Methylation Microarray Research Articles

A new dimensionality reduction technique based on the Wavelet Transform for cancer classification

ProblemDNA methylation and hydroxymethylation have become important epigenetic markers for early detection of cancer. In recent years, there has been a significant increase in both the number of research works on this topic and the number and size of labeled databases with some type of cancer. Although the advent of methylation microarrays such as the HumanMethylation450 platform has greatly reduced the dimensionality of the problem from billions to 450K positions, this data size is still too large to be processed by machine learning algorithms for cancer prediction and classification.AimIn the particular case of methylation, an efficient dimensionality reduction technique should also preserve the spatial information of the original data in order to properly predict and classify cancer.MethodThis work proposes a new approach for data dimensionality reduction technique based on the Discrete Wavelet Transform (DWT), which preserves spatial information. We have evaluated the proposed technique with a dataset collected from the most important cancer databases according to their social impact, and we have compared our proposal to five well-known dimensionality reduction techniques: PCA, ReliefF, Isomap, LLE and UMAP.ResultsThe performance evaluation results show that the proposed technique significantly reduces both the computational resources and the execution time required for dimensionality reduction. In addition, it significantly improves the accuracy achieved in the classification by a support vector machine when it uses as input data the resulting dataset yielded by each technique.ConclusionsThe proposed approach based on the DWT can be considered as an efficient alternative for those cases where dimensionality reduction must preserve spatial information.

AI-driven feature selection and epigenetic pattern analysis: A screening strategy of CpGs validated by pyrosequencing for body fluid identification.

Identification of body fluid stain at crime scene is one of the important tasks of forensic evidence analysis. Currently, body fluid-specific CpGs detected by DNA methylation microarray screening, have been widely studied for forensic body fluid identification. However, some CpGs have limited ability to distinguish certain body fluid types. The ongoing need is to discover novel methylation markers and fully validate them to enhance their evidentiary strength in complex forensic scenarios. This research gathered forensic-related DNA methylation microarrays data from the Gene Expression Omnibus (GEO) database. A novel screening strategy for marker selection was developed, combining feature selection algorithms (elastic net, information gain ratio, feature importance based on Random Forest, and mutual information coefficient) with epigenetic pattern analysis, to identify CpG markers for body fluid identification. The selected CpGs were validated through pyrosequencing on peripheral blood, saliva, semen, vaginal secretions, and menstrual blood samples, and machine learning classification models were constructed based on the sequencing results. Pyrosequencing results revealed 14 CpGs with high specificity in five types of body fluid samples. A machine learning classification model, developed based on the pyrosequencing results, could effectively distinguish five types of body fluid samples, achieving 100 % accuracy on the test set. Utilizing six CpG markers, it was also feasible to attain ideal efficacy in identifying body fluid stains. Our research proposes a systematic and scientific strategy for screening body fluid-specific CpGs, contributing new insights and methods to forensic body fluid identification.

Methylation changes and INS-IGF2 expression predict progression in early-stage Wilms tumor.

Wilms tumor, the most common pediatric kidney cancer, accounts for 5% of childhood cancers and is classified by stage and histological subtype. Despite high survival rates (80-85%), approximately 15% of patients experience relapse, reducing survival to around 50%. Epigenetic changes, particularly DNA methylation, play a critical role in Wilms tumor pathogenesis. This study investigates the prognostic potential of DNA methylation in stage I and II patients with favorable histology, aiming to identify early relapse biomarkers. Genome-wide methylation was assessed using methylation microarrays in tumor tissues from relapsed patients (n = 9) and those with complete responses (n = 9), alongside normal tissues (n = 3 each). Differentially methylated probes and regions were analyzed, with additional ROC and survival analyses. Real-time PCR was used to measure IGF2 and INS-IGF2 gene expression. The analysis revealed hypomethylation in intergenic regions in remission patients, identifying 14 differentially methylated positions as potential biomarkers. Increased INS-IGF2 expression was associated with relapse, suggesting its role in disease progression. While the study concentrated on stages I and II patients, where relapse rates are lower, this focus inherently led to a smaller sample size. Despite this, the findings provide valuable insights into the potential role of DNA methylation markers for monitoring disease progression and guiding personalized treatment in Wilms tumor patients.

