scholarly journals CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data

2018 ◽  
Vol 46 (15) ◽  
pp. e89-e89 ◽  
Author(s):  
Wenyuan Li ◽  
Qingjiao Li ◽  
Shuli Kang ◽  
Mary Same ◽  
Yonggang Zhou ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cancer Detection ◽  
Invasive Cancer ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Free Dna

scholarly journals DISMIR: a deep learning-based cancer-detection method by integrating DNA sequence and methylation information of individual cell-free DNA reads

2021 ◽  
Author(s):  
Jiaqi Li ◽  
Lei Wei ◽  
Xianglin Zhang ◽  
Wei Zhang ◽  
Haochen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Cancer Detection ◽  
High Throughput Sequencing ◽  
Detection Method ◽  
Low Cost ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Free Dna ◽  
Novel Approach

ABSTRACTDetecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel non-invasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise prediction with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as “switching region” to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state, and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultra-low sequencing depths. Analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites’ methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

scholarly journals CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Shuli Kang ◽  
Qingjiao Li ◽  
Quan Chen ◽  
Yonggang Zhou ◽  
Stacy Park ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Invasive Cancer ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Free Dna ◽  
Tissue Of Origin

scholarly journals Detection and characterization of lung cancer using cell-free DNA fragmentomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dimitrios Mathios ◽  
Jakob Sidenius Johansen ◽  
Stephen Cristiano ◽  
Jamie E. Medina ◽  
Jillian Phallen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Detection ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Free Dna ◽  
Genome Wide

AbstractNon-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.

scholarly journals Detection of glioma and prognostic subtypes by non-invasive circulating cell-free DNA methylation markers

2019 ◽  
Author(s):  
H Noushmehr ◽  
TS Sabedot ◽  
TM Malta ◽  
K Nelson ◽  
J Snyder ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cerebrospinal Fluid ◽  
Tumor Tissue ◽  
Standard Of Care ◽  
Pancreatic Islet Transplantation ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Free Dna ◽  
Genome Wide ◽  
Low Sensitivity

SUMMARYGenome-wide DNA methylation profiling has shown that epigenetic abnormalities are biologically important in glioma and can be used to classify these tumors into distinct prognostic groups. Thus far, DNA profiling has required surgically resected glioma tissue; however, gliomas release tumoral material into biofluids, such as blood and cerebrospinal fluid, providing an opportunity for a minimally invasive testing. While prior studies have shown that genetic and epigenetic markers can be detected in blood or cerebrospinal fluid (e.g., liquid biopsy [LB]), there has been low sensitivity for tumor-specific markers. We hypothesize that the low sensitivity is due to the targeted assay methods. Therefore, we profiled the genome-wide CpG methylation levels in DNA of tumor tissue and cell-free DNA in serum of glioma patients, to identify non-invasive epigenetic LB (eLB) markers in the serum that reflect the characteristics of the tumor tissue. From the epigenetic profiles of serum from patients diagnosed with glioma (N=15IDHmutant and N=7IDHwildtype) and with epilepsy (N=3), we defined glioma-specific andIDH-specific eLB signatures (Glioma-eLB andIDH-eLB, respectively). The epigenetic profiles of the matched tissue demonstrate that these eLB signatures reflected the signature of the tumor. Through cross-validation we show that Glioma-eLB can accurately predict a patient’s glioma from those with other neoplasias (N=6 Colon; N=14 Pituitary; N=3 Breast; N=4 Lung), non-neoplastic immunological conditions (N=22 sepsis; N=9 pancreatic islet transplantation), and from healthy individuals (sensitivity: 98%; specificity: 99%). Finally,IDH-eLB includes promoter methylated markers associated with genes known to be involved in glioma tumorigenesis (PVT1andCXCR6). The application of the non-invasive eLB signature discovered in this study has the potential to complement the standard of care for patients harboring glioma.

Epigenetic biomarkers for noninvasive detection of colorectal cancer.

