DNA Methylation Array Data Research Articles

Meningiomas: Sex-Specific Differences and Prognostic Implications of a Chromosome X Loss.

Meningiomas are the most common primary intracranial tumours in adults. Several studies proposed new stratification systems with a more accurate risk prediction than the WHO grading, e.g. based on methylation and copy number variations (CNVs). Yet, common shortcomings in these analyses are either a lack of stratification by sex of patients or excluding the gonososmes from CNV assessment. Within this study, DNA methylation array data from 7,424 meningioma samples as well as targeted sequencing, clinical annotations and morphology subtyping of 796 samples were examined for differences between females and males regarding mutations, methylation classes, copy number variations and histology. Meningiomas from females accounted for about 53 % of the malignant tumours and present a loss of one X chromosome in 57 % of these malignant cases. In the group of benign tumours, females comprised about 75 % of the patients. Therein, a loss of one X chromosome was detected in only about 10 % of the cases but was associated with a significantly worse progression free survival. Although genomic instability is a common feature of malignant meningiomas, particularly loss of the X chromosome in tumours of female patients in otherwise histologically and molecularly low-risk tumours confers higher risk. Hence, the gonosomal copy number status can be leveraged for increased diagnostic accuracy.

BS-clock, advancing epigenetic age prediction with high-resolution DNA methylation bisulfite sequencing data.

DNA methylation patterns provide precise and accurate estimates of biological age due to their robustness and predictable changes associated with aging processes. Although several methylation aging clocks have been developed in recent years, they are primarily designed for DNA methylation array data, which has limited CpG coverage and detection sensitivity compared to bisulfite sequencing data. Here, we present BS-clock, a novel DNA methylation clock for human aging based on bisulfite sequencing data. Using BS-seq data from 529 samples retrieved from four tissues, our BS-clock achieves higher correlations with chronological age in multiple tissue types compared to existing array-based clocks. Our study revealed age-dependent aging rates across different age stages and disease conditions, and overall low cross-tissue prediction capability by applying the model trained on one tissue type to others. In summary, BS-clock overcomes limitations of array-based techniques, offering genome-wide CpG site coverage and more robust and accurate aging quantification. This research paves the way for advanced epigenetic studies of aging and holds promise for developing targeted interventions to promote healthy aging. All analysis codes for reproducing the results of the study are publicly available at https://github.com/hucongcong97/BS-clock. Supplementary data are available at Bioinformatics online.

Novel bioinformatic analyses of somatic cell contamination in sperm samples

The assessment of epigenetic profiles in sperm is sensitive to somatic cell contamination, which can influence methylation signals at gene promoters. This contamination is particularly problematic in the assessment of DNA methylation in samples with low sperm counts, where fractional amounts of somatic cell DNA can lead to significant shifts in measured methylation state. In this study, a new method of detecting possible somatic cell contamination is proposed through two multi-region bioinformatic models: a traditional differential methylation analysis and a machine learning logistic regression model. These models were trained on publicly available sperm (n = 489) and blood (n = 1029) DNA methylation array data and tested on a contamination set, wherein the sperm of four donors with normal sperm counts were run on a 450k methylation array with four permutations each, including pure blood, half blood and half sperm by DNA concentration, half blood and half sperm by cell count, and pure sperm (n = 16). The DMR and logistic regression model classified the contamination testing set with 100% and 94% accuracy, respectively. These new methods of detecting the effects of somatic cell contamination allow for more accurate differentiation between epigenetic profiles that contain a biological somatic-like shift and those that have somatic-like signatures because of contamination.

Abstract 3424: Assessing a DNA methylation-based telomere length estimator in individuals with a heavy smoking history

