BIOM-19. DECIPHERING THE METHYLATION SIGNATURE OF CIRCULATING EXTRACELLULAR VESICLE DNA FOR CNS TUMOR CLASSIFICATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi14-vi14
Author(s):  
Franz Ricklefs ◽  
Tammo Ricklefs ◽  
Cecile Maire ◽  
Amanda Salviano da Silva ◽  
Kathrin Wollman ◽  
...  
Keyword(s):  
Empirical Bayes ◽  
Methylation Status ◽  
Extracellular Vesicle ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Tumor Patients ◽  
Cns Tumor ◽  
Genome Wide ◽  
Tumor Entities ◽  
Methylation Profiling

Abstract Genome-wide methylation profiling has recently been developed into a tool that allows subtype tumor classification in central nervous system (CNS) tumors. We previously showed that extracellular vesicle (EV) DNA faithfully reflects the tumor methylation class, including information on the IDH mutation and MGMT promoter methylation status. Furthermore we showed that circulating plasma EVs are elevated in CNS tumor patients in comparison to non-tumor donors (HD) controls with tumor related protein profiles. We now investigated, whether the methylation signatures of circulating DNA (both EV and cfNDA) can be used in liquid biopsy approaches for CNS tumor detection and classification. We isolated DNA from circulating EVs (n=27), cfDNA (n=27) and tumor tissue DNA (n=90) of patients with glioblastoma (GBM), meningioma (MGN) and cerebral metastases (CM). Patients undergoing epilepsy surgery as well as aneurysm clipping were used as non-tumor controls (HD, n= 7). EVs were classified by nanoparticle analysis, immunoblotting, imaging flow cytometry and electron microscopy. Isolated EV-DNA comprised many sorts of molecular weight (up tp >10Kb) in comparison to cfDNA (130-140bp). Healthy donors and tumor patients showed not differences in their DNA size profiles. We performed genome-wide methylation profiling by 850k Illumina EPIC arrays for all DNA analytes and tumor entities. Linear models and empirical Bayes methods identified significant differentially methylated CpGs (GBM vs. HD, MGN, vs HD, CM vs. HD), that revealed tumor specific signatures to detect and discriminate different CNS tumor entities. Visualization of differentially methylated CPGs by dimension reduction (PCA, t-SNE, Umap) verified tumor specific clusters. cfDNA and EV-DNA exhibited distinctive individual CpG profiles. Our study shows that the methylation signature of circulating EV DNA and cfDNA can be used to separate healthy individuals from tumor patients and could potentially complement standard-of-care imaging to improve tumor detection, classification and surveillance.

scholarly journals GENE-22. GENOME-WIDE METHYLATION PROFILING OF GLIOBLASTOMA EXTRACELLULAR VESICLE DNA ALLOWS TUMOR CLASSIFICATION

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi102-vi102
Author(s):  
Franz Ricklefs ◽  
Cecile Maire ◽  
Katharina Kolbe ◽  
Mareike Holz ◽  
Manfred Westphal ◽  
...  
Keyword(s):  
Copy Number ◽  
Methylation Status ◽  
Extracellular Vesicle ◽  
Copy Number Variations ◽  
Tumor Classification ◽  
Cns Tumors ◽  
Original Tumor ◽  
Genome Wide ◽  
Potential Biomarker ◽  
Methylation Profiling