2019 ◽  
Vol 37 (8_suppl) ◽  
pp. 45-45
Author(s):  
Dhruvajyoti Roy ◽  
David Taggart ◽  
Lianghong Zheng ◽  
Dan Liu ◽  
Gen Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Stage I ◽  
Cancer Stage ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Epigenetic Biomarkers ◽  
Free Dna ◽  
Diagnostic Potential ◽  
Target Sites

45 Background: Aberrant DNA hypermethylation is known to be a major mechanism for inactivation of cancer-associated genes, including tumor suppressor genes, in colorectal cancer (CRC) and in other human cancers. Cancer-specific DNA methylation patterns of cell-free DNA (cfDNA) isolated from blood samples is a non-invasive method to obtain representative epigenetic information from solid tumors. In the present study, we identified and validated colorectal cancer-specific methylation markers for diagnosis of the disease with high sensitivity and specificity. We also compared the relative amount of DNA methylation at these target sites in relation to colorectal cancer stage. Methods: For marker validation, a total of 154 samples drawn from 68 subjects diagnosed with colorectal cancer (Stage I to IV), 42 healthy donors, 14 subjects with benign colorectal diseases, and 30 subjects diagnosed with other cancer types (breast, liver and lung cancer: 10 cases each) were obtained for a randomized, blinded study. Cell-free DNA was then extracted from the samples, bisulfite converted, and DNA methylation was quantified by using the IvyGene Platform. Results: By quantifying DNA methylation at the target sites, colorectal cancer samples were differentiated from samples drawn from healthy subjects or subjects with benign disease with an overall sensitivity of 93% (95% CI: 86-99) and specificity of 100% (95% CI: 85-100). All stages (I to IV) of colorectal cancer were identified with sensitivities ranging from 67% to 100%. None of the 30 samples drawn from subjects diagnosed with breast, liver or lung cancers were incorrectly identified as a colorectal cancer by the assay, for a calculated analytical specificity of 100%. Conclusions: These results demonstrate the high diagnostic potential of cfDNA methylation markers isolated from blood for the detection of colorectal cancer. Taken together, these findings establish the utility of methylation biomarkers for the detection of colorectal cancers as early as Stage I. In addition, a quantitative analysis of cfDNA provides an opportunity for non-invasive detection and monitoring of disease.

scholarly journals Genome-wide discovery and validation of diagnostic DNA methylation-based biomarkers for hepatocellular cancer detection in circulating cell free DNA

Theranostics ◽  
2019 ◽  
Vol 9 (24) ◽  
pp. 7239-7250 ◽  
Author(s):  
Ryan A. Hlady ◽  
Xia Zhao ◽  
Xiaoyu Pan ◽  
Ju Dong Yang ◽  
Fowsiyo Ahmed ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cancer Detection ◽  
Hepatocellular Cancer ◽  
Cell Free Dna ◽  
Free Dna ◽  
Genome Wide ◽  
Circulating Cell Free Dna

scholarly journals Bioinformatics Analysis for Circulating Cell-Free DNA in Cancer

Cancers ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 805 ◽  
Author(s):  
Chiang-Ching Huang ◽  
Meijun Du ◽  
Liang Wang
Keyword(s):  
Bioinformatics Analysis ◽  
Rapid Development ◽  
Genetic Mutation ◽  
Molecular Characteristics ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Sequencing Technologies ◽  
Free Dna ◽  
Promising Source

Molecular analysis of cell-free DNA (cfDNA) that circulates in plasma and other body fluids represents a “liquid biopsy” approach for non-invasive cancer screening or monitoring. The rapid development of sequencing technologies has made cfDNA a promising source to study cancer development and progression. Specific genetic and epigenetic alterations have been found in plasma, serum, and urine cfDNA and could potentially be used as diagnostic or prognostic biomarkers in various cancer types. In this review, we will discuss the molecular characteristics of cancer cfDNA and major bioinformatics approaches involved in the analysis of cfDNA sequencing data for detecting genetic mutation, copy number alteration, methylation change, and nucleosome positioning variation. We highlight specific challenges in sensitivity to detect genetic aberrations and robustness of statistical analysis. Finally, we provide perspectives regarding the standard and continuing development of bioinformatics analysis to move this promising screening tool into clinical practice.

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