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80 results for “cfDNA”
Technical Feasibility of the cfDNA Test for Non-invasive Cytogenetic Analysis of Early Miscarriages Versus the Gold Standard Microarray
ClinicalTrials.gov study NCT05900076. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Efficacy of Dd-cfDNA in Routine Patient Care in Kidney Transplant Recipients"
ClinicalTrials.gov study NCT06406179. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of Early Detection and Prognosis Based on cfDNA Methylation in Multiple Myeloma
ClinicalTrials.gov study NCT05693012. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study to Detect V-Raf Murine Sarcoma Viral Oncogene Homolog B1 (BRAF) V600 Mutation on Cell-Free Deoxyribonucleic Acid (cfDNA) From Plasma in Participants With Advanced Melanoma
ClinicalTrials.gov study NCT02768207. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Immunosuppression Reduction in Failed Allograft Guided by cfDNA
ClinicalTrials.gov study NCT04560582. IPD Sharing: Not stated. Countries: 1. Publications: 0.
cfDNA Assay Prospective Observational Validation for Early Cancer Detection and Minimal Residual Disease
ClinicalTrials.gov study NCT05366881. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Frequency of Circulating Tumor Cells (CTCs) and Amount of Cell-free DNA (cfDNA) in Cirrhotic Patients With Hepatocellular Carcinoma (HCC)
ClinicalTrials.gov study NCT03162198. IPD Sharing: NO. Countries: 1. Publications: 0.
Vascular Invasion Signatures in cfDNA Support Re-staging of Liver Cancer
ClinicalTrials.gov study NCT05540925. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Application of Quantum Detection-Driven Artificial Intelligence Algorithms for Single-Molecule cfDNA Characterization in the Early Diagnosis of Prostate Cancer
ClinicalTrials.gov study NCT07238959. IPD Sharing: NO. Countries: 1. Publications: 0.
cfDNA sequencing with TACS-TOPO scheme
GEO Series GSE222918. Homo sapiens. 1 samples. Type: Other.
Sequencing of cfDNA derived from the plasma of individuals of different ages
GEO Series GSE114511. Homo sapiens. 12 samples. Type: Other.
CUPiD, A cfDNA methylation-based tissue-of-origin classifier for Cancers of Unknown Primary - classifier data and code
<p>This repository holds code behind the article "A cfDNA methylation-based tissue-of-origin classifier for Cancers of Unknown Primary" by Conway, Pearce, Clipson et al, <a href="https://www.nature.com/articles/s41467-024-47195-7">published in Nature Communications</a>. This contains the code and data required to generate the CUPiD classifier itself.</p> <p>Data and code to reproduce the figures in the paper are available from <a href="../records/10684337">https://zenodo.org/uploads/10684337</a> (unrestricted).</p> <p>Methyl-Binding Domain protein sequencing (MBD-Seq) was applied to circulating cell-free DNA (cfDNA) samples derived from patients with a range of known cancer types (143 patients), as well as 106 non-cancerous controls (79 used in training). </p> <p>The objects deposited here include R data files containing qseaSets from the R package qsea, which includes the read counts per sample per 300 base pair window across the genome, as well as information on copy number variation and metadata tables. These are provided in the <code>inputFiles/nextflowOutput</code> folder, and are some of the outputs of the nextflow pipeline.</p> <p>The scripts folder contains numbered sub-folders, with numbered scripts within them, which should be ran in order. The scripts are setup to be run on a PBS-Torque system; files ending ".pbs" should be submitted via qsub, files ending ".sh" should be ran on a node and will submit individual jobs within a loop. R scripts without an associated .pbs or .sh file should just be ran directly. All files should be submitted from the base of the repository (e.g. <code>qsub scripts/01-downloadData/01-getRawData.pbs</code>) to set the paths appropriately via the environment variable <code>PBS_O_WORKDIR</code>. </p> <ul> <li><code>01-downloadData</code> contains scripts to download and preprocess all the required data.</li> <li><code>02-qseaSetNextFlowPipeline</code> contains our custom in-house DSL2 Nextflow pipeline which takes fastq files to processed qseaSets, including QC checks. This requires the fastq files which will be deposited in EGA.</li> <li><code>03-convertArrays</code> converts downloaded (pre-processed) arrays into estimated qseaSets (containing solely the array sample), and then mixes each array with each NCC cfDNA at varying proportions.</li> <li><code>04-DMRs</code> calculates pairwise DMRs between each class.</li> <li><code>05-prepForClassifier</code> selects up to 10000 mixture sets per class, and generates a large table suitable for input into the ML model.</li> <li><code>06-fitClassifier</code> fits the ML model using <code>xgboost</code> within the <code>tidymodels</code> framework. This is repeated 100 times with different subsets of the mixture sets as input.</li> <li><code>07-applyClassifier</code> applies these classifiers to the "independent test cohort" - the set of 143 known tumour types, 27 additional NCCs and the 41 patients with CUP. These have been ran through the Nextflow pipeline separately to the 79 NCCs used to derive CUPiD, and have not been used to derive the classifier.</li> <li><code>08-UMAPs</code> generates some UMAPs on the array data.</li> </ul> <p>A subset of these output files are provided in <a href="../records/10684337">https://zenodo.org/uploads/10684337</a> , along with the code to reproduce the figures.</p>
Measurement of Circulating Cell-free DNA (cfDNA) for Diagnosis and Prognosis of Various Diseases and Evaluation of Tissue Damage
ClinicalTrials.gov study NCT05094960. IPD Sharing: NO. Countries: 0. Publications: 0.
