Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures

Standard

Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures. / Schwalbe, E C; Hicks, D; Rafiee, G; Bashton, M; Gohlke, H; Enshaei, A; Potluri, S; Matthiesen, J; Mather, M; Taleongpong, P; Chaston, R; Silmon, A; Curtis, A; Lindsey, J C; Crosier, S; Smith, A J; Goschzik, T; Doz, F; Rutkowski, S; Lannering, B; Pietsch, T; Bailey, S; Williamson, D; Clifford, S C.

in: SCI REP-UK, Jahrgang 7, Nr. 1, 18.10.2017, S. 13421.

Publikationen: SCORING: Beitrag in Fachzeitschrift/ZeitungSCORING: ZeitschriftenaufsatzForschungBegutachtung

Harvard

Schwalbe, EC, Hicks, D, Rafiee, G, Bashton, M, Gohlke, H, Enshaei, A, Potluri, S, Matthiesen, J, Mather, M, Taleongpong, P, Chaston, R, Silmon, A, Curtis, A, Lindsey, JC, Crosier, S, Smith, AJ, Goschzik, T, Doz, F, Rutkowski, S, Lannering, B, Pietsch, T, Bailey, S, Williamson, D & Clifford, SC 2017, 'Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures', SCI REP-UK, Jg. 7, Nr. 1, S. 13421. https://doi.org/10.1038/s41598-017-13644-1

APA

Schwalbe, E. C., Hicks, D., Rafiee, G., Bashton, M., Gohlke, H., Enshaei, A., Potluri, S., Matthiesen, J., Mather, M., Taleongpong, P., Chaston, R., Silmon, A., Curtis, A., Lindsey, J. C., Crosier, S., Smith, A. J., Goschzik, T., Doz, F., Rutkowski, S., ... Clifford, S. C. (2017). Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures. SCI REP-UK, 7(1), 13421. https://doi.org/10.1038/s41598-017-13644-1

Vancouver

Bibtex

@article{c6761aed86514f16bcc764cd385203e3,
title = "Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures",
abstract = "Rapid and reliable detection of disease-associated DNA methylation patterns has major potential to advance molecular diagnostics and underpin research investigations. We describe the development and validation of minimal methylation classifier (MIMIC), combining CpG signature design from genome-wide datasets, multiplex-PCR and detection by single-base extension and MALDI-TOF mass spectrometry, in a novel method to assess multi-locus DNA methylation profiles within routine clinically-applicable assays. We illustrate the application of MIMIC to successfully identify the methylation-dependent diagnostic molecular subgroups of medulloblastoma (the most common malignant childhood brain tumour), using scant/low-quality samples remaining from the most recently completed pan-European medulloblastoma clinical trial, refractory to analysis by conventional genome-wide DNA methylation analysis. Using this approach, we identify critical DNA methylation patterns from previously inaccessible cohorts, and reveal novel survival differences between the medulloblastoma disease subgroups with significant potential for clinical exploitation.",
keywords = "Journal Article",
author = "Schwalbe, {E C} and D Hicks and G Rafiee and M Bashton and H Gohlke and A Enshaei and S Potluri and J Matthiesen and M Mather and P Taleongpong and R Chaston and A Silmon and A Curtis and Lindsey, {J C} and S Crosier and Smith, {A J} and T Goschzik and F Doz and S Rutkowski and B Lannering and T Pietsch and S Bailey and D Williamson and Clifford, {S C}",
year = "2017",
month = oct,
day = "18",
doi = "10.1038/s41598-017-13644-1",
language = "English",
volume = "7",
pages = "13421",
journal = "SCI REP-UK",
issn = "2045-2322",
publisher = "NATURE PUBLISHING GROUP",
number = "1",

}

RIS

TY - JOUR

T1 - Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures

AU - Schwalbe, E C

AU - Hicks, D

AU - Rafiee, G

AU - Bashton, M

AU - Gohlke, H

AU - Enshaei, A

AU - Potluri, S

AU - Matthiesen, J

AU - Mather, M

AU - Taleongpong, P

AU - Chaston, R

AU - Silmon, A

AU - Curtis, A

AU - Lindsey, J C

AU - Crosier, S

AU - Smith, A J

AU - Goschzik, T

AU - Doz, F

AU - Rutkowski, S

AU - Lannering, B

AU - Pietsch, T

AU - Bailey, S

AU - Williamson, D

AU - Clifford, S C

PY - 2017/10/18

Y1 - 2017/10/18

N2 - Rapid and reliable detection of disease-associated DNA methylation patterns has major potential to advance molecular diagnostics and underpin research investigations. We describe the development and validation of minimal methylation classifier (MIMIC), combining CpG signature design from genome-wide datasets, multiplex-PCR and detection by single-base extension and MALDI-TOF mass spectrometry, in a novel method to assess multi-locus DNA methylation profiles within routine clinically-applicable assays. We illustrate the application of MIMIC to successfully identify the methylation-dependent diagnostic molecular subgroups of medulloblastoma (the most common malignant childhood brain tumour), using scant/low-quality samples remaining from the most recently completed pan-European medulloblastoma clinical trial, refractory to analysis by conventional genome-wide DNA methylation analysis. Using this approach, we identify critical DNA methylation patterns from previously inaccessible cohorts, and reveal novel survival differences between the medulloblastoma disease subgroups with significant potential for clinical exploitation.

AB - Rapid and reliable detection of disease-associated DNA methylation patterns has major potential to advance molecular diagnostics and underpin research investigations. We describe the development and validation of minimal methylation classifier (MIMIC), combining CpG signature design from genome-wide datasets, multiplex-PCR and detection by single-base extension and MALDI-TOF mass spectrometry, in a novel method to assess multi-locus DNA methylation profiles within routine clinically-applicable assays. We illustrate the application of MIMIC to successfully identify the methylation-dependent diagnostic molecular subgroups of medulloblastoma (the most common malignant childhood brain tumour), using scant/low-quality samples remaining from the most recently completed pan-European medulloblastoma clinical trial, refractory to analysis by conventional genome-wide DNA methylation analysis. Using this approach, we identify critical DNA methylation patterns from previously inaccessible cohorts, and reveal novel survival differences between the medulloblastoma disease subgroups with significant potential for clinical exploitation.

KW - Journal Article

U2 - 10.1038/s41598-017-13644-1

DO - 10.1038/s41598-017-13644-1

M3 - SCORING: Journal article

C2 - 29044166

VL - 7

SP - 13421

JO - SCI REP-UK

JF - SCI REP-UK

SN - 2045-2322

IS - 1

ER -