Cell-free DNA Methylation Research Articles

More isn't always better: Expanding and refining prognostic methylation signatures for pancreatic ductal adenocarcinoma.

769 Background: In our previous study, we developed protein-informed methylation signatures from a chemoresistance gene panel (RGp, n=122) curated from the literature, identifying 28 signatures with strong prognostic value in The Cancer Genome Atlas (TCGA) pancreatic ductal adenocarcinoma (PDA) database. To enhance the clinical utility of this panel, we incorporated methylation changes from genes with known prognostic (PGp) and diagnostic (DGp) significance in PDA to form a composite gene panel (CGp-PDA) and repeated our analysis. We hypothesized that epigenetic changes in these genes reflect protein expression and that deriving signatures from them could help identify patients at risk of treatment failure. Methods: We assembled a composite gene panel (CGp-PDA) of 206 genes (122 RGp + 26 PGp + 82 DGp) through a literature review spanning from January 1970 to April 2024. These PGp and DGp genes are unique, as cell-free DNA methylation changes in them have established prognostic and diagnostic value, respectively in PDA. The same cohort of pancreatic ductal adenocarcinoma (PDA) patients (n=184) from our previous study was analyzed. We applied elastic net multivariate analysis to calculate survival outcomes and generated Kaplan-Meier plots using our gene panel, optimizing values for 𝛼 and λ over 100 iterations. Results: Our analysis identified 20 signatures with cytosines followed by a guanine residue (CpG) site in genes from our panel. The number of CpG sites in these signatures ranged from 3 to 4248. The most effective signature featured 17 CpG sites (22 months [m] vs. 67m, p=0.03), followed by signatures with 3 (16m vs. 49m, p=0.009) and 12 (17m vs. 36m, p=0.02) CpG sites. The 4248 CpG site signature demonstrated the poorest prognostic value among the signatures (17m vs. 21m, p=0.01). Some signatures included multiple CpG sites within a single gene. Notably, KCNH2 reappeared in several signatures, as in the prior study. Expanding our RGp to CGp-PDA did not produce more (20 vs. 28) signatures or improved prognostic value. Conclusions: Targeted methylation signatures, supported by robust preclinical or translational evidence of their impact on treatment resistance, can be clinically valuable for informing treatment selection and prognosis. We may need to shift our focus from machine learning-based broad methylation or transcriptomic analyses to more targeted investigations.

High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma

PurposeRenal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease.MethodsHigh-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively.ResultsComparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7).ConclusionsOur findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications.

Therapy response monitoring in blood plasma from esophageal adenocarcinoma patients using cell-free DNA methylation profiling

Esophageal adenocarcinoma (EAC) is an aggressive cancer characterized by a high risk of relapse post-surgery. Current follow-up methods (serum carcinoembryonic antigen detection and PET-CT) lack sensitivity and reliability, necessitating a novel approach. Analyzing cell-free DNA (cfDNA) from blood plasma emerges as a promising avenue. This study aims to evaluate the cost-effective and genome-wide cell-free reduced representation bisulfite sequencing (cfRRBS) method combined with computational deconvolution for effective disease monitoring in EAC patients. cfDNA methylation profiling with cfRRBS was performed on 162 blood plasma samples from 33 EAC cancer patients and 28 blood plasma samples from 20 healthy donors. The estimated tumor fraction for EAC patients at the time of diagnosis was significantly different from the healthy donor plasma samples (one-sided Wilcoxon rank-sum test: p-value = 0.032). Tumor fractions above 15% and focal gains/amplifications in MYC (chr8), KRAS (chr12), EGFR (chr7) and NOTCH2 (chr1) were observed in four samples of distinct patients at the time metastatic disease was detected. This study showed feasibility to estimate tumor fractions in blood plasma of EAC patients based on cfDNA methylation using cfRRBS and computational deconvolution. Nevertheless, in this study only cancer patients with evidence of metastatic disease show high tumor fractions and copy number alterations.