Abstract A016: Early pancreatic cancer detection using extracellular vesicle DNA methylation signatures in blood

Abstract In 2020, there were 495,773 new cases and 466,003 deaths for pancreatic cancer worldwide. Furthermore, pancreatic cancer is emerging to be the leading cause of cancer related deaths in the U.S. by the year 2030, surpassing lung and colon cancer. The majority of pancreatic cancer patients (∼80%) are diagnosed at the most advanced (metastatic) stage, for which the 5-year survival is 3%. Dramatically, studies show that if it can be detected at an early stage when surgical removal is feasible, the 5-year survival rate significantly increased to 37%. This data highlights the crucial importance of early detection for pancreatic cancer treatment with curative intent. Unfortunately, currently there are no reliable techniques for routine screening to identify pancreatic cancer at early stages. In this Abstract, we developed and validated the performance of those DNA methylation markers to detect pancreatic cancer in a cohort of pancreatic cancer patients. Firstly, we collected a large amount of genome-wide methylation microarray data (targeting over 450,000 methylation sites) of tissue samples from several publicly available databases, including samples of solid pancreatic tumors (n = 384), noncancerous pancreatic tissues (n = 126) and healthy whole blood (n = 453). The raw microarray data were preprocessed using a pipeline implemented in R, which removes inaccurate measurements and corrects for bias caused by technical issues. A two-stage feature selection procedure was designed and implemented in Python, which makes use of various filters and a multivariate embedded method, support vector machine recursive feature elimination (SVM-RFE). The features were evaluated using two metrics, classification performance and stability, based on a tradeoff of which we selected a set of differentially methylated loci for discriminating the samples. The set of prospective markers includes 129 hypermethylated and 142 hypomethylated tumor-specific loci, 8 hypermethylated and 8 hypomethylated pancreatic tissue specific loci. To validate the performance of those pancreatic cancer-specific methylation markers, as a feasibility study, we isolated extracellular vesicles from cancer cells and plasma of cancer patients using lipid nanoprobe (LNP). Extracellular vesicles (EVs) are lipid-bilayer-enclosed vesicles of sub- micrometer size that are secreted by virtually all cell types. In a pilot study encompassing 11 pancreatic cancer patients (Stage I-IV) and age-matched 10 healthy donors (HD), we employed quantitative methylation-specific polymerase chain reaction assays (qMSP) to examine the promoter methylation status of the genes within plasma EV DNA. The receiver operating characteristic (ROC) curves were employed to assess the performance of a combined promoter methylation signature in EV DNA for the detection of pancreatic cancer. Our findings indicate that the promoter methylation of genes can be specific to pancreatic cancer, suggesting the efficient packaging of methylation information from genomic DNA into EVs. Citation Format: Hongzhang He, Siyang Zheng. Early pancreatic cancer detection using extracellular vesicle DNA methylation signatures in blood [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A016.

MethylCallR : a comprehensive analysis framework for Illumina Methylation Beadchip

DNA methylation is a molecular process that mediates gene-environment interactions. Epigenome-wide association studies (EWAS) using the Illumina Human Methylation BeadChip are powerful tools for quantifying the relationship between DNA methylation and phenotypes. Recently, the Illumina Methylation EPICv2 BeadChip (EPICv2) was released, which includes new features, such as duplicated probes and changed probe names. Several published algorithms have been updated to address these features in EPICv2. However, appropriate EPICv2 preprocessing and integration with previous microarray versions remain complex. Therefore, MethylCallR, an open-source R package designed to provide standard procedures for performing EWAS using Illumina methylation microarrays including EPICv2, was developed. MethylCallR can be used to control duplicated probes in EPICv2, by using pre-set data implemented in MethylCallR or new customized data. MethylCallR includes a straightforward conversion function between different types of Illumina Human Methylation BeadChips. Using MethylCallR, potential outlier sample detection and statistical power estimation were conducted and used to select meaningful probes. Publicly available data was analyzed using MethylCallR and the findings were compared to that of a previous study.

DNA Methylation Signatures Characterize Gene Expression Modulation in Lung Cancer Patients Affected by Anorexia.

