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  • Review
  • Open Access

Towards precision medicine: advances in 5-hydroxymethylcytosine cancer biomarker discovery in liquid biopsy

Cancer Communications201939:12

https://doi.org/10.1186/s40880-019-0356-x

  • Received: 18 September 2018
  • Accepted: 19 March 2019
  • Published:

Abstract

Robust and clinically convenient biomarkers for cancer diagnosis, early detection, and prognosis have great potential to improve patient survival and are the key to precision medicine. The advent of next-generation sequencing technologies enables a more sensitive and comprehensive profiling of genetic and epigenetic information in tumor-derived materials. Researchers are now able to monitor the dynamics of tumorigenesis in new dimensions, such as using circulating cell-free DNA (cfDNA) and tumor DNA (ctDNA). Mutation-based assays in liquid biopsy cannot always provide consistent results across studies due partly to intra- and inter-tumoral heterogeneity as well as technical limitations. In contrast, epigenetic analysis of patient-derived cfDNA is a promising alternative, especially for early detection and disease surveillance, because epigenetic modifications are tissue-specific and reflect the dynamic process of cancer progression. Therefore, cfDNA-based epigenetic assays are emerging to be a highly sensitive, minimally invasive tool for cancer diagnosis and prognosis with great potential in future precise care of cancer patients. The major obstacle for applying epigenetic analysis of cfDNA, however, has been the lack of enabling techniques with high sensitivity and technical robustness. In this review, we summarized the advances in epigenome-wide profiling of 5-hydroxymethylcytosine (5hmC) in cfDNA, focusing on the detection approaches and potential role as biomarkers in different cancer types.

Keywords

  • Liquid biopsy
  • Cell-free DNA
  • Epigenetics
  • Cancer biomarker
  • 5-Hydroxymethylcytosine

Background

Robust and clinically convenient cancer biomarkers are of great importance for the successful delivery of precision medicine and better clinical care for cancer patients. Firstly, the latency for cancers is long, and cancer patients usually exhibit symptoms at advanced stages when curative treatment may no longer be available. Therefore, screening and early detection of cancers at asymptomatic and/or curable stages are especially critical in improving patients’ survival and quality of life [13], as well as reducing the burden of healthcare system. Secondly, the dynamics of tumorigenesis is the main hurdle to effective treatments in cancer patients [4] and remains a grand challenge for cancer precision medicine. Tumor heterogeneity and clonal evolution are the two major consequences of the dynamics of tumorigenesis, which can simultaneously drive tumor evolution [5], posing challenges in the selection of anticancer drugs as well as the optimal doses of these drugs. Thirdly, patients with metastatic cancers of unknown primary sites usually have poor prognosis and dismal survival rate because site-specific targeted therapies are not effective [6, 7]. To reduce cancer mortality and improve the overall quality of healthcare outcomes and well-being of the patients, there is an urgent need to develop minimally-invasive biomarkers that are sensitive and specific enough for clinical applications such as early cancer detection, longitudinal surveillance of dynamic tumor progression under drug treatment, and the selection of targeted therapies for cancers of unknown primary sites. In this review, we overview the current states of molecular biomarkers in cancer diagnosis and prognosis, as well as discuss the potential benefits and limitations of the new approaches for detecting a novel class of epigenetic biomarkers, 5-hydroxymethylcytosine (5hmC) biomarkers, in cell-free DNA (cfDNA). Furthermore, we provide an update and current perspectives for 5hmC alterations in patients with different cancer types and discuss their potential role as cancer biomarkers. Finally, we point out the improvements and future work required to make these biomarkers effective in the clinic.

Current strategies of cancer biomarker discovery

Tissue biopsy versus liquid biopsy

Tumor tissues are the gold-standard sources for identifying cancer-specific biomarkers. However, tissue biopsy has some intrinsic limitations as it can be invasive and clinically risky. Tissue biopsy usually requires surgical resection to obtain tumor tissues, and surgery entails risks such as bleeding and infection [8, 9]. In addition, information acquired from a single-region tissue biopsy only provides a spatially limited snap-shot of a tumor and might fail to reflect the intra-tumor heterogeneity [4, 10]. This could lead to an inaccurate diagnosis of cancer type and stage or unreliable prognostic assessment of relapse risk and survival [11]. Multi-region tissue biopsy offers an alternative way to capture the intra-tumor heterogeneity [10, 12]; however, its clinical application is limited due to the volume and accessibility of tumor tissues [9]. Furthermore, drug treatment confers selective pressure on tumor cells, resulting in adaptive clonal evolution and possibly subsequent drug resistance [4, 13, 14]. While longitudinal profiling of tumor heterogeneity provides valuable information in treatment response evaluation and therapy optimization, this again is of limited clinical feasibility given the difficulty of obtaining tumor tissues for multiple time points [15].

