Evaluation of circulating tumor DNA by electropherogram analysis and methylome profiling in high-risk neuroblastomas.

DNA mehtylation ctDNA / normal cfDNA ratio enzymatic methyl-sequencing (EM-seq) high-risk neuroblastoma liquid biopsy

Journal

Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867

Informations de publication

Date de publication:
2023
Historique:
received: 05 09 2022
accepted: 24 04 2023
medline: 30 5 2023
pubmed: 30 5 2023
entrez: 30 5 2023
Statut: epublish

Résumé

Liquid biopsy has emerged as a promising, non-invasive diagnostic approach in oncology because the analysis of circulating tumor DNA (ctDNA) reflects the precise status of the disease at diagnosis, progression, and response to treatment. DNA methylation profiling is also a potential solution for sensitive and specific detection of many cancers. The combination of both approaches, DNA methylation analysis from ctDNA, provides an extremely useful and minimally invasive tool with high relevance in patients with childhood cancer. Neuroblastoma is an extracranial solid tumor most common in children and responsible for up to 15% of cancer-related deaths. This high death rate has prompted the scientific community to search for new therapeutic targets. DNA methylation also offers a new source for identifying these molecules. However, the limited blood sample size which can be obtained from children with cancer and the fact that ctDNA content may occasionally be diluted by non-tumor cell-free DNA (cfDNA) complicate optimal quantities of material for high-throughput sequencing studies. In this article, we present an improved method for ctDNA methylome studies of blood-derived plasma from high-risk neuroblastoma patients. We assessed the electropherogram profiles of ctDNA-containing samples suitable for methylome studies, using 10 ng of plasma-derived ctDNA from 126 samples of 86 high-risk neuroblastoma patients, and evaluated several bioinformatic approaches to analyze DNA methylation sequencing data. We demonstrated that enzymatic methyl-sequencing (EM-seq) outperformed bisulfite conversion-based method, based on the lower proportion of PCR duplicates and the higher percentage of unique mapping reads, mean coverage, and genome coverage. The analysis of the electropherogram profiles revealed the presence of nucleosomal multimers, and occasionally high molecular weight DNA. We established that 10% content of the mono-nucleosomal peak is sufficient ctDNA for successful detection of copy number variations and methylation profiles. Quantification of mono-nucleosomal peak also showed that samples at diagnosis contained a higher amount of ctDNA than relapse samples. Our results refine the use of electropherogram profiles to optimize sample selection for subsequent high-throughput analysis and support the use of liquid biopsy followed by enzymatic conversion of unmethylated cysteines to assess the methylomes of neuroblastoma patients.

Sections du résumé

Background UNASSIGNED
Liquid biopsy has emerged as a promising, non-invasive diagnostic approach in oncology because the analysis of circulating tumor DNA (ctDNA) reflects the precise status of the disease at diagnosis, progression, and response to treatment. DNA methylation profiling is also a potential solution for sensitive and specific detection of many cancers. The combination of both approaches, DNA methylation analysis from ctDNA, provides an extremely useful and minimally invasive tool with high relevance in patients with childhood cancer. Neuroblastoma is an extracranial solid tumor most common in children and responsible for up to 15% of cancer-related deaths. This high death rate has prompted the scientific community to search for new therapeutic targets. DNA methylation also offers a new source for identifying these molecules. However, the limited blood sample size which can be obtained from children with cancer and the fact that ctDNA content may occasionally be diluted by non-tumor cell-free DNA (cfDNA) complicate optimal quantities of material for high-throughput sequencing studies.
Methods UNASSIGNED
In this article, we present an improved method for ctDNA methylome studies of blood-derived plasma from high-risk neuroblastoma patients. We assessed the electropherogram profiles of ctDNA-containing samples suitable for methylome studies, using 10 ng of plasma-derived ctDNA from 126 samples of 86 high-risk neuroblastoma patients, and evaluated several bioinformatic approaches to analyze DNA methylation sequencing data.
Results UNASSIGNED
We demonstrated that enzymatic methyl-sequencing (EM-seq) outperformed bisulfite conversion-based method, based on the lower proportion of PCR duplicates and the higher percentage of unique mapping reads, mean coverage, and genome coverage. The analysis of the electropherogram profiles revealed the presence of nucleosomal multimers, and occasionally high molecular weight DNA. We established that 10% content of the mono-nucleosomal peak is sufficient ctDNA for successful detection of copy number variations and methylation profiles. Quantification of mono-nucleosomal peak also showed that samples at diagnosis contained a higher amount of ctDNA than relapse samples.
Conclusions UNASSIGNED
Our results refine the use of electropherogram profiles to optimize sample selection for subsequent high-throughput analysis and support the use of liquid biopsy followed by enzymatic conversion of unmethylated cysteines to assess the methylomes of neuroblastoma patients.

