Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)
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ABSTRACT:
SUBMITTER: Cui P
PROVIDER: S-EPMC10898085 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:MotivationMicrosatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach.ResultsWe developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines.AvailabilityMSIsensor-ct is freely available at https://github.com/niu-lab/MSIsensor-ct.Supplementary informationSupplementary data are available at Briefings in Bioinformatics online.
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Project description:This study aimed to evaluate the cost-effective and genome-wide cell-free reduced representation bisulfite sequencing (cfRRBS) method combined with computational deconvolution for effective disease monitoring in patients with esophageal adenocarcinoma (EAC). cfDNA methylation profiling with cfRRBS was performed on 162 blood plasma samples from 33 EAC cancer patients and 28 blood plasma samples from 20 healthy donors. In addition, for reproducibility testing purposes of the method, 9 plasma samples were re-prepped (library was re-made) and re-sequenced once (n=9) or twice (n=1). As a reference for the data deconvolution cfRRBS was performed on 7 EAC tumor tissue (FFPE) samples.
2024-12-04 | E-MTAB-14370 | biostudies-arrayexpress
Project description:BackgroundUpper gastrointestinal cancer (UGC) is an important cause of cancer death in China, with low five-year survival rates due to the majority of UGC patients being diagnosed at an advanced stage. Therefore, there is an urgent need to develop cost-effective, reliable and non-invasive methods for the early detection of UGC.MethodsA novel plasma-based methylation panel combining simultaneous detection of three methylated biomarkers (ELMO1, ZNF582 and TFPI2) and an internal control gene were developed and used to examine plasma samples from 186 UGC patients and 190 control subjects.ResultsThe results indicated excellent PCR amplification efficiency and reproducibility of ELMO1, ZNF582 and TFPI2 in the range of 10-100,000 copies per PCR reaction of fully methylated genomic DNA. The methylation levels of ELMO1, ZNF582 and TFPI2 were significantly higher in UGC samples than those in control subjects. The sensitivities of ELMO1, ZNF582 and TFPI2 alone for UGC detection were 32.3%, 61.3% and 30.6%, respectively; when three markers were combined, the sensitivity was improved to 71.0%, with a specificity of 90.0%, and the area under the curve (AUC) was 0.870 (95% CI: 0.832-0.902).ConclusionMethylated ELMO1, ZNF582 and TFPI2 were specific for UGC and the three-methylated gene panel provided an alternative non-invasive choice for UGC early detection.
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Project description:Epithelial ovarian cancer (EOC) is the deadliest women's cancer and has a poor prognosis. Early detection is the key for improving survival (a 5-year survival rate in stage I/II is over 70% compared to that of 25% in stage III/IV) and can be achieved through methylation markers from circulating cell-free DNA (cfDNA) using a liquid biopsy. In this study, we first identify top 500 EOC markers differentiating EOC from healthy female controls from 3.3 million methylome-wide CpG sites and validated them in 1,800 independent cfDNA samples. We then utilize a pretrained AI transformer system called MethylBERT to develop an EOC diagnostic model which achieves 80% sensitivity and 95% specificity in early-stage EOC diagnosis. We next develop a simple digital droplet PCR (ddPCR) assay which archives good performance, facilitating early EOC detection.
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Project description:IntroductionThe exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.MethodsThe Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7416 non-Hispanic white (NHW) participants (3150 cognitively normal older adults (CN), 4266 AD).Results30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog.DiscussionThe Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies.HighlightsGrowing genomic datasets require advanced tools to identify genetic loci in sequencing.Deep-Block, a novel AI framework, was used to process large-scale ADSP WGS data.Deep-Block identified both known and novel AD-associated genetic loci.rs429358 (APOE) was key; rs11556505 (TOMM40), rs34342646 (NECTIN2) were significant.The AI framework uses biological knowledge to enhance detection of Alzheimer's loci.
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Project description:Epigenetic alterations by promoter DNA hypermethylation and gene silencing in cancer have been reported over the past few decades. DNA hypermethylation has great potential to serve as a screening marker, a prognostic marker, and a therapeutic surveillance marker in cancer clinics. Some bodily fluids, such as stool or urine, were obtainable without any invasion to the body. Thus, such bodily fluids were suitable samples for high throughput cancer surveillance. Analyzing the methylation status of bodily fluids around the cancer tissue may, additionally, lead to the early detection of cancer, because several genes in cancer tissues are reported to be cancer-specifically hypermethylated. Recently, several studies that analyzed the methylation status of DNA in bodily fluids were conducted, and some of the results have potential for future development and further clinical use. In fact, a stool DNA test was approved by the U.S. Food and Drug Administration (FDA) for the screening of colorectal cancer. Another promising methylation marker has been identified in various bodily fluids for several cancers. We reviewed studies that analyzed DNA methylation in bodily fluids as a less-invasive cancer screening.
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