In contrast, liquid biopsy is emerging as a minimally invasive tool with great potential in cancer management. It uses circulating materials such as cfDNA, circulating tumor cells (CTCs), and exosomes to detect molecular alterations that are indicative of cancer progression. cfDNA is fragmented cellular DNA released into the bloodstream by cells undergoing apoptosis and necrosis, and possibly through active secretion [16, 17]. In healthy individuals, cfDNA is primarily derived from apoptotic hematopoietic cells [18], whereas, in cancer patients, cfDNA can be of tumor tissue and tumor microenvironment origin, reflecting the genetic and epigenetic alterations of tumor tissues and their corresponding microenvironment [19]. As a result, cfDNA has been extensively studied recently, especially in the field of early cancer detection, cancer staging and subtyping, and disease surveillance. The origins of biomarkers for liquid biopsy are summarized in Fig. 1.
Fig. 1
Fig. 1

The origins of biomarkers for liquid biopsy. Molecular alterations of circulating materials, such as cell-free DNA, circulating tumor cells, exosomes, and circulating nucleosomes and RNA, can be used as biomarkers for cancer diagnosis and prognosis. Genetic biomarkers can be identified via mutational profiling of nucleic acids extracted from circulating tumor cells. Epigenetic biomarkers can be obtained via methylation profiling and nucleosome foot printing of cell-free DNA, circulating tumor cells, and circulating nucleosomes and RNA

Genetic biomarkers versus epigenetic biomarkers

Clinical trials based on mutational profiling of circulating tumor DNA (ctDNA; cfDNA of tumor origin) are increasing. Several studies have demonstrated the feasibility of applying ctDNA analysis to capture the heterogeneity of cancer evolution, therefore, making ctDNA analysis a tool for cancer surveillance and care [20]. For example, longitudinal sampling of ctDNA levels not only indicated the presence of cancer but also had good association with therapy response and disease progression in pancreatic cancer [21]. Additionally, driver mutations identified in ctDNA samples showed a high concordance with that from matched tumor samples in prostate cancer [19] and multiple myeloma [22]. In metastatic breast cancer, estrogen receptor 1 (ESR1) mutations are responsible for the resistance to aromatase inhibitors [23]. The mutational profile of ESR1 in ctDNA was associated with that in tumor tissues and therefore reflected the clonal evolution of breast cancer under the treatment of aromatase inhibitors [23]. This evidence indicates that ESR1 mutations in ctDNA are potential biomarkers in treatment monitoring. Despite several studies have demonstrated that the mutational signatures in ctDNA were consistent with those in corresponding tumor tissues, there is currently insufficient evidence of clinical validity and utility for the majority of ctDNA-based mutational assays in advanced cancer, and there is no evidence supports that they can be applied to early cancer detection [20, 24, 25].

In addition, merely relying on the identification of tumor-derived driver mutations in ctDNA cannot capture the whole complexity of tumor biology [26]. Unlike mutations, the reversible epigenetic modifications are more plastic and can reflect the changes of tumor microenvironment and tissue of origin [27, 28]. Epigenetic modifications such as DNA methylation may represent a novel and promising analytical tool for biomarker discovery with broad potential applications in risk assessment, early cancer detection, prognosis, and prediction of response to therapy [2931]. To date, DNA methylation-based assay, Epi proColon, has been approved by the US Food and Drug Administration (USFDA) for colon cancer detection [32]. In the early stages of carcinogenesis, many epigenetic changes have occurred in normal tissues before somatic mutations and histopathological changes can be detected [33]. Therefore, epigenetic analysis of cfDNA combined with mutation-based analysis may contribute to a better understanding of the interplay across molecular alterations in the cancer genome, epigenome, and tumor microenvironment in tumor heterogeneity and clonal evolution [27, 28, 30, 3436].

Despite the promises, the applications of these genetic or epigenetic biomarkers in population screening and very early stage cancer detection can still be challenging. Like traditional biomarkers, they also suffer from the same issues of low sensitivity and specificity because of the limited amount of circulating materials and the noise in the detection [37].

Advances in epigenetic cancer biomarker discovery in liquid biopsy

The most extensively studied epigenetic feature for cancer biomarker discovery in cfDNA is DNA methylation, especially the 5-methylcytosine (5mC) modification at CpG dinucleotides [29, 35, 3841]. In hepatocellular carcinoma, 5mC biomarkers derived from ctDNA showed better diagnostic and prognostic values than currently used indicators (such as serum-based alpha-fetoprotein [AFP] and TNM staging) [35]. In addition, repetitive elements such as long intersperse nucleotide element 1 (LINE-1) and Alu are known proxies for global DNA methylation [42]. In diffuse large B cell lymphoma, LINE-1 methylation in cfDNA has been shown to be strongly associated with clinical outcomes, demonstrating its potential as a prognostic biomarker [43]. Another approach in 5mC biomarker discovery is to identify tissue-specific methylation haplotypes as biomarkers to estimate tumor burden and tissue-of-origin in cfDNA [40]. These multi-CpG haplotypes have been shown to outperform the traditional single-CpG methylation biomarker in cancer classification [40]. Recently, other epigenetic features such as 5-hydroxymethylcytosine (5hmC) and nucleosome positioning and occupancy on cfDNA have also been utilized to infer tissue of origin and cancer progression [27, 4447]. Although genome-wide nucleosome distribution of cfDNA provides valuable information in the deconvolution of pooled cfDNA to infer tissue of origin, its clinical application has not been extensively studied [27, 48]. In this review, we summarize the advances in the genome-wide profiling of 5hmC dynamics in cfDNA for cancer biomarker discovery based on the unique features and distinct biological functions of modified cytosines.

