RESEARCH

2023

Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy

Papadakis VM, Cheimonidi C, Panagopoulou M, Karaglani M, Apalaki P, Katsara K, Kenanakis G, Theodosiou T, Constantinidis TC, Stratigi K, Chatzaki E.

Int J Mol Sci. 2023 Aug 3;24(15):12384

doi: 10.3390/ijms241512384. PMID: 37569759; PMCID: PMC10418917

Abstract

Circulating cell-free DNA (ccfDNA) is a liquid biopsy biomaterial attracting significant attention for the implementation of precision medicine diagnostics. Deeper knowledge related to its structure and biology would enable the development of such applications. In this study, we employed Raman spectroscopy to unravel the biomolecular profile of human ccfDNA in health and disease. We established reference Raman spectra of ccfDNA samples from healthy males and females with different conditions, including cancer and diabetes, extracting information about their chemical composition. Comparative observations showed a distinct spectral pattern in ccfDNA from breast cancer patients taking neoadjuvant therapy. Raman analysis of ccfDNA from healthy, prediabetic, and diabetic males uncovered some differences in their biomolecular fingerprints. We also studied ccfDNA released from human benign and cancer cell lines and compared it to their respective gDNA, confirming it mirrors its cellular origin. Overall, we explored for the first time Raman spectroscopy in the study of ccfDNA and provided spectra of samples from different sources. Our findings introduce Raman spectroscopy as a new approach to implementing liquid biopsy diagnostics worthy of further elaboration.

2022

Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning

Karaglani M, Panagopoulou M, Cheimonidi C, Tsamardinos I, Maltezos E, Papanas N, Papazoglou D, Mastorakos G, Chatzaki E.

J Clin Med. 2022 Feb 17;11(4):1045

doi: 10.3390/jcm11041045. PMID: 35207316; PMCID: PMC8876363

Abstract

Background: The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of β-pancreatic cell loss is emerging. Here, we focused on studying circulating cell-free DNA (ccfDNA) as a liquid biopsy biomaterial for accurate diagnosis/monitoring of T2DM. Methods: ccfDNA levels were directly quantified in sera from 96 T2DM patients and 71 healthy individuals via fluorometry, and then fragment DNA size profiling was performed by capillary electrophoresis. Following this, ccfDNA methylation levels of five β-cell-related genes were measured via qPCR. Data were analyzed by automated machine learning to build classifying predictive models

Results: ccfDNA levels were found to be similar between groups but indicative of apoptosis in T2DM. INS (Insulin), IAPP (Islet Amyloid Polypeptide-Amylin), GCK (Glucokinase), and KCNJ11 (Potassium Inwardly Rectifying Channel Subfamily J member 11) levels differed significantly between groups. AutoML analysis delivered biosignatures including GCK, IAPP and KCNJ11 methylation, with the highest ever reported discriminating performance of T2DM from healthy individuals (AUC 0.927). Conclusions: Our data unravel the value of ccfDNA as a minimally invasive biomaterial carrying important clinical information for T2DM. Upon prospective clinical evaluation, the built biosignature can be disruptive for T2DM clinical management.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Karaglani M, Panagopoulou M, Baltsavia I, Apalaki P, Theodosiou T, Iliopoulos I, Tsamardinos I, Chatzaki E.

Int J Mol Sci. 2022 Mar 9;23(6):2959

doi: 10.3390/ijms23062959. PMID: 35328380; PMCID: PMC8952417

Abstract

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature’s applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

ENPP2 Promoter Methylation Correlates with Decreased Gene Expression in Breast Cancer: Implementation as a Liquid Biopsy Biomarker

Panagopoulou M, Drosouni A, Fanidis D, Karaglani M, Balgkouranidou I, Xenidis N, Aidinis V, Chatzaki E.

Int J Mol Sci. 2022 Mar 28;23(7):3717

doi:10.3390/ijms23073717. PMID: 35409077; PMCID: PMC8998992

Abstract

Autotaxin (ATX), encoded by the ctonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) gene, is a key enzyme in lysophosphatidic acid (LPA) synthesis. We have recently described ENPP2 methylation profiles in health and multiple malignancies and demonstrated correlation to its aberrant expression. Here we focus on breast cancer (BrCa), analyzing in silico publicly available BrCa methylome datasets, to identify differentially methylated CpGs (DMCs) and correlate them with expression. Numerous DMCs were identified between BrCa and healthy breast tissues in the gene body and promoter-associated regions (PA). PA DMCs were upregulated in BrCa tissues in relation to normal, in metastatic BrCa in relation to primary, and in stage I BrCa in relation to normal, and this was correlated to decreased mRNA expression. The first exon DMC was also investigated in circulating cell free DNA (ccfDNA) isolated by BrCa patients; methylation was increased in BrCa in relation to ccfDNA from healthy individuals, confirming in silico results. It also differed between patient groups and was correlated to the presence of multiple metastatic sites. Our data indicate that promoter methylation of ENPP2 arrests its transcription in BrCa and introduce first exon methylation as a putative biomarker for diagnosis and monitoring which can be assessed in liquid biopsy.