Abstract Telomere length (TL) and DNA methylation are associated with age and smoking exposure, as well as aging-related outcomes including lung cancer and mortality. A DNA methylation-based TL score (DNAmTL) was developed to estimate TL using DNA methylation array data, both in blood. DNAmTL effectively estimates telomere-related aging using 140 CpGs that are moderately enriched in sub-telomeric regions and not included in existing epigenetic clocks. We examined DNAmTL in individuals with a heavy smoking history (mean 56 pack years, range 11-185) in a nested lung cancer case-control study of 350 cases and 350 controls from the β-Carotene and Retinol Efficacy Trial identified through 2005. Twenty-two controls became cases with additional follow-up through 2013, so 328 controls and 372 cases were considered for these analyses. Leukocyte TL was measured using quantitative polymerase chain reaction (T/S ratio, log2-transformed), and DNA methylation was assayed on the Illumina EPIC array, in blood samples obtained on average 4.9 years prior to diagnosis in cases. We observed weak positive Pearson correlations between DNAmTL and TL in cases (r=0.19, p=0.0003) and controls (r=0.14, p=0.009) that persisted after age-adjustment: r=0.16, p=0.002 and r=0.14, p=0.014, respectively. Age-adjusted DNAmTL, but not TL, was longer for female compared to male cases (T=3.6, p=0.0004) and controls (T=2.2, p=0.03), and for those in the placebo compared to the intervention arm in cases (T=2.1, p=0.03) and controls (T=2.3, p=0.02). Linear regression models of TL and DNAmTL adjusted for age, sex, and race and ethnicity were not statistically significant for smoking variables at blood draw (pack years, current vs former smoking status, cigarettes smoked per day during smoking history, years smoked, years since quit), nor asbestos exposure in either cases or controls. Longer age-adjusted DNAmTL was associated with a lower hazard ratio (HR) for time to lung cancer death (0.60, 95% Confidence Interval (CI): 0.37-0.97) among cases (n=313 lung cancer-specific mortality events) in Cox proportional hazards models adjusted for age, sex, race and ethnicity, and time from blood draw to diagnosis with stage as a stratification variable (early I/II, advanced III/IV, unknown). Analogous age-adjusted TL model showed a similar direction but did not reach statistical significance (HR 0.86, CI: 0.71-1.05). Further adjustment of these models by DNA methylation-estimated leukocyte proportions of granulocytes, monocytes, natural killer, CD8+T, and CD4+T (excluding B cells) yielded similar results for age-adjusted DNAmTL (HR 0.54, CI: 0.31-0.92) and age-adjusted TL (HR 0.87, CI: 0.71-1.05). Our results illustrate that while DNAmTL may not be closely associated with smoking exposure among heavily exposed individuals, shorter DNAmTL may be more strongly associated with lung cancer mortality than TL in individuals with a heavy smoking history. Citation Format: Laurie Grieshober, Stefan Graw, Matt J. Barnett, Gary E. Goodman, Chu Chen, Devin C. Koestler, Carmen J. Marsit, Jennifer A. Doherty. Assessing a DNA methylation-based telomere length estimator in individuals with a heavy smoking history [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3424.

Abstract 3439: Factors affecting the performance of existing epigenetic clocks to predict chronological age in colorectal tumor and normal tissues

Abstract Epigenetic age (EA) may serve as a risk stratification biomarker for early-onset colorectal cancer (EOCRC, <50 years old), which has been rising substantially in recent decades. We utilized publicly available data in a descriptive analysis to compare epigenetic clocks in colorectal tissue and to investigate differences between chronological age (CA) and biological age, as measured by EA, in relation to demographic and clinical features. We analyzed DNA methylation (DNAm) array data on 144 participants (96 CRC cases, 48 healthy controls) from the Colonomics Study (CLX; Barcelona, Spain) and 331 CRC cases (51 EOCRC, 316 average-onset (≥50) colorectal cancer (AOCRC)) from The Cancer Genome Atlas (TCGA) as well as corresponding demographic and clinical variables. Among CLX participants, the 96 CRC cases provided colon tumor and paired normal mucosa samples, while the 48 controls provided healthy colon mucosa samples. TCGA included primary tumor samples from 331 cases with colon or rectal adenocarcinoma (TCGA-COADREAD). The 2013 Horvath clock estimated EA based on CA, while epiTOC, estimated the relative stem cell division rate from tissue biospecimens. We examined the correlations between CA and EA stratified by case/control status, sample type, sex, and age of onset (TCGA only). Tissue from CLX controls demonstrated a strong, positive correlation (r>0.9) between CA and EA for both the 2013 Horvath and epiTOC clocks. Using the Horvath clock, CA and EA correlations in normal mucosa and tumor tissue samples from CLX varied, with r = 0.74 and r = 0.41, respectively, and were weaker using the epiTOC clock, with r = 0.26 (normal) and r = 0.10 (tumor). When CLX cases were stratified by sex, the CA and EA (Horvath) correlations among female cases were r = 0.80 (normal) and r = -0.12 (tumor), while the correlations for males were stronger at r = 0.58 (normal) and r = 0.31 (tumor). This is in comparison to the epiTOC clock with females showing correlations of r = -0.02 (normal) and r = 0.41 (tumor), while males had r = 0.06 (normal) and r = 0.11 (tumor). In TCGA, correlations for EOCRC were r = -0.20 (Horvath) and r = -0.08 (epiTOC) and for AOCRC were r = 0.29 and 0.20, respectively. By sex, correlations for females with EOCRC for Horvath and epiTOC were r = -0.09 and r = -0.07, respectively, while correlations for males with EOCRC were r = -0.02 and r = -0.08, respectively. For TCGA cases with AOCRC, the correlations between CA and EA were r = 0.35 and r = 0.22 for females, and r = 0.26 and r = 0.20 for males, respectively for Horvath and epiTOC. This study revealed substantial variability in the ability of existing epigenetic clocks to accurately predict chronological age, with tissue type and sex strongly influencing the resulting correlations. Our findings highlight the importance of considering these factors when choosing an epigenetic clock. Citation Format: Christopher L. Benson, Ferran Moratalla-Navarro, Anna Diez-Villanueva, Matthew A. Devall, Victor Moreno, Stephanie L. Schmit, Fredrick R. Schumacher. Factors affecting the performance of existing epigenetic clocks to predict chronological age in colorectal tumor and normal tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3439.