Abstract BACKGROUND Genome-wide methylation profiling has recently been developed into a tool that allows subtype tumor classification in central nervous system (CNS) tumors. Extracellular vesicles (EVs) are released by CNS tumor cells and contain high molecular weight tumor DNA, rendering EVs a potential biomarker source to identify tumor subgroups, stratify patients and monitor therapy by liquid biopsy. We investigated whether the DNA in glioma-derived EVs reflects genome-wide tumor methylation profiles and allows tumor subtype classification. METHODS DNA was isolated from EVs secreted by cultured glioma stem-like cells (GSC) as well as from the cells of origin and from the original tumor samples (n=3 patients). EVs were classified by nanoparticle analysis (NTA), immunoblotting, imaging flow cytometry (IFCM), multiplex EV assays and electron microscopy. Genome-wide DNA methylation profiling was performed using an 850k Illumina EPIC array and results were classified according to the DKFZ brain tumor classifier. RESULTS The size range of GSC-derived EVs was 120–150 nm, as measured by NTA. The majority of secreted EVs exhibited high expression of common EV markers (i.e. CD9, CD63 and CD81), as characterized by IFCM and multiplex EV assays. Genome-wide methylation profiling of GSC-derived EVs correctly identified the methylation class of the original tumor, including information on the IDH mutation status and subclass classification (RTK1, RTK2). In addition, copy number alterations and the MGMT metyhlation status matched the pattern of the parental GSCs and original tumor samples. CONCLUSION EV DNA faithfully reflects the tumor methylation class as well as the MGMT methylation status and copy number variations present in the parental cells and the original tumor. Methylation profiling of circulating tumor EV DNA could become a useful tool to detect and classify CNS tumors.

scholarly journals Genome-wide methylation profiling of glioblastoma cell-derived extracellular vesicle DNA allows tumor classification

2021 ◽  
Author(s):  
Cecile L Maire ◽  
Marceline M Fuh ◽  
Kerstin Kaulich ◽  
Krystian D Fita ◽  
Ines Stevic ◽  
...  
Keyword(s):  
Copy Number ◽  
Methylation Status ◽  
Tumor Classification ◽  
Glioblastoma Cell ◽  
Glioblastoma Cells ◽  
High Molecular Weight Dna ◽  
Genome Wide ◽  
Tumor Identification ◽  
Potential Biomarker ◽  
Methylation Profiling

Abstract Background Genome-wide DNA methylation profiling has recently been developed into a tool that allows tumor classification in central nervous system tumors. Extracellular vesicles (EVs) are released by tumor cells and contain high molecular weight DNA, rendering EVs a potential biomarker source to identify tumor subgroups, stratify patients and monitor therapy by liquid biopsy. We investigated whether the DNA in glioblastoma cell-derived EVs reflects genome-wide tumor methylation and mutational profiles and allows non-invasive tumor subtype classification. Methods DNA was isolated from EVs secreted by glioblastoma cells as well as from matching cultured cells and tumors. EV-DNA was localized and quantified by direct stochastic optical reconstruction microscopy. Methylation and copy number profiling was performed using 850k arrays. Mutations were identified by targeted gene panel sequencing. Proteins were differentially quantified by mass spectrometric proteomics. Results Genome-wide methylation profiling of glioblastoma-derived EVs correctly identified the methylation class of the parental cells and original tumors, including the MGMT promoter methylation status. Tumor-specific mutations and copy number variations (CNV) were detected in EV-DNA with high accuracy. Different EV isolation techniques did not affect the methylation profiling and CNV results. DNA was present inside EVs and on the EV surface. Proteome analysis did not allow specific tumor identification or classification but identified tumor-associated proteins that could potentially be useful for enriching tumor-derived circulating EVs from biofluids. Conclusions This study provides proof of principle that EV-DNA reflects the genome-wide methylation, CNV and mutational status of glioblastoma cells and enables their molecular classification.

scholarly journals Genome-wide methylation profiling of glioblastoma extracellular vesicle DNA allows tumor classification

2021 ◽  
Vol 1 ◽  
pp. 100549
Author(s):  
F. Ricklefs ◽  
C. Maire ◽  
H. Heiland ◽  
M. Westphal ◽  
U. Schüller ◽  
...  
Keyword(s):  
Extracellular Vesicle ◽  
Tumor Classification ◽  
Genome Wide ◽  
Methylation Profiling

scholarly journals The central nervous system tumor methylation classifier changes neuro-oncology practice for challenging brain tumor diagnoses and directly impacts patient care

