Methylation Class Research Articles

Supratentorial non-RELA, ZFTA-fused ependymomas: a comprehensive phenotype genotype correlation highlighting the number of zinc fingers in ZFTA-NCOA1/2 fusions

The cIMPACT-NOW Update 7 has replaced the WHO nosology of “ependymoma, RELA fusion positive” by “Supratentorial-ependymoma, C11orf95-fusion positive”. This modification reinforces the idea that supratentorial-ependymomas exhibiting fusion that implicates the C11orf95 (now called ZFTA) gene with or without the RELA gene, represent the same histomolecular entity. A hot off the press molecular study has identified distinct clusters of the DNA methylation class of ZFTA fusion-positive tumors. Interestingly, clusters 2 and 4 comprised tumors of different morphologies, with various ZFTA fusions without involvement of RELA. In this paper, we present a detailed series of thirteen cases of non-RELA ZFTA-fused supratentorial tumors with extensive clinical, radiological, histopathological, immunohistochemical, genetic and epigenetic (DNA methylation profiling) characterization. Contrary to the age of onset and MRI aspects similar to RELA fusion-positive EPN, we noted significant histopathological heterogeneity (pleomorphic xanthoastrocytoma-like, astroblastoma-like, ependymoma-like, and even sarcoma-like patterns) in this cohort. Immunophenotypically, these NFκB immunonegative tumors expressed GFAP variably, but EMA constantly and L1CAM frequently. Different gene partners were fused with ZFTA: NCOA1/2, MAML2 and for the first time MN1. These tumors had epigenetic homologies within the DNA methylation class of ependymomas-RELA and were classified as satellite clusters 2 and 4. Cluster 2 (n = 9) corresponded to tumors with classic ependymal histological features (n = 4) but also had astroblastic features (n = 5). Various types of ZFTA fusions were associated with cluster 2, but as in the original report, ZFTA:MAML2 fusion was frequent. Cluster 4 was enriched with sarcoma-like tumors. Moreover, we reported a novel anatomy of three ZFTA:NCOA1/2 fusions with only 1 ZFTA zinc finger domain in the putative fusion protein, whereas all previously reported non-RELA ZFTA fusions have 4 ZFTA zinc fingers. All three cases presented a sarcoma-like morphology. This genotype/phenotype association requires further studies for confirmation. Our series is the first to extensively characterize this new subset of supratentorial ZFTA-fused ependymomas and highlights the usefulness of ZFTA FISH analysis to confirm the existence of a rearrangement without RELA abnormality.

Analyses of DNA Methylation Profiling in the Diagnosis of Intramedullary Astrocytomas

Intramedullary astrocytomas (IMAs) consist of a heterogeneous group of rare central nervous system (CNS) tumors associated with variable outcomes. A DNA methylation-based classification approach has recently emerged as a powerful tool to further classify CNS tumors. However, no DNA methylation-related studies specifically addressing to IMAs have been performed yet. In the present study, we analyzed 16 IMA samples subjected to morphological and molecular analyses, including DNA methylation profiling. Among the 16 samples, only 3 cases were classified in a reference methylation class (MC) with the recommended calibrated score (≥0.9). The remaining cases were either considered “no-match” cases (calibrated score <0.3, n = 7) or were classified with low calibrated scores (ranging from 0.32 to 0.53, n = 6), including inconsistent classification. To obtain a more comprehensive tool for pathologists, we used different unsupervised analyses of DNA methylation profiles, including our data and those from the Heidelberg reference cohort. Even though our cohort included only 16 cases, hypotheses regarding IMA-specific classification were underlined; a potential specific MC of PA_SPINE was identified and high-grade IMAs, probably consisting of H3K27M wild-type IMAs, were mainly associated with ANA_PA MC. These hypotheses strongly suggest that a specific classification for IMAs has to be investigated.

Current Treatment of Juvenile Myelomonocytic Leukemia.

