Distinct Methylation Patterns Research Articles

InterpolatedXY: a two-step strategy to normalize DNA methylation microarray data avoiding sex bias.

MotivationData normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes; thus, it poses a significant challenge to normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately.ResultsHere, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalization methods. By this new strategy, the autosomal CpGs are first normalized independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome-linked CpGs are estimated as the weighted average of their nearest neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalization effect.Availability and implementationThe proposed methods are available by calling the function ‘adjustedDasen’ or ‘adjustedFunnorm’ in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi.Supplementary information Supplementary data are available at Bioinformatics online.

MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites

MotivationIntermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns.ResultsWe proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest.Availability and implementationMeConcord is available at https://github.com/WangLabTHU/MeConcord.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 3581: Multi-omic analysis identifies mechanisms of resistance to CD19 CAR T-cell therapy in children with acute lymphoblastic leukemia

Abstract Background: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Despite the survival rate of 90% for newly diagnosed children with ALL, the outcome for relapsed patients is historically poor with a less than 30% survival. CD19 CAR T-cell therapy (CART19) has shown remarkable response rates, between 80-90% in relapsed/refractory disease. Little is known about antigen-independent factors that predict initial resistance to CART19. We hypothesized that leukemias that are resistant to CART19 are distinct from sensitive leukemias and that these differences can be detected prior to therapy. Methods: To interrogate differences between resistant and sensitive leukemias, we obtained pre-treatment bone marrow aspirates (BMAs) from patients enrolled in a clinical trial at Seattle Children’s Hospital (PLAT-02). Samples were categorized based on patient response, with non-response defined as not achieving and maintaining minimal residual disease negativity at Day +63. Our study included 7 resistant and 7 sensitive leukemias as controls. We performed whole exome sequencing, bulk RNA-seq, PacBio-seq of the CD19 locus, array-based methylation, ATAC-seq, scRNA-seq, and CyTOF. Results: We found that non-response to CART19 is independent of leukemic subtype. Despite blasts being CD19+ in all patients by flow cytometry, we identified alternative splicing of CD19 in one non-responder, while the remaining non-responders expressed high levels of wildtype CD19. We discovered a distinctive DNA methylation pattern in the non-responders characterized by hypermethylation of PRC2 targets in embryonic and cancer stem cells (p = 8.15E-25) Furthermore, using gene set enrichment analysis of ATAC-seq data, we found increased accessibility of chromatin at regions associated with stem cell proliferation (NES = 2.31; p < 0.0001) and cell cycling (NES = 2.27; p < 0.0001). We found a greater similarity between accessibility patterns of non-responders to hematopoietic progenitors, including hematopoietic stem cells (p = 0.037) and common myeloid progenitors (p = 0.047). These findings were supported by an increased frequency of cell subpopulations expressing a multi-lineage phenotype (CD19, CD20, CD33, CD34; p = 0.009). Moreover, we find decreased expression of antigen presentation and processing pathways across all leukemic cells relative to responders (p = 0.0001). Conclusions: This study, one of the most comprehensive multi-omic analyses of samples from patients treated with CAR T-cells, identified resistance mechanisms that can be detected prior to treatment. We report the novel association of a stem cell phenotype, lineage plasticity, and decreased antigen presentation with resistance. These results support further refinement of eligibility for CART19 for children with leukemia and highlights the need for alternative of complimentary approaches for these patients. Citation Format: Katherine E. Masih, Rebecca Gardner, Hsien-Chao Chou, Abdalla Abdelmaksoud, Young K. Song, Luca Mariani, Vineela Gangalapudi, Berkley E. Gryder, Ashley Wilson, Serifat O. Adebola, Benjamin Z. Stanton, Chaoyu Wang, Xinyu Wen, Gregoire Altan-Bonnet, Michael C. Kelly, Jun S. Wei, Martha L. Bulyk, Michael C. Jensen, Rimas J. Orentas, Javed Khan. Multi-omic analysis identifies mechanisms of resistance to CD19 CAR T-cell therapy in children with acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3581.

