Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

9ae63e13-9cd3-4767-8c81-209bb0a1ed5d - samples

ABSTRACT: Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

PROVIDER: EGAD00001006237 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

8997e924-0e0b-40ba-8b88-79ed81bfce62 - samples

Project description:Set of 19 patients afflicted with colorectal cancer with matching preoperative and postoperative blood plasma, PBMC, and tumor biopsy sequencing data. Originally referenced by Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med. 2020, Zviran et. al.

| EGAD00001011186 | EGA

Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

Project description:In many areas of oncology, we lack sensitive tools to track low burden disease. While cell-free DNA (cfDNA) shows promise in detecting cancer mutations, we found that the combination of low tumor fraction (TF) and limited number of DNA fragments restricts low disease burden monitoring through the prevailing deep targeted sequencing paradigm. We reasoned that breadth may supplant depth of sequencing to overcome the barrier of cfDNA abundance. Whole genome sequencing (WGS) of cfDNA allowed ultra-sensitive detection, capitalizing on the cumulative signal of thousands of somatic mutations observed in solid malignancies, with TF detection sensitivity as low as 10-5. The WGS approach enabled dynamic tumor burden tracking and post-operative residual disease detection, associated with adverse outcome. Thus, we present an orthogonal framework for cfDNA cancer monitoring via genome wide mutational integration, enabling ultra-sensitive detection, overcoming the limitation of cfDNA abundance, and empowering treatment optimization in low-disease burden oncology care

| EGAS00001004406 | EGA

Non-disruptive in vitro monitoring of cellular states with cell-free DNA methylation

Project description:Regenerative cell therapies and stem-based in vitro models all aspire the approximation of a given start cell to an alternate (often more mature) cell state. However, ‘monitoring’, hence controlling, such a dynamic process requires disruption of the precious sample, destroying architecture and introducing variation. Here, we establish real time-compatible, fully non-invasive monitoring of cell fates through simple cell-free DNA medium sampling. We demonstrate that cell-free DNA methylation non-disruptively reveals genome-wide DNA methylation transitions during epigenetic drug treatment in stem cells, and reports the gradual maturation of 3D hepatocytes grown in bioreactors. Our cell-free DNA methylation platform enables monitoring a broad variety of in vitro systems, has implications for the real-time evaluation human therapeutic cells, and the potential to be readily integrated into large-scale bioreactor workflows.

2026-01-29 | GSE293866 | GEO

Sensitive monitoring of xenograft cancer models using cell-free DNA

Project description:Sensitive monitoring of xenograft cancer models using cell-free DNA

| PRJNA1172290 | ENA

Heat selection enables highly scalable methylome profiling in cell-free DNA for noninvasive monitoring of cancer patients

Project description:Heat selection enables highly scalable methylome profiling in cell-free DNA for noninvasive monitoring of cancer patients

| PRJNA836767 | ENA

Heat selection enables highly scalable methylome profiling in cell-free DNA for noninvasive monitoring of cancer patients [CRC]

Project description:Heat selection enables highly scalable methylome profiling in cell-free DNA for noninvasive monitoring of cancer patients [CRC]

| PRJNA860193 | ENA

A Sensitive Approach to Map Genome-wide 5-Hydroxymethylcytosine and 5-Formylcytosine at Single-base Resolution

Project description:Mapping genome-wide 5-hydroxymethylcytosine (5hmC) and 5-formylcytosine (5fC) at single-base resolution is important to understand their biological functions. We present a cost-efficient mapping method that combines 5hmC-specific restriction enzyme PvuRts1I with a 5hmC enrichment method. The sensitive method enables detection of low abundant 5hmC sites, providing a more complete 5hmC landscape than available bisulfite-based methods. This method generated the first genome-wide 5fC map at single-base resolution. Parallel analyses revealed that 5hmC and 5fC existed with lower abundance and more dynamically in non-CpG context than in CpG context. In the genic region, distribution of 5hmCpG and 5fCpG differed from 5hmCH and 5fCH (H=A, T, C). 5hmC and 5fC were distributed distinctly at regulatory protein-DNA binding sites, depleted in permissive transcription factor binding sites, and enriched at active and poised enhancers. This sensitive bisulfite-conversion free method can be applied to biological samples with limited starting material or low abundance of cytosine modifications. Sensitive mapping of genome-wide 5-hydroxymethylcytosine and 5-formylcytosine in mouse embryonic stem cell at single-base resolution by combining 5-hydroxymethylcytosine specific restriction enzyme PvuRts1I and 5-hydroxymethylcytosine enrichment method (selective chemical labeling or SEAL)

2014-12-22 | E-GEOD-64186 | biostudies-arrayexpress

Plasma cfDNA and Peripheral Blood gDNA Provide Complementary Information for Molecular Monitoring in Myeloproliferative Neoplasms

Project description:This project examines the complementary roles of plasma cell-free DNA (cfDNA) and peripheral blood genomic DNA (gDNA) for molecular monitoring in myeloproliferative neoplasms (MPNs).

2026-03-23 | GSE325660 | GEO

Project description:PRISM-Seq: An Ultra-sensitive Sequencing Approach For Mapping Lentiviral Integration Sites

| PRJNA1460434 | ENA

Robust classification of pediatric brain tumors from cell-free DNA methylomes

Project description:Cerebrospinal fluid (CSF) liquid biopsies serve as a rich source of tumor-derived cell-free DNA (cfDNA) for evaluating patients with central nervous system (CNS) tumors. However, challenges stemming from trace cfDNA yields and low mutational burden have hindered sensitivity, whereas first-generation clinical assays have relied on genetic alterations as biomarkers. Leveraging the diagnostic utility of DNA methylation classification in CNS tumors, we developed M-PACT (Methylation-based Predictive Algorithm for CNS Tumors), a robust deep neural network that accurately classifies tumors from sub-nanogram input cfDNA methylomes acquired through enzymatic methylation sequencing. In addition to tumor classification, this workflow enables methylation-based cellular deconvolution and sensitive copy number variation (CNV) detection. We benchmark our methodology in pediatric CNS embryonal tumors and further demonstrate accurate classification of intra-operative CSF, balanced tumor genomes, and secondary malignancies. Altogether, we provide a blueprint for CNS tumor classification from low input cfDNA methylomes, motivating prospective validation for future clinical implementation.

2025-11-17 | GSE292312 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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