Juvenile myelomonocytic leukemia (JMML) is a rare pediatric leukemia characterized by mutations in five canonical RAS pathway genes. The diagnosis is made by typical clinical and hematological findings associated with a compatible mutation. Although this is sufficient for clinical decision-making in most JMML cases, more in-depth analysis can include DNA methylation class and panel sequencing analysis for secondary mutations. NRAS-initiated JMML is heterogeneous and adequate management ranges from watchful waiting to allogeneic hematopoietic stem cell transplantation (HSCT). Upfront azacitidine in KRAS patients can achieve long-term remissions without HSCT; if HSCT is required, a less toxic preparative regimen is recommended. Germline CBL patients often experience spontaneous resolution of the leukemia or exhibit stable mixed chimerism after HSCT. JMML driven by PTPN11 or NF1 is often rapidly progressive, requires swift HSCT and may benefit from pretransplant therapy with azacitidine. Because graft-versus-leukemia alloimmunity is central to cure high risk patients, the immunosuppressive regimen should be discontinued early after HSCT.

A novel DNA methylation signature is associated with androgen receptor activity and patient prognosis in bone metastatic prostate cancer

BackgroundPatients with metastatic prostate cancer (PC) are treated with androgen deprivation therapy (ADT) that initially reduces metastasis growth, but after some time lethal castration-resistant PC (CRPC) develops. A better understanding of the tumor biology in bone metastases is needed to guide further treatment developments. Subgroups of PC bone metastases based on transcriptome profiling have been previously identified by our research team, and specifically, heterogeneities related to androgen receptor (AR) activity have been described. Epigenetic alterations during PC progression remain elusive and this study aims to explore promoter gene methylation signatures in relation to gene expression and tumor AR activity.Materials and methodsGenome-wide promoter-associated CpG methylation signatures of a total of 94 tumor samples, including paired non-malignant and malignant primary tumor areas originating from radical prostatectomy samples (n = 12), and bone metastasis samples of separate patients with hormone-naive (n = 14), short-term castrated (n = 4) or CRPC (n = 52) disease were analyzed using the Infinium Methylation EPIC arrays, along with gene expression analysis by Illumina Bead Chip arrays (n = 90). AR activity was defined from expression levels of genes associated with canonical AR activity.ResultsIntegrated epigenome and transcriptome analysis identified pronounced hypermethylation in malignant compared to non-malignant areas of localized prostate tumors. Metastases showed an overall hypomethylation in relation to primary PC, including CpGs in the AR promoter accompanied with induction of AR mRNA levels. We identified a Methylation Classifier for Androgen receptor activity (MCA) signature, which separated metastases into two clusters (MCA positive/negative) related to tumor characteristics and patient prognosis. The MCA positive metastases showed low methylation levels of genes associated with canonical AR signaling and patients had a more favorable prognosis after ADT. In contrast, MCA negative patients had low AR activity associated with hypermethylation of AR-associated genes, and a worse prognosis after ADT.ConclusionsA promoter methylation signature classifies PC bone metastases into two groups and predicts tumor AR activity and patient prognosis after ADT. The explanation for the methylation diversities observed during PC progression and their biological and clinical relevance need further exploration.