Abstract 7006: Identification of endothelial-cell-specific DNA methylation alterations in breast cancer with deconvolution of the tumor microenvironment

Abstract The tumor microenvironment (TME) plays a pivotal role in cancer progression, with tumor endothelial cells (TECs) emerging as key orchestrators of angiogenesis and vascular remodeling. Tumor-associated vasculature can have increased permeability and irregular blood flow, impacting drug delivery and tumor immune response. Understanding the molecular alterations within TECs may help identify targeted therapies and improve treatment outcomes with existing therapies. We combined TME cell-type deconvolution with modeling to identify cell-specific methylation alterations of TECs in breast cancer. Genome-scale DNA methylation array data accessed in GEO and GTEx from preinvasive, mixed, and invasive breast cancer tissue (n=609) & normal breast tissue (n=230). We applied HiTIMED, a reference-based cell-type deconvolution algorithm, to estimate endothelial and other cell-type proportions in the TME. Next, we applied CellDMC, a statistical algorithm that identifies cell-specific driven differential methylation, to our cell fraction results, unraveling differentially methylated positions occurring in the endothelial compartment of the angiogenic component of our breast tissue DNA methylation profiles. We identified 29 TEC driven differentially methylated positions (FDR ≤ 8e-03), including loci within genes in angiogenic pathways regulated by VEGF, endothelial inflammation and activation genes, and tumor-associated vasculature factors in the TME. Our findings suggest that variation in DNA methylation between TECs and normal endothelial cells is crucial to understanding the TME and providing insights into unknown cancer cell behaviors that contribute to tumor growth and metastasis, thus leading to the design of novel efficient therapies that target the malignant vascular component. Our study demonstrates the power of combining cell-type deconvolution and a modeling framework that identifies cell-specific somatic alterations to unravel the intricate characteristics of TECs in breast cancer. Furthermore, this study highlights the potential of DNA methylation profiling as a robust tool for characterizing TEC heterogeneity and elucidating the molecular basis of angiogenesis beyond breast tissue in the context of diverse cancer types. Our integrative approach holds promise for advancing precision oncology, paving the way for effective and targeted therapies aimed at modulating the tumor vasculature to impede cancer progression in breast cancer and other cancer types. Citation Format: Barbara Karakyriakou, Ze Zhang, Irma Vlasac, Pauline Mochama, Lucas Salas, Brock Christensen. Identification of endothelial-cell-specific DNA methylation alterations in breast cancer with deconvolution of the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7006.

Associations between Sperm Epigenetic Age and Semen Parameters: An Evaluation of Clinical and Non-Clinical Cohorts.

The well-documented relationship between chronological age and the sperm methylome has allowed for the construction of epigenetic clocks that estimate the biological age of sperm based on DNA methylation, which we previously termed sperm epigenetic age (SEA). Our lab demonstrated that SEA is positively associated with the time taken to achieve pregnancy; however, its relationship with semen parameters is unknown. A total of 379 men from the Longitudinal Investigation of Fertility and Environment (LIFE) study, a non-clinical cohort, and 192 men seeking fertility treatment from the Sperm Environmental Epigenetics and Development Study (SEEDS) were included in the study. Semen analyses were conducted for both cohorts, and SEA was previously generated using a machine learning algorithm and DNA methylation array data. Association analyses were conducted via multivariable linear regression models adjusting for BMI and smoking status. We found that SEA was not associated with standard semen characteristics in SEEDS and LIFE cohorts. However, SEA was significantly associated with higher sperm head length and perimeter, the presence of pyriform and tapered sperm, and lower sperm elongation factor in the LIFE study (p < 0.05). Based on our results, SEA is mostly associated with defects in sperm head morphological factors that are less commonly evaluated during male infertility assessments. SEA shows promise to be an independent biomarker of sperm quality to assess male fecundity.