Identifiants

pubmed: 37251933
doi: 10.3389/fonc.2023.1037342
pmc: PMC10213460
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1037342

Informations de copyright

Copyright © 2023 Trinidad, Juan-Ribelles, Pisano, Castel, Cañete, Gut, Heath and Font de Mora.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

Clin Chem. 2021 Nov 1;67(11):1554-1566
pubmed: 34626187
Cancers (Basel). 2022 Apr 21;14(9):
pubmed: 35565208
Clin Cancer Res. 2023 Jan 17;29(2):410-421
pubmed: 36007103
Genes Chromosomes Cancer. 2018 Mar;57(3):123-139
pubmed: 29205637
Nat Commun. 2022 Oct 29;13(1):6467
pubmed: 36309516
Nat Rev Dis Primers. 2016 Nov 10;2:16078
pubmed: 27830764
Front Oncol. 2022 Jul 29;12:946257
pubmed: 35965534
Nature. 2018 Mar 15;555(7696):321-327
pubmed: 29489754
Br J Cancer. 2022 Feb;126(3):391-400
pubmed: 35027672
Oncotarget. 2017 Jul 11;8(28):45286-45297
pubmed: 28423360
Clin Cancer Res. 2022 Feb 15;28(4):728-737
pubmed: 34753780
Nat Rev Cancer. 2011 Sep 23;11(10):726-34
pubmed: 21941284
Clin Cancer Res. 2022 May 2;28(9):1809-1820
pubmed: 35247920
Sci Transl Med. 2010 Dec 8;2(61):61ra91
pubmed: 21148127
Clin Cancer Res. 2016 Nov 15;22(22):5564-5573
pubmed: 27440268
Biochim Biophys Acta. 2016 Jan;1863(1):157-65
pubmed: 26529550
Clin Chem Lab Med. 2021 Feb 05;59(7):1181-1200
pubmed: 33544478
Clin Chem. 2020 Jan 1;66(1):149-160
pubmed: 31628139
PLoS Genet. 2014 Dec 11;10(12):e1004868
pubmed: 25501653
Genome Res. 2021 Jun 17;:
pubmed: 34140313
PLoS Genet. 2016 Jul 18;12(7):e1006162
pubmed: 27428049
Nat Rev Clin Oncol. 2013 Aug;10(8):472-84
pubmed: 23836314
Cancer Discov. 2022 Dec 2;12(12):2800-2819
pubmed: 36108156
Nat Genet. 2013 Mar;45(3):279-84
pubmed: 23334666
Clin Cancer Res. 2018 Feb 15;24(4):939-949
pubmed: 29191970
Cancer Res. 1977 Mar;37(3):646-50
pubmed: 837366
Biomol Detect Quantif. 2019 Mar 18;17:100087
pubmed: 30923679
JCO Precis Oncol. 2022 Apr;6:e2100535
pubmed: 35544728
Cancer Res. 2001 Feb 15;61(4):1659-65
pubmed: 11245480
Ann Oncol. 2022 May;33(5):500-510
pubmed: 35306155
BMC Cancer. 2011 Feb 11;11:66
pubmed: 21314941
Sci Adv. 2022 Sep 9;8(36):eabn4030
pubmed: 36083902
N Engl J Med. 2018 Nov 01;379(18):1754-1765
pubmed: 30380390
Epigenomics. 2015 Aug;7(5):813-28
pubmed: 26366945
Trends Mol Med. 2021 May;27(5):482-500
pubmed: 33500194
Clin Cancer Res. 2023 Jan 4;29(1):209-220
pubmed: 36269794
JCO Precis Oncol. 2022 Sep;6:e2200148
pubmed: 36170624
J Hematol Oncol. 2022 Oct 1;15(1):137
pubmed: 36183093
Pancreas. 1998 Jul;17(1):89-97
pubmed: 9667526
Epigenetics. 2022 Oct;17(10):1195-1204
pubmed: 34709110
Nat Med. 2008 Sep;14(9):985-90
pubmed: 18670422
Cancer Metastasis Rev. 2021 Mar;40(1):173-189
pubmed: 33404859
Oncotarget. 2018 Apr 24;9(31):22184-22193
pubmed: 29774131
Proc Natl Acad Sci U S A. 2013 Nov 19;110(47):18761-8
pubmed: 24191000
Nature. 2018 Nov;563(7732):579-583
pubmed: 30429608
Proc Natl Acad Sci U S A. 2022 Nov;119(44):e2209852119
pubmed: 36288287
Heliyon. 2018 Jul 25;4(7):e00699
pubmed: 30094369
Clin Chem. 2022 Nov 3;68(11):1381-1390
pubmed: 35962648
Epigenetics. 2022 May;17(5):518-530
pubmed: 33975521
Brief Bioinform. 2023 Jan 19;24(1):
pubmed: 36611239
Cancer Metastasis Rev. 2016 Sep;35(3):347-76
pubmed: 27392603
N Engl J Med. 2010 Jun 10;362(23):2202-11
pubmed: 20558371
JCO Precis Oncol. 2022 Oct;6:e2200261
pubmed: 36265119
Children (Basel). 2019 Jan 17;6(1):
pubmed: 30658459