OMIC-07. FEASIBILITY AND UTILITY OF EPIGENOMIC PROFILING FOR CHILDHOOD CNS TUMORS IN HONG KONG

Genome-wide DNA methylation profiling has emerged as an important diagnostic tool that complements histopathology for CNS tumors in children and adults. Literature describing its application in Asian countries is nonetheless limited. Herein, we report the feasibility and utility of adopting such platform for children diagnosed with CNS tumors in Hong Kong. A multi-institutional cohort (n=94, 97% of Chinese ethnicity) with CNS embryonal or high grade neuroepithelial tumors (HGNET) diagnosed in Hong Kong from 1996–2020 was assembled based on tissue availability. DNA was extracted from FFPE tumor material (median 301ng, range 13-1000ng), bisulfite converted and profiled with the Infinium Methylation EPIC BeadChip kit. Raw data were analyzed on the German Cancer Research Center MNP 2.0 classifier and through unsupervised dimensionality-reduction analysis (t-SNE) referencing a published CNS tumor reference dataset (GSE90496). The radio-histologic diagnosis included medulloblastoma (n=65), ATRT (n=9), pineal parenchymal tumors (n=7), ETMR (n=5), CNS-PNET (n=4) and other embryonal tumors/HGNETs (n=4). Methylation class could be assigned based on results from MNP 2.0 (calibrated score ≥ 0.9) in 62 patients (66%, including 2 clustering with control) and t-SNE in 22 (23%), while no-match was encountered in 10 (11%). Methylation-based analysis allowed confirmation of diagnosis and assignment of molecular subgroup in 64 patients (68%), confirmation of histologic diagnosis alone in 5 (5%) and resulted in revision/reassignment of diagnosis in 13 (14%). Among medulloblastoma samples that were assigned molecular tumor classes (n=57), 8 clustered with WNT-activated medulloblastoma, 13 with SHH-activated medulloblastoma, 10 with Group 3 medulloblastoma, 21 with Group 4 medulloblastoma, and 5 with non-medulloblastoma entities (high-grade gliomas=3, ETMR=1, ATRT=1). In conclusion, epigenomic profiling allowed refinement of disease classification for pediatric CNS tumors. Availability of such methodology in Asia sets the stage for international collaborations in molecularly-driven trials.

LGG-06. COMPREHENSIVE GENOMIC CHARACTERIZATION AND INTEGRATED CLINICAL ANALYSIS OF LOW-GRADE GLIOMAS IN CHILDREN WITH NEUROFIBROMATOSIS TYPE 1

BackgroundLow-grade gliomas (LGGs) arising in children with neurofibromatosis type 1 (NF1) are usually not biopsied. To identify secondary genetic alterations or molecular features that may contribute to pathogenesis and correlate with clinical behavior, we initiated a comprehensive molecular and clinical analysis of pediatric NF1-LGGs. MethodsNF1-LGGs were analysed by whole-genome sequencing (31), targeted gene panel sequencing (9), RNAseq transcriptomal profiling (33) and genome-wide DNA methylation analysis (67). Clinical annotation was available for 48 subjects. ResultsMost LGGs harbored bi-allelic NF1 inactivation as the sole genetic abnormality, but 11% had additional alterations (FGFR1 mutation, n=3; PIK3CA mutation, n=2; homozygous 9p21 deletion, n=2; MYB:QKI fusion, n=1; SETD2 mutation, n=1; EGFR amplification, n=1). FGFR1 mutation conferred additional growth advantage in multiple complementary murine Nf1 models. 88% of NF1-LGGs resembled sporadic pilocytic astrocytoma (PA) by methylation, higher than that based on histology. Non-PA methylation patterns included low-grade glial/glioneuronal tumors, rosette-forming glioneuronal tumors, MYB/MYBL1-altered glioma, and high-grade astrocytoma with piloid features (2 tumors histologically diagnosed as LGG). In total, 18% of samples were classified as non-PA and/or harbored an additional non-NF1 mutation. Non-PA methylation class tumors were more likely to harbor an additional non-NF1 mutation (p=0.005). 7.7% of optic pathway hypothalamic gliomas (OPHGs) had other mutations or were not classified by methylation as PA, compared with 20.6% of NF1-LGGs arising elsewhere. There was no difference based on age for the presence of an additional non-NF1 mutation or non-PA methylation class. ConclusionsGiven the overall low occurrence of non-NF1 mutations or non-PA methylation class tumors in this series, routine clinical biopsy of typically-appearing NF1-LGG may not be indicated, particularly for children with OPHG. Biopsy should be considered for non-OPHG tumors refractory to conventional treatment. As additional agents are developed and treatment strategies evolve, the rationale for biopsy of NF1-LGG may become stronger.

DNA methylation-based profiling of bone and soft tissue tumours: a validation study of the 'DKFZ Sarcoma Classifier'.

