Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Longitudinal analysis of personal cfDNA methylome patterns in metastatic prostate cancer [plasma]

ABSTRACT: Disease progression and therapeutic resistance are hallmarks of advanced stage prostate cancer (PCa), which remains a major cause of cancer-related mortality around the world. Longitudinal studies, coupled with the use of liquid biopsies, offer a potentially new and minimally invasive platform to study the dynamics of tumour progression. Our study aimed to investigate the dynamics of personal methylomic profiles of metastatic PCa (mPCa) patients, during disease progression and therapy administration. 52 plasma samples from 9 patients with mPCa were collected, longitudinally, over 13-21 months. After cell-free DNA (cfDNA) isolation, DNA methylation was profiled using the Infinium MethylationEPIC BeadChip and analysed using the minfi software. The top 5% most variable probes across time, within each individual, were utilised to study dynamic methylation patterns during disease progression and therapeutic response. Statistical testing was carried out to identify and validate ctDNA differentially methylated genes (DMGs). Personal cfDNA global methylation patterns were temporally stable throughout disease course. A proportion of analysed CpG sites presented a dynamic temporal pattern that was consistent with clinical events, such as therapy administration, and were prominently associated with immune response pathways. Study of ctDNA identified >2,000 DMGs with dynamic methylation patterns. We concluded that longitudinal assessment of cfDNA methylation in mPCa patients unveiled dynamic patterns associated with the occurrence of specific clinical events, thus highlighting the potential of using liquid biopsies to study PCa progression.

ORGANISM(S): Homo sapiens

PROVIDER: GSE157273 | GEO | 2021/09/01

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Longitudinal analysis of personal cfDNA methylome patterns in metastatic prostate cancer [plasma]

Project description:Longitudinal analysis of personal cfDNA methylome patterns in metastatic prostate cancer [plasma]

| PRJNA660746 | ENA

Cell-free DNA methylation markers in diagnosis and prognosis of common cancers (COAD)

Project description:To identify diagnostic and prognostic biomarkers, we compared methylation profiles of COAD tissues and normal blood at 485,000 CpG markers and identified a marker panel differently methylated in COAD. We developed diagnostic and prognostic prediction models with the selected panel and compared their efficacy in ctDNA to current available approaches. Our data indicate that cfDNA methylation patterns provide reliable biomarkers in the diagnosis, surveillance, and prognosis of COAD.

2020-01-01 | GSE97923 | GEO

Cell-free DNA methylation markers in diagnosis and prognosis of common cancers (lung cancer)

Project description:To identify diagnostic and prognostic biomarkers, we compared methylation profiles of LUNC tissues and normal blood at 485,000 CpG markers and identified a marker panel differently methylated in LUNC. We developed diagnostic and prognostic prediction models with the selected panel and compared their efficacy in ctDNA to current available approaches. Our data indicate that cfDNA methylation patterns provide reliable biomarkers in the diagnosis, surveillance, and prognosis of LUNC.

2020-01-01 | GSE97932 | GEO

Targeted next generation sequencing of cfDNA from lung cancer patients enrolled in the GALAXY-1 trial (NCT01348126)

Project description:We evaluated whether targeted next-generation sequencing (NGS) using the Ion Torrent Personal Genome Sequencer of cfDNA could identify prognostic or predictive factors for overall survival (OS) or progression free survival (PFS) within a large cohort of patients with advanced lung adenocarcinoma enrolled in the GALAXY-1 trial.

2018-01-10 | E-MTAB-5749 | biostudies-arrayexpress

Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes

Project description:Small-cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, each with unique therapeutic vulnerabilities. Implementing subtyping in the clinic has remained challenging due to limited tissue availability, particularly for longitudinal monitoring. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that there would be subtype-specific patterns of DNA methylation that could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts of totally 179 SCLC patients and machine learning approaches, we developed a highly accurate DNA methylation-based classifier (SCLC-DMC) that could distinguish SCLC subtypes using clinical tumor samples with 95.8% accuracy in the testing set compared to mRNA-based profiling. We further adjusted the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC) we could demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. Furthermore, methylation-based subtyping predicted response to a wide variety of drugs in preclinical models and clinical outcomes were indistinguishable in cohorts of patients subtyped using mRNA or SCLC-DMC. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and guide precision SCLC therapy.

2024-01-22 | GSE241673 | GEO

Dynamic Monitoring of ctDNA Methylation to Predict Relapse in Colorectal Cancer After Radical Resection (POSTCA)

Project description:This is a prospective, multicenter, observational, single-blinded controlled study. Dynamic monitoring of patients with resectable colorectal cancer was performed using the previously established colorectal tumor-specific plasma ctDNA methylation markers (Multigene methylation detection). Dynamic monitoring of plasma ctDNA methylation before and after treatment and at regular follow-up in patients with colorectal cancer after radical resection of tumor, to explore the predictive effect of postoperative plasma ctDNA methylation on postoperative recurrence and whether dynamic monitoring of postoperative ctDNA methylation could be earlier than imaging examination to indicate tumor recurrence.

| 2289240 | ecrin-mdr-crc

Distinct DNA methylation patterns of rheumatoid arthritis peripheral blood and synovial tissue T cells