Advancing diagnosis and early risk assessment of preeclampsia through noninvasive cell-free DNA methylation profiling

BackgroundAberrant embryo implantation and suboptimal placentation can lead to (severe) complications such as preeclampsia and fetal growth restriction later in pregnancy. Current identification of high-risk pregnancies relies on a combination of risk factors, biomarkers, and ultrasound examinations, a relatively inaccurate approach.Previously, aberrant DNA methylation due to placental hypoxia has been identified as a potential marker of placental insufficiency and, hence, potential (future) pregnancy complications. The goal of the Early Prediction of prEgnancy Complications Testing, or the ExPECT study, is to validate a genome-wide, cell-free DNA (cfDNA) methylation strategy to diagnose preeclampsia accurately. More importantly, the predictive potential of this strategy is also explored to reliably identify high-risk pregnancies early in gestation. Furthermore, a longitudinal study was conducted, including sequential blood samples from pregnant individuals experiencing both uneventful and complicated gestations, to assess the methylation dynamics of cfDNA throughout these pregnancies.A significant strength of this study is its enzymatic digest, which enriches CpG-rich regions across the genome without the need for proprietary reagents or prior selection of regions of interest. This makes it useful for the cost-effective discovery of novel markers.ResultsInvestigation of methylation patterns throughout pregnancy showed different methylation trends between unaffected and affected pregnancies. We detected differentially methylated regions (DMRs) in pregnancies complicated with preeclampsia as early as 12 weeks of gestation, with distinct differences in the methylation profile between early and late pregnancy. Two classification models were developed to diagnose and predict preeclampsia, demonstrating promising results on a small set of validation samples.ConclusionsThis study offers valuable insights into methylation changes at specific genomic regions throughout pregnancy, revealing critical differences between normal and complicated pregnancies. The power of noninvasive cfDNA methylation profiling was successfully proven, suggesting the potential to integrate this noninvasive approach into routine prenatal care.

General anesthesia induces acute cell-free DNA methylation changes in peripheral blood.

Short-term and long-term adverse events could occur after general anesthesia (GA) and the specific mechanism driving these effects has not yet been well-characterized. In this study, we aimed to evaluate the global effect of GA on DNA methylation in the cell-free DNA (cfDNA) of surgical lung-nodule patients. This large retrospective cohort study enrolled 1,006 surgical lung nodule patients (529 pre-anesthesia, and 477 post-anesthesia). Methylation profiles of the cfDNA isolated from plasma were analyzed by targeted bisulfite sequencing using an enrichment panel covering 12,899 biologically informative methylation regions and 105,844 CpG sites. By comparing the pre-anesthesia to the post-anesthesia group, a total of 4,562 differentially methylated regions (DMRs) were identified as GA-induced DMRs. Pathway enrichment analysis annotated with cellular processes including pattern specification process, head/heart/bone/tissues development and morphogenesis pathways, cell-adhesion, extra-cellular matrix (ECM) remodeling pathways, and signaling pathways including PI3K-AKT pathway, Ca2+ dependent pathway and RAS/extracellular signal-regulated kinase (RAS/ERK) signaling pathway. Prediction models using 20 DMR markers were derived using Random Forest, which could accurately predict biochemical indicators for post-operative abnormal coagulation function including activated-partial-thromboplastin-time [APTT, area under curve (AUC) 0.81], international normalized ratio (INR, AUC 0.87), D-dimer (AUC 0.82), neutrophil (AUC 0.84) and monocyte (AUC 0.79). Low methylation level in one of the top DMR markers, cg02032606 (DLX-4 gene), was found to be associated with worse overall survival in both lung adenocarcinoma and squamous carcinoma patients. This study demonstrated that GA could result in acute DNA methylation changes, which were associated with tissue damage and repair responses. These GA-induced methylation changes were associated with postoperative coagulation functions and could serve as a promising predictive biomarker for coagulation disorders after surgery.

Identifying targeted cell-free DNA methylation regions in head and neck cancer via paired methylome analysis.