Diagnosing bone and soft tissue neoplasms remains challenging because of the large number of subtypes, many of which lack diagnostic biomarkers. DNA methylation profiles have proven to be a reliable basis for the classification of brain tumours and, following this success, a DNA methylation‐based sarcoma classification tool from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has been developed. In this study, we assessed the performance of their classifier on DNA methylation profiles of an independent data set of 986 bone and soft tissue tumours and controls. We found that the ‘DKFZ Sarcoma Classifier’ was able to produce a diagnostic prediction for 55% of the 986 samples, with 83% of these predictions concordant with the histological diagnosis. On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper. The classifier performed best when diagnosing mesenchymal chondrosarcomas (CHSs, 88% sensitivity), chordomas (85% sensitivity), and fibrous dysplasia (83% sensitivity). Amongst the subtypes least often classified correctly were clear cell CHSs (14% sensitivity), malignant peripheral nerve sheath tumours (27% sensitivity), and pleomorphic liposarcomas (29% sensitivity). The classifier predictions resulted in revision of the histological diagnosis in six of our cases. We observed that, although a higher tumour purity resulted in a greater likelihood of a prediction being made, it did not correlate with classifier accuracy. Our results show that the DKFZ Classifier represents a powerful research tool for exploring the pathogenesis of sarcoma; with refinement, it has the potential to be a valuable diagnostic tool.

DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas

BackgroundDNA methylation profiling has facilitated and improved the classification of a wide variety of tumors of the central nervous system. In this study, we investigated the potential utility of DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade glioma (dLGG) according to WHO 2016 classification system. We also evaluated whether methylation profiling could provide improved molecular characterization and identify prognostic differences beyond the classical histological WHO grade together with IDH mutation status and 1p/19q codeletion status. All patients diagnosed with dLGG in the period 2007–2016 from the Västra Götaland region in Sweden were assessed for inclusion in the study.ResultsA total of 166 dLGG cases were subjected for genome-wide DNA methylation analysis. Of these, 126 (76%) were assigned a defined diagnostic methylation class with a class prediction score ≥ 0.84 and subclass score ≥ 0.50. The assigned methylation classes were highly associated with their IDH mutation status and 1p/19q codeletion status. IDH-wildtype gliomas were further divided into subgroups with distinct molecular features.ConclusionThe stratification of the patients by methylation profiling was as effective as the integrated WHO 2016 molecular reclassification at predicting the clinical outcome of the patients. Our study shows that DNA methylation profiling is a reliable and robust approach for the classification of dLGG into molecular defined subgroups, providing accurate detection of molecular markers according to WHO 2016 classification.

Consistency of the S5 DNA methylation classifier in formalin-fixed biopsies versus corresponding exfoliated cells for the detection of pre-cancerous cervical lesions.

Methylation biomarkers are promising tools for diagnosis and disease prevention. The S5 classifier is aimed at the prevention of cervical cancer by the early detection of cervical intraepithelial neoplasia (CIN). S5 is based on pyrosequencing a promoter region of EPB41L3 and five late regions of HPV types 16, 18, 31, and 33 following bisulfite conversion of DNA. Good biomarkers should perform well in a variety of sample types such as exfoliated cells, fresh frozen or formalin‐fixed paraffin‐embedded (FFPE) materials. Here, we tested the performance of S5 on 315 FFPE biopsies with paired exfoliated cervical samples using four different conversion kits (Epitect Bisulfite, Epitect Fast Bisulfite, EZ DNA Methylation, and EZ DNA Methylation‐Lightning). The S5 values from FFPE biopsies for all kits were significantly correlated with those obtained from their paired exfoliated cells. For the EZ DNA Methylation kit, we observed an average increased methylation of 4.4% in FFPE. This was due to incomplete conversion of DNA (73% for FFPE vs. 95% for cells). The other kits had a DNA conversion rate in FFPE similar to the cells (95%–97%). S5 performed well at discriminating <CIN2 lesions from CIN2+ lesions on the FFPE with all kits given optimized adjustments to the cut‐off. The area under the curve (AUC) for S5 on FFPE was not significantly different from the paired cells (0.74–0.79 vs. 0.81). The best sensitivity and specificity were obtained for EZ DNA Methylation after the adjustment of the cut‐off to reflect its lower conversion rate. Consistent methylation results can be obtained from FFPE material regardless of the conversion kit used. The S5 classifier performed as well on FFPE material as on exfoliated cells with adjusted cut‐off allowing easier clinical implementation.