Abstract 3396: Identification of small cell lung cancer stage-specific DNA methylation in patients using liquid biopsies

Abstract Introduction: Small cell lung cancer (SCLC) is a deadly disease and patients often suffer from recurrent disease. Biologic mechanisms of recurrence are unclear. Epigenetic mechanisms, like DNA methylation, may be operant. SCLC is rarely resected; therefore, the SCLC methylome is understudied due to scarce tumor tissue. Methods: In this study, we examined the pre-treatment methylome of 72 SCLC patients at our institution through cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) on plasma cell-free DNA (cfDNA) and on sheared genomic DNA from paired peripheral blood leukocytes (PBLs) (n = 72) to increase tumor signal specificity. cfMeDIP-seq was also performed on an independent cohort of healthy non-cancer controls (n = 20) and on tumors from circulating tumor cell-derived xenograft (CDX) models (n = 12 CDXs of 72 patients). cfMeDIP-seq libraries were sequenced at a depth of 100 million reads, paired-end, on the NovaSeq 6000. For all bioinformatic analyses, chromosomes 1-22 were binned into 300-bp windows (n = 9.6e6 genome-wide windows); reads from cfMeDIP-seq were tallied per bin. To filter out noise from non-tumor cells, ENCODE blacklist regions (n = 1e6 windows) were removed and CG-rich (>5 CGs per window), PBL-associated windows with MeDEStrand-converted beta-values < 0.2 were kept (n = 190,769 windows). Subsequent cfDNA analyses were performed using these 190,769 windows. Results: 33 (45.8%) and 39 (54.2%) of the 72 patients had limited-stage (LS) and extensive-stage (ES) SCLC, respectively. Most were current or former smokers (65/72, 90.3%). Using filtered windows (n = 190,769), methylated cfDNA from SCLC were distinguished from healthy controls: SCLC cfDNA was enriched for hypermethylated CpG islands and shore regions, whereas controls had more methylated open-sea regions. SCLC cohort by consensus clustering of the top 5000 most variant windows revealed two distinct cfDNA methylation patterns. Cluster A (9 ES, 22 LS) and Cluster B (33 ES, 11 LS) were significantly different by stage (X2(1) = 13.79, p < 0.001). There was no significant difference in sex (p = 0.81). Patients in Cluster A had a lower concentration of cfDNA (median = 7.2ng/ml) compared to Cluster B (median = 15.9ng/ml) but not statistically significant (p = 0.33). High concordance between genome-wide methylome profiles was found between paired patient cfDNA and CDX tumor, demonstrating that cfDNA is representative of tumor methylation (Pearson's correlation median r = 0.86). Conclusion: We identified stage-specific methylation patterns in the plasma of SCLC patients using the cfMeDIP-seq assay that may reveal novel epigenetic and biologic mechanisms of SCLC disease progression. Moreover, CDXs recapitulate the methylome of SCLC cell-free patient samples highlighting their utility in future work as representative models for methylome analysis. Citation Format: Sami Ul Haq, Sabine Schmid, Mansi K. Aparnathi, Katrina Hueniken, Luna J. Zhan, Danielle Sacdalan, Janice J.N. Li, Devalben Patel, Dangxiao Cheng, Vivek Philip, Geoffrey Liu, Scott V. Bratman, Benjamin H. Lok. Identification of small cell lung cancer stage-specific DNA methylation in patients using liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3396.

Abstract 3755: DNA methylation patterns, but not genetic sequence alterations, are concordant across multiply sampled sites within individuals with unilateral and bilateral Wilms tumors

Abstract Wilms tumor, the most common renal malignancy of childhood, is a disease marked by heterogeneity. One tumor can harbor multiple histologic subtypes and multifocal or bilateral disease is common occurring in up to 10% of patients. Several molecular features have been associated with Wilms tumors and some are used for risk stratification. However, it is unclear which, if any, features are consistent throughout a single tumor or shared between tumors in cases of bilateral disease. To answer this question, we generated genomic and epigenomic data from multiply sampled Wilms tumors from 10 patients - 3 with unilateral disease and 7 with bilateral disease. Multiple samples from each tumor as well as matched normal tissues were included when available. All tumors and matched normal tissues had DNA methylation array data available. Segmental chromosomal aberrations (SCAs) were inferred from methylation array data. Sequencing data were available for all bilateral patients but not for unilateral patients. We found that SCAs were discordant both between pairs of bilateral tumors and within multiply sampled unilateral tumors. This discordance included SCAs used for clinical risk stratification (chromosome 1q gain, 1p loss). Sequence variants were also rarely shared between pairs of bilateral tumors. On the other hand, DNA methylation patterns were concordant across all samples for each individual. We previously described 3 distinct DNA methylation patterns in Wilms tumors and found that all samples from each patient classified into the same subgroup. Furthermore, DNA methylation at Wilms tumor-specific and subgroup-specific differentially methylated regions had high (>0.8) correlation in all samples from all cases. Our data demonstrate that, in contrast to genomic changes such as SCAs and sequence alterations, DNA methylation patterns in Wilms tumors are frequently shared between paired tumors in bilateral disease and in multiple regions within individual tumors. This is consistent with earlier studies demonstrating that gain of methylation at the H19 imprinted region is often the earliest event in Wilms tumors and in some cases likely occurs in the embryonic intermediate mesoderm prior to the development of individual kidneys. Our findings demonstrate the utility of Wilms tumor DNA methylation patterns in overcoming tumor heterogeneity for risk stratification in cases of bilateral or multifocal Wilms tumors. Citation Format: Jack Brzezinski, Sanaa Choufani, Cheryl Shuman, Rodrigo Romao, Armando Lorenzo, Rosanna Weksberg. DNA methylation patterns, but not genetic sequence alterations, are concordant across multiply sampled sites within individuals with unilateral and bilateral Wilms tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3755.

