scholarly journals Deoxyribonucleic Acid 5-Hydroxymethylation in Cell-Free Deoxyribonucleic Acid, a Novel Cancer Biomarker in the Era of Precision Medicine

Author(s):  
Ling Xu ◽  
Yixin Zhou ◽  
Lijie Chen ◽  
Abdul Saad Bissessur ◽  
Jida Chen ◽  
...  

Aberrant methylation has been regarded as a hallmark of cancer. 5-hydroxymethylcytosine (5hmC) is recently identified as the ten-eleven translocase (ten-eleven translocase)-mediated oxidized form of 5-methylcytosine, which plays a substantial role in DNA demethylation. Cell-free DNA has been introduced as a promising tool in the liquid biopsy of cancer. There are increasing evidence indicating that 5hmC in cell-free DNA play an active role during carcinogenesis. However, it remains unclear whether 5hmC could surpass classical markers in cancer detection, treatment, and prognosis. Here, we systematically reviewed the recent advances in the clinic and basic research of DNA 5-hydroxymethylation in cancer, especially in cell-free DNA. We further discuss the mechanisms underlying aberrant 5hmC patterns and carcinogenesis. Synergistically, 5-hydroxymethylation may act as a promising biomarker, unleashing great potential in early cancer detection, prognosis, and therapeutic strategies in precision oncology.

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 306
Author(s):  
Ryan Tsz-Hei Tse ◽  
Hongda Zhao ◽  
Christine Yim-Ping Wong ◽  
Carol Ka-Lo Cheng ◽  
Angel Wing-Yan Kong ◽  
...  

Urinary bladder cancer is a common urological cancer. Although flexible cystoscopy is widely employed in bladder cancer detection, it is expensive, invasive, and uncomfortable to the patients. Recently, urinary cell-free DNA (ucfDNA) isolated from urine supernatant has been shown to have great potential in bladder cancer detection and surveillance. Molecular features, such as integrity and concentration of ucfDNA, have been shown to be useful for differentiating bladder cancer patients from healthy controls. Besides, bladder cancer also exhibits unique genetic features that can be identified from sequencing and expression of ucfDNA. Apart from bladder cancer detection, ucfDNA is also useful for molecular classification. For example, ucfDNA exhibits significant differences, both molecularly and genetically, in non-muscle-invasive and muscle-invasive bladder cancers. There is no doubt that ucfDNA is a very promising tool for future applications in the field of bladder cancer.


Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5611
Author(s):  
Jinyong Huang ◽  
Alex C. Soupir ◽  
Brian D. Schlick ◽  
Mingxiang Teng ◽  
Ibrahim H. Sahin ◽  
...  

Cell-free DNA (cfDNA) methylation has emerged as a promising biomarker for early cancer detection, tumor type classification, and treatment response monitoring. Enrichment-based cfDNA methylation profiling methods such as cfMeDIP-seq have shown high accuracy in the classification of multiple cancer types. We have previously optimized another enrichment-based approach for ultra-low input cfDNA methylome profiling, termed cfMBD-seq. We reported that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of high-CpG-density regions, such as CpG islands. However, the clinical feasibility of cfMBD-seq is unknown. In this study, we applied cfMBD-seq to profiling the cfDNA methylome using plasma samples from cancer patients and non-cancer controls. We identified 1759, 1783, and 1548 differentially hypermethylated CpG islands (DMCGIs) in lung, colorectal, and pancreatic cancer patients, respectively. Interestingly, the vast majority of DMCGIs were overlapped with aberrant methylation changes in corresponding tumor tissues, indicating that DMCGIs detected by cfMBD-seq were mainly driven by tumor-specific DNA methylation patterns. From the overlapping DMCGIs, we carried out machine learning analyses and identified a set of discriminating methylation signatures that had robust performance in cancer detection and classification. Overall, our study demonstrates that cfMBD-seq is a powerful tool for sensitive detection of tumor-derived epigenomic signals in cfDNA.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vida Ungerer ◽  
Abel J. Bronkhorst ◽  
Priscilla Van den Ackerveken ◽  
Marielle Herzog ◽  
Stefan Holdenrieder