5-Hydroxymethylation

In the human genome, 5mC is the most abundant and well-known DNA methylation variant that plays an important role in the regulation of gene expression [49]. The 5mC-associated methylation patterns are usually tumor- and tissue-specific, reflecting the origin of the metastatic tumors and their altered epigenomes [36]. With the mediation of ten-eleven translocation (Tet) proteins, 5mC can be further oxidized to 5hmC, 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) [50]. Among them, 5hmC is the most abundant and stably oxidized product [51]. In contrast to about 8% of cytosine that is methylated in the human genome, only 0.5%–1% of cytosines are hydroxymethylated. The scarcity of 5hmC in the genome may pose challenges in distinguishing true signals from noise upon sequencing. However, it also has the potential to improve the statistical power in biomarker discovery because of the reduced multiple hypothesis burden [39, 5254]. Unlike the uniform distribution of 5mC outside of the promoter regions, satellites, and repeat DNA sequences [55], 5hmC has distinct distributions across different functional regions, and its abundance varies across different tissues and cell types [56, 57], with tissue type playing a dominant role in determining the distribution patterns of 5hmC [58]. 5hmC is enriched primarily in the distal regulatory regions, gene bodies of actively expressing genes and promoters, indicating its connection with active transcription [59]. Genome-wide analysis of 5mC has indicated the global hypomethylation pattern in tumor tissues, whereas depletion of 5hmC has also been associated with the hypermethylation of gene bodies in various cancers [58, 60, 61]. Significant enrichment of 5hmC is observed in both tissue-specific and cancer-specific differentially methylated regions as compared with that of 5mC [62]. Thus, genome-wide analysis of 5hmC dynamics can further refine our understanding of the relationship between cancer and methylome.

Enabling technologies for profiling 5hmc

Because 5hmC and 5mC dynamics can be informative of tumorigenesis, epigenome-wide analysis of cfDNA has also been conducted to identify minimally-invasive cfDNA-derived biomarkers for better cancer management [35, 45, 46, 6366]. An overview of the most common genome-wide 5hmC quantification methods are summarized in Table 1.
Table 1

Overview of genome-wide 5hmC quantification methods

Technique

Advantages

Disadvantages

Whole-genome TAB-Seq [70] (direct measurement of 5hmC)

Whole-genome

Single nucleotide resolution

Absolute methylation value

High sequencing depth required

Input > 100 ng DNA

Whole-genome oxBS-Seq [71, 74] (inference of 5hmC)

RRBS TAB-Seq [91] (direct measurement of 5hmC)

Single nucleotide resolution

Absolute methylation value

Targets CpG islands

Input > 500 ng DNA

RRBS oxBS-Seq [52] (inference of 5hmC)

~ 60 million PE reads/sample

Input > 500 ng DNA

Nano-hmC-Seal [53] (direct measurement of 5hmC)

Input 5 ng DNA

Excellent genomic coverage

High specificity for 5hmC

~ 30 million PE reads/sample

Lower resolution

5hmC, 5-hydroxymethlcytosine; TAB-Seq, Tet-assisted bisulfite sequencing; oxBS-Seq, oxidative bisulfite sequencing; PE, paired-end

Bisulfite-based whole-genome sequencing and reduced representation bisulfite sequencing (RRBS) are conventional methods for methylation profiling and have been applied in biomarker discovery with cfDNA [40, 67]. However, there are several limitations of applying bisulfite-based methods on cfDNA. First, traditional bisulfite-based methods cannot distinguish between 5mC and 5hmC, and thus are not capable of capturing the dynamics of these two distinct modification types [68, 69]. Second, although modified bisulfite-based methods such as Tet-assisted bisulfite sequencing (TAB-seq) [70] and oxidative bisulfite sequencing (oxBS-seq) [71] can detect and quantify 5hmC at nucleotide resolution, they require an amount of DNA input (> 100 ng) that is not feasible for cfDNA from blood samples (1–2 ng cfDNA from 2 to 3 mL of plasma). This makes its application in early cancer detection challenging [72]. Third, bisulfite-based methods require a high sequencing depth, which is costly and further limits their application in the clinical setting. Restriction enzyme-based methods like reduced representation 5-hydroxymethylcytosine profiling rely on the efficiency of enzymes. As a result, their detection capacity can be limited as inefficient digestion might result in the loss of information on certain 5hmC sites [73]. In contrast, enrichment-based methods such as hME-Seal [53] and the nano-hmC-Seal [59] rely on the selective chemical binding of 5hmC to enrich for 5hmC-containing DNA fragments. These fragments are then sequenced to obtain genome-wide information of 5hmC [53, 59]. Because the size of these chemically selected fragments is a major determinant of the resolution of 5hmC mapping [56], these enrichment-based methods can provide good coverage and high specificity despite low resolution [74]. Given that these enrichment-based approaches can achieve a balance between cost and detection capacity, they are likely to have great potential to be applied widely in the cfDNA-based liquid biopsy in cancer management for large cohorts in the clinical setting [75]. However, lack of a consensus computational framework to analyze these enrichment-based sequencing data may also hamper the interpretation of the results from the clinicians’ end. Although 5hmC-Seal technology is the state-of-the-art in genome-wide profiling of 5hmC dynamics in cfDNA, it relies on relative abundance to infer absolute modification levels and cannot provide single-base resolution 5hmC information. Development of statistical approaches to infer base resolution 5hmC modification levels from enrichment counts would provide more insights into the dynamics of 5hmC. In addition, many of the intrinsic features of 5hmC enrichment-based sequencing data must be taken into consideration to build this computational framework. GC content, copy number variation, strand-specific and asymmetric 5hmC distribution need to be statistically corrected before downstream analysis. Therefore, an integrative computational framework must be established for 5hmC-enrichment sequencing data to increase the sensitivity and specificity of 5hmC-derived biomarkers in cancer research and enable their easy clinical application in the future. The computational analysis of 5hmC enrichment-based sequencing data can be decomposed into the following components: (1) modeling (to model 5hmC enrichment signal taking into account factors such as enrichment bias, local density bias, and copy number variations); (2) quantification (to infer modification level from normalized regional count-based enrichment data [76]); (3) construction of cancer prediction models (to identify differentially modified sites or regions among different conditions [76]). Another approach is to construct a model to predict cancer status or stage based on the estimated proportions and the tissue-of-origin of tumor-derived cfDNA in the blood sample [39].