The dural angioleiomyoma harbors frequent GJA4 mutation and a distinct DNA methylation profile

The International Society for the Study of Vascular Anomalies (ISSVA) has defined four vascular lesions in the central nervous system (CNS): arteriovenous malformations, cavernous angiomas (also known as cerebral cavernous malformations), venous malformations, and telangiectasias. From a retrospective central radiological and histopathological review of 202 CNS vascular lesions, we identified three cases of unclassified vascular lesions. Interestingly, they shared the same radiological and histopathological features evoking the cavernous subtype of angioleiomyomas described in the soft tissue. We grouped them together with four additional similar cases from our clinicopathological network and performed combined molecular analyses. In addition, cases were compared with a cohort of 5 soft tissue angioleiomyomas. Three out 6 CNS lesions presented the same p.Gly41Cys GJA4 mutation recently reported in hepatic hemangiomas and cutaneous venous malformations and found in 4/5 soft tissue angioleiomyomas of our cohort with available data. Most DNA methylation profiles were not classifiable using the CNS brain tumor (version 12.5), and sarcoma (version 12.2) classifiers. However, using unsupervised t-SNE analysis and hierarchical clustering analysis, 5 of the 6 lesions grouped together and formed a distinct epigenetic group, separated from the clusters of soft tissue angioleiomyomas, other vascular tumors, inflammatory myofibroblastic tumors and meningiomas. Our extensive literature review identified several cases similar to these lesions, with a wide variety of denominations. Based on radiological and histomolecular findings, we suggest the new terminology of “dural angioleiomyomas” (DALM) to designate these lesions characterized by a distinct DNA methylation pattern and frequent GJA4 mutations.

Evaluation of site-specific methylation of the CMV promoter and its role in CHO cell productivity of a recombinant monoclonal antibody.

We previously demonstrated that increased monoclonal antibody productivity in dihydrofolate reductase (DHFR)-amplified CHO cells correlates with phosphorylated transcription factor-cytomegalovirus (CMV) promoter interactions. In this article, we extend the characterization to include CMV promoter methylation and its influence on NFκB and CREB1 transcription factor binding to the CMV promoter in two families of DHFR-amplified CHO cell lines. CMV promoter methylation was determined using bisulfite sequencing. To overcome Sanger-sequencing limitations due to high CG bias and multiple transgenes copies, pyrosequencing was used to determine the frequency of methylated cytosines in regions proximal to and containing the NFκB and CREB1 transcription-factor consensus binding sites. Chromatin immunoprecipitation was performed to interrogate transcription factor–DNA interactions. Antibodies to CREB1 and NFκB were used to immunoprecipitate formaldehyde-crosslinked protein-DNA fractions, followed by reverse transcription quantitative real-time polymerase chain reaction to quantitate the number of copies of CMV-promoter DNA bound to the various transcription factors. The relative unmethylated fraction at the CREB1 and NFκB consensus binding sites determined by pyrosequencing was correlated with transcription factor binding as determined by chromatin immunoprecipitation. Azacytidine treatment reduced methylation in all treated samples, though not at all methylation sites, while increasing transcription. Distinct promoter methylation patterns arise upon clonal selection in different families of cell lines. In both cell line families, increased methylation was observed upon amplification. In one family, the NFκB binding-site methylation was accompanied by increased CREB1 interaction with the promoter. In the other cell line family, lower methylation frequency at the NFκB consensus binding site was accompanied by more NFκB recruitment to the promoter region.

Clinical Significance of the Expression of Co-Stimulatory Molecule B7-H3 in Papillary Thyroid Carcinoma.

