The data access committee for genome-wide cell-free DNA fragmentation in patients with cancer
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PROVIDER: EGAC00001001180 | EGA |
REPOSITORIES: EGA
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Project description:Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer.
| S-EPMC6774252 | biostudies-literature
Project description:Genome-wide cell-free DNA fragmentation in patients with cancer
| PRJNA543357 | ENA
Project description:Genome-wide cell-free DNA fragmentation in patients with cancer
| PRJNA543358 | ENA
Project description:Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer. However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.
| EGAS00001003611 | EGA
Project description:BackgroundGenome-wide chromosomal instability, instead of specific somatic mutations or copy-number alterations in selected genes, is a significant property of cancer and may suggest a new strategy for treatment. Here we utilized cell-free DNA (cfDNA) sequencing to display the whole picture of chromosomal instability in patients with metastatic breast cancer (MBC), and evaluate its predictive value for patient survival.MethodsThe clinical data of 65 patients who had frozen plasma and planned to change the therapeutic regimen were retrospectively enrolled. Low-coverage whole-genome sequencing of cfDNA was performed to generate the chromosomal instability represented by chromosomal instability (CIN) score.ResultsTumors with diverse status of hormone receptor and HER2 represented diverse chromosomal instability across the whole genome. According to the receiver operating characteristic curve and the statistical distribution, CIN score exceed 3881 was defined as "High". 32 (53.3%) patients with high CIN score had similar clinicopathologic characteristics compared with low CIN score patients. The median overall survival of patients with high CIN score was 21.2 months (95% CI 14.1-28.3), which was significantly inferior to those with low CIN score (not reached, P = 0.006). Regardless of various treatment regimens, the median progression free survival in patients with high CIN score was 7.3 months, which was significantly worse than those in the low CIN score population (11.0 months, P = 0.034). Multivariate analysis revealed that CIN score was an independent prognostic factor, with hazard ratio of 3.563 (P = 0.005).ConclusionsTo our knowledge, this is the first study illustrating the prognostic value of chromosomal instability derived from cfDNA in MBC.
| S-EPMC7503795 | biostudies-literature
Project description:As a noninvasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma has raised an increasing interest due to diagnostic applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. Compared with the healthy individuals, 330 differentially methylated regions (DMRs) at gene promoters were identified in lung cancer patients with 33 hypermethylated and 297 hypomethylated regions, respectively. Moreover, these hypermethylated genes were validated with the publicly available DNA methylation data, yielding a set of ten significant differentially methylated genes in lung cancer, including B3GAT2, BCAR1, HLF, HOPX, HOXD11, MIR1203, MYL9, SLC9A3R2, SYT5, and VTRNA1-3. Our study demonstrated MeDIP-seq could be effectively used for cfDNA methylation profiling and identified a set of potential biomarker genes with clinical application for lung cancer.
| S-EPMC6379867 | biostudies-literature
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
Project description:We carried out a genome-wide cfDNA methylation profiling study of pancreatic ductal adenocarcinoma (PDAC) patients by Methylated DNA Immunoprecipitation coupled with high-throughput sequencing (MeDIP-seq). Compared with healthy individuals, 775 differentially methylated regions (DMRs) located in promoter regions were identified in PDAC patients with 761 hypermethylated and 14 hypomethylated regions; meanwhile, 761 DMRs in CpG islands (CGIs) were identified in PDAC patients with 734 hypermethylated and 27 hypomethylated regions (p-value < 35 0.0001). 143 hypermethylated DMRs were further selected which were located in promoter regions and completely overlapped with CGIs. A total of 8 probes from 8 genes were found to fairly distinguish PDAC patients from the healthy individuals, including TRIM73, FAM150A, EPB41L3, SIX3, MIR663, MAPT, LOC100128977 and LOC100130148.
2020-10-29 | E-MTAB-9678 | biostudies-arrayexpress
Project description:The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p < 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at >1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater
clinical implementation.
| EGAS00001005747 | EGA
Project description:The structure, fragmentation patterns and terminal sequences of cell-free DNA (cfDNA) are altered by nucleases and biological mechanisms in the blood of cancer patients.
The cfDNA fragment-end composition recovered from low coverage WGS (<1 fold coverage) using a bespoke software (FrEIA) is aberrant in the plasma from cancer patient (n = 418, 655 samples) compared to controls (n = 117). As a standalone test FrEIA allows detection down to ~0.2% tumor fraction in vitro and in silico at 95% specificity, leading to a sensitivity of ~71% for detecting lung cancer (14/22 stage I-II, 27/38 stage III, 92/127 stage IV) and ~68% for detecting esophageal adenocarcinoma (26/44 stage II, 46/62 stage III).
Additional cfDNA biological patterns can be combined with FrEIA increasing the diagnostic potential of low coverage WGS at minimal cost (mean AUROC = 0.96). Integrating multiple cfDNA biological signal augments the diagnostic performance of liquid biopsy.
| EGAS00001006142 | EGA