Despite the critical need, progress in developing cell-free DNA (cfDNA) liquid biopsy biomarkers for the diagnosis and risk stratification of head and neck squamous cell carcinoma (HNSC) has been limited. In this study, we present a comprehensive paired-sample differential methylation region (psDMR) analysis in HNSC, aimed at identifying reliable and HNSC-specific regions for cfDNA biomarker discovery. Traditional DMR analyses often overlook paired-sample information and fail to account for the heterogeneity within HNSC tissues. Our findings reveal a substantial overlap of hypermethylated DMRs across two independent HNSC methylation datasets, demonstrating the robustness and clinical relevance of these regions for cfDNA biomarker development. Furthermore, we identified consistent DMRs in both HNSC and lung squamous cell carcinoma (LSCC), suggesting the potential for pan-squamous cell carcinoma biomarkers. Notably, gene clusters such as HOXD, ZNF, and NKX2 were frequently hypermethylated, providing new insights into the shared epigenetic landscape of HNSC and LSCC. This study underscores the importance of incorporating matched normal tissues in cancer methylome analyses and establishes a foundation for advancing cfDNA-based biomarker discovery across squamous cell carcinomas.

Abstract B005: Investigating distinct methylation signatures characteristic of breast cancer subtypes in residual disease via cell free DNA methylation

Abstract Breast cancer is the most common type of cancer in females and recurrence increases over time, unlike many other cancers. Treatment for recurrent cancer is often the same as the primary with no additional biopsies taken. Current research suggests that subtype switching and tumor character changes frequently occur between primary and recurrent breast cancers. Therefore, it may be beneficial to patients to switch treatment based on these changes. Since physical biopsies are cumbersome and not always feasible, liquid biopsies open a way to monitor tumor changes less invasively and more comprehensively. It has been previously established that cell free DNA (cfDNA) shed into the bloodstream from dying cells can reflect cell type of origin via methylation pattern and rate of death via concentration of cfDNA. In this pilot study, we seek to look at the cfDNA of fifteen late stage pre-/ post-surgical breast cancer patients who also received radiation treatment. We will perform cfDNA extraction on the serum of these patients and both whole genome bisulfite sequencing (WGBS), which chemically converts unmethylated cytosine to uracil/thymine in the DNA and is the current gold-standard of methylation sequencing, and a newer method, enzymatic methylation sequencing (EM-seq), which enzymatically converts (TET2/APOBEC) unmethylated cytosine to uracil/thymine and potentially preserves more of the cfDNA. To validate any signatures found in the cfDNA of the breast cancer patients, we have begun the genomic DNA (gDNA) extraction and WGBS/ EM-seq protocols on a variety of breast cancer cell lines including: MCF10A, MCFDCIS, MCF7, T47D, BT474 MDA MB453, MDA MB436 and MDA MB231 (including in-lab brain, bone, and lung metastatic clones). Bioanalyzer traces are produced from the extracted cfDNA/gDNA and also for the final sequencing libraries. The success of the methylation conversion is evaluated after sequencing data is returned and conversion rates of the cytosine to uracil/thymine are compared to unmethylated DNA control (lambda) and methylated DNA control (pUC19). Once this sequencing data is obtained, we use an in-lab deconvolution algorithm to detect cell types of origin and intend to make the algorithm more robust for cancer cell types as well. We have currently produced breast cancer cell line methylation sequencing libraries and are in the process of producing the libraries for the patient samples. Our current data suggests that there are changes in the cfDNA general fragmentation patterns and cfDNA concentrations between pre-/post- surgery samples. Once our sequencing data is obtained for the patient samples, we will run our deconvolution algorithm. The potential of characterizing breast cancer subtype and progression signatures in cfDNA of late stage pre-/ post-surgical breast cancer patients can have significant impact on patient treatment options. Identifying these breast cancer signatures less invasively and, therefore, more frequently may allow for early and more targeted intervention to improve breast cancer patient outcomes. Citation Format: Amber Alley, Megan McNamara, Sidharth Jain, Anton Wellstein. Investigating distinct methylation signatures characteristic of breast cancer subtypes in residual disease via cell free DNA methylation [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 B005.

