scholarly journals BIOM-62 SENSITIVE DETECTION AND DISCRIMINATION OF INTRACRANIAL TUMORS BY BLOOD

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii15-ii15
Author(s):  
Farshad Nassiri ◽  
Ankur Chakravarthy ◽  
Shengrui Feng ◽  
Roxana Shen ◽  
Romina Nejad ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Brain Tumors ◽  
Model Performance ◽  
High Sensitivity ◽  
Circulating Tumor Dna ◽  
Low Grade ◽  
Learning Approaches ◽  
Intracranial Tumors ◽  
Cell Of Origin ◽  
Tumor Dna

Abstract BACKGROUND The diagnosis of intracranial tumors relies on tissue specimens obtained by invasive surgery. Non-invasive diagnostic approaches, particularly for patients with brain tumours, provide an opportunity to avoid surgery and mitigate unnecessary risk to patients. We reasoned that DNA methylation profiles of circulating tumor DNA in blood can be used as a clinically useful biomarker for patients with brain tumors, given the specificity of DNA methylation profiles for cell-of-origin. METHODS We generated methylation profiles on the plasma of 608 patients with cancer (219 intracranial, 388 extracranial) and 60 healthy controls using a cell-free methylated DNA immunoprecipitation combined with deep sequencing (cfMeDIP-seq) approach. Using machine-learning approaches we generated and evaluated models to distinguish brain tumors from extracranial cancers that may metastasize to the brain, as well as additional models to discriminate common brain tumors included in the differential diagnosis of solitary extra-axial and intra-axial tumors. RESULTS We observed high sensitivity and discriminative capacity for our models to distinguish gliomas from other cancerous and healthy patients (AUC=0.99, 95%CI 0.96–1), with similar performance in IDH mutant and wildtype gliomas as well as in lower- and high-grade gliomas. Excluding non-malignant contributors to plasma methylation did not change model performance (AUC=0.982, 95%CI 0.93–1). Models generated to discriminate intracranial tumors from each other also demonstrated high accuracy for common extra-axial tumors (AUCmeningioma=0.89, 95%CI 0.80–0.97; AUChemangiopericytoma=0.95, 95%CI 0.73–1) as well as intra-axial tumors ranging from low-grade indolent glial-neuronal tumors (AUC 0.93, 95%CI 0.80 – 1) to diffuse intra-axial gliomas with distinct molecular composition (AUCIDH-mutant glioma = 0.82, 95%CI 0.66 -0.98; AUCIDH-wildtype-glioma = 0.71, 95%CI 0.53 – 0.9). Plasma cfMeDIP-seq signals originated from corresponding tumor tissue DNA methylation signals (r=0.37, p< 2.2e-16). CONCLUSIONS These results demonstrate the potential for cfMeDIP-seq profiles to not only detect circulating tumor DNA, but to accurately discriminate common intracranial tumors that share cell-of-origin lineages.

scholarly journals Electroanalytical Biosensors for Circulating Tumor DNA Detection—A Brief Review

2021 ◽  
Author(s):  
Zhijia Peng ◽  
Xiaogang Lin ◽  
Weiqi Nian ◽  
Xiaodong Zheng ◽  
Jayne Wu
Keyword(s):  
Real Time ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
High Specificity ◽  
Circulating Tumor Dna ◽  
Minimal Invasive ◽  
Diagnosis And Treatment ◽  
Detection Technology ◽  
Tumor Dna

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.

Abstract 3154: Cell-free circulating tumor DNA methylation in high-grade serous ovarian cancer

2016 ◽  
Author(s):  
Kayleigh R. Davis ◽  
Kirsty J. Flower ◽  
Jane V. Borley ◽  
Charlotte SM Wilhelm-Benartzi ◽  
Robert Brown
Keyword(s):  
Ovarian Cancer ◽  
Dna Methylation ◽  
Circulating Tumor Dna ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Tumor Dna

Circulating tumor DNA analysis to reveal genomic profiles for epithelial ovarian cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 5543-5543
Author(s):  
Yang Xiang ◽  
Shan Zhu ◽  
Weiran Wang ◽  
Dongyan Cao ◽  
Xi-Run Wan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ovarian Carcinoma ◽  
Epithelial Ovarian Cancer ◽  
Circulating Tumor Dna ◽  
Stage Iii ◽  
Signal Pathways ◽  
Low Grade ◽  
Serous Ovarian Carcinoma ◽  
Ctdna Analysis ◽  
Tumor Dna