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Shirin Karimi ◽  
Jeffrey A. Zuccato ◽  
Yasin Mamatjan ◽  
Sheila Mansouri ◽  
Suganth Suppiah ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Central Nervous System ◽  
Nervous System ◽  
Patient Care ◽  
Classification Systems ◽  
Tumor Classification ◽  
Tumor Board ◽  
Molecular Diagnostic ◽  
Cns Tumor ◽  
Methylation Profiling

Abstract Background Molecular signatures are being increasingly incorporated into cancer classification systems. DNA methylation-based central nervous system (CNS) tumor classification is being recognized as having the potential to aid in cases of difficult histopathological diagnoses. Here, we present our institutional clinical experience in integrating a DNA-methylation-based classifier into clinical practice and report its impact on CNS tumor patient diagnosis and treatment. Methods Prospective case review was undertaken at CNS tumor board discussions over a 3-year period and 55 tumors with a diagnosis that was not certain to two senior neuropathologists were recommended for methylation profiling based on diagnostic needs. Tumor classification, calibrated scores, and copy number variant (CNV) plots were obtained for all 55 cases. These results were integrated with histopathological findings to reach a final diagnosis. We retrospectively reviewed each patient's clinical course to determine final neuro-pathology diagnoses and the impact of methylation profiling on their clinical management, with a focus on changes that were made to treatment decisions. Results Following methylation profiling, 46 of the 55 (84%) challenging cases received a clinically relevant diagnostic alteration, with two-thirds having a change in the histopathological diagnosis and the other one-third obtaining clinically important molecular diagnostic or subtyping alterations. WHO grading changed by 27% with two-thirds receiving a higher grade. Patient care was directly changed in 15% of all cases with major changes in clinical decision-making being made for these patients to avoid unnecessary or insufficient treatment. Conclusions The integration of methylation-based CNS tumor classification into diagnostics has a substantial clinical benefit for patients with challenging CNS tumors while also avoiding unnecessary health care costs. The clinical impact shown here may prompt the expanded use of DNA methylation profiling for CNS tumor diagnostics within prominent neuro-oncology centers globally.

scholarly journals Genome Wide MeDIP-Seq Profiling of Wild and Cultivated Olives Trees Suggests DNA Methylation Fingerprint on the Sensory Quality of Olive Oil

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1405
Author(s):  
Oussama Badad ◽  
Naoufal Lakhssassi ◽  
Nabil Zaid ◽  
Abdelhalim El Baze ◽  
Younes Zaid ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Phenotypic Plasticity ◽  
Secondary Metabolites ◽  
Olive Oil ◽  
Sensory Quality ◽  
Methylation Status ◽  
Genome Wide ◽  
Pharmaceutical Properties ◽  
Methylation Profiling

Secondary metabolites are particularly important to humans due to their pharmaceutical properties. Moreover, secondary metabolites are key compounds in climate change adaptation in long-living trees. Recently, it has been described that the domestication of Olea subspecies had no major selection signature on coding variants and was mainly related to changes in gene expression. In addition, the phenotypic plasticity in Olea subspecies was linked to the activation of transposable elements in the genes neighboring. Here, we investigated the imprint of DNA methylation in the unassigned fraction of the phenotypic plasticity of the Olea subspecies, using methylated DNA immuno-precipitation sequencing (MeDIP-seq) for a high-resolution genome-wide DNA methylation profiling of leaves and fruits during fruit development in wild and cultivated olives from Turkey. Notably, the methylation profiling showed a differential DNA methylation in secondary metabolism responsible for the sensory quality of olive oil. Here, we highlight for the first time the imprint of DNA methylation in modulating the activity of the Linoleate 9S lipoxygenase in the biosynthesis of volatile aromatic compounds. Unprecedently, the current study reveals the methylation status of the olive genome during fruit ripening.

scholarly journals BIOM-57. PEDIATRIC TUMOR CLASSIFICATION THROUGH GENOME-WIDE METHYLATION PROFILING OF EXTRACELLULAR VESICLE DNA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii14-ii14
Author(s):  
Franz Ricklefs ◽  
Cecile Maire ◽  
Krys Fita ◽  
Friederike Fritzsche ◽  
Gertrud Kammler ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Tumor Cells ◽  
Copy Number ◽  
Cns Tumors ◽  
Copy Number Alterations ◽  
Cns Tumor ◽  
Original Tumor ◽  
Genome Wide ◽  
Dna Methylation Profiling ◽  
Methylation Profiling