Abstract PR019: Transfer learning for accurate tissue of origin classification from cfDNA methylation

Abstract Accurately predicting the tissue of origin (TOO) is required for blood-based multi-cancer screening to guide follow-up imaging and care when cancer is detected. However, precisely determining the TOO using cell-free DNA (cfDNA) methylation remains challenging due to low and variable concentrations of circulating tumor DNA and cfDNA and the complex, poorly characterized nature of tissue-specific methylation patterns. To address these challenges, we developed a transfer learning approach that leverages methylation profiles learned from tissue biopsy samples to predict the TOO of cfDNA cancer samples. Our model achieved 89% balanced accuracy (BA) in classifying the TOO across 10 cancer groups and outperformed models trained solely on plasma cfDNA data. Unmatched tissue biopsy (N=517) and blood (N=1,155, NCT05435066) samples were collected from treatment-naïve cancer patients across 21 tumor types consolidated into 17 cancer groups. All samples were analyzed using a custom targeted bisulfite sequencing hybrid capture assay. Five unique metrics were used to quantify informative methylation signal at each TOO region of interest. A feed-forward neural network was first trained on biopsy methylation features, where the cancer signal is strongest. Subsequently, the model was fine-tuned using plasma cfDNA methylation features, which have significantly lower signal-to-noise ratio, allowing the model to adapt its predictions to the noisier data. The Adam optimizer was employed for both training and fine-tuning, with early stopping implemented to prevent overfitting. Dropout layers were incorporated to enhance model generalization and performance was evaluated using 10-fold cross validation on plasma cfDNA samples across 10 cancer groups for which at least 25 plasma samples were available (N=1,041). The same architecture was trained exclusively on plasma cfDNA samples for comparison. Results are reported for samples with tumor content greater than 0.1% (N=415). Our TOO prediction achieved 73% BA in top-one predictions, where the true label matched the highest probability prediction, and 89% BA in top-two predictions, where the true label matched the highest or the second-highest probability prediction. Multi-class classification revealed our method significantly outperforms a model trained only on plasma cfDNA data. The plasma only model achieved 69% and 82% BA in top-one and top-two predictions, respectively. Accuracy per indication revealed that training on biopsy data significantly improves the detection of tumor types that shed DNA at low rates compared to training on plasma data alone (breast cancer +14%, prostate cancer +12%, and uterine cancer +20% points). In conclusion, our transfer learning approach, utilizing tissue biopsy methylation data, significantly improves cfDNA-based TOO detection accuracy. This approach highlights the capability of transfer learning to enable automated feature extraction from signal-rich data to enhance the analysis of more challenging, heterogeneous cfDNA samples, advancing non-invasive cancer diagnostics. Citation Format: Shiva Farashahi, Amirali Kia, Dorna Kashef, Esther Brown, Feras Hantash, Kieran I Chacko. Transfer learning for accurate tissue of origin classification from cfDNA methylation [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 PR019.

BIOM-72. INTEGRATION OF ULTRASENSITIVE ELECTROLUMINESCENT IMMUNOASSAY AND CELL-FREE DNAMETHYLATION ANALYSIS FOR NON-INVASIVE MONITORING OF ADULT DIFFUSE GLIOMAS

Abstract Gliomas are highly aggressive brain tumors with nearly universal recurrence rate. Despite this, the ability to accurately predict and identify tumor recurrence relies solely on serial MRI imaging, which is marred by treatment effects such as radiation necrosis, and necessarily identifies progression retrospectively. It is believe that tumor undergo molecular changes upon tumor progression, however the resampling of tumors upon recurrence imposes significant risks. This highlights the need for novel non-invasive biomarkers capable of identifying tumor recurrence. Due to the low accuracies of individual biomarkers, we have proposed the use of an integrated, multi-platform approach to biomarker discovery. A cohort of 107 glioma plasma samples, including 30 pairs, underwent plasma proteomic analysis, consisting of a panel of serum proteins (FABP4, GFAP, NFL, Tau and MMP3,4 &7) quantified through ultrasensitive electrochemiluminescence multiplexed immunoassays, and plasma DNA methylation analysis, captured through cell-free methylated DNA immunoprecipitation and high-throughput sequencing. Unsupervised hierarchal clustering revealed robust separation of primary and recurrent tumors through plasma proteomics, associated with a distinct plasma methylation signature. NFL, Tau and MMP3 levels differed between primary and recurrent samples; pair-wise analysis revealed increased in NFL and Tau concentrations upon recurrence. Tau levels predicted outcome independent of WHO Grade and IDH status. A predictive generalized linear regression model created through the integration of the proteomic and methylation signatures allowed for the discrimination of primary and recurrent samples in 83% of cases. This work suggests that the combination of DNA methylation and plasma proteomics may improve the ability of these techniques for the serial monitoring of gliomas patients.