Central Nervous System Efficacy of Capmatinib in NSCLC With Brain Metastases With cfDNA Positive MET Alterations
ClinicalTrials.gov study NCT05567055. IPD Sharing: NO. Countries: 0. Publications: 0.
A Single-arm, Prospective, Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for the Staging Diagnosis of Breast Cancer Lesions and Lymph Nodes
ClinicalTrials.gov study NCT07304934. IPD Sharing: NO. Countries: 0. Publications: 0.
cfDNA methylome profiling for detection and subtyping of Small Cell Lung Cancers
<p>Methyl-Binding Domain protein sequencing (MBD-Seq) was applied to samples derived from patients with small cell lung cancer (SCLC), as well as non-cancerous controls. This included circulating tumour cell derived explants (CDX) or patient derived Xenograft (PDX) preclinical models derived from 33 patients with SCLC, circulating cell-free DNA (cfDNA) from 78 patients with SCLC, cfDNA from 79 non-cancer controls and 13 non-cancerous lung tissue samples.</p> <p>The objects deposited here include R data files containing qseaSets from the R package qsea, which includes the read counts per sample per 300 base pair window across the genome, as well as information on copy number variation and metadata tables and the scripts used to generate and analyse them.<br> <br> Details of files:</p> <p><strong>DX_All_min50_max1000_w300_q10.rds</strong></p> <p> A qseaSet containing all 97 CDX/PDX samples (including replicates) and the 13 normal lung tissue samples. Note that min50_max1000_w300_q10 refers to including paired reads with between 50 and 1000 base pairs (bp), a window size of 300bp and a minimum MAPQ score of 10.</p> <p><strong>DX_merged.rds</strong></p> <p> A qseaSet with the biological replicates of each CDX merged together (formed from the above dataset).</p> <p><strong>cfDNA_All_min50_max1000_w300_q10.rds</strong></p> <p> A qseaSet containing 157 cfDNA samples used in the main body of the paper.</p> <p><strong>NCCsForTrain_All_min50_max1000_w300_q10.rds</strong></p> <p> A qseaSet containing the 38 NCC cfDNA samples used in the mixture sets for training the tumour/normal classifier. These samples are a subset of those in the cfDNA_All_min50_max1000_w300_q10.rds object.</p> <p><strong>ValidationSet_All_min50_max1000_w300_q10.rds</strong></p> <p> A qseaSet containing the 41 NCC cfDNA samples and 78 SCLC cfDNA samples used to validate the tumour/normal classifier. A subset of the cfDNA_All_min50_max1000_w300_q10.rds object.</p> <p><strong>cfDNApostTreatment_All_min50_max1000_w300_q10.rds</strong></p> <p> A qseaSet containing 7 cfDNA samples which were collected at a later, post-treatment, timepoint (mostly disease progression) from the same patients as in the main cfDNA object. Used only for Extended Figure 5, not any other part of the manuscript.</p> <p><strong>varyDNAinput_All_min50_max1000_w300_q10.rds</strong></p> <p> A qseaSet containing independent replicates of the cell line H1975, with different ng amounts of starting DNA (1-75ng). Used only for Figure 1B.</p> <p><strong>TNmixSets_combined_regionsFiltered_redo2.rds</strong></p> <p> A qseaSet containing the synthetic mixture sets generated by mixing either a CDX/PDX sample and a NCC sample or two NCC samples. Used to train the tumour/normal classifier. This object is restricted to only the windows used in the classifier for size regions, but the original mixtures are across the whole genome.</p> <p><strong>CDXarrayWide.csv</strong></p> <p> Pre-processed 450k Infinium Methylation array beta values for 8 CDX samples which were previously sequenced. Used for Supplementary Table 7 only.