Abstract BACKGROUND Genome-wide methylation profiling reliably classifies pediatric central nervous system (CNS) tumors. Extracellular vesicles (EVs) are released by pediatric CNS tumor cells (pCC) and contain high molecular weight tumor DNA, rendering EVs a potential biomarker source to identify tumor subgroups, stratify patients and monitor therapy by liquid biopsy. We investigated whether the DNA in pCC-derived EVs reflects genome-wide tumor methylation profiles and allows tumor subtype classification. METHODS DNA was isolated from EVs secreted by pediatric CNS tumor cells (pCC) as well as from the shortly cultured tumor cells and from the original tumor samples (n=4 patients). Pediatric Fibroblasts and EVs derived thereof were used as a non-tumorous control. EVs were classified by nanoparticle analysis (NTA), immunoblotting, imaging flow cytometry (IFCM and electron microscopy. Genome-wide DNA methylation profiling was performed using an 850k Illumina EPIC array and results were classified according to the DKFZ brain tumor classifier and further analysed by t-SNE and Copy number alteration analysis (CNA). RESULTS The size range of pCC-derived EVs was 120–150 nm, as measured by NTA. The majority of secreted EVs exhibited high expression of common EV markers (i.e. CD9, CD63 and CD81), as characterized by IFCM. Genome-wide DNA methylation profiling of pCC-derived EVs correctly identified the methylation class of the original tumor (i.e. pilocytic astrocytoma, medulloblastoma). In addition, t-SNE analysis and copy number alterations matched the pattern of the parental pCC and original tumor samples. CONCLUSION EV DNA faithfully reflects the tumor methylation class and copy number alterations present in the parental cells and the original tumor. Methylation profiling of circulating tumor EV DNA could become a useful tool to detect and classify pediatric CNS tumors.

scholarly journals P.099 Integrating DNA methylation profiling in brain tumour diagnosis directly changes patient oncological care

2019 ◽  
Vol 46 (s1) ◽  
pp. S40
Author(s):  
JA Zuccato ◽  
S Karimi ◽  
S Mansouri ◽  
Y Mamatjan ◽  
S Suppiah ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Patient Care ◽  
Tumor Classification ◽  
World Health ◽  
Molecular Diagnostic ◽  
Who Grade ◽  
Cns Tumor ◽  
Idh Mutations ◽  
Dna Methylation Profiling ◽  
Methylation Profiling

Background: Molecular signatures are being increasing used to classify central nervous system (CNS) tumors with incorporation into World Health Organization (WHO) classifications. A recently published genome-wide DNA methylation-based CNS tumor classifier assisted in diagnostically challenging cases. However its impact on patient care has not been reported, limiting translation to other centres. Methods: All 55 challenging CNS tumour diagnoses over three years underwent DNA methylation profiling. Tumor classification along with copy number variant (CNV) plot results were integrated with histopathological findings to determine final diagnoses and corresponding clinical impact was assessed. Results: After methylation profiling 46/55 (84%) received clinically relevant diagnostic changes, 30 (55%) with a new diagnosis or resolved differential diagnosis and 16 (29%) with clinically important molecular diagnostic or subtyping changes. WHO grade changed in 15 (27%), with two-thirds upgraded. Nine new IDH mutations in gliomas, four new molecular subtypes in medulloblastomas/ependymomas, and three false positive 1p/19q codeletions were identified. Patient care was directly changed by methylation profiling in 7/47 (15%) followed-up cases to avoid unnecessary treatment in three, insufficient treatment in three, and medically assisted death in one. Conclusions: This real-world use of methylation-based CNS tumor classification substantially impacts patient care for diagnostically challenging tumors and also avoids misdiagnosis-related uncessary resource use.