5543 Background: Circulating tumor DNA (ctDNA) analysis in epithelial ovarian cancer (EOC) was previously reported, however with limited samples or limited genes. Here, we reported an analysis of ctDNA in EOC cohort using targeted sequencing with a 1021-gene panel. Methods: Patients with EOC were enrolled, and treatment-naïve tumor tissues and blood samples were collected. We utilized a 1021-gene NGS panel in matched tissue DNA and ctDNA to identify somatic mutations with white blood cell DNA as a germline control. Results: Mutations were identified in all of the 65 tissues and in 53 (81.5%) ctDNA. The median ctDNA mutation allelic frequency was 2.5%, ranging from 0.1% to 36.2%. A median of 66.7% (12.5%-100.0%) of tissue derived mutations were observed in ctDNA. Besides, there were 91 ctDNA private mutations, including TP53 gene mutations. The most frequently mutated genes were TP53 (55.4%), PIK3CA (13.8%) and ARID1A (12.3%) in ctDNA analysis, which were consistent with tissue analysis (60.0%, 26.2% and 20.0% of tissues with TP53, PIK3CA and ARID1A mutations, respectively). Mutations of TP53 (37/42) in high-grade serous ovarian carcinoma (HGSOC), PIK3CA (10/11) and ARID1A (8/11) in ovarian clear cell carcinoma, BRAF (4/5) in low-grade serous ovarian carcinoma and PIK3CA (3/5), ARID1A (2/5) and PTEN (2/5) in endometrioid carcinoma were observed as the most commonly genetic aberrations in ctDNA in different sub-types of EOC, which located in different signal pathways and suggested different pathogenesis. In total, 90.5% (38/42) of HGSOC were ctDNA positive, comparing with 65.2% (15/23) of other EOC subtypes (p = 0.012). In addition, 56.5% (13/23) of stage I~II EOC were ctDNA positive, comparing with 94.7% (36/38) of stage III (p = 0.002). No association between ctDNA positivity and other clinic characteristics was observed, including pathological differentiation, CA125, lesion density (solid vs. cystic-solid and cystic). Multivariable analysis suggested FIGO stage III (p = 0.008) as an independent predictor of ctDNA detection. Conclusions: In summary, genomic characterization of EOC may offer insights into tumorigenesis and identify potential therapeutic targets in this disease.

Endonuclease-assisted hydrogel bead array for digital analysis of circulating tumor DNA methylation

2020 ◽  
Vol 304 ◽  
pp. 127381
Author(s):  
Yunlong Liu ◽  
Haiping Wu ◽  
Wancun Zhang ◽  
Hang Zhang ◽  
Honghong Chen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Circulating Tumor Dna ◽  
Digital Analysis ◽  
Hydrogel Bead ◽  
Tumor Dna

Methylated circulating tumor DNA (Met-DNA) as an independent prognostic factor in metastatic pancreatic adenocarcinoma (mPAC) patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 4136-4136 ◽  
Author(s):  
Daniel Pietrasz ◽  
Shufang Wang-Renault ◽  
Laetitia Dahan ◽  
Julien Taieb ◽  
Karine Le Malicot ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Prognostic Factor ◽  
Prognostic Value ◽  
Progression Free Survival ◽  
Circulating Tumor Dna ◽  
Independent Prognostic Factor ◽  
Oncologic Outcomes ◽  
Phase Ii Trials ◽  
Training Cohort ◽  
Tumor Dna

4136 Background: Circulating tumor DNA has emerged as prognostic biomarker in oncology. Many different genes can be mutated within a tumor, complicating procedures, even with highly sensitive next-generation sequencing (NGS). DNA methylation in promotor of specific genes is an early key epigenetic change during oncogenesis. Specific methylated genes could be a potential relevant cancer biomarker that may substitute for NGS panels. The aim of this study was to assess the prognostic value of Met-DNA in mPAC. Methods: Prognostic value of Met-DNA was assessed in a prospective cohort (PLAPAN) of mPAC (training cohort), correlated with NGS, then in two prospective independent validation cohorts from two randomized phase II trials (PRODIGE 35 and 37). Plasma samples were collected before chemotherapy on EDTA-coated tubes. Met-DNA was quantified using two specific markers of pancreatic DNA methylation by digital droplet PCR and correlated with prospectively registered patient (pts) characteristics and oncologic outcomes (progression free survival (PFS) and overall survival (OS)). Results: 330 patients (pts) were enrolled. 60% (n = 58) of the 96 pts of the training cohort had at least one Met-DNA marker. The correlation with NGS assessment was R = 0.93 (Pearson; p < 0.001). 59.5% (n = 100/168) and 59% (n = 39/66) of pts had detectable Met-DNA in the 2 validation cohorts. In the training cohort, Met-DNA was correlated with poor OS (HR = 1.82; 95%CI 1.07-2.42; p = 0.026). In validation cohorts, Met-DNA was a prognostic factor of PFS (HR = 1.62; 95%CI 1.17-2.25, p = 004) and OS (HR = 1.79; 95%CI 1.28-2.49, p < 0.001) in PRODIGE 35, as in PRODIGE 37: PFS HR = 1.79 (95%CI 1.07-2.99; p = 0.026) and OS HR = 2.08 (95%CI [1.18-3.68], p = 0.01), respectively. In multivariate analysis adjusted on gender, age, CA19-9 > 40UI.mL, treatment arm, number of metastatic sites and stratified on center, Met-DNA was independently associated with poor OS in both trials: HR = 1.81 (95%CI 1.10-2.98; p = 0.02) and HR = 3.62 (95%CI: 1.32-9.93; p = 0.01). Conclusions: This study demonstrates that Met-DNA is a strong independent prognostic factor in mPAC. These results argue for patient’s stratification on ctDNA status for further randomized trials. Clinical trial information: NCT02827201 and NCT02352337.

DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Antonios Papanicolau-Sengos ◽  
Kenneth Aldape
Keyword(s):  
Dna Methylation ◽  
Cancer Diagnosis ◽  
New Technology ◽  
Circulating Tumor Dna ◽  
Great Promise ◽  
Annual Review ◽  
Publication Date ◽  
Dna Methylation Profiling ◽  
Tumor Dna ◽  
Methylation Profiling

Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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