Just Add Data: automated predictive modeling for knowledge discovery and feature selection

Tsamardinos I, Charonyktakis P, Papoutsoglou G, Borboudakis G, Lakiotaki K, Zenklusen JC, Juhl H, Chatzaki E, Lagani V.

NPJ Precision Oncology, 2022 Jun 16;6(1):38

doi: 10.1038/s41698-022-00274-8. PMID: 35710826; PMCID: PMC9203777

Abstract

Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.

Autotaxin in Breast Cancer: Role, Epigenetic Regulation and Clinical Implications

Drosouni A, Panagopoulou M, Aidinis V, Chatzaki E.

Cancers (Basel). 2022 Nov 4;14(21):5437

doi: 10.3390/cancers14215437. PMID: 36358855; PMCID: PMC9658281

Abstract

Autotaxin (ATX), the protein product of Ectonucleotide Pyrophosphatase Phosphodiesterase 2 (ENPP2), is a secreted lysophospholipase D (lysoPLD) responsible for the extracellular production of lysophosphatidic acid (LPA). ATX-LPA pathway signaling participates in several normal biological functions, but it has also been connected to cancer progression, metastasis and inflammatory processes. Significant research has established a role in breast cancer and it has been suggested as a therapeutic target and/or a clinically relevant biomarker. Recently, ENPP2 methylation was described, revealing a potential for clinical exploitation in liquid biopsy. The current review aims to gather the latest findings about aberrant signaling through ATX-LPA in breast cancer and discusses the role of ENPP2 expression and epigenetic modification, giving insights with translational value.

2021

Circulating Cell-Free DNA in Breast Cancer: Searching for Hidden Information towards Precision Medicine

Panagopoulou M, Esteller M, Chatzaki E.

Cancers (Basel). 2021 Feb 10;13(4):728

doi: 10.3390/cancers13040728. PMID: 33578793; PMCID: PMC7916622

Abstract

Breast cancer (BC) is a leading cause of death between women. Mortality is significantly raised due to drug resistance and metastasis, while personalized treatment options are obstructed by the limitations of conventional biopsy follow-up. Lately, research is focusing on circulating biomarkers as minimally invasive choices for diagnosis, prognosis and treatment monitoring. Circulating cell-free DNA (ccfDNA) is a promising liquid biopsy biomaterial of great potential as it is thought to mirror the tumor’s lifespan; however, its clinical exploitation is burdened mainly by gaps in knowledge of its biology and specific characteristics. The current review aims to gather latest findings about the nature of ccfDNA and its multiple molecular and biological characteristics in breast cancer, covering basic and translational research and giving insights about its validity in a clinical setting.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Panagopoulou M, Karaglani M, Manolopoulos VG, Iliopoulos I, Tsamardinos I, Chatzaki E.

Cancers (Basel). 2021 Apr 2;13(7):1677

doi: 10.3390/cancers13071677. PMID: 33918195; PMCID: PMC8037759

Abstract

DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982–1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921–1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920–1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

Methylation Status of Corticotropin-Releasing Factor (CRF) Receptor Genes in Colorectal Cancer

Panagopoulou M, Cheretaki A, Karaglani M, Balgkouranidou I, Biziota E, Amarantidis K, Xenidis N, Kakolyris S, Baritaki S, Chatzaki E.