The pathophysiology of cancer anorexia is multifactorial and unclear. Transcriptomic analysis from PBMCs RNA showed diverse patterns of gene expression pathways in anorexic cancer patients. We assessed whether the different transcriptomic signatures are modulated by DNA methylation in lung cancer patients presenting with poor appetite. Lung cancer patients and controls were enrolled, and anorexia was assessed by the FAACT-score questionnaire. Genome-wide DNA methylation was determined by Human Infinium MethylationEPIC BeadChip Kit. Data from genome-wide methylation analysis were merged with those from gene expression analysis, previously obtained by RNA sequencing (NGS). Four groups of genes were identified for each comparison: hypermethylated repressed, hypermethylated induced, hypomethylated repressed, and hypomethylated induced. Cancer patients (n = 16) showed 382 differentially methylated genes when compared with controls (n = 8). Anorexic patients (n = 8) presented 586 hypomethylated and 174 hypermethylated genes compared with controls. In anorexic patients vs. non-anorexic (n = 8), 211 genes were identified as hypomethylated and 90 hypermethylated. When microarray methylation data were merged with transcriptomic data by RNA sequencing, we observed significant differences in anorexic patients vs. controls; a total of 42 genes resulted as hypomethylated and induced, 5 hypermethylated repressed, 10 hypermethylated induced, and 15 hypomethylated repressed. The CG sites analyzed by targeted bisulfite NGS in four genes of interest (FLNA, PGRMC1, GNL3L, and FHL1) resulting as hypomethylated in anorexic vs. controls allowed the validation of the data obtained from DNA methylation. Interestingly, the four genes resulted as hypomethylated in anorexic patients vs. non-anorexic patients and vs. controls (p < 0.0001). Our data support that methylation is implicated in cancer-associated anorexia and nutritional derangements among lung cancer patients.

Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

Decoding the Methylation Patterns of ABC Transporters in Colorectal Cancer

Colorectal cancer (CRC) is a significant global health concern, posing a major threat to morbidity and mortality rates. The molecular mechanisms that drive CRC, particularly the DNA methylation patterns in ATP-binding cassette (ABC) genes, have attracted considerable attention because of their potential roles in CRC drug resistance, initiation, and progression. This study aimed to investigate the methylation patterns of ABC genes in CRC using a high-throughput microarray technology. Microarray methylation data from CRC and adjacent normal tissues were subjected to preprocessing, differential methylation analysis, and correlation analysis using the MethSurv tool for a detailed study of methylation patterns. The study revealed global hypomethylation of 43 ABC transporters and two pseudogenes in CRC compared to normal tissues. Receiver operating characteristic curve analysis identified 369 CpG sites as potential biomarkers for CRC diagnosis, with area under the curve values ranging from acceptable to outstanding. Survival analysis demonstrated the correlation between DNA methylation of individual CpG sites and patient survival probability in colon and rectal adenocarcinoma datasets from The Cancer Genome Atlas. The study provides insights into the epigenetic landscape of ABC genes in CRC, highlighting their potential roles in drug resistance, disease initiation, and progression. The findings offer opportunities for developing innovative therapeutic approaches and improving patient outcomes in the fight against CRC. Further research is needed to validate these results and investigate the functional implications of ABC gene methylation in CRC pathogenesis and treatment response.

DNA demethylation triggers cell free DNA release in colorectal cancer cells

BackgroundLiquid biopsy based on cell-free DNA (cfDNA) analysis holds significant promise as a minimally invasive approach for the diagnosis, genotyping, and monitoring of solid malignancies. Human tumors release cfDNA in the bloodstream through a combination of events, including cell death, active and passive release. However, the precise mechanisms leading to cfDNA shedding remain to be characterized. Addressing this question in patients is confounded by several factors, such as tumor burden extent, anatomical and vasculature barriers, and release of nucleic acids from normal cells. In this work, we exploited cancer models to dissect basic mechanisms of DNA release.MethodsWe measured cell loss ratio, doubling time, and cfDNA release in the supernatant of a colorectal cancer (CRC) cell line collection (N = 76) representative of the molecular subtypes previously identified in cancer patients. Association analyses between quantitative parameters of cfDNA release, cell proliferation, and molecular features were evaluated. Functional experiments were performed to test the impact of modulating DNA methylation on cfDNA release.ResultsHigher levels of supernatant cfDNA were significantly associated with slower cell cycling and increased cell death. In addition, a higher cfDNA shedding was found in non-CpG Island Methylator Phenotype (CIMP) models. These results indicate a positive correlation between lower methylation and increased cfDNA levels. To explore this further, we exploited methylation microarrays to identify a subset of probes significantly associated with cfDNA shedding and derive a methylation signature capable of discriminating high from low cfDNA releasers. We applied this signature to an independent set of 176 CRC cell lines and patient derived organoids to select 14 models predicted to be low or high releasers. The methylation profile successfully predicted the amount of cfDNA released in the supernatant. At the functional level, genetic ablation of DNA methyl-transferases increased chromatin accessibility and DNA fragmentation, leading to increased cfDNA release in isogenic CRC cell lines. Furthermore, in vitro treatment of five low releaser CRC cells with a demethylating agent was able to induce a significant increase in cfDNA shedding.ConclusionsMethylation status of cancer cell lines contributes to the variability of cfDNA shedding in vitro. Changes in methylation pattern are associated with cfDNA release levels and might be exploited to increase sensitivity of liquid biopsy assays.