Background: B7-H3, also known as CD276, an important immune checkpoint member of the B7-CD28 family, is confirmed as a promising target after PD-L1 in clinical trials. Although the overexpression of B7-H3 has been associated with invasive metastatic potential and poor prognosis in multiple types of cancer, nothing is known regarding the expression profiles of B7-H3 in papillary thyroid carcinoma (PTC). In this study, we carried out a large-scale analysis of B7-H3 expression in PTC patients and evaluated the potential clinical significance of B7-H3. Methods: In total, data from 1,210 samples, including 867 cases from TCGA and four GEO datasets, were collected for B7-H3–related transcriptome analyses, and 343 postoperative, whole-tumor sections were collected from patients with PTC at our institute for B7-H3–specific immunohistochemistry (IHC) staining. The statistical analysis was primarily accomplished using the R project for statistical computing. Results: B7-H3 positivity was found in 84.8% of PTC patients (291/343), and the mRNA and protein expression levels of B7-H3 in PTC were markedly higher than those of para-tumor tissues (p < 0.001), demonstrating that B7-H3 can serve as a potential diagnostic biomarker for PTC. The significant upregulation of B7-H3 in PTC is caused by distinct patterns of CNVs and CpG DNA methylation. Functional enrichment analysis confirmed that high B7-H3 expression was significantly associated with specific immune features and angiogenesis. High B7-H3 protein expression was associated with tumor size (p = 0.022), extrathyroidal extension (ETE) (p = 0.003), and lymph node metastasis (LNM) (p < 0.001). More importantly, multivariate analysis confirmed that B7-H3 was an independent predictor of relapse-free survival (RFS) (p < 0.05). In the subgroup analysis, positive B7-H3 staining was associated with worse RFS in patients with primary tumor size ≥2 cm (p < 0.05), age ≥55 years (p < 0.05), LNM (p = 0.07), multifocality (p < 0.05), and ETE (p < 0.05). In addition, Circos plots indicated that B7-H3 was significantly associated with other immune checkpoints in the B7-CD28 family. Conclusion: This is the first comprehensive study to elucidate the expression profile of B7-H3 in PTC. Our observations revealed that B7-H3 is a novel independent biomarker for predicting LNM and disease recurrence for PTC patients, and it thus may serve as an indicator that could be used to improve risk-adapted therapeutic strategies and a novel target for immunotherapy strategies for patients who undergo an aggressive disease course.

Comparison of tumor and two types of paratumoral tissues highlighted epigenetic regulation of transcription during field cancerization in non-small cell lung cancer

BackgroundField cancerization is the process in which a population of normal or pre-malignant cells is affected by oncogenic alterations leading to progressive molecular changes that drive malignant transformation. Aberrant DNA methylation has been implicated in early cancer development in non-small cell lung cancer (NSCLC); however, studies on its role in field cancerization (FC) are limited. This study aims to identify FC-specific methylation patterns that could distinguish between pre-malignant lesions and tumor tissues in NSCLC.MethodsWe enrolled 52 patients with resectable NSCLC and collected resected tumor (TUM), tumor-adjacent (ADJ) and tumor-distant normal (DIS) tissue samples, among whom 36 qualified for subsequent analyses. Methylation levels were profiled by bisulfite sequencing using a custom lung-cancer methylation panel.ResultsADJ and DIS samples demonstrated similar methylation profiles, which were distinct from distinct from that of TUM. Comparison of TUM and DIS profiles led to identification of 1740 tumor-specific differential methylated regions (DMRs), including 1675 hypermethylated and 65 hypomethylated (adjusted P < 0.05). Six of the top 10 tumor-specific hypermethylated regions were associated with cancer development. We then compared the TUM, ADJ, and DIS to further identify the progressively aggravating aberrant methylations during cancer initiation and early development. A total of 332 DMRs were identified, including a predominant proportion of 312 regions showing stepwise increase in methylation levels as the sample drew nearer to the tumor (i.e. DIS < ADJ < TUM) and 20 regions showing a stepwise decrease pattern. Gene set enrichment analysis (GSEA) for KEGG and GO terms consistently suggested enrichment of DMRs located in transcription factor genes, suggesting a central role of epigenetic regulation of transcription factors in FC and tumorigenesis.ConclusionWe revealed distinct methylation patterns between pre-malignant lesions and malignant tumors, suggesting the essential role of DNA methylation as an early step in pre-malignant field defects. Moreover, our study also identified differentially methylated genes, especially transcription factors, that could potentially be used as markers for lung cancer screening and for mechanistic studies of FC and early cancer development.

Candidate Epigenetic Biomarker of Cognitive Trajectory: The Chicken or the Egg?