scholarly journals Diagnosis and Prognostication of Ductal Adenocarcinomas of the Pancreas Based on Genome-Wide DNA Methylation Profiling by Bacterial Artificial Chromosome Array-Based Methylated CpG Island Amplification

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Masahiro Gotoh ◽  
Eri Arai ◽  
Saori Wakai-Ushijima ◽  
Nobuyoshi Hiraoka ◽  
Tomoo Kosuge ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bacterial Artificial Chromosome ◽  
Cpg Island ◽  
Methylation Status ◽  
Artificial Chromosome ◽  
Prognostic Indicators ◽  
Bac Clones ◽  
Genome Wide ◽  
Dna Methylation Profiling ◽  
Methylation Profiling

To establish diagnostic criteria for ductal adenocarcinomas of the pancreas (PCs), bacterial artificial chromosome (BAC) array-based methylated CpG island amplification was performed using 139 tissue samples. Twelve BAC clones, for which DNA methylation status was able to discriminate cancerous tissue (T) from noncancerous pancreatic tissue in the learning cohort with a specificity of 100%, were identified. Using criteria that combined the 12 BAC clones, T-samples were diagnosed as cancers with 100% sensitivity and specificity in both the learning and validation cohorts. DNA methylation status on 11 of the BAC clones, which was able to discriminate patients showing early relapse from those with no relapse in the learning cohort with 100% specificity, was correlated with the recurrence-free and overall survival rates in the validation cohort and was an independent prognostic factor by multivariate analysis. Genome-wide DNA methylation profiling may provide optimal diagnostic markers and prognostic indicators for patients with PCs.

scholarly journals EPCT-15. RAPID EPIGENOMIC CLASSIFICATION OF BRAIN TUMORS ENABLES INTRAOPERATIVE NEUROSURGICAL RISK MODULATION

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i50-i50
Author(s):  
Luna Djirackor ◽  
Skarphedinn Halldorsson ◽  
Pitt Niehusmann ◽  
Henning Leske ◽  
Luis P Kuschel ◽  
...  
Keyword(s):  
Frozen Section ◽  
Methylation Status ◽  
Tumor Resection ◽  
Preoperative Imaging ◽  
Who Grade ◽  
Genome Wide ◽  
Oxford Nanopore ◽  
Clear Identification ◽  
Tumor Entities

Abstract Background Clear identification of tumor subtype is the main predictor of patient outcome and ultimately what is considered an adequate level of surgical risk. At brain tumor resection, imaging modalities and intraoperative histology often give an ambigious diagnosis, complicating intraoperative surgical decision-making. Here, we report a nanopore DNA methylation analysis (NDMA) sequencing approach combined with machine learning for classification of tumor entities that could be used intraoperatively. Methods We analyzed 50 biopsies obtained from biobanked tissue (43, prospective) or sampled at surgery (7, intraoperative) from 20 female and 30 male patients with a median age of 8 years. DNA was extracted using spin columns, quantified on a Qubit fluorometer and assessed for purity using NanoDrop spectrophotometer. DNA was then barcoded with the Rapid Barcoding kit from Oxford Nanopore technologies and loaded onto a MinION flow cell. Sequencing was performed for 3 hours (intraoperative) and 24 hours (prospective). Raw reads were basecalled using the Guppy algorithm, then fed into a snakemake workflow (nanoDx pipeline). This generated a report showing the copy number profile, genome-wide methylation status and subclassification of the tumor according to the Heidelberg reference cohort. Results Twelve different tumor classes were discovered within our cohort spanning from WHO Grade I to Grade IV. The results generated by NDMA were concordant with standard neuropathological diagnosis in 43 out of 50 cases (86%). Of the discordant cases, six were due to the biological complexity of the tumor and one case was misclassified by the pipeline. NDMA enabled correct subclassification of 6/7 intraop cases within a mean of 129 minutes. Conclusion NDMA can accurately subclassify tumor entities intraoperatively and guide surgical procedures when preoperative imaging and frozen section evaluation are unclear.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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