Applicability of epigenetic age models to next-generation methylation arrays

BackgroundEpigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.MethodsWe tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.ResultsThe results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.ConclusionExisting epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the “epigenetic age” determined by these first-generation models.

Technical and biological sources of unreliability of Infinium probes on Illumina methylation microarrays

The Illumina Methylation array platform has facilitated countless epigenetic studies on DNA methylation (DNAme) in health and disease, yet relatively few studies have so studied its reliability, i.e., the consistency of repeated measures. Here we investigate the reliability of both type I and type II Infinium probes. We propose a method for excluding unreliable probes based on dynamic thresholds for mean intensity (MI) and ‘unreliability’, estimated by probe-level simulation of the influence of technical noise on methylation β values using the background intensities of negative control probes. We validate our method in several datasets, including newly generated Illumina MethylationEPIC BeadChip v1.0 data from paired whole blood samples taken six weeks apart and technical replicates spanning multiple sample types. Our analysis revealed that specifically probes with low MI exhibit higher β value variability between repeated samples. MI was associated with the number of C-bases in the respective probe sequence and correlated negatively with unreliability scores. The unreliability scores were substantiated through validation in a new EPIC v1.0 (blood and cervix) and a publicly available 450k (blood) dataset, as they effectively captured the variability observed in β values between technical replicates. Finally, despite promising higher robustness, the newer version v2.0 of the MethylationEPIC BeadChip retained a substantial number of probes with poor unreliability scores. To enhance current pre-processing pipelines, we developed an R package to calculate MI and unreliability scores and provide guidance on establishing optimal dynamic score thresholds for a given dataset.

DNA methylation of bone morphogenetic protein 7 in leukocytes as a possible biomarker for hand osteoarthritis: A pilot study.

Hand osteoarthritis (HOA), characterized by an earlier onset age and reduced susceptibility to mechanical stress compared with knee and hip osteoarthritis, is considered a suitable disease for identifying predictive biomarkers of osteoarthritis. In particular, DNA methylation variants, expected to contribute to HOA susceptibility, hold potential as osteoarthritis biomarkers. In this study, leukocyte DNA methylation patterns were analyzed in blood samples from patients with HOA, aiming to identify disease-specific biomarkers for osteoarthritis. Using DNA methylation microarrays, we analyzed samples from three subjects with HOA and three age- and gender-matched healthy individuals. For validation, pyrosequencing analysis was conducted using samples from 16 to 9 subjects with and without HOA, respectively. From 735,026 probes in the DNA methylation array, the Top 100 CpG sites associated with HOA, based on low adjusted P-values, including those targeting bone morphogenetic protein 7 (BMP7), SBF2-AS1, PLOD2, ICOS, and CSF1R were identified. Validation analysis revealed significantly higher methylation levels in the BMP7-related site in the HOA group compared with the control group, even after adjusting for age, gender, and body mass index (p = 0.037). In contrast, no significant difference was observed in the other selected CpG sites between the HOA and control groups. This study highlights the significantly increased frequency of methylation at the specific BMP7 site in leukocytes of patients with HOA, suggesting its potential as a biomarker for HOA. Measurement of methylation levels at the CpG sites identified in this study offers a potential approach to prevent future osteoarthritis progression, providing valuable insights into disease management.