Genome-wide DNA methylation profiling is able to identify prefibrotic PMF cases at risk for progression to myelofibrosis

BackgroundPatients suffering from the BCR-ABL1-negative myeloproliferative disease prefibrotic primary myelofibrosis (pre-PMF) have a certain risk for progression to myelofibrosis. Accurate risk estimation for this fibrotic progression is of prognostic importance and clinically relevant. Commonly applied risk scores are based on clinical, cytogenetic, and genetic data but do not include epigenetic modifications. Therefore, we evaluated the assessment of genome-wide DNA methylation patterns for their ability to predict fibrotic progression in PMF patients.ResultsFor this purpose, the DNA methylation profile was analyzed genome-wide in a training set of 22 bone marrow trephines from patients with either fibrotic progression (n = 12) or stable disease over several years (n = 10) using the 850 k EPIC array from Illumina. The DNA methylation classifier constructed from this data set was validated in an independently measured test set of additional 11 bone marrow trephines (7 with stable disease, 4 with fibrotic progress). Hierarchical clustering of methylation β-values and linear discriminant classification yielded very good discrimination between both patient groups. By gene ontology analysis, the most differentially methylated CpG sites are primarily associated with genes involved in cell–cell and cell–matrix interactions.ConclusionsIn conclusion, we could show that genome-wide DNA methylation profiling of bone marrow trephines is feasible under routine diagnostic conditions and, more importantly, is able to predict fibrotic progression in pre-fibrotic primary myelofibrosis with high accuracy.

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation is shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB® and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

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

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 noninvasive tumor subtype classification. 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. 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. 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.

Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder.

Major depressive disorder (MDD) is a mental illness with high incidence and complex etiology, that poses a serious threat to human health and increases the socioeconomic burden. Currently, high-accuracy biomarkers for MDD diagnosis are urgently needed. This paper aims to identify novel blood-based diagnostic biomarkers for MDD. Whole blood DNA methylation data and gene expression data from the Gene Expression Omnibus database are downloaded. Then, differentially expressed/methylated genes (DEGs/DMGs) are identified. In addition, we made a systematic analysis of the DNA methylation on 5′-C-phosphate-G-3′ (CpGs) in all of the gene regions, as well as different gene regions, and then we defined a “dominant” region. Subsequently, integrated analysis is employed to identify the robust MDD-related blood biomarkers. Finally, a gene expression classifier and a methylation classifier are constructed using the random forest algorithm and the leave-one-out cross-validation method. Our results demonstrate that DEGs are mainly involved in the inflammatory response-associated pathways, while DMGs are primarily concentrated in the neurodevelopment- and neuroplasticity-associated pathways. Our integrated analysis identified 46 hypo-methylated and up-regulated (hypo-up) genes and 71 hyper-methylated and down-regulated (hyper-down) genes. One gene expression classifier and two DNA methylation classifiers, based on the CpGs in all of the regions or in the dominant regions are constructed. The gene expression classifier possessed the best predictive ability, followed by the DNA methylation classifiers, based on the CpGs in both the dominant regions and all of the regions. In summary, the integrated analysis of DNA methylation and gene expression has identified 46 hypo-up genes and 71 hyper-down genes, which could be used as diagnostic biomarkers for MDD.