BIOM-67. DIFFERENTIAL DNA METHYLATION PATTERNS IN THE CSF OF PATIENTS WITH DIFFUSE GLIOMAS

Abstract Studies have shown that different central nervous system (CNS) tumor types exhibit unique DNA methylation profiles. DNA methylation-based machine learning classification of CNS tumor tissue has been demonstrated to be highly-accurate in differentiating between tumor types, including between diffuse glioma subtypes. While similar analyses have been performed using cell-free DNA (cfDNA) from blood, few studies have looked at the potential of DNA methylation as a biomarker for diffuse gliomas using CSF-derived cfDNA. In this study, we analyzed CSF cfDNA methylation patterns in diffuse glioma patients. DNA methylation profiling of CSF of patients with glioblastoma, IDH-wildtype (GBM) (n=37), IDH-mutant diffuse gliomas (n=12), and non-neoplastic conditions (n=5) was performed using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). Methylation quantification was estimated for each genomic region. Significance testing was performed between diagnostic groups to discover differentially-methylated regions (DMRs). The 300 most differentially-methylated regions (DMRs) for each comparison (IDH-wildtype vs. IDH-mutant, IDH-mutant vs. control, IDH-wildtype vs. control), for a total of 900 DMRs, were utilized as features in dimensionality reduction analysis and machine learning. Out of ~1×107 defined genomic regions, there were ~7×105 DMRs between GBM and control samples and ~1.5×106 DMRs between GBM and IDH-mutant samples, accounting for ~7% and ~15% of the genome, respectively. Uniform manifold approximation and projection (UMAP) and hierarchical clustering analysis revealed that samples were mostly clustered by diagnosis. Random forest analysis of the 900 DMRs with an 80/20 train/test split resulted in an overall accuracy of 0.815. In conclusion, CSF cfDNA methylation profiles of diffuse glioma patients exhibit differential methylation patterns between IDH-mutant and IDH-wildtype tumors, as well as between tumor and control, and could be informative biomarkers for the diagnosis of patients with diffuse gliomas.

BIOM-19. METHYLATION PROFILING OF PLASMA CELL-FREE DNA IN PEDIATRIC BRAIN TUMOR PATIENTS

Abstract BACKGROUND Circulating tumor DNA (ctDNA) assays are being evaluated to inform clinical decisions in cancer care in adult patients, but the study in pediatric brain tumor patients is rare partly due to very low levels of ctDNA in blood. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. The aim of this study is to utilize methylomes from plasma ctDNA for non-invasive diagnosis in pediatric brain tumor patients. METHODS Using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq), which with low input DNA requirement, we measured methylation profiles of plasma samples from 77 pediatric brain tumor patients and 16 patients with non-neoplastic diseases like epilepsy. Binomial GLMnet classifiers of tumor or tumor subtype were built, in 2000 iterations of 60% training sets. Performance was evaluated in 40% test sets. RESULTS. These 77 pediatric brain tumor patients were a mixture of the most common pediatric brain tumors, include circumscribed astrocytic gliomas (N=23), glioneuronal tumors (N=16), embryonal tumor (N=9), ependymal tumor (N=7), pediatric type diffuse high grade glioma (N= 5), glioma NOS (N=4), mesenchymal tumor (N=4) and other pediatric brain tumors (N=9). The methylation profile differentiated brain tumors from non-neoplastic diseases with 82.9% accuracy (precision = 92.6%, sensitivity = 86.2%, specificity = 66.7 %). For subtype analysis, the methylation profile detected circumscribed astrocytic glioma from non-neoplastic diseases with 85.7% accuracy (precision = 87.5%, sensitivity = 87.5 %, specificity = 83.3 %), and glioneuronal tumors from non- neoplastic diseases with 83.3% accuracy (all precision, sensitivity, and specificity = 83.3 %). CONCLUSIONS The results suggest that methylation profiling of plasma cell-free DNA has the potential to discriminate between pediatric brain tumor patients and patients with non- neoplastic diseases. Larger population cohorts to train even more accurate classifiers will be needed.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