Potential prognostic determinants for FET::CREB fusion-positive intracranial mesenchymal tumor

Intracranial mesenchymal tumor (IMT), FET::CREB fusion-positive is a provisional tumor type in the 2021 WHO classification of central nervous system tumors with limited information available. Herein, we describe five new IMT cases from four females and one male with three harboring an EWSR1::CREM fusion and two featuring an EWSR1::ATF1 fusion. Uniform manifold approximation and projection of DNA methylation array data placed two cases to the methylation class “IMT, subclass B”, one to “meningioma-benign” and one to “meningioma-intermediate”. A literature review identified 74 cases of IMTs (current five cases included) with a median age of 23 years (range 4–79 years) and a slight female predominance (female/male ratio = 1.55). Among the confirmed fusions, 25 (33.8%) featured an EWSR1::ATF1 fusion, 24 (32.4%) EWSR1::CREB1, 23 (31.1%) EWSR1::CREM, one (1.4%) FUS::CREM, and one (1.4%) EWSR1::CREB3L3. Among 66 patients with follow-up information available (median: 17 months; range: 1–158 months), 26 (39.4%) experienced progression/recurrences (median 10.5 months; range 0–120 months). Ultimately, three patients died of disease, all of whom underwent a subtotal resection for an EWSR1::ATF1 fusion-positive tumor. Outcome analysis revealed subtotal resection as an independent factor associated with a significantly shorter progression free survival (PFS; median: 12 months) compared with gross total resection (median: 60 months; p < 0.001). A younger age (< 14 years) was associated with a shorter PFS (median: 9 months) compared with an older age (median: 49 months; p < 0.05). Infratentorial location was associated with a shorter overall survival compared with supratentorial (p < 0.05). In addition, the EWSR1::ATF1 fusion appeared to be associated with a shorter overall survival compared with the other fusions (p < 0.05). In conclusion, IMT is a locally aggressive tumor with a high recurrence rate. Potential risk factors include subtotal resection, younger age, infratentorial location, and possibly EWSR1::ATF1 fusion. Larger case series are needed to better define prognostic determinants in these tumors.

A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation

Higher birth order is associated with altered risk of many disease states. Changes in placentation and exposures to in utero growth factors with successive pregnancies may impact later life disease risk via persistent DNA methylation alterations. We investigated birth order with Illumina DNA methylation array data in each of 16 birth cohorts (8164 newborns) with European, African, and Latino ancestries from the Pregnancy and Childhood Epigenetics Consortium. Meta-analyzed data demonstrated systematic DNA methylation variation in 341 CpGs (FDR adjusted P < 0.05) and 1107 regions. Forty CpGs were located within known quantitative trait loci for gene expression traits in blood, and trait enrichment analysis suggested a strong association with immune-related, transcriptional control, and blood pressure regulation phenotypes. Decreasing fertility rates worldwide with the concomitant increased proportion of first-born children highlights a potential reflection of birth order-related epigenomic states on changing disease incidence trends.

Refinement of prognostication for IDH-mutant astrocytomas using DNA methylation-based classification.

The 2021 World Health Organization (WHO) grading system of isocitrate dehydrogenase (IDH)-mutant astrocytomas relies on histological features and the presence of homozygous deletion of the cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A/B). DNA methylation profiling has become highly relevant in the diagnosis of central nervous system (CNS) tumors including gliomas, and it has been incorporated into routine clinical diagnostics in some countries. In this study, we, therefore, examined the value of DNA methylation-based classification for prognostication of patients with IDH-mutant astrocytomas. We analyzed histopathological diagnoses, genome-wide DNA methylation array data, and chromosomal copy number alteration profiles from a cohort of 385 adult-type IDH-mutant astrocytomas, including a local cohort of 127 cases and 258 cases from public repositories. Prognosis based on WHO 2021 CNS criteria (histological grade and CDKN2A/B homozygous deletion status), other relevant chromosomal/gene alterations in IDH-mutant astrocytomas and DNA methylation-based subclassification according to the molecular neuropathology classifier were assessed. We demonstrate that DNA methylation-based classification of IDH-mutant astrocytomas can be used to predict outcome of the patients equally well as WHO 2021 CNS criteria. In addition, methylation-based subclassification enabled the identification of IDH-mutant astrocytoma patients with poor survival among patients with grade 3 tumors and patients with grade 4 tumors with a more favorable outcome. In conclusion, DNA methylation-based subclassification adds prognostic information for IDH-mutant astrocytomas that can further refine the current WHO 2021 grading scheme for these patients.