Auteurs

Eva María Trinidad (EM)

Laboratory of Cellular and Molecular Biology, Health Research Institute Hospital La Fe, Valencia, Spain.
Clinical and Translational Research in Cancer, Health Research Institute Hospital La Fe, Valencia, Spain.

Antonio Juan-Ribelles (A)

Clinical and Translational Research in Cancer, Health Research Institute Hospital La Fe, Valencia, Spain.
Pediatric Oncology Unit, La Fe University Hospital, Valencia, Spain.

Giulia Pisano (G)

Clinical and Translational Research in Cancer, Health Research Institute Hospital La Fe, Valencia, Spain.
Pediatric Oncology Unit, La Fe University Hospital, Valencia, Spain.

Victoria Castel (V)

Clinical and Translational Research in Cancer, Health Research Institute Hospital La Fe, Valencia, Spain.
Pediatric Oncology Unit, La Fe University Hospital, Valencia, Spain.

Adela Cañete (A)

Clinical and Translational Research in Cancer, Health Research Institute Hospital La Fe, Valencia, Spain.
Pediatric Oncology Unit, La Fe University Hospital, Valencia, Spain.
School of Medicine, University of Valencia, Valencia, Spain.

Marta Gut (M)

National Center for Genomic Analysis - Centre for Genomic Regulation (CNAG-CRG), Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Universitat Pompeu Fabra (UPF), Barcelona, Spain.

Simon Heath (S)

National Center for Genomic Analysis - Centre for Genomic Regulation (CNAG-CRG), Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Universitat Pompeu Fabra (UPF), Barcelona, Spain.

Jaime Font de Mora (J)

Laboratory of Cellular and Molecular Biology, Health Research Institute Hospital La Fe, Valencia, Spain.
Clinical and Translational Research in Cancer, Health Research Institute Hospital La Fe, Valencia, Spain.

Classifications MeSH