Project description:Objective: To study epigenetic patterns in T lymphocytes that accumulate in rheumatoid arthritis (RA) synovium, we characterized DNA methylation of CD3+ T cells in peripheral blood and synovial tissue in RA and osteoarthritis (OA) patients. Methods: Genomic DNA of CD3+ T cells was isolated from RA (n = 8) and OA (n = 5) patients from blood or synovium at the time of arthroplasty using antibodies and magnetic beads. Methylation was measured using Illumina Infinium MethylationEPIC Kit. Differentially methylated loci (DMLs) and genes (DMGs) were identified using Welch’s t-test. Principal component analysis (PCA), hierarchical clustering and pathway analysis were used to determine relationships among groups. Results: Comparing DNA methylation of CD3+ T cells between peripheral blood and synovial tissue within each disease, 4615 and 164 DMLs were identified in RA and OA samples respectively, resulting in 832 and 36 DMGs. PCA showed that methylation differences in T cells were greater based on location (blood vs. synovium) than based on disease (RA vs. OA). Differentially modified pathways were significantly enriched between RA blood and synovial T cells, especially in genes related to complement, integrin cell surface interactions and P53 pathway. The limited number of DMGs identified between OA blood and synovial T cells did not conform to biologic pathways. Conclusion: The patterns of DNA methylation in RA show location-specific differences related to immune pathways, while methylation differences in OA are limited. The RA joint-specific signatures could be due to selective accumulation of T cell populations or expansion of differentially marked adaptive immune cells. Understanding epigenetic patterns could provide clues to the types of T cells that accumulate in the RA joint and identify potential therapeutic targets.

2021-02-01 | GSE164468 | GEO

Linear Amplification based Bisulfite Sequencing Enables Ultrasensitive Cancer Detection from Cell-free DNA

Project description:Although methylation-based liquid biopsy assays demonstrate great potential to detect and classify tumor information from circulating plasma cell-free DNA (cfDNA), limitations of the current technologies hinder the introduction of these assays into clinical practice. Most assays require significant amounts of input DNA, which can be challenging and inconvenient. Furthermore, underrepresented circulating tumor DNA (ctDNA) fragments in the cfDNA pool may go undetected during the exponential amplification step of a traditional sequencing library construction. Cell type deconvolution has yet to be achieved with existing methods. Here we report LABS (Linear Amplification based Bisulfite Sequencing) that can linearly amplify bisulfite-treated DNA fragments in a genome-wide, unbiased fashion, making it feasible to detect cancer abnormalities with sub-nanogram inputs (<1 ng). We applied LABS to patient plasma-derived cfDNA samples from human subjects with 50 colorectal cancer (CRC), 16 pancreatic ductal adenocarcinoma (PDAC), and 34 healthy controls. By exploiting LABS methylome data using a multi-faceted approach, we detected cancer-specific methylation patterns, copy number alterations, and gene promoter alterations in CRC and PDAC samples. Furthermore, the ability to identify tissue-of-origin and immune cell composition further enhances the accuracy of cancer detection.

2024-04-24 | GSE186007 | GEO

Reliable tumor detection using cerebrospinal fluid cell-free DNA methylomes in pediatric medulloblastoma

Project description:Medulloblastoma (MB) is an embryonal tumor of the cerebellum and a highly malignant childhood brain tumor. Cell-free circulating tumor DNA (ctDNA) from the cerebrospinal fluid (CSF) of patients with brain tumors faithfully represent genomic alterations of brain tumors. Distinct epigenetic signatures among subgroups of MB allow us to detect epigenetic alterations in CSF to aid classify and guide therapy of MB tumors. Here, we evaluate DNA methylation and hydroxymethylation of ctDNA derived from small amount of CSF (200 µL) and matched tumor DNA from 3 MB patients. We find highly concordance of DNA methylation and hydroxymethylation between CSF ctDNA and tumor DNA, especially in CpG islands. Importantly, CSF ctDNA can mostly recapitulate the dynamic changes of DNA methylation and hydroxymehtylation in tumor species compared to healthy cerebellums. Those MB tumor signature CpGs’ DNA methylation status are recovered in CSF ctDNA can clearly distinguish MB subgroups by utilizing public large cohort data. We further identified potential diagnostic and prognostic DNA methylation markers in CSF ctDNA. Our results show that CSF ctNDA methylation and hydroxymethylation can be a minimal invasive method to assess epigenetic alterations of MB, which is complementary to current diagnoses of MB tumors.

2020-10-28 | GSE142241 | GEO

Genome-wide plasma cell-free DNA methylation profiling in lung cancer patients using MeDIP-seq

Project description:As a non-invasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma is raising increasing interest due to its diagnostic and biology applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. Through enriching methylated cfDNA fragments with MeDIP followed by deep sequencing, we aimed to characterize cfDNA methylome in cancer patients. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. MEDIPS package was used for the identification of differentially methylated regions (DMRs) between patients and normal ones. Overall, we identified 330 differentially methylated regions (DMRs) in gene promoter regions, 33 hypermethylation and 297 hypomethylation respectively, by comparing lung cancer patients and healthy individuals as controls. The 33 hypermethylation regions represent 32 genes. Some of the genes had been previously reported to be associated with lung cancers, such as GAS7, AQP10, HLF, CHRNA9 and HOPX. Taken together, our study provided an alternative method of cfDNA methylation analysis in lung cancer patients with potential clinical applications.

2019-03-31 | E-MTAB-7163 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data