Azacytidine treatment affects the methylation pattern of genomic and cell-free DNA in uveal melanoma cell lines

BackgroundUveal melanoma (UM) is the most common primary intraocular tumour in adults, and approximately 50% of patients will develop metastasis. Epigenetic changes are a major factor in cancer progression. We aimed to determine whether methylation profiles could be altered using a DNA methyltransferase (DNMT) inhibitor in UM cell lines.MethodsFour primary and metastatic UM cell lines were treated with azacytidine and analysed for cell proliferation, colony formation, and BAP1 protein expression. Genomic and cell-free (cf)DNA methylation were compared.ResultsIn all cell lines, azacytidine treatment resulted in dose-dependent effects on proliferation, colony formation, and radiosensitivity. Methylation profiling revealed differences in methylation between cell lines according to BAP1 expression. Matched primary and metastatic cell lines showed very similar patterns. Alterations were seen in pathways known to be important in UM progression, such as PI3K/Akt and MAPK signaling, and in pathways involved in cancer progression, such as regulation of stemlike potential, cell motility, and invasion. These changes were maintained in genomic and cell-free DNA.ConclusionsThis data suggests that DNMT inhibitors cause changes in UM cells that are maintained in cfDNA. The results suggest that targeting methylation in UM treatment and monitoring response to treatment using cfDNA methylation could be a valuable tool.

Global cell-free DNA methylation in patients with active tuberculosis and tuberculosis contacts with latent tuberculosis infection

IntroductionTo investigate whether the methylation of circulating cell-free DNA (cfDNA) differentiates active tuberculosis (TB) from latent TB infection (LTBI). MethodsPatients with pulmonary TB, contacts with LTBI, and healthy controls were enrolled (2018–2021). Plasma cfDNA was extracted, and using a 5-methylcytosine (5mC) DNA ELISA kit, the global methylation of cfDNA (5mC-cfDNA) was measured. Results59 TB, 63 LTBI, 39 healthy controls were included. The 5mC-cfDNA level was higher in TB (6.4 %) than LTBI (4.1 %) and healthy controls (4.9 %) (both p<0.05). Independent TB factors were 5mC-cfDNA ≥6.6 % and CRP ≥0.32 mg/dL (adjusted odds ratio (aOR) 4.594 [95 % CI:1.628–12.965], p=0.004 and 5.338 [1.659–17.176], p=0.005). Having one or both factors increased TB odds 8- and 16-fold (aOR 8.688 [3.229–23.378], p <0.001 and 16.080 [3.092–83.632], p =0.001). ConclusionThe global cfDNA methylation level was higher in TB than contacts without TB and helped differentiate patients with TB from contacts with LTBI.

