Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning

Liang, Ning; Li, Bingsi; Jia, Ziqi; Wang, Chenyang; Wu, Pancheng; Zheng, Tao; Wang, Yanyu; Qiu, Fujun; Wu, Yijun; Su, Jing; Xu, Jiayue; Xu, Feng; Chu, Huiling; Fang, Shuai; Yang, Xingyu; Wu, Chunyan; Cao, Zhili; Cao, Lei; Zhu, Bing; Liu, Huan; Li, Li; Huang, Cheng; Qin, Yingzhi; Cui, Yuming; Han‐Zhang, Han; Xiang, Jianxing; Líu, Hao; Guo, Xin; Li, Shanqing; Zhao, Heng; Zhang, Zhihong

doi:10.1038/s41551-021-00746-5

Cited by 100 publications

(84 citation statements)

References 57 publications

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“…As previously described [ 13 ], public data sets including TCGA and GEO databases (tumor = 4366, normal = 1008; HumanMethylation450K array) and in-house generated functional methylome (targeted methylation panel, 5.5 million CpG sites) sequencing data (tumor = 116, normal = 131) were used in the present study. The methylation data of TCGA datasets ( https://portal.gdc.cancer.gov/ ) was analyzed by limma (R package) along with the in-house data to select differentially methylated CpG sites (Benjamini–Hochberg-corrected FDR < 0.05).…”

Section: Methodsmentioning

confidence: 99%

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Discovery and validation of methylation signatures in circulating cell-free DNA for early detection of esophageal cancer: a case-control study

Qiao

Zhuang

Dong

et al. 2021

BMC Med

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Background Plasma cell-free DNA (cfDNA) methylation has shown promising results in the early detection of multiple cancers recently. Here, we conducted a study to investigate the performance of cfDNA methylation in the early detection of esophageal cancer (ESCA). Methods Specific methylation markers for ESCA were identified and optimized based on esophageal tumor and paired adjacent tissues (n = 24). Age-matched participants with ESCA (n = 85), benign esophageal diseases (n = 10), and healthy controls (n = 125) were randomized into the training and test sets to develop a classifier to differentiate ESCA from healthy controls and benign esophageal disease. The classifier was further validated in an independent plasma cohort of ESCA patients (n = 83) and healthy controls (n = 98). Results In total, 921 differentially methylated regions (DMRs) between tumor and adjacent tissues were identified. The early detection classifier based on those DMRs was first developed and tested in plasma samples, discriminating ESCA patients from benign and healthy controls with a sensitivity of 76.2% (60.5–87.9%) and a specificity of 94.1% (85.7–98.4%) in the test set. The performance of the classifier was consistent irrespective of sex, age, and pathological diagnosis (P > 0.05). In the independent plasma validation cohort, similar performance was observed with a sensitivity of 74.7% (64.0–83.6%) and a specificity of 95.9% (89.9–98.9%). Sensitivity for stage 0–II was 58.8% (44.2–72.4%). Conclusion We demonstrated that the cfDNA methylation patterns could distinguish ESCAs from healthy individuals and benign esophageal diseases with promising sensitivity and specificity. Further prospective evaluation of the classifier in the early detection of ESCAs in high-risk individuals is warranted.

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Section: Methodsmentioning

confidence: 99%

“…The score for each DMR was calculated according to both depth of coverage and the distance between the adjacent CpG sites as follows [ 13 ]: …”

Section: Methodsmentioning

confidence: 99%

Discovery and validation of methylation signatures in circulating cell-free DNA for early detection of esophageal cancer: a case-control study

Qiao

Zhuang

Dong

et al. 2021

BMC Med

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“…The sensitivity and specificity of NGS analysis depend upon the type of platform used, such as deep sequencing, Tam-seq, Safe-SEQs, CAPP-Seq, MCTA-Seq, FASTSeqS, etc [79] . A study by Liang et al, demonstrated that a combination of deep methylation sequencing with machine learning can provide better efficiency concerning cancer identification in comparison to ultradeep sequencing [80] .…”

Section: Computational Issues Related To Cfdna Methylation Detection Techniquesmentioning

confidence: 99%

Computational challenges in detection of cancer using cell-free DNA methylation

Sharma

Verma

Kumar

et al. 2022

Computational and Structural Biotechnology Journal

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Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

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“…Therefore, improvements in technologies are still needed before sensitive assays for early cancer diagnosis can be generally applied in clinical settings. A recent study showed that detection of ctDNA via deep methylation sequencing aided by machine learning identified nearly twice as many patients with can cer as those identified by ultradeep mutation sequencing analysis [43]. It also enabled the detection of tumor-derived signals at dilution factors as low as 1 in 10,000, showing its great potential for being used as an ultrasensitive method for early cancer diagnosis [43].…”

Section: Detection Of Ctdna Mutations For Early Cancer Diagnosismentioning

confidence: 99%

“…A recent study showed that detection of ctDNA via deep methylation sequencing aided by machine learning identified nearly twice as many patients with can cer as those identified by ultradeep mutation sequencing analysis [43]. It also enabled the detection of tumor-derived signals at dilution factors as low as 1 in 10,000, showing its great potential for being used as an ultrasensitive method for early cancer diagnosis [43]. Furthermore, other studies demonstrated that the tissue-of-origin of ctDNA can be tracked by profiling the methylation patterns, demonstrating its potential for early diagnosis of asymptomatic cancer patients [44,45].…”

Section: Detection Of Ctdna Mutations For Early Cancer Diagnosismentioning

confidence: 99%

Liquid Biopsy, ctDNA Diagnosis through NGS

Chen

Liu

Zheng

et al. 2021

Life

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Liquid biopsy with circulating tumor DNA (ctDNA) profiling by next-generation sequencing holds great promise to revolutionize clinical oncology. It relies on the basis that ctDNA represents the real-time status of the tumor genome which contains information of genetic alterations. Compared to tissue biopsy, liquid biopsy possesses great advantages such as a less demanding procedure, minimal invasion, ease of frequent sampling, and less sampling bias. Next-generation sequencing (NGS) methods have come to a point that both the cost and performance are suitable for clinical diagnosis. Thus, profiling ctDNA by NGS technologies is becoming more and more popular since it can be applied in the whole process of cancer diagnosis and management. Further developments of liquid biopsy ctDNA testing will be beneficial for cancer patients, paving the way for precision medicine. In conclusion, profiling ctDNA with NGS for cancer diagnosis is both biologically sound and technically convenient.

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Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning

Cited by 100 publications

References 57 publications

Discovery and validation of methylation signatures in circulating cell-free DNA for early detection of esophageal cancer: a case-control study

Discovery and validation of methylation signatures in circulating cell-free DNA for early detection of esophageal cancer: a case-control study

Computational challenges in detection of cancer using cell-free DNA methylation

Liquid Biopsy, ctDNA Diagnosis through NGS

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