Supratentorial ependymoma in childhood: more than just RELA or YAP

Two distinct genetically defined entities of ependymoma arising in the supratentorial compartment are characterized by the presence of either a C11orf95-RELA or a YAP-MAMLD1 fusion, respectively. There is growing evidence that supratentorial ependymomas without these genetic features exist. In this study, we report on 18 pediatric non-RELA/non-YAP supratentorial ependymomas that were systematically characterized by means of their histology, immunophenotype, genetics, and epigenomics. Comprehensive molecular analyses included high-resolution copy number analysis, methylation profiling, analysis of fusion transcripts by Nanostring technology, and RNA sequencing. Based upon histological and immunohistochemical features two main patterns were identified—RELA-like (n = 9) and tanycytic ependymomas (n = 6). In the RELA-like group histologically assigned to WHO grade III and resembling RELA-fused ependymomas, tumors lacked nuclear expression of p65-RelA as a surrogate marker for a pathological activation of the NF-κB pathway. Three tumors showed alternative C11orf95 fusions to MAML2 or NCOA1. A methylation-based brain tumor classifier assigned two RELA-like tumors to the methylation class “EP, RELA-fusion”; the others demonstrated no significant similarity score. Of the tanycytic group, 5/6 tumors were assigned a WHO grade II. No gene fusions were detected. Methylation profiling did not show any association with an established methylation class. We additionally identified two astroblastoma-like tumors that both presented with chromothripsis of chromosome 22 but lacked MN1 breaks according to FISH analysis. They revealed novel fusion events involving genes in chromosome 22. One further tumor with polyploid cytogenetics was interpreted as PFB ependymoma by the brain tumor methylation classifier but had no relation to the posterior fossa. Clinical follow-up was available for 16/18 patients. Patients with tanycytic and astroblastoma-like tumors had no relapse, while 2 patients with RELA-like ependymomas died. Our data indicate that in addition to ependymomas discovered so far, at least two more supratentorial ependymoma types (RELA-like and tanycytic) exist.

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

An Eight-CpG-based Methylation Classifier for Preoperative Discriminating Early and Advanced-Late Stage of Colorectal Cancer.

AimTo develop and validate a CpG-based classifier for preoperative discrimination of early and advanced-late stage colorectal cancer (CRC).MethodsWe identified an epigenetic signature based on methylation status of multiple CpG sites (CpGs) from 372 subjects in The Cancer Genome Atlas (TCGA) CRC cohort, and an external cohort (GSE48684) with 64 subjects by LASSO regression algorithm. A classifier derived from the methylation signature was used to establish a multivariable logistic regression model to predict the advanced-late stage of CRC. A nomogram was further developed by incorporating the classifier and some independent clinical risk factors, and its performance was evaluated by discrimination and calibration analysis. The prognostic value of the classifier was determined by survival analysis. Furthermore, the diagnostic performance of several CpGs in the methylation signature was evaluated.ResultsThe eight-CpG-based methylation signature discriminated early stage from advanced-late stage CRC, with a satisfactory AUC of more than 0.700 in both the training and validation sets. This methylation classifier was identified as an independent predictor for CRC staging. The nomogram showed favorable predictive power for preoperative staging, and the C-index reached 0.817 (95% CI: 0.753–0.881) and 0.817 (95% CI: 0.721–0.913) in another training set and validation set respectively, with good calibration. The patients stratified in the high-risk group by the methylation classifier had significantly worse survival outcome than those in the low-risk group. Combination diagnosis utilizing only four of the eight specific CpGs performed well, even in CRC patients with low CEA level or at early stage.ConclusionsOur classifier is a valuable predictive indicator that can supplement established methods for more accurate preoperative staging and also provides prognostic information for CRC patients. Besides, the combination of multiple CpGs has a high value in the diagnosis of CRC.

Enhanced neoplasia detection in chronic ulcerative colitis: the ENDCaP-C diagnostic accuracy study