</p> <p><strong>ArrayCDXs_percent100.rds</strong></p> <p> A qseaSet containing the 8 CDX samples processed on 450k Infinium Methylation arrays (CDXarrayWide.csv) converted to estimated reads. </p> <p><strong>KeyTFsincYAP.csv</strong></p> <p> Variance stabilised transform (vst) values generated from RNASeq for the CDX/PDX samples, for the key genes involved in the subtype classifications.</p> <p><strong>infinium-methylationepic-v-1-0-b5-manifest-file.csv</strong></p> <p> A lookup file for the Infinium EPIC arrays, as downloaded from https://emea.support.illumina.com/downloads/infinium-methylationepic-v1-0-product-files.html.</p> <p><strong>SCLC_transcript_expression_adjusted_for_batch_effects.csv</strong></p> <p><strong>SCLC_methylation_beta_values_of_individual_probes_after_QC_and_filtering_out_SNVs.csv</strong></p> <p> Pre-processed transcript and methylation beta values from Infinium EPIC arrays for SCLC cell lines, as downloaded from sclccelllines.cancer.gov/sclc/downloads.xhtml (data timestamped as December 2019).</p> <p><strong>CellLine_100percent.rds</strong></p> <p> A qseaSet containing the SCLC cell lines converted to estimated MBD-Seq reads.</p> <p><strong>Cellline_mixtureSets.rds</strong></p> <p> A qseaSet containing the synthetic mixture sets generated by mixing converted cell lines with a NCC sample. Used to train the subtype classifier. </p> <p><strong>DilutionSeries_CDX13_min50_max1000_w300_q10.rds</strong></p> <p><strong>DilutionSeries_CDX29_min50_max1000_w300_q10.rds</strong></p> <p><strong>DilutionSeries_CDX32_min50_max1000_w300_q10.rds</strong></p> <p> Three qseaSets containing the results of an <em>in silico</em> dilution of a CDX (CDX13 = POU2F3, CDX29 = NEUROD1, CDX32 = ASCL1) with a single NCC, used to test limit of detection of the subtype classifier. Reads were mixed at the fastq level, prior to the NextFlow pipeline being used.</p> <p><strong>DilutionSeries_H446_Rep*_min50_max1000_w300_q10.rds</strong></p> <p> Eleven qseaSets containing the results of an <em>in silico</em> dilution of a SCLC cell line H446 with a single NCC (not used to build the classifier), used to determine limit of detection of the tumour/normal classifier. Reads were mixed at the fastq level with different random seeds, prior to a NextFlow pipeline being used.</p> <p><strong>poirier_oncogene.rda<br> PoirierEtAl_Oncogene2015_SuppTab1.csv<br> PoirierEtAl_Oncogene2015_SuppTab2.csv</strong></p> <p> Processed data object for the 2015 Oncogene paper Poirier<em> et al</em> (PMID:25746006), with 450k array data for SCLC tumours and normal lungs, along with two of the supplementary tables from that paper.</p> <p> </p>
Data from: "IL-6 and cfDNA monitoring throughout COVID-19 hospitalization are accurate markers of its outcomes".
<p>Data supporting the manuscript title: <strong>IL-6 and cfDNA monitoring throughout COVID-19 hospitalization are accurate markers of its outcomes</strong></p>
Genome-wide analysis of cfDNA fragmentation patterns and nucleosome positioning in dual enzyme-digested NIPT quality control materials (DENQCMs) and maternal plasma
GEO Series GSE124686. Homo sapiens. 2 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
Detecting small cell transformation in patients with advanced EGFR mutant lung adenocarcinoma through epigenomic cfDNA profiling
GEO Series GSE269746. Homo sapiens. 128 samples. Type: Methylation profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
phylogenetically informed cfdna
<p>phylogenetically informed cfdna</p>
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