AbstractRecent advances in basic research have unveiled several strategies for improving the sensitivity and specificity of cell-free DNA (cfDNA) based assays, which is a prerequisite for broadening its clinical use. Included among these strategies is leveraging knowledge of both the biogenesis and physico-chemical properties of cfDNA towards the identification of better disease-defining features and optimization of methods. While good progress has been made on this front, much of cfDNA biology remains uncharted. Here, we correlated serial measurements of cfDNA size, concentration and nucleosome histone modifications with various cellular parameters, including cell growth rate, viability, apoptosis, necrosis, and cell cycle phase in three different cell lines. Collectively, the picture emerged that temporal changes in cfDNA levels are rather irregular and not the result of constitutive release from live cells. Instead, changes in cfDNA levels correlated with intermittent cell death events, wherein apoptosis contributed more to cfDNA release in non-cancer cells and necrosis more in cancer cells. Interestingly, the presence of a ~ 3 kbp cfDNA population, which is often deemed to originate from accidental cell lysis or active release, was found to originate from necrosis. High-resolution analysis of this cfDNA population revealed an underlying DNA laddering pattern consisting of several oligo-nucleosomes, identical to those generated by apoptosis. This suggests that necrosis may contribute significantly to the pool of mono-nucleosomal cfDNA fragments that are generally interrogated for cancer mutational profiling. Furthermore, since active steps are often taken to exclude longer oligo-nucleosomes from clinical biospecimens and subsequent assays this raises the question of whether important pathological information is lost.


Author(s):  
Saifur Rahaman ◽  
Xiangtao Li ◽  
Jun Yu ◽  
Ka-Chun Wong

Abstract Motivation The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge. Methods A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from the blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques. Results Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK published in Science, 2018) for cancer detection. The results reveal that our proposed method (i.e., CancerEMC) can achieve the best performance result for both binary cancer classification with 99.1748% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.1214% accuracy (AUC = 0.938). For addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.4966% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data. Availability https://github.com/saifurcubd/Cancer-Detection


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3043-3043
Author(s):  
Grace Q. Zhao ◽  
Yun Bao ◽  
Heng Wang ◽  
Wanping Hu ◽  
John Coller ◽  
...  

3043 Background: Assessing the genomic and epigenomic changes on plasma cell-free DNA (cfDNA) using next-generation sequencing (NGS) has become increasingly important for cancer detection and treatment selection guidance. However, two major hurdles of existing targeted NGS methods make them impractical for the clinical setting. First, there is no comprehensive, end to end, kit solution available for targeted methylation sequencing (TMS), let alone one that analyzes both mutation and methylation information in one assay. Second, the low yield of cfDNA from clinical blood samples presents a major challenge for conducting multi-omic analysis. Thus, an assay that is capable of both genomic and epigenomic analysis would be advantageous for clinical research and future diagnostic assays. Methods: Here, we report the performance of Point-n-SeqTM dual analysis, a kit solution that can provide in-depth DNA analysis with highly flexible and customizable focused panels to enable both genomic and epigenomic analysis without sample splitting. With custom panels of tens to thousands of markers designed with > 99% first-pass success rate, we conducted both performance validation and multi-center, multi-operator, reproducibility studies. Using spike-in titration of cancer cell-line gDNA with known mutation and methylation profiles, Point-n-Seq assay achieved a reliable detection level down to 0.003% of tumor DNA with a linear relationship between the measured and expected fractions. Benchmarked with conventional targeted sequencing and methylation sequencing, Point-n-Seq solution also demonstrated improved performance, speed and shortened hands-on time. Results: In a pilot clinical study, a colorectal cancer (CRC) TMS panel covering 560 methylation markers and a mutation panel with > 350 hotspot mutations in 22 genes were used in the dual assay. Using 1ml of plasma from late-stage CRC patients, cancer-specific methylation signals were detected in all samples tested, and oncogenic mutations. In an early-stage cohort (33 stage I/II CRC patient ), comparison of the analysis between tumor-informed, personalized-mutation panels (̃100 private SNVs) for each patient and the tumor-independent CRC methylation panels were conducted. The initial results showed that tumor-independent TMS assay achieved a comparable detection compared to the personalized tumor-informed approach. Moreover, cfDNA size information (fragmentome) is also integrated into the analysis of the same Point-n-Seq workflow to improve the assay sensitivity. Conclusions: Point-n-Seq dual analysis is poised to advance both research and clinical applications of early cancer detection, minimal residual disease (MRD), and monitoring.


Author(s):  
Oscar D. Pons-Belda ◽  
Amaia Fernandez-Uriarte ◽  
Annie Ren ◽  
Eleftherios P. Diamandis

2021 ◽  
Author(s):  
Alan H. Bryce ◽  
Minetta C. Liu ◽  
Michael V. Seiden ◽  
David D. Thiel ◽  
Donald Richards ◽  
...  

2021 ◽  
Author(s):  
Jiaqi Li ◽  
Lei Wei ◽  
Xianglin Zhang ◽  
Wei Zhang ◽  
Haochen Wang ◽  
...  

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.


2018 ◽  
Vol 25 (3) ◽  
pp. 915-923 ◽  
Author(s):  
Barbara Kinga Barták ◽  
Alexandra Kalmár ◽  
Orsolya Galamb ◽  
Barnabás Wichmann ◽  
Zsófia Brigitta Nagy ◽  
...  

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