Background Chronic ulcerative colitis is a large bowel inflammatory condition associated with increased colorectal cancer risk over time, resulting in 1000 colectomies per year in the UK. Despite intensive colonoscopic surveillance, 50% of cases progress to invasive cancer before detection. Detecting early (precancer) molecular changes by analysing biopsies from routine colonoscopy should increase neoplasia detection. Objectives To establish a deoxyribonucleic acid (DNA) marker panel associated with early neoplastic changes in ulcerative colitis patients. To develop the DNA methylation test for high-throughput analysis within the NHS. To prospectively evaluate the test within the existing colonoscopy surveillance programme. Design Module 1 analysed 569 stored biopsies from neoplastic and non-neoplastic sites/patients using pyrosequencing for 11 genes that were previously reported to have altered promoter methylation associated with colitis-associated neoplasia. Classifiers were constructed to predict neoplasia based on gene combinations. Module 2 translated analysis to a NHS laboratory, assessing next-generation sequencing to increase speed and reduce cost. Module 3 applied the molecular classifiers within a prospective diagnostic accuracy study, in the existing ulcerative colitis surveillance programme. Comparisons were made between baseline and reference colonoscopies undertaken in a stratified patient sample 6–12 months later. Setting Thirty-one UK hospitals. Participants Patients with chronic ulcerative colitis, either for at least 10 years and extensive disease, or with primary sclerosing cholangitis. Interventions An optimised DNA methylation classifier tested on routine mucosal biopsies taken during colonoscopy. Main outcome Identifying ulcerative colitis patients with neoplasia. Results Module 1 selected five genes with specificity for neoplasia. The optimism-adjusted area under the receiver operating characteristic curve for neoplasia was 0.83 (95% confidence interval 0.79 to 0.88). Precancerous neoplasia showed a higher area under the receiver operating characteristic curve of 0.88 (95% confidence interval 0.84 to 0.92). Background mucosa had poorer discrimination (optimism-adjusted area under the receiver operating characteristic curve was 0.68, 95% confidence interval 0.62 to 0.73). Module 2 was unable to develop a robust next-generation sequencing assay because of the low amplification rates across all genes. In module 3, 818 patients underwent a baseline colonoscopy. The methylation assay (testing non-neoplastic mucosa) was compared with pathology assessments for neoplasia and showed a diagnostic odds ratio of 2.37 (95% confidence interval 1.46 to 3.82; p = 0.0002). The probability of dysplasia increased from 11.1% before testing to 17.7% after testing (95% confidence interval 13.0% to 23.2%), with a positive methylation result suggesting added value in neoplasia detection. To determine added value above colonoscopy alone, a second (reference) colonoscopy was performed in 193 patients without neoplasia. Although the test showed an increased number of patients with neoplasia associated with primary methylation changes, this failed to reach statistical significance (diagnostic odds ratio 3.93; 95% confidence interval 0.82 to 24.75; p = 0.09). Limitations Since the inception of ENDCaP-C, technology has advanced to allow whole-genome or methylome testing to be performed. Conclusions Methylation testing for chronic ulcerative colitis patients cannot be recommended based on this study. However, following up this cohort will reveal further neoplastic changes, indicating whether or not this test may be identifying a population at risk of future neoplasia and informing future surveillance programmes. Trial registration Current Controlled Trials ISRCTN81826545. Funding This project was funded by the Efficacy and Mechanism Evaluation programme, a Medical Research Council and National Institute for Health Research (NIHR) partnership, and will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 1. See the NIHR Journals Library website for further project information.

PATH-20. METHYLATION ARRAY PROFILING OF PEDIATRIC BRAIN TUMORS; SINGLE CENTRE EXPERIENCE

BACKGROUNDSignificant heterogeneity of pediatric brain tumors poses major challenge on diagnostics. Therefore, we aimed to evaluate feasibility of methylation array in the diagnostic process.METHODSMethylation array (Infinium MethylationEPIC, Illumina) was performed on DNA extracted from fresh frozen tissue from prospective newly diagnosed and selected retrospective patients. Results from Heidelberg classifier (www.molecularneuropathology.org) were compared to the histological diagnosis and further genetic testing was performed to establish integrated morphological/molecular diagnosis.RESULTSWithin years 2018–2019, we performed methylation array profiling of 102 samples consisting mainly of ependymoma, medulloblastoma high-grade and low-grade glioma. High calibrated score (>0.9) was achieved in 62 patients (61%). In 46 cases (74%) with score >0.9, the histological diagnosis matched the methylation class (MC). In the remaining cases (16) that were classified by histopathology mainly as ependymomas, the methylation profiles were classified as novel molecular entities (HGNET_BCOR, HGNET_MN1, etc.) or different tumor type. In 40 cases (39%) with the score <0.9, six were found to have high normal tissue content. Nine cases had no match in the classifier and 25 were assigned MC with score 0.3 to 0.89. In 20 out of 34 cases with low score, the molecular diagnosis could be confirmed based on copy number variants inferred from the methylation array or using additional testing for gene fusions and mutations.CONCLUSIONSOur experience on the first 100+ cases demonstrated that methylation array could be integral part of diagnostic process in order to establish integrated morphological and molecular diagnosis of pediatric brain tumors.