Advances in 5hmc-based cancer biomarker discovery

Several recent studies have demonstrated that 5hmC signatures in cfDNA are reliable and sensitive epigenetic markers that are indicative of types and stages of cancers [4446]. These cfDNA-derived 5hmC biomarkers are found to achieve higher detection sensitivity than classical biomarkers [45].

Colorectal cancer (CRC)

CRC is a commonly diagnosed cancer [1, 77]. Despite recent declines in CRC incidence in the United States and other developed countries, the incidence and mortality continue to increase in the rest of the world [1]. This reduction in CRC incidence is largely attributed to screening via colonoscopy and to cancer prevention efforts [1, 77]. However, the invasive nature of colonoscopy leads to poor patient compliance. Stool and blood DNA methylation assays based on candidate genes show great diagnostic and prognostic values, but the sensitivities and specificities of the assays vary and are usually inconsistent [78]. In a Chinese cohort study evaluating cfDNA-derived 5hmC analysis in 80 colorectal cancer patients and 90 healthy individuals [45], a total of 989 differentially methylated 5hmC loci in gene bodies were selected as biomarkers to train the machine learning algorithm for cancer classification. The classifier achieved 83% sensitivity and 94% specificity (area under curve [AUC] = 0.95) in the validating dataset (24 patients and 35 controls) and 88% sensitivity and 89% specificity (AUC = 0.94) in another independent validating dataset (32 patients and 37 controls) for cancer classification [45]. The discriminatory performance of these cfDNA-derived 5hmC biomarkers was not only comparable to that of 5hmC tissue biomarkers but also significantly outperformed the current USFDA approved blood-based methylation test Epi proColon [45, 79]. Epi proColon relies on the methylation status of the single gene septin 9 (SEPT9) to infer the presence of cancer and can only achieve a detection sensitivity of 0.48 [79]. To be noted, the sensitivity and specificity of the cancer classifier often vary with cancer stages. Because the majority of colorectal cancer patients in this study are at TNM stages III and IV, the performance of this classifier cannot be over-interpreted [45].

Gastric cancer (GC)

GC is a common digestive cancer with 26,240 new cases and 10,800 deaths estimated in 2018 in the United States [1]. Early-stage GC is asymptomatic and exhibits high genomic heterogeneity, making endoscopic or surgical biopsy-based molecular testing rather inaccurate and non-representative [80]. Recent studies suggested the potential role of cfDNA-based molecular profiling in future clinical applications such as diagnosis and targeted therapy selection. However, the epigenetic alterations on cfDNA remain understudied [8183]. A pilot study has explored the 5hmC alterations in cfDNA from GC patients as compared to that from healthy individuals in a Chinese cohort [45]. Patients with GC and controls were divided into discovery (7 patients and 18 controls) and validation groups (25 patients and 35 controls) [45]. In total, 1431 differentially methylated 5hmC loci in gene bodies were identified and trained on the cancer classifier, and the classifier achieved 92% sensitivity and 91% specificity (AUC = 0.93) in the validating dataset and 90% sensitivity and 97% specificity (AUC = 0.97) in another independent validating dataset [45]. Again, consistent with the study in CRC, these cfDNA-derived 5hmC biomarkers performed better than classical early diagnosis biomarkers, such as carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA19-9), and other epidemiological factors, such as smoking and alcohol [45]. In both CRC and GC, the classifiers derived from cfDNA are disease-, clinical stage-, and cancer type-specific, suggesting their potential values as diagnostic cancer biomarkers [45].

Esophageal cancer (EC)

EC is among the five leading causes of cancer-related death in male patients between age 40 and 59 [1]. Similar to GC, evidence for the effects of molecular biomarkers on diagnosis and treatment guidance are limited because of the challenges in detecting genomic alterations or clinical symptoms at early stages and in resolving the discrepancy of genomic profiling between primary tumors and metastatic lesions [81]. One study conducted genome-wide 5hmC profiling in 150 newly diagnosed EC patients and 177 healthy controls in China [46]. The classifier achieved 93.75% sensitivity and 85.71% specificity with an average AUC of 0.947. Consistent with the observations in CRC and GC, the 5hmC signatures on cfDNA were indicative of clinical stages of EC. The probability of predicting cancer based on the 5hmC classifier increased with the progression of cancer stage. In addition, EC patients with lymph node metastases were predicted to have significantly higher cancer probability as compared with patients without lymph node metastases [46].

Lung cancer (LC)

LC is the leading cause of cancer-related death in both genders with 83,550 estimated deaths in males and 70,500 estimated deaths in females in 2018 [1]. Unlike the steady increase of 5-year survival rate in most cancer types, the 5-year survival rate of LC patients is the second lowest with little improvement over the past years [1]. Minimally-invasive early LC detection is urgently needed to improve the survival rate because the low-dose computed tomography screening not only fails to provide low false positive results with high predictive values but also requires additional invasive testing procedures afterwards [66, 84]. One study evaluated the diagnostic value of 5hmC signatures in 15 LC patients from China and 40 healthy controls from the United States and found that global cell-free 5hmC levels were gradually depleted during the development from early non-metastatic to late metastatic stages [44]. Another independent study on a Chinese cohort identified 2459 genes with differential 5hmC levels by comparing the genome-wide 5hmC profiles among 66 non-small cell lung cancer patients and 67 healthy individuals [85]. Candidate 5hmC biomarker panel derived from this study was observed to achieve better detection sensitivity as compared with that of known clinical biomarkers such as CEA, carbohydrate antigen 125 (CA125) and neuron-specific enolase (NSE).