Discovery of DNA methylation signature in the peripheral blood of individuals with history of antenatal exposure to valproic acid

PurposeValproic acid or valproate is an effective antiepileptic drug; however, embryonic exposure to valproate can result in a teratogenic disorder referred to as fetal valproate syndrome (OMIM #609442). Currently there are no diagnostic biomarkers for the condition. This study aims to define an episignature biomarker for teratogenic antenatal exposure to valproate. MethodsDNA extracted from peripheral blood of individuals with teratogenic antenatal exposure to valproate was processed using DNA methylation microarrays. Subsequently, methylation profiling and construction of support vector machine classifiers were performed in R. ResultsGenomic DNA methylation analysis was applied, and a distinct DNA methylation profile was identified in the majority of affected individuals. This profile was used to develop a diagnostic episignature classifier. The valproate exposure episignature exhibited high sensitivity and specificity relative to a large reference data set of unaffected controls and individuals with a wide spectrum of syndromic disorders with episignatures. Gene set enrichment analysis demonstrated an enrichment for terms associated with cell adhesion, including significant overrepresentation of the cadherin superfamily. ConclusionThis study provides evidence of a robust peripheral blood-based diagnostic epigenetic biomarker for a prenatal teratogenic disorder.

MethylGenotyper: Accurate Estimation of SNP Genotypes and Genetic Relatedness from DNA Methylation Data.

Epigenome-wide association studies (EWAS) are susceptible to widespread confounding caused by population structure and genetic relatedness. Nevertheless, kinship estimation is challenging in EWAS without genotyping data. Here, we proposed MethylGenotyper, a method that for the first time enables accurate genotyping at thousands of single nucleotide polymorphisms (SNPs) directly from commercial DNA methylation microarrays. We modeled the intensities of methylation probes near SNPs with a mixture of three beta distributions corresponding to different genotypes and estimated parameters with an expectation-maximization algorithm. We conducted extensive simulations to demonstrate the performance of the method. When applying MethylGenotyper to the Infinium EPIC array data of 4662 Chinese samples, we obtained genotypes at 4319 SNPs with a concordance rate of 98.26%, enabling the identification of 255 pairs of close relatedness. Furthermore, we showed that MethylGenotyper allows for the estimation of both population structure and cryptic relatedness among 702 Australians of diverse ancestry. We also implemented MethylGenotyper in a publicly available R package (https://github.com/Yi-Jiang/MethylGenotyper) to facilitate future large-scale EWAS.

Novel genome-wide DNA methylation profiling reveals distinct epigenetic landscape, prognostic model and cellular composition of early-stage lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) has been a leading cause of cancer-related mortality worldwide. Early intervention can significantly improve prognosis. DNA methylation could occur in the early stage of tumor. Comprehensive understanding the epigenetic landscape of early-stage LUAD is crucial in understanding tumorigenesis.MethodsEnzymatic methyl sequencing (EM-seq) was performed on 23 tumors and paired normal tissue to reveal distinct epigenetic landscape, for compared with The Cancer Genome Atlas (TCGA) 450K methylation microarray data. Then, an integrative analysis was performed combined with TCGA LUAD RNA-seq data to identify significant differential methylated and expressed genes. Subsequently, the prognostic risk model was constructed and cellular composition was analyzed.ResultsMethylome analysis of EM-seq comparing tumor and normal tissues identified 25 million cytosine-phosphate-guanine (CpG) sites and 30,187 differentially methylated regions (DMR) with a greater number of untraditional types. EM-seq identified a significantly higher number of CpG sites and DMRs compared to the 450K microarray. By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset. Finally, cell deconvolution based on the in-house EM-seq methylation profile was used to estimate cellular composition of early-stage LUAD.ConclusionsThis study firstly delves into novel pattern of epigenomic DNA methylation and provides a multidimensional analysis of the role of DNA methylation revealed by EM-seq in early-stage LUAD, providing distinctive insights into its potential epigenetic mechanisms.

Abstract PO2-01-08: Differential HOXB13 gene expression and promoter methylation analysis in breast cancer