RARE-39. MOLECULARLY CONFIRMED ATYPICAL CHOROID PLEXUS PAPILLOMA WITH INTRACRANIAL DISSEMINATION

INTRODUCTIONAmong choroid plexus tumors (CPTs), metastasis occurs more frequently as pathological grading increases. There could be an underestimation of pathological diagnosis if disseminated CPTs are diagnosed with lower grade tumors such as choroid plexus papilloma (CPP) or atypical choroid plexus papilloma (aCPP). Thus, molecular diagnosis using genome-wide DNA methylation profiling may be useful to clarify malignant potential among thetumor entity. Here, we report about a case of aCPP with intracranial dissemination that was molecularly diagnosed by methylation profiling. CASE DESCRIPTION: A 2-year-old girl presented with a history of vomiting. Brain magnetic resonance imaging showed a large tumor mass in the right lateral ventricle and diffuse enhancement surrounding her brainstem, which suggested dissemination. Gross total resection of the mass was performed. Intraoperative findings revealed multiple spot metastatic lesions on the inner wall of lateral ventricle. The pathological diagnosis was aCPP owing to the presence of a glandular structure with a papillary pattern suggesting a neoplasm of epithelial origin, increased cellularity, several necrotic areas, and an intermediate number of mitoses. The CPT-SIOP-2000 treatment protocol was followed without radiation therapy, and the disseminated lesion was disappeared during the chemotherapy. Methylation data of the current case was entered into a recently published classifier, and the tumor was classified as methylation class “plexus tumor, subclass pediatric A” with high confidence (calibrated score 0.96), which includes cases diagnosed as CPP and aCPPs.CONCLUSIONOur case indicates the clinical significance of molecular confirmation of diagnosis among CPTs, particularly lower grade tumors with dissemination.

PATH-23. ADULT SPINAL CORD ASTROBLASTOMA WITH EWSR1-BEND2 FUSION

The most recurrent fusion of CNS high-grade neuroepithelial tumor with MN1alteration(HGNET-MN1) is MN1- BEN Domain Containing 2(BEND2) fusion. Recently, there was a report of a 3-month-old boy with spinal astroblastoma, classified as CNS HGNET-MN1 by DKFZ methylation classification but positive for EWSR1-BEND2 fusion(Yamasaki, 2019). Here, we report a 36-year old man with a spinal cord astroblastoma with EWSR1 alternation. The patient presented with back pain, gait disorder and dysesthesia in lower extremities and trunk was referred to our hospital. MRI showed intramedullary tumor in Th3-5 level, displaying low-intensity on T1 weighted image, high-intensity on T2 weighted image, and homogeneous gadolinium enhancement. Partial removal was performed with the laminectomy. The tumor extended to extramedullary and its boundary was unclear. Histological examinations showed the epithelium-like tumor cells with eosinophilic cytoplasm with high cellularity palisade, intracellar fibrosis, and mitosis. Immunohistochemical staining showed positive for Olig2, GFAP, EMA, SSTR2, S-100, but negative for p53, PgRAE1/AE3. The tumor was diagnosed as astroblastoma, and was classified as HGNET-MN1 by the DKFZ methylation classifier. However, the MN1 alternation was not detected by fluorescence in situ hybridization, instead EWSR1 and BEND2 alternations which suggested EWSR1-BEND2 fusion were detected. After radiation therapy of 54Gy/30fr with bevacizumab and temozolomide, the residual tumor reduced the size and his symptoms improved. This case provides evidence that EWSR1-BEND2 fusion is recurrent in HGNET-MN1 and, as previously reported, suggests the importance of BEND2 in this entity. These two cases suggested that it may be the BEND2 alteration that biologically defines the HGNET-MN1 subclass rather than MN1.