Brain health throughout the life course depends upon both genetic and epigenetic features. The most widely studied epigenetic mark in the brain has been DNA methylation owing to its stability and convenience to assay from widely available DNA biospecimens. Evidence suggests that distinct DNA methylation patterns serve as a wide-ranging biomarker including but not limited to disease status, disease risk, predictor of life expectancy and mortality, aging, and prior environmental exposures. Emerging research is suggesting the utility of DNA methylation biomarkers in neuroimaging and neurocognition epigenetics.

DNMT1 Deficiency Impacts on Plasmacytoid Dendritic Cells in Homeostasis and Autoimmune Disease.

Dendritic cells (DCs) are heterogeneous immune regulators involved in autoimmune diseases. Epigenomic mechanisms orchestrating DC development and DC subset diversification remain insufficiently understood but could be important to modulate DC fate for clinical purposes. By combining whole-genome methylation assessment with the analysis of mice expressing reduced DNA methyltransferase 1 levels, we show that distinct DNA methylation levels and patterns are required for the development of plasmacytoid DC and conventional DC subsets. We provide clonal in vivo evidence for DC lineage establishment at the stem cell level, and we show that a high DNA methylation threshold level is essential for Flt3-dependent survival of DC precursors. Importantly, reducing methylation predominantly depletes plasmacytoid DC and alleviates systemic lupus erythematosus in an autoimmunity mouse model. This study shows how DNA methylation regulates the production of DC subsets and provides a potential rationale for targeting autoimmune disease using hypomethylating agents.

Epigenetic dysregulation in meningiomas.

BackgroundMeningiomas are the most common primary brain tumor. Though typically benign with a low mutational burden, tumors with benign histology may behave aggressively and there are no proven chemotherapies. Although DNA methylation patterns distinguish subgroups of meningiomas and have higher predictive value for tumor behavior than histologic classification, little is known about differences in DNA methylation between meningiomas and surrounding normal dura tissue.MethodsWhole-exome sequencing and methylation array profiling were performed on 12 dura/meningioma pairs (11 WHO grade I and 1 WHO grade II). Single-nucleotide polymorphism (SNP) genotyping and methylation array profiling were performed on an additional 19 meningiomas (9 WHO grade I, 5 WHO grade II, 4 WHO grade III).Results Using multimodal studies of meningioma/dura pairs, we identified 4 distinct DNA methylation patterns. Diffuse DNA hypomethylation of malignant meningiomas readily facilitated their identification from lower-grade tumors by unsupervised clustering. All clusters and 12/12 meningioma-dura pairs exhibited hypomethylation of the gene promoters of a module associated with the craniofacial patterning transcription factor FOXC1 and its upstream lncRNA FOXCUT. Furthermore, we identified an epigenetic continuum of increasing hypermethylation of polycomb repressive complex target promoters with increasing histopathologic grade.ConclusionThese findings support future investigations of the role of epigenetic dysregulation of FOXC1 and cranial patterning genes in meningioma formation as well as studies of the utility of polycomb inhibitors for the treatment of malignant meningiomas.

Workflow to Mine Frequent DNA Co-methylation Clusters in DNA Methylome Data.

The advances in high-throughput nucleotide sequencing technology revolutionized biomedical research. Vast amount of genomic data rapidly accumulates in a daily basis, which in turn calls for the development of powerful bioinformatics tools and efficient workflows to analyze them. One of the approaches to address the "big data" issue is to mine highly correlated clusters/networks of biological molecules, which may provide rich yet hidden information about the underlying functional, regulatory, or structural relationships among genes, proteins, genomic loci or various types of biological molecules or events. A network mining algorithm lmQCM has recently been developed, which can be applied to mine tightly connected correlation clusters (networks) in large biological data with big sample size, and it guarantees a lower bound of the cluster density. This algorithm has been used in a variety of cancer transcriptomic data to mine gene co-expression networks (GCNs), but it can be applied to any correlational matrix. lmQCM is available through R package lmQCM as well as the online tool package TSUNAMI ( https://biolearns.medicine.iu.edu ).In this study, the purpose is to establish an analytical workflow to apply lmQCM for frequent (consensus)cluster mining in multiple DNA methylation datasets in different cancers and extract the underlying common co-methylation networks for genes.Specifically, the workflow is applied to analyze DNA methylome data across different cancer types using lmQCM. It mines frequent DNA methylation clusters based on individual clustering mining results, identifying common as well as distinctive DNA methylation patterns among different cancer types. This workflow has successfully identified frequent GCNs in 33 types of cancers, thus proven to be a powerful tool to analyze large biological data. It helps to identify common features as well as distinctions among different diseases, disease subtypes, or among different biological processes. The resulted frequent clusters may provide new insights on the pathway/function networks. In the case of disease study, the results lead to new directions for biomarker and drug target discovery. The advantages of this workflow include the highly efficient processing of large biological data generated from high-throughput experiments, quick identification of highly correlated interaction networks, substantial reduction of the data dimensionality to a manageable number of variables for downstream comparative analysis, and consequently increased statistical power for detecting differences between conditions.