Abstract Background: The Breast Cancer Index (BCI) is a gene expression-based signature comprising two functional biomarker panels: the Molecular Grade Index (MGI) and BCI (H/I), which is a ratio of the HOXB13 and IL17BR gene expression. Several studies have shown that BCI (H/I) is a predictive biomarker for extended adjuvant endocrine therapy benefit in hormone receptor-positive (HR+) early-stage breast cancer. Molecular mechanisms underlying endocrine responsiveness remain to be fully explored. The objective of this analysis is to evaluate the correlation between gene expression and methylation in breast cancer patients displaying different HOXB13 mRNA expression levels from The Cancer Genome Atlas (TCGA) project. Methods: Data from methylation microarrays and mRNA sequencing were examined in combination with clinical metadata from 1,095 TCGA breast cancer patients. After removing outliers, HR+ samples were ordered based on their HOXB13 expression levels and the top 10% of samples (HOXB13-high; n=95) and bottom 10% (HOXB13-low; n=95) were selected for further analysis. Differentially expressed genes (DEGs) were determined by comparing all protein-coding genes between the HOXB13-high and HOXB13-low sets, based on an absolute log-fold change above 2 and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Among the subset of samples with matching methylation data (n=60 for the HOXB13-high group and n=48 for the HOXB13-low group), hypo-methylated and hyper-methylated differentially methylated probes (DMPs) were identified based on at least 10% change of β (i.e., average promoter methylation) and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Motif and transcription factor (TF) enrichment analyses were identified at a minimum incidence of 10 and a lower boundary of 1.5 for the 95% confidence interval of the odds ratio. Results: Our analysis identified a total of 613 DEGs between the HOXB13-high and HOXB13-low groups. Gene ontology analysis of DEGs revealed a statistically significant enrichment of genes associated with PTK6 regulated cell cycle, oncogene-induced cellular senescence, and regulation of T cell activation. Analysis of protein-protein interactions of differentially expressed genes revealed differences in key biological processes including changes in cell cycle regulation, p53 pathway, and IL-17 signaling. A total of 5,295 hyper-methylated and 580 hypo-methylated DMPs were discovered when comparing HOXB13-high and HOXB13-low samples. Changes in global methylation patterns enabled classification of breast cancer samples with high or low HOXB13 expression with an accuracy of 0.90. Reconstruction of gene regulatory networks from DNA methylation and transcriptome profiles elucidated significant pairs of DMPs and differentially expressed genes. Specifically, a total of 197 differentially expressed genes were attributed to hyper-methylated DMPs, including genes involved in response to estrogen, activation of HOX genes during differentiation, and drug-mediated inhibition of CDK4/6 activity. Additionally, 8 differentially expressed genes were attributed to hypo-methylated DMPs and were associated with proteasomal protein catabolic processes. Lastly, motif analysis of transcriptional factors for the hyper-methylated DMPs revealed enrichment of binding motifs attributed to FOXA1, ESR1, XBR1, and FOXP1. Conclusion: A comprehensive and comparative analysis of mRNA expression levels and methylation patterns between TCGA breast cancer samples with high and low expression of HOXB13 revealed key signaling pathways and biological processes that may provide insights on the molecular mechanism of HOXB13 in the regulation of response to endocrine therapy in HR+ breast cancer. Citation Format: Natalia Siuliukina, Yi Zhang, Kai Treuner, Mark Pegram. Differential HOXB13 gene expression and promoter methylation analysis in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-08.

Exploring TSPAN4 promoter methylation as a diagnostic biomarker for tuberculosis.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a persistent infectious disease threatening human health. The existing diagnostic methods still have significant shortcomings, including a low positivity rate in pathogen-based diagnoses and the inability of immunological diagnostics to detect active TB. Hence, it is urgent to develop new techniques to detect TB more accurate and earlier. This research aims to scrutinize and authenticate DNA methylation markers suitable for tuberculosis diagnosis. Concurrently, Providing a new approach for tuberculosis diagnosis. Blood samples from patients with newly diagnosed tuberculosis and healthy controls (HC) were utilized in this study. Examining methylation microarray data from 40 whole blood samples (22TB + 18HC), we employed two procedures: signature gene methylated position analysis and signature region methylated position analysis to pinpoint distinctive methylated positions. Based on the screening results, diagnostic classifiers are constructed through machine learning, and validation was conducted through pyrosequencing in a separate queue (22TB + 18HC). Culminating in the development of a new tuberculosis diagnostic method via quantitative real-time methylation specific PCR (qMSP). The combination of the two procedures revealed a total of 10 methylated positions, all of which were located in the promoter region. These 10 signature methylated positions facilitated the construction of a diagnostic classifier, exhibiting robust diagnostic accuracy in both cross-validation and external test sets. The LDA model demonstrated the best classification performance, achieving an AUC of 0.83, specificity of 0.8, and sensitivity of 0.86 on the external test set. Furthermore, the validation of signature methylated positions through pyrosequencing demonstrated high agreement with screening outcomes. Additionally, qMSP detection of 2 potential hypomethylated positions (cg04552852 and cg12464638) exhibited promising results, yielding an AUC of 0.794, specificity of 0.720, and sensitivity of 0.816. Our study demonstrates that the validated signature methylated positions through pyrosequencing emerge as plausible biomarkers for tuberculosis diagnosis. The specific methylation markers in the TSPAN4 gene, identified in whole blood samples, hold promise for improving tuberculosis diagnosis. This approach could significantly enhance diagnostic accuracy and speed, offering a new avenue for early detection and treatment.