Multiple myeloma (MM)

MM is the second most common hematological malignancy with profound disruption of epigenomes. A recent proof-of-concept study applied the nano-hmC-Seal technology to explore the potential of cfDNA-derived 5hmC biomarkers to improve the current invasive and expensive bone marrow biopsy-based biomarkers [86]. This study profiled the genome-wide 5hmC modifications in 9 MM patients, 5 patients with premalignant precursor condition defined as monoclonal gammopathy of undetermined significance, 5 patients with another premalignant precursor condition defined as smoldering multiple myeloma, and 19 newly diagnosed, treatment-naïve European American patients as controls. In total, 183 genes containing differential 5hmC loci were identified, and MM patients were separated from the patients in precursor conditions utilizing these 5hmC signatures. In addition, MM patients with different relapse statuses could also be separated using 5hmC signatures [86]. These preliminary findings again highlighted the values of 5hmC signatures as independent diagnostic and prognostic biomarkers in MM.

Conclusions and future directions

With the biological understanding that 5hmC is at the nexus of glucose metabolism and cancer epigenetics [61], recent studies have demonstrated the clinical prospects of using genome-wide 5hmC dynamics on cfDNA to improve cancer management. However, several challenges remain, limiting the translational success of cfDNA-derived 5hmC biomarkers in clinical settings. Current studies of 5hmC biomarker discovery have been mostly focused on genic regions, partly because of their genomic enrichment pattern and putative gene regulatory role. Expanding 5hmC biomarker discovery beyond genic regions to for example other cis-regulatory elements or unbiased genome-wide scans will potentially provide opportunities to identify the optimal clinically useful 5hmC biomarkers and enhance our knowledge of their biological relevance. Another challenge is that circulating cfDNA can originate from various sources; therefore, the genome-wide analysis of cfDNA is possibly hampered by the effect of genetic or epigenetic heterogeneity. A comprehensive cross-tissue comparison will be needed to establish highly tissue-specific 5hmC features in cfDNA [87]. Well-controlled animal models such as the patient-derived xenograft mouse model can also be utilized to evaluate experimentally the tumor relevance of 5hmC signals in cfDNA [45, 88]. Moreover, emerging technologies such as single-cell epigenetic assays could help determine the contributions of cfDNA from various sources [89]. Furthermore, considering their distinct biological functions and genomic distributions, integrating both 5mC and 5hmC modification markers together with nucleosome foot printing in the future would be promising to maximize the detection sensitivity in early-stage cancer and tissue-of-origin [90]. Finally, since the current 5hmC cancer biomarker discovery studies are generally small in sample size, future better statistically powered case–control and longitudinal studies will be necessary to identify more reliable 5hmC biomarkers, and evaluate the relationships between 5hmC and various potential confounding factors as well as the dynamic changes of 5hmC in patients (e.g., after treatment), thus facilitating the clinical applications of this promising tool in cancer precision medicine.

Abbreviations

ctDNA: 

circulating tumor DNA

cfDNA: 

cell-free DNA

5mC: 

5-methylcytosine

5hmC: 

5-hydroxymethlcytosine

5fC: 

5-formylcytosine

5caC: 

5-carboxylcytosine

CTC: 

circulating tumor cell

AFP: 

alpha-fetoprotein

TNM staging: 

tumor staging based on primary tumor (T), reginal lymph nodes (N) and distant metastasis (M)

Tet: 

ten-eleven translocation

DMR: 

differentially methylated region

TAB-Seq: 

tet-assisted bisulfite sequencing

oxBS-Seq: 

oxidative bisulfite sequencing

RRBS: 

reduced representation bisulfite sequencing

RRHP: 

reduced representation 5-hydroxymethylcytosine profiling

CRC: 

colorectal cancer

AUC: 

area under the receiver operating characteristic curve

GC: 

gastric cancer

CEA: 

carcinoembryonic antigen

CA19-9: 

cancer antigen 19-9

CA125: 

carbohydrate antigen 125

NSE: 

neuron-specific enolase

EC: 

esophageal cancer

LC: 

lung cancer

LDCT: 

low-dose computed tomography

NSCLC: 

non-small-cell lung cancer

MM: 

multiple myeloma

PE: 

paired-end

Declarations

Authors’ contributions

All authors contributed substantially to the writing of this review. All authors read and approved the final manuscript.

Acknowledgements

We would like to thank the Taishan Scholars Program of Shandong Province, P. R. China.

Competing interests

W.Z. advises Shanghai Epican Genetech Co., Ltd., which holds a license of the 5hmC-Seal technology. Other authors declare that they have no competing interests.