Prognostic and therapeutic value of LSM5 gene in human brain cancer Glioma: An omics database exploration approach

This study explored the diagnostic and therapeutic values of the LSM5 gene in two commonest types of gliomas, i.e., lower-grade glioma (LGG) and glioblastoma multiforme (GBM) by exploiting a bioinformatics-based database mining pipeline. LSM5 gene was reported to be upregulated at both mRNA and protein levels in both LGG and GBM tissues compared to adjacent normal tissues. Moreover, overexpression of LSM5 was later observed to be linked with glioma patients' demographic status and advancing cancer stages. Additionally, a differential and distinct methylation pattern of the LSM5 promoter and coding sequence in glioma tissues was reported. The gene of our interest was also discovered to undergo multiple amplification events (frequency of alteration: 1.1%) in both LGG and GBM patients. Furthermore, LSM5 overexpression was reported to be correlated with the poor overall and relapse-free survival of glioma patients. Subsequently, the immune phenotype analysis revealed that the expression of LSM5 correlates with the abundance levels of various immune cells i.e., B cell, T cell, dendritic cell, and immunomodulators, i.e., IL10RB, CD48, CD274 in glioma microenvironment. Finally, functional enrichment analysis of the neighbor genes of LSM5 showed that most of the genes are involved in different pivotal cellular processes like RNA splicing, DNA mismatch repair, and DNA replication. Overall, this study suggests that LSM5 and its downstream products are potential candidates for glioma diagnosis and treatment.

Hepatitis C Virus Core Protein Down-Regulates Expression of Src-Homology 2 Domain Containing Protein Tyrosine Phosphatase by Modulating Promoter DNA Methylation.

Hepatitis C virus (HCV) is the major causative pathogen associated with liver cirrhosis and hepatocellular carcinoma. The main virion component, the core (C) protein, has been implicated in several aspects of HCV pathology including oncogenesis and immune subversion. Here we show that expression of the C protein induced specific tyrosine phosphorylation of the TCR-related signaling proteins ZAP-70, LAT and PLC-γ in the T cells. Stable expression of the C protein specifically reduced Src homology domain 2-containing protein tyrosine phosphatase 1 (SHP-1) mRNA and protein accumulation. Quantitative CpG methylation analysis revealed a distinct CpG methylation pattern at the SHP-1 gene promoter in the C protein expressing cells that included specific hypermethylation of the binding site for Sp1 transcription factor. Collectively, our results suggest that HCV may suppress immune responses and facilitate its own persistence by deregulating phosphotyrosine signaling via repressive epigenetic CpG modification at the SHP-1 promoter in the T cells.

Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma.

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.

Risk-focused differences in molecular processes implicated in SARS-CoV-2 infection: corollaries in DNA methylation and gene expression