Abstract 1029: Role of PCDHGA12/PRRX1 methylation for the diagnosis of lung cancer using bronchial washing fluid

Abstract Bronchoscopy is a frequently used initial diagnostic procedure for patients with suspected lung cancer (LC). Cytological examinations of bronchial washing (BW) samples, obtained during bronchoscopy, often yield inconclusive results in the diagnosis of LC. Aberrant DNA methylation has previously been shown to be a useful biomarker for LC. DNA methylation analysis in BW specimens could serve as a valuable clinical adjunct to cytology. In this study, to identify potential methylation biomarkers for LC, methylation microarray analyses were performed on primary tumors and compared with adjacent non-tumor tissues of 13 patients with LC at various stages (I - IV). A stepwise validation process using bisulfite-pyrosequencing of cell lines and tissues identified six methylated genes (ADAMTS20, FOXC2, NKX2-5, OLIG3, PCDHGA12, and PRRX1) associated with LC. To assess methylation in BW samples, a highly sensitive and accurate 3-plex Linear Target Enrichment (LTE)-quantitative methylation-specific PCR (qMSP) in a single closed tube was developed. Clinical validation was performed using BW samples from patients with LC (n = 68) and non-LC (benign) (n = 33) and identified PCDHGA12 and PRRX1 methylation as the best performing marker combination for detecting LC. The 2-marker combination exhibited a sensitivity of 82.4% and a specificity of 87.9%, with an area under the curve of 0.891. Notably, its sensitivity was 100% for small cell LC. The 2-marker combination had a positive predictive value of 93.3% and a negative predictive value of 70.7%. Its sensitivity outperformed cytology. The methylation status of the 2-marker showed no correlation with gender, age or stage, but was associated with tumor location and histology. In conclusion, we identified PCDHGA12 and PRRX1 methylation as high potential biomarkers for LC diagnosis. This study shows that these biomarkers could be used as a diagnostic adjunct to cytology in the clinical practice for diagnosing LC in BW specimens. Citation Format: Ji Woong Son, Tae Jeong Oh, Minheyok Lee, Chang Ryul Park, Sungwhan An. Role of PCDHGA12/PRRX1 methylation for the diagnosis of lung cancer using bronchial washing fluid [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 1029.

Abstract 3497: Glioma Immune Microenvironment Composition Calculator (GIMiCC): A method of estimating the proportions of eighteen key cell types from human brain tissue assayed using Illumina DNA methylation microarray technology

Abstract Precise detection of both tumor subclass and immune composition is critical in the pursuit of personalized immunotherapeutic treatment strategies for glioma. DNA methylation (DNAm) biomarkers are promising on both fronts. DNAm-based biomarkers for molecular classification have been developed and integrated into World Health Organization (WHO) diagnostic criteria. However, prediction of the tumor microenvironment is not as well defined. To fill this gap, we present GIMiCC, an approach for cellular deconvolution of DNAm microarray data for four WHO 2021 tumor subclasses: glioblastoma, oligodendroglioma, grade 2/3 astrocytoma and grade 4 astrocytoma. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries and test the method on independent datasets from the Cancer Genome Atlas (TCGA) and the DKFZ molecular neuropathology classifier (PMID: 29539639). We utilize GIMiCC to illustrate that DNAm-based glioma classification is unlikely to be biased by compositional variation. In addition, we utilize GIMiCC to identify composition independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma with this new tool. Citation Format: Steven C. Pike, John K. Wiencke, Ze Zhang, Annette M. Molinaro, Devin C. Koestler, Brock C. Christensen, Karl T. Kelsey, Lucas A. Salas. Glioma Immune Microenvironment Composition Calculator (GIMiCC): A method of estimating the proportions of eighteen key cell types from human brain tissue assayed using Illumina DNA methylation microarray technology [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 3497.

DNA methylation microarray analysis of adult gliomas: A pilot study at Groote Schuur Hospital

DNA methylation microarray analysis of adult gliomas: A pilot study at Groote Schuur Hospital