J Clin Med. 2021 Jun 18;10(12):2680

doi: 10.3390/jcm10122680. PMID: 34207031; PMCID: PMC8234503

Abstract

The corticotropin-releasing factor (CRF) system has been strongly associated with gastrointestinal pathophysiology, including colorectal cancer (CRC). We previously showed that altered expression of CRF receptors (CRFRs) in the colon critically affects CRC progression and aggressiveness through regulation of colonic inflammation. Here, we aimed to assess the potential of CRFR methylation levels as putative biomarkers in CRC. In silico methylation analysis of CRF receptor 1 (CRFR1) and CRF receptor 2 (CRFR2) was performed using methylome data derived by CRC and Crohn’s disease (CD) tissues and CRC-derived circulating cell-free DNAs (ccfDNAs). In total, 32 and 33 differentially methylated sites of CpGs (DMCs) emerged in CRFR1 and CRFR2, respectively, between healthy and diseased tissues. The methylation patterns were verified in patient-derived ccfDNA samples by qMSP and associated with clinicopathological characteristics. An automated machine learning (AutoML) technology was applied to ccfDNA samples for classification analysis. In silico analysis revealed increased methylation of both CRFRs in CRC tissue and ccfDNA-derived datasets. CRFR1 hypermethylation was also noticed in gene body DMCs of CD patients. CRFR1 hypermethylation was further validated in CRC adjuvant-derived ccfDNA samples, whereas CRFR1 hypomethylation, observed in metastasis-derived ccfDNAs, was correlated to disease aggressiveness and adverse prognostic characteristics. AutoML analysis based on CRFRs methylation status revealed a three-feature high-performing biosignature for CRC diagnosis with an estimated AUC of 0.929. Monitoring of CRFRs methylation-based signature in CRC tissues and ccfDNAs may be of high diagnostic and prognostic significance in CRC.

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets

Papoutsoglou G, Karaglani M, Lagani V, Thomson N, Røe OD, Tsamardinos I, Chatzaki E. 

Scientific Reports 2021 Jul 23;11(1):15107

doi: 10.1038/s41598-021-94501-0. PMID: 34302024; PMCID: PMC8302755

Abstract

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723-0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865-0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899-0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.

2020

Somatic copy number aberrations detected in circulating tumor DNA can hold diagnostic value for early detection of hepatocellular carcinoma

Chatzaki E, Tsamardinos I.

EBioMedicine. 2020 Jul;57:102851

doi: 10.1016/j.ebiom.2020.102851. Epub 2020 Jul 7. PMID: 32650268; PMCID: PMC7341368

Abstract

The study reported by Tao et al., 2020 appearing in the recent issue of EBioMedicine [1] targets the significant unmet clinical need for a blood-based minimally invasive accurate biomarker for the early detection of hepatocellular carcinoma (HCC). Circulating tumor DNA (ctDNA), a most promising liquid biopsy biomaterial holding clinically relevant information is analyzed. ctDNA had been shown to carry genetic and epigenetic aberrations that mirror the growing tumor [2,3], therefore could be used to monitor its lifespan. The present study focuses on the detection of somatic copy number aberrations (SCNAs) in ctDNA, not previously assessed in HCC. As a liquid biopsy parameter, SCNAs present some advantages over others, as they contribute larger number of ctDNA fragments to the overall cfDNA pool, span larger genomic regions, whereas compared to methylation, they remain much less affected by confounders such as age, diet, and life style.

Association between SOX17, Wif-1 and RASSF1A promoter methylation status and response to chemotherapy in patients with metastatic gastric cancer

Karamitrousis E, Balgkouranidou I, Xenidis N, Amarantidis K, Biziota E, Koukaki T, Trypsianis G, Karayiannakis A, Bolanaki H, Chatzaki E, Kolios G, Lianidou E, Lambropoulou M, Kakolyris S.

Clin Chem Lab Med. 2020 Sep 1;59(2):e73-e75

doi: 10.1515/cclm-2020-0662. PMID: 32870805

To the Editor,

Gastric cancer is one of the most frequently diagnosed cancers worldwide [1]. In 2020, it is estimated that there will be more than 27,000 new cases and more than 11,000 patients will die from the disease, in the United States [2]. A global epigenetic marker is methylation of specific DNA bases. Several genes are generally hypomethylated in cancer patients, whereas tumor suppressor genes are hypermethylated in the vast majority of cases [3]. One of the most important events in carcinogenesis is the hypermethylation of CpG islands of the promoter region of these genes [4]. This procedure may affect the function of the aforementioned genes, involved in critical cell functions, such as DNA repair, cell cycle regulation, metabolism, cell–cell interactions, angiogenesis, apoptosis etc., leading to cancer development [5]. Methylation of the promoter region of tumor suppressor genes leads to inactivation of them, whereas unmethylated promoters are considered active, so the methylation patterns of cancer cells is translated into gene expression patterns. Cell-free DNA methylation may be used for the detection of treatment resistance in cancer patients. This resistance can be due to the reversal of existent or novel DNA changes induced by drugs. In many studies, DNA methylation patterns have been evaluated as predictors of treatment response in cancer patients, including breast and colon cancer patients. The relapse of the disease after chemotherapy is likely to be due, to some extent, to changes in the methylation pattern of DNA, which are induced by chemotherapy itself. On the other hand, the relapse of the disease may be due to methylation patterns already present in the tumor before the onset of any treatment, so that specific methylation patterns cause a more aggressive phenotype with a poor response to chemotherapy [6]. SOX17 has a very important role in regulating the growth and function of progenitor cells located in bone marrow via suppression of the activation of wnt signaling pathway. It seems that methylation of this gene results in activation of the wnt signaling pathway [7]. RASSF1A gene is responsible for the production of RASSF1A protein which is involved in the regulation of microtubules, the maintenance of genomic stability, the modification of apoptosis, the mobility of cells, the regulation of cell cycle and the control of tumor infiltration [8, 9]. Wif-1 gene (wnt inhibitory factor-1) acts as an antagonist of the wnt signaling pathway and is frequently methylated in many cancer patients [10]. In the present study, we tried to analyze the promoter methylation status of tumor-suppressor genes SOX-17, Wif-1 and RASSF1A.