BackgroundUnderstanding the molecular basis of susceptibility factors to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global health imperative. It is well-established that males are more likely to acquire SARS-CoV-2 infection and exhibit more severe outcomes. Similarly, exposure to air pollutants and pre-existing respiratory chronic conditions, such as asthma and chronic obstructive respiratory disease (COPD) confer an increased risk to coronavirus disease 2019 (COVID-19).MethodsWe investigated molecular patterns associated with risk factors in 398 candidate genes relevant to COVID-19 biology. To accomplish this, we downloaded DNA methylation and gene expression data sets from publicly available repositories (GEO and GTEx Portal) and utilized data from an empirical controlled human exposure study conducted by our team.ResultsFirst, we observed sex-biased DNA methylation patterns in autosomal immune genes, such as NLRP2, TLE1, GPX1, and ARRB2 (FDR < 0.05, magnitude of DNA methylation difference Δβ > 0.05). Second, our analysis on the X-linked genes identified sex associated DNA methylation profiles in genes, such as ACE2, CA5B, and HS6ST2 (FDR < 0.05, Δβ > 0.05). These associations were observed across multiple respiratory tissues (lung, nasal epithelia, airway epithelia, and bronchoalveolar lavage) and in whole blood. Some of these genes, such as NLRP2 and CA5B, also exhibited sex-biased gene expression patterns. In addition, we found differential DNA methylation patterns by COVID-19 status for genes, such as NLRP2 and ACE2 in an exploratory analysis of an empirical data set reporting on human COVID-9 infections. Third, we identified modest DNA methylation changes in CpGs associated with PRIM2 and TATDN1 (FDR < 0.1, Δβ > 0.05) in response to particle-depleted diesel exhaust in bronchoalveolar lavage. Finally, we captured a DNA methylation signature associated with COPD diagnosis in a gene involved in nicotine dependence (COMT) (FDR < 0.1, Δβ > 0.05).ConclusionOur findings on sex differences might be of clinical relevance given that they revealed molecular associations of sex-biased differences in COVID-19. Specifically, our results hinted at a potentially exaggerated immune response in males linked to autosomal genes, such as NLRP2. In contrast, our findings at X-linked loci such as ACE2 suggested a potentially distinct DNA methylation pattern in females that may interact with its mRNA expression and inactivation status. We also found tissue-specific DNA methylation differences in response to particulate exposure potentially capturing a nitrogen dioxide (NO2) effect—a contributor to COVID-19 susceptibility. While we identified a molecular signature associated with COPD, all COPD-affected individuals were smokers, which may either reflect an association with the disease, smoking, or may highlight a compounded effect of these two risk factors in COVID-19. Overall, our findings point towards a molecular basis of variation in susceptibility factors that may partly explain disparities in the risk for SARS-CoV-2 infection.

Latency-associated DNA methylation patterns among HIV-1 infected individuals with distinct disease progression courses or antiretroviral virologic response

DNA methylation is one of the epigenetic modifications that configures gene transcription programs. This study describes the DNA methylation profile of HIV-infected individuals with distinct characteristics related to natural and artificial viremia control. Sheared DNA from circulating mononuclear cells was subjected to target enrichment bisulfite sequencing designed to cover CpG-rich genomic regions. Gene expression was assessed through RNA-seq. Hypermethylation in virologic responders was highly distributed closer to Transcription Start Sites (p-value = 0.03). Hyper and hypomethylation levels within TSS adjacencies varied according to disease progression status (Kruskal–Wallis, p < 0.001), and specific differentially methylated regions associated genes were identified for each group. The lower the promoter methylation, the higher the gene expression in subjects undergoing virologic failure (R = − 0.82, p = 0.00068). Among the inversely correlated genes, those supporting glycolysis and its related pathways were hypomethylated and up-regulated in virologic failures. Disease progression heterogeneity was associated with distinct DNA methylation patterns in terms of rates and distribution. Methylation was associated with the expression of genes sustaining intracellular glucose metabolism in subjects undergoing antiretroviral virologic failure. Our findings highlight that DNA methylation is associated with latency, disease progression, and fundamental cellular processes.

EPCO-30. MACHINE-LEARNING PREDICTIVE MODELS BASED ON DNA METHYLATION SIGNATURES DETECTED IN LIQUID BIOPSY SPECIMENS ACCURATELY PREDICT THE DIAGNOSIS AND PROGNOSIS OF MENINGIOMAS

Abstract BACKGROUND Detection of distinct epigenetic features in circulating cell-free DNA (cfDNA) of liquid biopsy specimens (e.g. blood) provides an opportunity to diagnose and prognosticate central nervous system (CNS) tumors. Utilization of distinctive cell-type genome-wide DNA methylation patterns allows for development of machine-learning (ML) models with the ability to predict tumor diagnosis and prognosis, and remains widely unexplored in meningiomas using LB. METHODS We profiled the cfDNA methylome (EPIC array) in serum specimens from patients with meningiomas, other CNS tumors and nontumor conditions (n= 63, 190 and 18, respectively) and harnessed internal and external meningioma tissue methylome data. For diagnostic model development, we identified “meningioma specific” signatures through comparison of meningioma and non-meningioma serum specimens (Wilcoxon rank-sum test) which exhibited tissue methylation similarities named Meningioma- epigenetic Liquid Biopsy (MeLB), and were then employed to develop and cross-validate a Random-Forest derived “MeLB” score to discriminate meningiomas from the other conditions. To predict recurrence risk (RR), we classified a meningioma tissue cohort as ‘favorable’ or ‘unfavorable’ (low and high RR, respectively), using a validated ML outcome model and identified outcome-specific methylation markers with serum subgroup specificities used as input to train a Random-Forest to predict RR in serum-based specimens. RESULTS Prediction models based on meningioma-specific methylation markers detected in the serum presented a high accuracy in classification of samples as meningioma or not (Accuracy: 89.6%, Sensitivity: 80%, Specificity: 93.8%). The prognostic model using tissue-serum matching methylation markers was validated across an independent tissue-based cohort (Accuracy: 88%, Sensitivity: 86%, Specificity: 88%) and allowed for classification of serum samples according to RR. CONCLUSIONS Machine-learning models using DNA methylation markers identified in the serum can accurately diagnose and predict prognosis in patients with meningioma. After validation in an external cohort, these approaches may improve presurgical diagnosis and therapeutic management of patients with this type of tumor.