10206-GGE-21 MOLECULAR LANDSCAPE AND TUMOR EVOLUTION IN ASTROBLASTOMA

Abstract Astroblastoma is an extremely rare malignant brain tumor characterized by distinctive gene fusions, including MN1-BEND2 and EWSR1-BEND2 fusions. Despite its indolent nature, frequent local recurrence affects the quality of life for patients. However, there remains a lack of investigation into the genome of astroblastoma. To elucidate the molecular landscape and clonal evolution of astroblastoma, we conducted deep whole-genome sequencing, RNA-seq, and DNA methylation array on 11 cases, including multi-time point samples from 3 cases. Additionally, we integrated publicly available RNA-seq and/or DNA methylation array data from 9 cases. The cohort consists of 13 cases with MN1-BEND2 fusion and 7 cases with EWSR1-BEND2 fusion. Astroblastoma exhibits a relatively silent genome, with median mutation rate is 0.60/Mb (0.22 - 1.09), which is associated with patient age. While EWSR1-BEND2 fusions result from simple translocation, MN1-BEND2 fusions predominantly arise from complex chromosomal rearrangements between chromosome X and 22, frequently involving an additional chromosome, leading to massive oscillation of copy number states in the affected chromosome. Breakpoints in BEND2 within EWSR1-BEND2 fused cases are located near the N-terminus while breakpoints in BEND2 within MN1-BEND2 fused cases cluster in the middle of the gene, suggesting that functional region in BEND2 fusion is distinct based on its partner. Based on DNA methylation, astroblastoma can be divided into three subgroups: MN1-child, MN1-adult, and EWSR1-BEND2 fusion. Gene set enrichment analysis reveals the activation of mitochondrial metabolism in EWSR1-BEND2 fused cases. MN1-adult cases exhibit activation of oxidative stress and DNA replication, whereas MN1-child cases show activation of NOTCH signaling. Multi-time point analysis demonstrates the architecture of clonal evolution. Ionizing radiation appears to be associated with an increase in mutations and deletions, with a radiation-related signature. Our comprehensive analysis provides a detailed view of the genome and evolutionary trajectory of astroblastoma.

Deconvolution of immune cell composition and biological age of hepatocellular carcinoma using DNA methylation.

Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using publicly available and in-house datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging were significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.

JS04.5.A HEMIZYGOUSCDKN2A DELETION CONFERS WORSE SURVIVAL OUTCOMES IN IDH-MUTANT GLIOMAS

Abstract BACKGROUND The tumor suppressor gene CDKN2A is frequently deactivated in cancer. Homozygous CDKN2A deletion is a Grade 4 defining criterion for IDH-mutant gliomas. However, the prognostic value of hemizygous CDKN2A deletions is unclear. We assessed CNV profiles of 1238 initial and 479 recurrent IDHmut gliomas to evaluate the association of CDKN2A hemizygous deletions with overall survival (OS) outcomes. MATERIAL AND METHODS The GLASS primary/recurrent glioma dataset was used to infer CDKN2A status with DNAseq (n=210) and DNA methylation array (n=100) data. Validation was conducted using DFCI, MSKCC, TCGA and DKFZ cohorts. RESULTS Longitudinal analyses of IDH-mutant gliomas without 1p-/19q-codeletion showed significant increases in CDKN2A homozygous deletion from n=6 to n=23 (5% to 20%, P=0.0001, Fisher’s-exact test) and hemizygous deletion from n=19 to n=34 (17% to 30%, P=0.002) at recurrence. CDKN2A hemizygous deletions were significantly associated with poor OS at initial diagnosis (P&amp;lt;0.0001, log-rank test) and recurrence (P&amp;lt;0.0001), remaining significant in multivariable cox regression model that accounted for age, treatment with radiotherapy and/or alkylating agents, and 1p/19q-codeletion status: HR 2.40 (95%-CI: 1.31-4.37, P=0.004). Validation in independent datasets confirmed increased frequencies of CDKN2A hemizygous deletions in recurrent cases as well as the association between CDKN2A hemizygous deletion and worse OS in IDHmut gliomas. Using the DKFZ DNA methylation profiling dataset we further confirmed that CDKN2A status can be accurately assessed through DNAseq and DNA methylation array profiling. CONCLUSION CDKN2A hemizygous deletion is associated with significantly worse OS in initial and recurrent IDHmut gliomas. Similar to CDKN2A homozygous deletion, CDKN2A hemizygous deletion is enriched in post-treatment, recurrent IDHmut gliomas, confirming the value of CDKN2A status (re-)assessment in recurrences. Our results highlight the importance of incorporating CDKN2A status in diagnostic workup to inform prognosis and treatment strategies, revealing the previously unrecognized prognostic value of hemizygous CDKN2A deletions.

