoma. Cell Rep 23:313–26
    [Google Scholar]
  115. 115. 
    Linehan WM, Spellman PT, Ricketts CJ, Creighton CJ, Fei SS et al. 2016. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374:135–45
    [Google Scholar]
  116. 116. 
    Chen W, Zhuang J, Wang PP, Jiang J, Lin C et al. 2019. DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups. Cancer Cell Int 19:185
    [Google Scholar]
  117. 117. 
    Evelonn EA, Landfors M, Haider Z, Kohn L, Ljungberg B et al. 2019. DNA methylation associates with survival in non-metastatic clear cell renal cell carcinoma. BMC Cancer 19:65
    [Google Scholar]
  118. 118. 
    Slater AA, Alokail M, Gentle D, Yao M, Kovacs G et al. 2013. DNA methylation profiling distinguishes histological subtypes of renal cell carcinoma. Epigenetics 8:252–67
    [Google Scholar]
  119. 119. 
    Davis CF, Ricketts CJ, Wang M, Yang L, Cherniack AD et al. 2014. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26:319–30
    [Google Scholar]
  120. 120. 
    Malouf GG, Su X, Zhang J, Creighton CJ, Ho TH et al. 2016. DNA methylation signature reveals cell ontogeny of renal cell carcinomas. Clin. Cancer Res 22:6236–46
    [Google Scholar]
  121. 121. 
    Ruiz-Cordero R, Rao P, Li L, Qi Y, Atherton D et al. 2019. Hybrid oncocytic/chromophobe renal tumors are molecularly distinct from oncocytoma and chromophobe renal cell carcinoma. Mod. Pathol. 32:1698–707
    [Google Scholar]
  122. 122. 
    Sirohi D, Smith SC, Agarwal N, Maughan BL. 2018. Unclassified renal cell carcinoma: diagnostic difficulties and treatment modalities. Res. Rep. Urol. 10:205–17
    [Google Scholar]
  123. 123. 
    Hu S, Liu D, Tufano RP, Carson KA, Rosenbaum E et al. 2006. Association of aberrant methylation of tumor suppressor genes with tumor aggressiveness and BRAF mutation in papillary thyroid cancer. Int. J. Cancer 119:2322–29
    [Google Scholar]
  124. 124. 
    Mancikova V, Buj R, Castelblanco E, Inglada-Perez L, Diez A et al. 2014. DNA methylation profiling of well-differentiated thyroid cancer uncovers markers of recurrence free survival. Int. J. Cancer 135:598–610
    [Google Scholar]
  125. 125. 
    Bisarro dos Reis M, Barros-Filho MC, Marchi FA, Beltrami CM, Kuasne H et al. 2017. Prognostic classifier based on genome-wide DNA methylation profiling in well-differentiated thyroid tumors. J. Clin. Endocrinol. Metab. 102:4089–99
    [Google Scholar]
  126. 126. 
    Barros-Filho MC, Bisarro dos Reis M, Beltrami CM, de Mello JBH, Marchi FA et al. 2019. DNA methylation-based method to differentiate malignant from benign thyroid lesions. Thyroid 29:1244–54
    [Google Scholar]
  127. 127. 
    Jurmeister P, Bockmayr M, Seegerer P, Bockmayr T, Treue D et al. 2019. Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Sci. Transl. Med. 11:509eaaw8513
    [Google Scholar]
  128. 128. 
    Weinstein JN, Akbani R, Broom BM, Wang W, Verhaak RGW et al. 2014. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507:315–22
    [Google Scholar]
  129. 129. 
    Burk RD, Chen Z, Saller C, Tarvin K, Carvalho AL et al. 2017. Integrated genomic and molecular characterization of cervical cancer. Nature 543:378–84
    [Google Scholar]
  130. 130. 
    Shen H, Shih J, Hollern DP, Wang L, Bowlby R et al. 2018. Integrated molecular characterization of testicular germ cell tumors. Cell Rep 23:3392–406
    [Google Scholar]
  131. 131. 
    Cherniack AD, Shen H, Walter V, Stewart C, Murray BA et al. 2017. Integrated molecular characterization of uterine carcinosarcoma. Cancer Cell 31:411–23
    [Google Scholar]
  132. 132. 
    Abeshouse A, Ahn J, Akbani R, Ally A, Amin S et al. 2015. The molecular taxonomy of primary prostate cancer. Cell 163:1011–25
    [Google Scholar]
  133. 133. 
    Farshidfar F, Zheng S, Gingras MC, Newton Y, Shih J et al. 2017. Integrative genomic analysis of cholangiocarcinoma identifies distinct IDH-mutant molecular profiles. Cell Rep 19:2878–80
    [Google Scholar]
  134. 134. 
    Ally A, Balasundaram M, Carlsen R, Chuah E, Clarke A et al. 2017. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169:1327–41.e23
    [Google Scholar]
  135. 135. 
    Schweizer L, Thierfelder F, Thomas C, Soschinski P, Suwala A et al. 2020. Molecular characterization of CNS paragangliomas identifies cauda equina paragangliomas as a distinct tumor entity. Acta Neuropathol 140:893–906
    [Google Scholar]
  136. 136. 
    Bell D, Berchuck A, Birrer M, Chien J, Cramer DW et al. 2011. Integrated genomic analyses of ovarian carcinoma. Nature 474:609–15
    [Google Scholar]
  137. 137. 
    Robertson AG, Shih J, Yau C, Gibb EA, Oba J et al. 2017. Integrative analysis identifies four molecular and clinical subsets in uveal melanoma. Cancer Cell 32:204–20.e15
    [Google Scholar]
  138. 138. 
    Wehmas LC, Hester SD, Wood CE. 2020. Direct formalin fixation induces widespread transcriptomic effects in archival tissue samples. Sci. Rep. 10:14497
    [Google Scholar]
  139. 139. 
    Maros ME, Capper D, Jones DTW, Hovestadt V, von Deimling A et al. 2020. Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. Nat. Protoc. 15:479–512
    [Google Scholar]
  140. 140. 
    Grunau C, Clark SJ, Rosenthal A 2001. Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res 29:e65
    [Google Scholar]
  141. 141. 
    Tomczak K, Czerwinska P, Wiznerowicz M 2015. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19:A68–77
    [Google Scholar]
  142. 142. 
    Clough E, Barrett T. 2016. The Gene Expression Omnibus database. Methods Mol. Biol. 1418:93–110
    [Google Scholar]
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