Genome-wide DNA methylation profiling in anorexia nervosa discordant identical twins

Up until now, no study has looked specifically at epigenomic landscapes throughout twin samples, discordant for Anorexia nervosa (AN). Our goal was to find evidence to confirm the hypothesis that epigenetic variations play a key role in the aetiology of AN. In this study, we quantified genome-wide patterns of DNA methylation using the Infinium Human DNA Methylation EPIC BeadChip array (“850 K”) in DNA samples isolated from whole blood collected from a group of 7 monozygotic twin pairs discordant for AN. Results were then validated performing a genome-wide DNA methylation profiling using DNA extracted from whole blood of a group of non-family-related AN patients and a group of healthy controls. Our first analysis using the twin sample revealed 9 CpGs associated to a gene. The validation analysis showed two statistically significant CpGs with the rank regression method related to two genes associated to metabolic traits, PPP2R2C and CHST1. When doing beta regression, 6 of them showed statistically significant differences, including 3 CpGs associated to genes JAM3, UBAP2L and SYNJ2. Finally, the overall pattern of results shows genetic links to phenotypes which the literature has constantly related to AN, including metabolic and psychological traits. The genes PPP2R2C and CHST1 have both been linked to the metabolic traits type 2 diabetes through GWAS studies. The genes UBAP2L and SYNJ2 have been related to other psychiatric comorbidity.

LINE-1 and Alu methylation Signatures in Autism Spectrum Disorder and Their Function in The Regulation of Autism-Related Genes

BackgroundLong interspersed nucleotide element-1 (LINE-1) and Alu elements are retrotransposons whose abilities cause abnormal gene expression and genomic instability. Several studies have focused on DNA methylation profiling of gene regions, but the locus-specific methylation of LINE-1 and Alu elements has not been identified in autism spectrum disorder (ASD).MethodsHere, DNA methylation age was predicted using Horvath’s method. We interrogated locus- and family-specific methylation profiles of LINE-1 and Alu elements (22,352 loci) in ASD blood using publicly-available Illumina Infinium 450K methylation datasets from heterogeneous ASD (n = 52), ASD with 16p11.2 del (n = 7), and ASD with Chromodomain Helicase DNA-binding 8 (CHD8) variants (n = 15). The differentially methylated positions of LINE-1 and Alu elements corresponding to genes were combined with transcriptome data from multiple ASD studies. ROC curve was conducted to examine the specificity of target loci.ResultsEpigenetic age acceleration was significantly decelerated in ASD children over the age of 11 years. DNA methylation profiling revealed LINE-1 and Alu methylation signatures in each ASD risk loci by which global methylation were notably hypomethylated exclusively in ASD with CHD8 variants. When LINE-1 and Alu elements were clustered into subfamilies, we found methylation changes in a family-specific manner in L1P, L1H, HAL, AluJ, and AluS families in the heterogeneous ASD and ASD with CHD8 variants. Our results showed that LINE-1 and Alu methylation within target genes is inversely related to the expression level in each ASD variant. Moreover, LINE-1 and Alu methylation signatures can be used to predict ASD individuals from non-ASD.LimitationsIntegration of methylome and transcriptome datasets was performed from different ASD cohorts. The small sample size of the validation cohort used post-mortem brain tissues and necessitates future validation in a larger cohort.ConclusionsThe DNA methylation signatures of the LINE-1 and Alu elements in ASD, as well as their functional impact on ASD-related genes, have been studied. These findings are considered for further research into DNA methylation profiles and the expression of the LINE-1 and Alu elements in post-mortem brain tissue, which has been linked to ASD pathogenesis.

Identification of DNA methylation biomarkers with potential to predict response to neoadjuvant chemotherapy in triple-negative breast cancer

Neoadjuvant chemotherapy (NAC) is used to treat triple-negative breast cancer (TNBC) prior to resection. Biomarkers that accurately predict a patient’s response to NAC are needed to individualise therapy and avoid chemotoxicity from unnecessary chemotherapy. We performed whole-genome DNA methylation profiling on diagnostic TNBC biopsy samples from the Sequential Evaluation of Tumours Undergoing Preoperative (SETUP) NAC study. We found 9 significantly differentially methylated regions (DMRs) at diagnosis which were associated with response to NAC. We show that 4 of these DMRs are associated with TNBC overall survival (P < 0.05). Our results highlight the potential of DNA methylation biomarkers for predicting NAC response in TNBC.

DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis

Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

EPCO-17. METHYLATION ANALYSIS OF MATCHED PRIMARY AND RECURRENT IDHmt ASTROCYTOMA; AN UPDATE FROM THE GLIOMA LONGITUDINAL ANALYSIS NL (GLASS-NL) CONSORTIUM

The GLASS-NL consortium, was initiated to gain insight into the molecular mechanisms underlying glioma evolution and to identify markers of progression in IDH-mutant astrocytomas. Here, we present the first results of genome-wide DNA-methylation profiling of GLASS-NL samples. 110 adult patients were identified with an IDH-mutant astrocytoma at first diagnosis. All patients underwent a surgical resection of the tumor at least twice, separated by at least 6 months (median 40.9 months (IQR: 24.0, 64.7). In 37% and 18% of the cases, patients were treated with radiotherapy or chemotherapy respectively, before surgical resection of the recurrent tumor. DNA-methylation profiling was done on 235 samples from 103 patients (102 1st, 101 2nd, 29 3rd, and 3 4th resection). Copy number variations were also extracted from these data. Methylation classes were determined according to Capper et al. Overall survival (OS) was measured from date of first surgery. Of all primary tumors, the methylation-classifier assigned 85 (87%) to the low grade subclass and 10 (10%) to the high grade subclass. The relative proportion of high grade tumors increased ~three-fold at tumor recurrence (32/101, 32%) and even further in the second recurrence (15/29, 52%). Methylation classes were prognostic, both in primary and recurrent tumors. The overall DNA-methylation levels of recurrent samples was lower than that of primary samples. This difference is explained by the increased number of high grade samples at recurrence, since near identical DNA-methylation levels were observed in samples that remained low grade. In an unsupervised analysis, DNA-methylation data derived from primary and first recurrence samples of individual patients mostly (79%) cluster together. Recurrent samples that do not cluster with their primary tumor, form a separate group with relatively low genome-wide DNA-methylation. Our data demonstrate that methylation profiling identifies a shift towards a higher grade at tumor progression coinciding with reduced genome-wide DNA-methylation levels.

