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Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma

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Abstract
Neuroblastoma, a childhood tumor that originates from precursor cells of the sympathetic nervous system, is a heterogeneous disease with prognosis ranging from long-term survival for localized tumors to fatal outcome for metastatic disease. One of the main challenges in the management of neuroblastoma remains accurate outcome prediction, enabling the choice of risk-related therapy. Therefore, we aimed at establishing novel prognostic tumor DNA methylation biomarkers. In total, 396 low- and high-risk primary tumors were analyzed, of which 87 were profiled using methyl-CpG-binding domain (MBD) sequencing for differential methylation analysis between prognostic patient groups. Subsequently, methylation-specific PCR (MSP) assays were developed for 78 top-ranking differentially methylated regions and tested on two independent cohorts of 132 and 177 samples, respectively. Further, a new statistical framework was developed to identify a robust set of MSP assays of which the methylation score (i.e. the percentage of methylated assays) allows accurate outcome prediction. Survival analyses were performed on the individual target level, as well as on the combined multimarker signature. As a result of the unbiased differential DNA methylation assessment by MBD sequencing, 58 of the 78 MSP assays were designed in regions previously unexplored in neuroblastoma, and 36 are located in non-promoter or non-coding regions. In total, 5 individual MSP assays (located in CCDC177, NXPH1, lnc-MRPL3-2, lnc-TREX1-1 and one on a region from chromosome 8 with no further annotation) predict event-free survival and 4 additional assays (located in SPRED3, TNFAIP2, NPM2 and CYYR1) also predict overall survival. Furthermore, a robust 58-marker methylation signature predicting overall and event-free survival was established. In conclusion, this study encompasses the largest unbiased DNA methylation biomarker study in neuroblastoma so far. We identified and independently validated several novel prognostic biomarkers, as well as a prognostic 58-marker methylation signature.

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Chicago
Decock, Anneleen, Maté Ongenaert, Robrecht Cannoodt, Kimberly Verniers, Bram De Wilde, Genevieve Laureys, Nadine Van Roy, et al. 2016. “Methyl-CpG-binding Domain Sequencing Reveals a Prognostic Methylation Signature in Neuroblastoma.” In OncoPoint, 4th Research Seminar, Abstracts.
APA
Decock, A., Ongenaert, M., Cannoodt, R., Verniers, K., De Wilde, B., Laureys, G., Van Roy, N., et al. (2016). Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma. OncoPoint, 4th Research seminar, Abstracts. Presented at the 4th OncoPoint research seminar.
Vancouver
1.
Decock A, Ongenaert M, Cannoodt R, Verniers K, De Wilde B, Laureys G, et al. Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma. OncoPoint, 4th Research seminar, Abstracts. 2016.
MLA
Decock, Anneleen, Maté Ongenaert, Robrecht Cannoodt, et al. “Methyl-CpG-binding Domain Sequencing Reveals a Prognostic Methylation Signature in Neuroblastoma.” OncoPoint, 4th Research Seminar, Abstracts. 2016. Print.
@inproceedings{7165340,
  abstract     = {Neuroblastoma, a childhood tumor that originates from precursor cells of the sympathetic nervous system, is a heterogeneous disease with prognosis ranging from long-term survival for localized tumors to fatal outcome for metastatic disease. One of the main challenges in the management of neuroblastoma remains accurate outcome prediction, enabling the choice of risk-related therapy. Therefore, we aimed at establishing novel prognostic tumor DNA methylation biomarkers. In total, 396 low- and high-risk primary tumors were analyzed, of which 87 were profiled using methyl-CpG-binding domain (MBD) sequencing for differential methylation analysis between prognostic patient groups. Subsequently, methylation-specific PCR (MSP) assays were developed for 78 top-ranking differentially methylated regions and tested on two independent cohorts of 132 and 177 samples, respectively. Further, a new statistical framework was developed to identify a robust set of MSP assays of which the methylation score (i.e. the percentage of methylated assays) allows accurate outcome prediction. Survival analyses were performed on the individual target level, as well as on the combined multimarker signature. As a result of the unbiased differential DNA methylation assessment by MBD sequencing, 58 of the 78 MSP assays were designed in regions previously unexplored in neuroblastoma, and 36 are located in non-promoter or non-coding regions. In total, 5 individual MSP assays (located in CCDC177, NXPH1, lnc-MRPL3-2, lnc-TREX1-1 and one on a region from chromosome 8 with no further annotation) predict event-free survival and 4 additional assays (located in SPRED3, TNFAIP2, NPM2 and CYYR1) also predict overall survival. Furthermore, a robust 58-marker methylation signature predicting overall and event-free survival was established. In conclusion, this study encompasses the largest unbiased DNA methylation biomarker study in neuroblastoma so far. We identified and independently validated several novel prognostic biomarkers, as well as a prognostic 58-marker methylation signature.},
  author       = {Decock, Anneleen and Ongenaert, Mat{\'e} and Cannoodt, Robrecht and Verniers, Kimberly and De Wilde, Bram and Laureys, Genevieve and Van Roy, Nadine and Berbegall, Ana Pilar and Bienertova-Vasku, Julie and Bown, Nick and Cl{\'e}ment, Nathalie and Combaret, Val{\'e}rie and Haber, Michelle and Hoyoux, Claire and Murray, Jayne and Noguera, Rosa and Pierron, Ga{\"e}lle and Schleiermacher, Gudrun and Schulte, Johannes H and Stallings, Ray L and Tweddle, Deborah A and De Preter, Katleen and Speleman, Franki and Vandesompele, Jo},
  booktitle    = {OncoPoint, 4th Research seminar, Abstracts},
  language     = {eng},
  location     = {Ghent, Belgium},
  title        = {Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma},
  year         = {2016},
}