Availability of data and materials

Not applicable.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

This work was partly supported by a grant from the National Institutes of Health P30 C060553 Career Development Fund (to W.Z.).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
(2)
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL 60611, USA
(3)
Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
(4)
The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
(5)
Institute of Precision Medicine, Jining Medical University, Jining, 272067, Shandong, P. R. China

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. Cancer J Clin. 2018;68(1):7–30. https://doi.org/10.3322/caac.21442.View ArticleGoogle Scholar
  2. Bax C, Taverna G, Eusebio L, Sironi S, Grizzi F, Guazzoni G, et al. Innovative diagnostic methods for early prostate cancer detection through urine analysis: a review. Cancers. 2018. https://doi.org/10.3390/cancers10040123.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Martin LR, Williams SL, Haskard KB, DiMatteo MR. The challenge of patient adherence. Ther Clin Risk Manag. 2005;1(3):189–99.PubMedPubMed CentralGoogle Scholar
  4. Sabaawy HE. Genetic heterogeneity and clonal evolution of tumor cells and their impact on precision cancer medicine. J Leuk (Los Angel). 2013;1(4):1000124. https://doi.org/10.4172/2329-6917.1000124.View ArticleGoogle Scholar
  5. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:1. https://doi.org/10.1056/nejmoa1113205.View ArticleGoogle Scholar
  6. Varadhachary GR, Raber MN. Cancer of unknown primary site. N Engl J Med. 2014;371(8):757–65. https://doi.org/10.1056/NEJMra1303917.View ArticlePubMedGoogle Scholar
  7. Kato S, Krishnamurthy N, Banks KC, De P, Williams K, Williams C, et al. Utility of genomic analysis in circulating tumor DNA from patients with carcinoma of unknown primary. Cancer Res. 2017;77(16):4238–46. https://doi.org/10.1158/0008-5472.CAN-17-0628.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Loeb S, Vellekoop A, Ahmed HU, Catto J, Emberton M, Nam R, et al. Systematic review of complications of prostate biopsy. Eur Urol. 2013;64(6):876–92. https://doi.org/10.1016/j.eururo.2013.05.049.View ArticlePubMedGoogle Scholar
  9. Ilie M, Hofman P. Pros: can tissue biopsy be replaced by liquid biopsy? Transl Lung Cancer Res. 2016;5(4):420–3. https://doi.org/10.21037/tlcr.2016.08.06.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Gerlinger M, Horswell S, Larkin J, Rowan AJ, Salm MP, Varela I, et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet. 2014;46(3):225–33. https://doi.org/10.1038/ng.2891.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Hoffman RM, Gilliland FD, Adams-Cameron M, Hunt WC, Key CR. Prostate-specific antigen testing accuracy in community practice. BMC Fam Pract. 2002;3:19.View ArticleGoogle Scholar
  12. Zhang J, Fujimoto J, Zhang J, Wedge DC, Song X, Zhang J, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science. 2014;346(6206):256–9. https://doi.org/10.1126/science.1256930.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Friedman R. Drug resistance in cancer: molecular evolution and compensatory proliferation. Oncotarget. 2016;7(11):11746–55. https://doi.org/10.18632/oncotarget.7459.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Diaz LA Jr, Williams RT, Wu J, Kinde I, Hecht JR, Berlin J, et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature. 2012;486(7404):537–40. https://doi.org/10.1038/nature11219.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Khan KH, Cunningham D, Werner B, Vlachogiannis G, Spiteri I, Heide T, et al. Longitudinal liquid biopsy and mathematical modeling of clonal evolution forecast time to treatment failure in the PROSPECT-C phase II colorectal cancer clinical trial. Cancer Discov. 2018. https://doi.org/10.1158/2159-8290.cd-17-0891.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Jahr S, Hentze H, Englisch S, Hardt D, Fackelmayer FO, Hesch RD, et al. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 2001;61(4):1659–65.Google Scholar
  17. Wan JC, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer. 2017;17(4):223–38. https://doi.org/10.1038/nrc.2017.7.View ArticlePubMedGoogle Scholar
  18. Lehmann-Werman R, Neiman D, Zemmour H, Moss J, Magenheim J, Vaknin-Dembinsky A, et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci USA. 2016;113(13):E1826–34. https://doi.org/10.1073/pnas.1519286113.View ArticlePubMedGoogle Scholar
  19. Wyatt AW, Annala M, Aggarwal R, Beja K, Feng F, Youngren J, et al. Concordance of circulating tumor DNA and matched metastatic tissue biopsy in prostate cancer. J Natl Cancer Inst. 2017. https://doi.org/10.1093/jnci/djx118.View ArticlePubMedGoogle Scholar
  20. Ossandon MR, Agrawal L, Bernhard EJ, Conley BA, Dey SM, Divi RL, et al. Circulating tumor DNA assays in clinical cancer research. J Natl Cancer Inst. 2018;110(9):929–34. https://doi.org/10.1093/jnci/djy105.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Park G, Park JK, Son DS, Shin SH, Kim YJ, Jeon HJ, et al. Utility of targeted deep sequencing for detecting circulating tumor DNA in pancreatic cancer patients. Sci Rep. 2018;8(1):11631. https://doi.org/10.1038/s41598-018-30100-w.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Mishima Y, Paiva B, Shi J, Park J, Manier S, Takagi S, et al. The mutational landscape of circulating tumor cells in multiple myeloma. Cell Rep. 2017;19(1):218–24. https://doi.org/10.1016/j.celrep.2017.03.025.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Schiavon G, Hrebien S, Garcia-Murillas I, Cutts RJ, Pearson A, Tarazona N, et al. Analysis of ESR1 mutation in circulating tumor DNA demonstrates evolution during therapy for metastatic breast cancer. Sci Transl Med. 2015;7(313):313ra182. https://doi.org/10.1126/scitranslmed.aac7551.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Merker JD, Oxnard GR, Compton C, Diehn M, Hurley P, Lazar AJ, et al. Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J Clin Oncol. 2017. https://doi.org/10.1200/jco.2017.76.8671.View ArticleGoogle Scholar