P05.01.B PROGNOSTIC MARKERS OF DNA METHYLATION AND NGS SEQUENCING IN PROGRESSIVE GLIOBLASTOMA FROM THE EORTC-26101 TRIAL

Abstract BACKGROUND The EORTC-26101 study was a randomized phase 2 and 3 clinical trial of bevacizumab in combination with lomustine versus lomustine alone in progressive glioblastoma. Other than for progression-free survival (PFS), there was no benefit from addition of bevacizumab for overall survival (OS). However, molecular data allows for the rare opportunity to assess prognostic biomarkers from primary surgery for their impact in progressive glioblastoma. MATERIAL AND METHODS We analyzed DNA methylation array data and panel sequencing from 170 genes of 380 tumor samples of the EORTC-26101 study. These patients were comparable to the overall study cohort in regards of baseline characteristics, study treatment and survival. RESULTS 295/380 (78%) of patients’ samples were classified into one of the main glioblastoma groups receptor tyrosine kinase (RTK)1, RTK2 and mesenchymal. There were 10 patients (2.6%) with isocitrate dehydrogenase (IDH) mutant tumors in the biomarker cohort. Patients with RTK1 and RTK2 classified tumors had lower median OS compared to mesenchymal (7.6 vs. 9.2 vs. 10.5 months). O6-methylguanine DNA-methyltransferase (MGMT) promotor methylation was prognostic for PFS and OS. Neurofibromin (NF)1 mutations were predictive of response to bevacizumab treatment. CONCLUSION Thorough molecular classification is important for brain tumor clinical trial inclusion and evaluation. MGMT promoter methylation and RTK1 classifier assignment were prognostic in progressive glioblastoma. NF1 mutation may be a predictive biomarker for bevacizumab treatment.

Identification of a KDM6A somatic mutation responsible for Kabuki syndrome by excluding a conflicting KMT2D germline variant through episignature analysis

Kabuki syndrome (KS) is a congenital disorder caused by mutations in either KMT2D on chromosome 12 or KDM6A on chromosome X, encoding a lysine methyltransferase and a lysine demethylase, respectively. A 9-year-4-month-old male patient with a normal karyotype presented with KS and autism spectrum disorder. Genetic testing for KS was conducted by Sanger sequencing and episignature analysis using DNA methylation array data. The patient had a mosaic stop-gain variant in KDM6A and a heterozygous missense variant (rs201078160) in KMT2D. The KDM6A variant is expected to be deleterious. The KMT2D variant pathogenicity has been inconsistently reported in the ClinVar database. Using biobanking resources, we identified two heterozygous individuals possessing the rs201078160 variant. In a subsequent episignature analysis, the KS patient showed the KS episignature, but two control individuals with the rs201078160 variant did not. Our results indicate that the mosaic stop-gained variant in KDM6A, but not the rs201078160 variant in KMT2D, is responsible for the KS phenotype in the patient. This study further demonstrated the utility of DNA methylation information in diagnosing rare genetic diseases and emphasized the importance of a reference dataset containing both genotype and DNA methylation information.