Epigenetic Drug Combination Alters Methylation Patterns in Patient-Derived Xenograft Models of KMT2A-Rearranged Pediatric AML Treated In Vivo

Pediatric acute myeloid leukemia (AML) patients possessing rearrangement of the KMT2A (previously known as MLL) gene on 11q23 constitute a subclass with a particularly poor prognosis. The five-year survival rate for these patients is only about 44% due to poor response to conventional chemotherapy and frequent early relapse. Aberrant epigenetic modifications play an important role in leukemogenesis in KMT2A-rearranged leukemia. Accordingly, several epigenome modifying drugs have been tested in preclinical studies of KMT2A-rearranged leukemia. Acknowledging the co-regulatory effects of DNA methylation and histone modifications in determining chromatin structure and governing gene expression, we combined DNA hypomethylating agent azacitidine with histone deacetylase inhibitor panobinostat in the hopes of achieving greater efficacy. We showed that this epigenetic drug combination was more efficacious than single agents using cell line derived xenograft models of pediatric AML (Gopalakrishnapillai et al., Leuk Res, 2017).We evaluated the efficacy of this epigenetic drug combination in patient-derived xenograft models of KMT2A rearranged pediatric AML and observed that similar to MV4;11 model, this combination induced complete remission in NTPL-146 model with KMT2A-MLLT1 fusion (Fig. 1A, P<0.001). We analyzed the methylome of AML cells harvested from xenografted mice treated with control, azacitidine, panobinostat, or a combination of the two. Methylation sensitive restriction endonucleases were utilized to fragment genomic DNA prior to library construction for next generation sequencing. GenPro software platform designed for highly quantitative, sensitive, and low error-rate detection of methylation at individual CpG sites was used.Methylation patterns between treatment groups were discriminated using an ordinate analysis technique of non-metric multidimensional scaling (NMDS) (Fig. 1B). CpG methylation profiles were compared among the four groups analyzed to isolate patterns conserved within groups while also differing between groups. The first two component axes were plotted to locate the individual sample points in a 2D plane. Samples from distinct PDX models undergoing similar treatment clustered together. The panobinostat-treated samples showed minimal differences compared to the control, while the azacitidine-treated samples clustered away. Interestingly, the samples treated with the combination, did not overlap with either treatment, indicating that although panobinostat alone showed minimal impact on methylation patterns, panobinostat together with azacitidine produced a distinct methylation pattern. Venn intersection sets of statistically significant differentially methylated CpG sites in the 3-way analyses derived from the control group comparisons showed 2086 CpG sites exclusively altered in the combination treatment (Fig. 1C).In order to determine the effect of the combination treatment on global methylation, the differences in methylation load (dML) per each CpG site between control and the combination treatment were summed across 1MB genome intervals and the distribution of these dML was plotted (Fig. 1D). There was a strong shift in methylation signal, with the majority of the intervals being hypomethylated in the treatment group compared to the control. Although global hypomethylation was observed in combination treatment, the most statistically significant CpG sites were hypermethylated in the combination treatment compared to the control as seen in the volcano plot in which log fold-change was plotted against the p-value (Fig. 1E). Circular ideogram presented with a mean subtraction of CpG methylation scores to calculate a summation methylation load score across chromosomal domains (Fig. 1F). The correlative association between top CpG sites is shown as arcs tracking the highest correlation for each CpG site. Gene labels indicate the positions of the top 60 CpG sites, with green and red indicating higher methylation in control and in combination treatment respectively.In conclusion, we have identified differential methylation patterns following in vivo treatment of KMT2A rearranged pediatric AML xenograft models with azacitidine and panobinostat combination compared to azacitidine alone. These methylation changes are likely to influence the increased survival seen in mice receiving combination treatment. [Display omitted] DisclosuresGopalakrishnapillai: Geron: Research Funding. Marsh: Genome Profiling LLC: Current Employment. Barwe: Prelude Therapeutics: Research Funding.