  25. Pantel K. Blood-based analysis of circulating cell-free DNA and tumor cells for early cancer detection. PLoS Med. 2016;13(12):e1002205. https://doi.org/10.1371/journal.pmed.1002205.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Rachiglio AM, Esposito Abate R, Sacco A, Pasquale R, Fenizia F, Lambiase M, et al. Limits and potential of targeted sequencing analysis of liquid biopsy in patients with lung and colon carcinoma. Oncotarget. 2016;7(41):66595–605. https://doi.org/10.18632/oncotarget.10704.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell. 2016;164(1–2):57–68. https://doi.org/10.1016/j.cell.2015.11.050.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell. 2018;173(2):291–304. https://doi.org/10.1016/j.cell.2018.03.022.View ArticlePubMedGoogle Scholar
  29. Leygo C, Williams M, Jin HC, Chan MWY, Chu WK, Grusch M, et al. DNA methylation as a noninvasive epigenetic biomarker for the detection of cancer. Dis Markers. 2017;2017:3726595. https://doi.org/10.1155/2017/3726595.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Liu L, Toung JM, Jassowicz AF, Vijayaraghavan R, Kang H, Zhang R, et al. Targeted methylation sequencing of plasma cell-free DNA for cancer detection and classification. Ann Oncol. 2018;29(6):1445–53. https://doi.org/10.1093/annonc/mdy119.View ArticlePubMedGoogle Scholar
  31. Gordevicius J, Krisciunas A, Groot DE, Yip SM, Susic M, Kwan A, et al. Cell-free DNA modification dynamics in abiraterone acetate-treated prostate cancer patients. Clin Cancer Res. 2018;24(14):3317–24. https://doi.org/10.1158/1078-0432.ccr-18-0101.View ArticlePubMedGoogle Scholar
  32. Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V, et al. Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol. 2018. https://doi.org/10.1038/s41571-018-0004-4.View ArticlePubMedGoogle Scholar
  33. Peltomaki P. Mutations and epimutations in the origin of cancer. Exp Cell Res. 2012;318(4):299–310. https://doi.org/10.1016/j.yexcr.2011.12.001.View ArticlePubMedGoogle Scholar
  34. Statham AL, Taberlay PC, Kelly TK, Jones PA, Clark SJ. Genome-wide nucleosome occupancy and DNA methylation profiling of four human cell lines. Genom Data. 2015;3:94–6. https://doi.org/10.1016/j.gdata.2014.11.012.View ArticlePubMedGoogle Scholar
  35. Xu RH, Wei W, Krawczyk M, Wang W, Luo H, Flagg K, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat Mater. 2017;16(11):1155–61. https://doi.org/10.1038/nmat4997.View ArticlePubMedGoogle Scholar
  36. Wen Y, Wei Y, Zhang S, Li S, Liu H, Wang F, et al. Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature. Brief Bioinform. 2017;18(3):426–40. https://doi.org/10.1093/bib/bbw028.View ArticlePubMedGoogle Scholar
  37. Fiala C, Diamandis EP. Utility of circulating tumor DNA in cancer diagnostics with emphasis on early detection. BMC Med. 2018;16(1):166. https://doi.org/10.1186/s12916-018-1157-9.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Zeng H, He B, Yi C, Peng J. Liquid biopsies: DNA methylation analyses in circulating cell-free DNA. J Genet Genom. 2018. https://doi.org/10.1016/j.jgg.2018.02.007.View ArticleGoogle Scholar
  39. Feng H, Jin P, Wu H. Disease prediction by cell-free DNA methylation. Brief Bioinform. 2018;1:1. https://doi.org/10.1093/bib/bby029.View ArticleGoogle Scholar
  40. Guo S, Diep D, Plongthongkum N, Fung HL, Zhang K, Zhang K. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet. 2017;49(4):635–42. https://doi.org/10.1038/ng.3805.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Kang S, Li Q, Chen Q, Zhou Y, Park S, Lee G, et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 2017;18(1):53. https://doi.org/10.1186/s13059-017-1191-5.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Barchitta M, Quattrocchi A, Maugeri A, Vinciguerra M, Agodi A. LINE-1 hypomethylation in blood and tissue samples as an epigenetic marker for cancer risk: a systematic review and meta-analysis. PLoS ONE. 2014;9(10):e109478. https://doi.org/10.1371/journal.pone.0109478.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Wedge E, Hansen JW, Garde C, Asmar F, Tholstrup D, Kristensen SS, et al. Global hypomethylation is an independent prognostic factor in diffuse large B cell lymphoma. Am J Hematol. 2017;92(7):689–94. https://doi.org/10.1002/ajh.24751.View ArticlePubMedGoogle Scholar