MDB-28. DEEP LEARNING-BASED PERSONALIZED SURVIVAL PREDICTION FOR MEDULLOBLASTOMA

Abstract Medulloblastoma is one of the most common malignant brain tumors in children and a leading cause of cancer-related death in this age group (5-year survival rate of ~70%). However, survival outcomes are heterogeneous, and currently associated with only a small number of clinical and molecular parameters. Instead, in this work we aim at exploiting the wealth of hidden information in genome-wide DNA methylation array data to predict accurate personalized survival probability curves for Group 3 and 4 medulloblastoma patients, the subgroups associated with the most heterogeneous outcomes. To accomplish this, we implemented a sparse neural network trained on DNA methylation and copy-number variation (CNV) profiles of over 900 medulloblastoma patients. Our network is designed specifically to leverage censored data for training, and makes no assumptions about the underlying distribution of the output survival probability curves. Furthermore, the architecture of our network is biologically interpretable, enabling us to probe the underlying biology driving survival. Our results demonstrate excellent discrimination and calibration, with a 5-year AUROC of 0.8. We compare our results to currently used stratification schemes on validation cohorts, including SJMB03 clinical trial data, and demonstrate significantly improved risk stratification (c-index of 0.66 vs 0.74, p &amp;lt; 0.0001). We find several cases in which patients assumed to be low-risk were predicted to progress rapidly, and vice versa, motivating possible refinement of risk categories in clinical practice. Our approach, which is based entirely on DNA methylation array data, may facilitate the de-escalation of therapy for low-risk patients to minimize treatment-related side effects and the intensification of treatment for high-risk individuals. The approach can be extended to other cancer types, and highlights the value of AI in healthcare towards realizing the goal of personalized medicine.

Adiposity is associated with widespread transcriptional changes and downregulation of longevity pathways in aged skeletal muscle.

Amongst healthy older people, a number of correlates of impaired skeletal muscle mass and function have been defined. Although the prevalence of obesity is increasing markedly in this age group, information is sparse about the particular impacts of obesity on ageing skeletal muscle or the molecular mechanisms that underlie this and associated disease risk. Here, we examined genome-wide transcriptional changes using RNA sequencing in muscle biopsies from 40 older community-dwelling men from the Hertfordshire Sarcopenia Study with regard to obesity (body mass index [BMI] >30kg/m2 , n=7), overweight (BMI 25-30, n=19), normal weight (BMI<25, n=14), and per cent and total fat mass. In addition, we used EPIC DNA methylation array data to investigate correlations between DNA methylation and gene expression in aged skeletal muscle tissue and investigated the relationship between genes within altered regulatory pathways and muscle histological parameters. Individuals with obesity demonstrated a prominent modified transcriptional signature in muscle tissue, with a total of 542 differentially expressed genes associated with obesity (false discovery rate ≤0.05), of which 425 genes were upregulated when compared with normal weight. Upregulated genes were enriched in immune response (P=3.18×10-41 ) and inflammation (leucocyte activation, P=1.47×10-41 ; tumour necrosis factor, P=2.75×10-15 ) signalling pathways and downregulated genes enriched in longevity (P=1.5×10-3 ) and AMP-activated protein kinase (AMPK) (P=4.5×10-3 ) signalling pathways. Furthermore, differentially expressed genes in both longevity and AMPK signalling pathways were associated with a change in DNA methylation, with a total of 256 and 360 significant cytosine-phosphate-guanine-gene correlations identified, respectively. Similar changes in the muscle transcriptome were observed with respect to per cent fat mass and total fat mass. Obesity was further associated with a significant increase in type II fast-fibre area (P=0.026), of which key regulatory genes within both longevity and AMPK pathways were significantly associated. We provide for the first time a global transcriptomic profile of skeletal muscle in older people with and without obesity, demonstrating modulation of key genes and pathways implicated in the regulation of muscle function, changes in DNA methylation associated with such pathways and associations between genes within the modified pathways implicated in muscle regulation and changes in muscle fibre type.

Integrated multi-omics analysis identifies epigenetic alteration related to neurodegeneration development in post-traumatic stress disorder patients.

Post-traumatic stress disorder (PTSD), is associated with an elevated risk of neurodegenerative disorders, but the molecular mechanism was not wholly identified. Aberrant methylation status and miRNA expression pattern have been identified to be associated with PTSD, but their complex regulatory networks remain largely unexplored. The purpose of this study was to identify the key genes/pathways related to neurodegenerative disorder development in PTSD by evaluating epigenetic regulatory signature (DNA methylation and miRNA) using an integrative bioinformatic analysis. We integrated DNA expression array data with miRNA and DNA methylation array data - obtained from the GEO database- to evaluate the epigenetic regulatory mechanisms. Our results indicated that target genes of dysregulated miRNAs were significantly related to several neurodegenerative diseases. Several dysregulated genes in the neurodegeneration pathways interacted with some members of the miR-17 and miR-15/107 families. Our analysis indicated that APP/CaN/NFATs signaling pathway was dysregulated in the peripheral blood samples of PTSD. Besides, the DNMT3a and KMT2D genes, as the encoding DNA and histone methyltransferase enzymes, were upregulated, and DNA methylation and miRNA regulators were proposed as critical molecular mechanisms. Our study found dysregulation of circadian rhythm as the CLOCK gene was upregulated and hypomethylated at TSS1500 CpGs S_shores and was also a target of several dysregulated miRNAs. In conclusion, we found evidence of a negative feedback loop between stress oxidative, circadian rhythm dysregulation, miR-17 and miR-15/107 families, some essential genes involved in neuronal and brain cell health, and KMT2D/DNMT3a in the peripheral blood samples of PTSD.