Differential methylation of circulating free DNA assessed through cfMeDiP as a new tool for breast cancer diagnosis and detection of BRCA1/2 mutation

BackgroundRecent studies have highlighted the importance of the cell-free DNA (cfDNA) methylation profile in detecting breast cancer (BC) and its different subtypes. We investigated whether plasma cfDNA methylation, using cell-free Methylated DNA Immunoprecipitation and High-Throughput Sequencing (cfMeDIP-seq), may be informative in characterizing breast cancer in patients with BRCA1/2 germline mutations for early cancer detection and response to therapy.MethodsWe enrolled 23 BC patients with germline mutation of BRCA1 and BRCA2 genes, 19 healthy controls without BRCA1/2 mutation, and two healthy individuals who carried BRCA1/2 mutations. Blood samples were collected for all study subjects at the diagnosis, and plasma was isolated by centrifugation. Cell-free DNA was extracted from 1 mL of plasma, and cfMeDIP-seq was performed for each sample. Shallow whole genome sequencing was performed on the immuno-precipitated samples. Then, the differentially methylated 300-bp regions (DMRs) between 25 BRCA germline mutation carriers and 19 non-carriers were identified. DMRs were compared with tumor-specific regions from public datasets to perform an unbiased analysis. Finally, two statistical classifiers were trained based on the GLMnet and random forest model to evaluate if the identified DMRs could discriminate BRCA-positive from healthy samples.ResultsWe identified 7,095 hypermethylated and 212 hypomethylated regions in 25 BRCA germline mutation carriers compared to 19 controls. These regions discriminate tumors from healthy samples with high accuracy and sensitivity. We show that the circulating tumor DNA of BRCA1/2 mutant breast cancers is characterized by the hypomethylation of genes involved in DNA repair and cell cycle. We uncovered the TFs associated with these DRMs and identified that proteins of the Erythroblast Transformation Specific (ETS) family are particularly active in the hypermethylated regions. Finally, we assessed that these regions could discriminate between BRCA positives from healthy samples with an AUC of 0.95, a sensitivity of 88%, and a specificity of 94.74%.ConclusionsOur study emphasizes the importance of tumor cell-derived DNA methylation in BC, reporting a different methylation profile between patients carrying mutations in BRCA1, BRCA2, and wild-type controls. Our minimally invasive approach could allow early cancer diagnosis, assessment of minimal residual disease, and monitoring of response to therapy.

Evaluation of a plasma cell-free DNA methylation test for colorectal cancer diagnosis: a multicenter clinical study.

A blood-based diagnostic test is a promising strategy for colorectal cancer (CRC). The MethyDT test (IColohunter), which detects methylation levels of NTMT1 and MAP3K14-AS1, exhibited potential in discriminating CRC, but its clinical performance needs to be validated in large-scale populations. A multicenter, double-blinded, cross-sectional study that enrolled 1194 participants was performed. Plasma samples were collected to detect methylation levels of NTMT1 and MAP3K14-AS1 using quantitative methylation-specific PCR with the MethyDT test, and the accuracy was further evaluated by Sanger sequencing. The sensitivities of the MethyDT test for detecting CRC, early stages of CRC (I and II), advanced adenoma (AA), and high-grade intraepithelial neoplasia (HGIN) were 91.2% (95% confidence interval [CI], 88.4-94.0), 87.4% (95% CI, 82.5-92.2), 43.5% (95% CI, 35.7-51.4), and 72.7% (95% CI, 57.5-87.9), respectively. The specificities for participants with non-AA, interfering diseases (ID), and no evidence of disease (NED) were 85.0% (95% CI, 78.8-91.3), 93.7% (95% CI, 91.4-95.9) and 97.3% (95% CI, 90.5-99.7), respectively, and its overall specificity for all-controls was 92.4% (95% CI, 90.3-94.4). The consistency of the MethyDT test with pathology for CRC was high with a kappa value of 0.830 (95% CI, 0.795-0.865). Additionally, the MethyDT test was comparable to Sanger sequencing for detecting methylation with kappa values > 0.97. The MethyDT test demonstrates excellent sensitivity and specificity for CRC and high consistency with Sanger sequencing for methylation, suggesting it may serve as a potential noninvasive diagnostic tool for the detection of CRC. This clinical trial has been registered in ClinicalTrials.gov (NCT05508503).