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning

Karaglani M, Gourlia K, Tsamardinos I, Chatzaki E.

J Clin Med. 2020 Sep 18;9(9):3016

doi: 10.3390/jcm9093016. PMID: 32962113; PMCID: PMC7563988

Abstract

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

2019

Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers

Panagopoulou M, Karaglani M, Balgkouranidou I, Biziota E, Koukaki T, Karamitrousis E, Nena E, Tsamardinos I, Kolios G, Lianidou E, Kakolyris S, Chatzaki E.

Oncogene. 2019 May;38(18):3387-3401

doi: 10.1038/s41388-018-0660-y. Epub 2019 Jan 14. PMID: 30643192

Abstract

Blood circulating cell-free DNA (ccfDNA) is a suggested biosource of valuable clinical information for cancer, meeting the need for a minimally-invasive advancement in the route of precision medicine. In this paper, we evaluated the prognostic and predictive potential of ccfDNA parameters in early and advanced breast cancer. Groups consisted of 150 and 16 breast cancer patients under adjuvant and neoadjuvant therapy respectively, 34 patients with metastatic disease and 35 healthy volunteers. Direct quantification of ccfDNA in plasma revealed elevated concentrations correlated to the incidence of death, shorter PFS, and non-response to pharmacotherapy in the metastatic but not in the other groups. The methylation status of a panel of cancer-related genes chosen based on previous expression and epigenetic data (KLK10, SOX17, WNT5A, MSH2, GATA3) was assessed by quantitative methylation-specific PCR. All but the GATA3 gene was more frequently methylated in all the patient groups than in healthy individuals (all p < 0.05). The methylation of WNT5A was statistically significantly correlated to greater tumor size and poor prognosis characteristics and in advanced stage disease with shorter OS. In the metastatic group, also SOX17 methylation was significantly correlated to the incidence of death, shorter PFS, and OS. KLK10 methylation was significantly correlated to unfavorable clinicopathological characteristics and relapse, whereas in the adjuvant group to shorter DFI. Methylation of at least 3 or 4 genes was significantly correlated to shorter OS and no pharmacotherapy response, respectively. Classification analysis by a fully automated, machine learning software produced a single-parametric linear model using ccfDNA plasma concentration values, with great discriminating power to predict response to chemotherapy (AUC 0.803, 95% CI [0.606, 1.000]) in the metastatic group. Two more multi-parametric signatures were produced for the metastatic group, predicting survival and disease outcome. Finally, a multiple logistic regression model was constructed, discriminating between patient groups and healthy individuals. Overall, ccfDNA emerged as a highly potent predictive classifier in metastatic breast cancer. Upon prospective clinical evaluation, all the signatures produced could aid accurate prognosis.

Circulating cell-free DNA release in vitro: kinetics, size profiling, and cancer-related gene methylation

Panagopoulou M, Karaglani M, Balgkouranidou I, Pantazi C, Kolios G, Kakolyris S, Chatzaki E.

J Cell Physiol. 2019 Aug;234(8):14079-14089

doi: 10.1002/jcp.28097. Epub 2019 Jan 7. PMID: 30618174

Abstract

Circulating cell-free DNA (ccfDNA) is a biological entity of great interest due to its potential as liquid biopsy biomaterial carrying clinically valuable information. To better understand its nature, we studied ccfDNA in vitro in two human cancer cell lines MCF-7 and HeLa. Normalized indexes of ccfDNA per cell population decreased over time of culture but were significantly elevated after exposure to IC50 doses of the demethylating/apoptotic agent 5-azacytidine (5-AZA-CR). Fragment-size profiling was indicative of active release, whereas exposure to 5-AZA-CR induced the release of additional shorter fragments, indicative of apoptosis. Finally, the methylation profile of a panel of cancer-specific genes as assessed by quantitative methylation analysis in ccfDNA was identical to the corresponding genomic DNA and followed accurately changes caused by 5-AZA-CR. Overall, our in vitro findings support that ccfDNA can be a reliable biosource of clinically relevant information that can be further studied in these cell culture models.