Abstract LB131: Association between epigenetic heterogeneity of esophageal adenocarcinoma and response to first-line immunochemotherapy in LUD2015-005 Trial

Abstract Immune checkpoint inhibitors (ICI) were recently approved as a first-line treatment for inoperable esophageal adenocarcinomas (EAC) in combination with chemotherapy (CTX). Unfortunately, even though EAC has one of the highest tumor mutation burdens among all cancer types, response to immunochemotherapy (ICI+CTX) is highly variable, and the underlying molecular basis is incompletely understood. While genomic features such as mutations and copy number alterations in EAC are highly variable across samples, DNA methylation array data from numerous studies suggested that EAC can be clustered into a few consistent subtypes. We thus hypothesize that epigenetic heterogeneity of EAC may contribute to or associate with patients’ heterogeneous response to ICI+CTX, possibly through modulation of key genes or neoantigens. To test this hypothesis, we took advantage of a unique ICI+CTX LUD2015-005 trial in which inoperable EAC patients received first-line ICI for four weeks (ICI-4W), followed by ICI+CTX. Instead of methylation array, we also used a new DNA methylation sequencing technology, TET-Assisted Pyridine-Borane Sequencing (TAPS), on 64 tumor and 15 adjacent normal tissue samples collected from 23 EAC patients before and throughout treatment. Unlike prior studies that used methylation arrays which only cover ~2.5% of all CpG sites in the genome, TAPS detects genome-wide, base-resolution DNA methylation information. Furthermore, many previous studies did not account for variability in tumor content between samples, which could impact downstream DNA methylation analyses. In view of this, we proposed an analytical framework that includes tumor content and local copy number as key parameters, which estimates the tumor and stromal methylation and tests for differentially methylated regions (DMR). We also performed unsupervised clustering based on large scale genome-wide methylation pattern and revealed 2 major tumor clusters. Using the whole genome data, we identified a large set of shared tumor-specific DMRs, revealing that hypomethylation across wide regions of the genome and hypermethylation of certain gene bodies are common features in EAC. We also identified a set of shared outcome-associated DMRs in pre-treatment samples, which predicts better progression-free survival at 12 months. We further performed subgroup analysis of the 2 tumor clusters. Interestingly, we found higher numbers of cluster-specific prognostic DMRs with stronger effect sizes. This suggests that tumor subtypes may respond differently to treatment, and should be considered separately in statistical analyses. Altogether, these results indicate that a detailed understanding of tumor epigenetic heterogeneity will improve patient stratification in immunochemotherapy. Citation Format: Phil F. Xie, Jaeho Chang, Paulina Siejka-Zielińska, Masato Inoue, Magdalena Drożdż, Joseph A. Chadwick, Thomas M. Carroll, Richard P. Owen, Michael J. White, Joseph Kaplinsky, Robert Amess, Mark Middleton, Skirmantas Kriaucionis, Chunxiao Song, Benjamin Schuster-Böckler, Xin Lu. Association between epigenetic heterogeneity of esophageal adenocarcinoma and response to first-line immunochemotherapy in LUD2015-005 Trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB131.

MethylR: a graphical interface for comprehensive DNA methylation array data analysis.

DNA methylation analysis using arrays is a widely used method in research and clinical studies to study Epigenetics. Although several packages have been published to incur the results, most of them require a deep computational knowledge to perform the analysis. To resolve the limitation and to offer an easily accessible solution for researchers, we developed methylR a graphical tool that can analyze not only the raw data but also performs different downstream analyses with a few mouse clicks. We used standard and established open-source published packages or pipelines in methylR. We evaluated a publicly available dataset and compared the published results with those obtained with our tool. We implemented eight downstream analysis modules that can perform multidimensional analyses to pathway enrichment. Although the main application is designed for Illumina DNA methylation array data analysis, we made the accessory modules suitable for other kinds of data analysis as well. Freely available at Github: https://github.com/JD2112/methylr; Webserver: https://methylr.research.liu.se. Supplementary data are available at Bioinformatics online.