A promising application of kidney-specific cell-free DNA methylation markers in real-time monitoring sepsis-induced acute kidney injury

ABSTRACT Sepsis-induced acute kidney injury (SI-AKI) is a common clinical syndrome that is associated with high mortality and morbidity. Effective timely detection may improve the outcome of SI-AKI. Kidney-derived cell-free DNA (cfDNA) may provide new insight into understanding and identifying SI-AKI. Plasma cfDNA from 82 healthy individuals, 7 patients with sepsis non-acute kidney injury (SN-AKI), and 9 patients with SI-AKI was subjected to genomic methylation sequencing. We deconstructed the relative contribution of cfDNA from different cell types based on cell-specific methylation markers and focused on exploring the association between kidney-derived cfDNA and SI-AKI.Based on the deconvolution of the cfDNA methylome: SI-AKI patients displayed the elevated cfDNA concentrations with an increased contribution of kidney epithelial cells (kidney-Ep) DNA; kidney-Ep derived cfDNA achieved high accuracy in distinguishing SI-AKI from SN-AKI (AUC = 0.92, 95% CI 0.7801–1); the higher kidney-ep cfDNA concentrations tended to correlate with more advanced stages of SI-AKI; strikingly, SN-AKI patients with potential kidney damage unmet by SI-AKI criteria showed higher levels of kidney-Ep derived cfDNA than healthy individuals. The autonomous screening of kidney-Ep (n = 24) and kidney endothelial (kidney-Endo, n = 12) specific methylation markers indicated the unique identity of kidney-Ep/kidney-Endo compared with other cell types, and its targeted assessment reproduced the main findings of the deconvolution of the cfDNA methylome. Our study first demonstrates that kidney-Ep- and kidney-Endo-specific methylation markers can serve as a novel marker for SI-AKI emergence, supporting further exploration of the utility of kidney-specific cfDNA methylation markers in the study of SI-AKI.

Plasma cell-free DNA methylome-based liquid biopsy for accurate gastric cancer detection.

Early detection plays a critical role in mitigating mortality rates linked to gastric cancer. However, current clinical screening methods exhibit suboptimal efficacy. Methylation alterations identified from cell-free DNA (cfDNA) present a promising biomarker for early cancer detection. Our study focused on identifying gastric cancer-specific markers from cfDNA methylation to facilitate early detection. We enrolled 150 gastric cancer patients and 100 healthy controls in this study, and undertook genome-wide methylation profiling of cfDNA using cell-free methylated DNA immunoprecipitation and high-throughput sequencing. We identified 21 differentially methylated regions (DMRs) between the gastric tumor and nontumor groups using multiple algorithms. Subsequently, using the 21 DMRs, we developed a gastric cancer detection model by random forest algorithm in the discovery set, and validated the model in an independent set. The model was able to accurately discriminate gastric cancer with a sensitivity and specificity of 93.90% and 95.15% in the discovery set, respectively, and 88.38% and 94.23% in the validation set, respectively. These results underscore the efficacy and accuracy of cfDNA-derived methylation markers in distinguishing early stage gastric cancer. This study highlighted the significance of cfDNA methylation alterations in early gastric cancer detection.

MA02.09 An Effective Multimodel Based on Cell-Free DNA Methylation for Risk Stratification of Pulmonary Nodules

MA02.09 An Effective Multimodel Based on Cell-Free DNA Methylation for Risk Stratification of Pulmonary Nodules

A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules

Accurate, non-invasive, and cost-effective tools are needed to assist pulmonary nodule diagnosis and management due to increasing detection by low-dose computed tomography (LDCT). We perform genome-wide methylation sequencing on malignant and non-malignant lung tissues and designed a panel of 263 differential DNA methylation regions, which is used for targeted methylation sequencing on blood cell-free DNA (cfDNA) in two prospectively collected and retrospectively analyzed multicenter cohorts. We develop and optimize an integrative model for risk stratification of pulmonary nodules based on 40 cfDNA methylation biomarkers, age, and five simple computed tomography (CT) imaging features using machine learning approaches and validate its good performance in two cohorts. Using the two-threshold strategy can effectively reduce unnecessary invasive surgeries, overtreatment costs, and injury for patients with benign nodules while advising immediate treatment for patients with lung cancer, which can potentially improve the overall diagnosis of lung cancer following LDCT/CT screening.