PATH-27. MGMT PROMOTER STATUS IN IDH1/2 MUTANT ANAPLASTIC ASTROCYTOMA PATIENTS ASSESSED BY DNA METHYLATION PROFILING AND QMS-PCR: A REPORT FROM THE EORTC BRAIN TUMOR GROUP

BACKGROUND Temozolomide efficacy in high-grade glioma is related to MGMTp methylation. We compared the prognostic and predictive effect of MGMTp between DNA methylation profiling (MGMT-STP27 model) and qMS-PCR in IDH1/2mt anaplastic astrocytoma patients. METHODS The 2x2 factorial design phase III CATNON trial randomized 751 adult patients with newly diagnosed 1p/19q non-codeleted anaplastic glioma to 59.4Gy radiotherapy, radiotherapy with concurrent temozolomide, radiotherapy with 12 cycles of adjuvant temozolomide, or radiotherapy with concurrent and adjuvant temozolomide. MGMTp methylation status was assessed with the MGMT-STP27 model using 850k EPIC data, and qMS-PCR. IDH1/2 mutation status was determined with next-generation sequencing. OS was measured from randomization date. RESULTS We identified 444 IDH1/2mt anaplastic astrocytoma patients. MGMTp was methylated in 365/440 patients (83.0%) with MGMT-STP27 data, and 168/361 patients (46.5%) with qMS-PCR data. The agreement between both modalities is 59.9% (Cohen’s Kappa score 0.229). At database lock, 289 patients with MGMT-STP27 data were alive and 236 patients with qMS-PCR data. The median OS of MGMTp methylated glioma patients was 9.1 yrs [95%CI 7.5-not reached] for the MGMT-STP27 model, and not reached [95%CI 9.1-not reached] for qMS-PCR. For MGMTp unmethylated glioma patients, the median OS was 6.9 yrs [95%CI 6.2-not reached] for the MGMT-STP27 model, and 6.8 yrs [95%CI 6.2-9.7] for qMS-PCR. The HR for OS based on MGMTp methylation was 0.88 [95%CI 0.58-1.31] for the MGMT-STP27 model, and 0.72 [95%CI 0.50-1.03]) for qMS-PCR. The HR for OS after radiotherapy with any temozolomide vs radiotherapy alone for the MGMT-STP27 model was 0.53 [95%CI 0.37-0.78] for MGMTp methylated, and 0.54 [95%CI 0.25-1.18] for MGMTp unmethylated glioma patients; and for MS-PCR was 0.34 [95%CI 0.19-0.61] for MGMTp methylated, and 0.53 [95%CI 0.33-0.85] for MGMTp unmethylated glioma patients. CONCLUSION MGMTp methylation, regardless of assay, was neither prognostic nor predictive for outcome to temozolomide in IDH1/2mt anaplastic astrocytoma patients.

PATH-36. INTRATUMOR HETEROGENEITY AND BIOINFORMATIC DIFFERENCES INFLUENCE MENINGIOMA MOLECULAR CLASSIFICATION

Molecular alterations such as CDKN2A inactivation and TERT promoter mutation are new criteria for grade 3 meningiomas in the 5th edition of the WHO Classification of Tumors of the Central Nervous System. However, consensus approaches to identify copy number variants (CNVs) and short somatic variants in meningiomas are lacking. Here, we performed integrated DNA methylation profiling, RNA-sequencing, and targeted DNA mutational profiling on 10 stereotactically-collected, regionally-distinct samples from 4 meningiomas. Targeted DNA sequencing revealed numerous private short somatic variants from multiple sites within individual meningiomas, including a TERT promoter mutation in only 1 of 2 samples from the same tumor. DNA methylation profiling revealed differences in biologic groups and immune cell enrichment between regionally-distinct samples within individual meningiomas. CNV status was evaluated using DNA methylation profiling and RNA sequencing on 14 stereotactically-collected, regionally-distinct samples from 2 meningiomas. Phylogenetic architectures from DNA methylation profiling and targeted DNA sequencing were highly concordant and shared 99.12% of CNVs while RNA sequencing identified only 39% of the CNVs called from DNA based approaches. Finally, CNV analysis based on single-cell RNA sequencing revealed partially overlapping CNVs across meningioma cells within an individual tumor, suggesting subclonal populations may influence CNV-based meningioma molecular classification and underlie limitations in defining CNVs from bulk RNA-sequencing. In sum, these data highlight the relative strengths and weaknesses of various approaches for molecular analysis of meningiomas complicated by intratumor heterogeneity due to non-tumor cells and subclonal populations of meningioma cells. Future efforts to incorporate molecular analysis into the diagnostic paradigm for meningiomas may require orthogonal validation across multiple platforms or image-guided meningioma sampling to select the most aggressive regions for molecular profiling.