  44. Song CX, Yin S, Ma L, Wheeler A, Chen Y, Zhang Y, et al. 5-Hydroxymethylcytosine signatures in cell-free DNA provide information about tumor types and stages. Cell Res. 2017. https://doi.org/10.1038/cr.2017.106.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Li W, Zhang X, Lu X, You L, Song Y, Luo Z, et al. 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Res. 2017. https://doi.org/10.1038/cr.2017.121.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Tian X, Sun B, Chen C, Gao C, Zhang J, Lu X, et al. Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer. Cell Res. 2018. https://doi.org/10.1038/s41422-018-0014-x.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Ivanov M, Baranova A, Butler T, Spellman P, Mileyko V. Non-random fragmentation patterns in circulating cell-free DNA reflect epigenetic regulation. BMC Genom. 2015;16(Suppl 13):S1. https://doi.org/10.1186/1471-2164-16-S13-S1.View ArticleGoogle Scholar
  48. Lehmann-Werman R, Neiman D, Zemmour H, Moss J, Magenheim J, Vaknin-Dembinsky A, et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci. 2016;113(13):E1826–34. https://doi.org/10.1073/pnas.1519286113.View ArticlePubMedGoogle Scholar
  49. Laird PW. The power and the promise of DNA methylation markers. Nat Rev Cancer. 2003;3(4):253–66. https://doi.org/10.1038/nrc1045.View ArticlePubMedGoogle Scholar
  50. Ito S, Shen L, Dai Q, Wu SC, Collins LB, Swenberg JA, et al. Tet proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine. Science. 2011;333(6047):1300–3. https://doi.org/10.1126/science.1210597.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Branco MR, Ficz G, Reik W. Uncovering the role of 5-hydroxymethylcytosine in the epigenome. Nat Rev Genet. 2011;13(1):7–13. https://doi.org/10.1038/nrg3080.View ArticlePubMedGoogle Scholar
  52. Skvortsova K, Zotenko E, Luu P-L, Gould CM, Nair SS, Clark SJ, et al. Comprehensive evaluation of genome-wide 5-hydroxymethylcytosine profiling approaches in human DNA. Epigenet Chromat. 2017;10(1):16. https://doi.org/10.1186/s13072-017-0123-7.View ArticleGoogle Scholar
  53. Song CX, Szulwach KE, Fu Y, Dai Q, Yi C, Li X, et al. Selective chemical labeling reveals the genome-wide distribution of 5-hydroxymethylcytosine. Nat Biotechnol. 2011;29(1):68–72. https://doi.org/10.1038/nbt.1732.View ArticlePubMedGoogle Scholar
  54. Doerks T, Copley RR, Schultz J, Ponting CP, Bork P. Systematic identification of novel protein domain families associated with nuclear functions. Genome Res. 2002;12(1):47–56.View ArticleGoogle Scholar
  55. Song CX, Yi C, He C. Mapping recently identified nucleotide variants in the genome and transcriptome. Nat Biotechnol. 2012;30(11):1107–16. https://doi.org/10.1038/nbt.2398.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Yu M, Hon GC, Szulwach KE, Song CX, Zhang L, Kim A, et al. Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell. 2012;149(6):1368–80. https://doi.org/10.1016/j.cell.2012.04.027.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Nestor CE, Ottaviano R, Reddington J, Sproul D, Reinhardt D, Dunican D, et al. Tissue type is a major modifier of the 5-hydroxymethylcytosine content of human genes. Genome Res. 2012;22(3):467–77. https://doi.org/10.1101/gr.126417.111.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Thomson JP, Meehan RR. The application of genome-wide 5-hydroxymethylcytosine studies in cancer research. Epigenomics. 2017;9(1):77–91. https://doi.org/10.2217/epi-2016-0122.View ArticlePubMedGoogle Scholar
  59. Han D, Lu X, Shih AH, Nie J, You Q, Xu MM, et al. A highly sensitive and robust method for genome-wide 5hmC profiling of rare cell populations. Mol Cell. 2016;63(4):711–9. https://doi.org/10.1016/j.molcel.2016.06.028.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Chen K, Zhang J, Guo Z, Ma Q, Xu Z, Zhou Y, et al. Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer. Cell Res. 2016;26(1):103–18. https://doi.org/10.1038/cr.2015.150.View ArticlePubMedGoogle Scholar
  61. Vasanthakumar A, Godley LA. 5-hydroxymethylcytosine in cancer: significance in diagnosis and therapy. Cancer Genet. 2015;208(5):167–77. https://doi.org/10.1016/j.cancergen.2015.02.009.View ArticlePubMedGoogle Scholar
  62. Li X, Liu Y, Salz T, Hansen KD, Feinberg A. Whole-genome analysis of the methylome and hydroxymethylome in normal and malignant lung and liver. Genome Res. 2016;26(12):1730–41. https://doi.org/10.1101/gr.211854.116.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Warton K, Mahon KL, Samimi G. Methylated circulating tumor DNA in blood: power in cancer prognosis and response. Endocr Relat Cancer. 2016;23(3):R157–71. https://doi.org/10.1530/ERC-15-0369.View ArticlePubMedPubMed CentralGoogle Scholar
  64. Widschwendter M, Zikan M, Wahl B, Lempiainen H, Paprotka T, Evans I, et al. The potential of circulating tumor DNA methylation analysis for the early detection and management of ovarian cancer. Genome Med. 2017;9(1):116. https://doi.org/10.1186/s13073-017-0500-7.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Mastoraki S, Strati A, Tzanikou E, Chimonidou M, Politaki E, Voutsina A, et al. ESR1 methylation: a liquid biopsy-based epigenetic assay for the follow-up of patients with metastatic breast cancer receiving endocrine treatment. Clin Cancer Res. 2018;24(6):1500–10. https://doi.org/10.1158/1078-0432.ccr-17-1181.View ArticlePubMedGoogle Scholar
  66. Hulbert A, Jusue-Torres I. Lung cancer recurrence epigenetic liquid biopsy. J Thorac Dis. 2018;10(1):4–6. https://doi.org/10.21037/jtd.2017.11.124.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Chan KC, Jiang P, Chan CW, Sun K, Wong J, Hui EP, et al. Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc Natl Acad Sci USA. 2013;110(47):18761–8. https://doi.org/10.1073/pnas.1313995110.View ArticlePubMedGoogle Scholar
  68. Huang Y, Pastor WA, Shen Y, Tahiliani M, Liu DR, Rao A. The behaviour of 5-hydroxymethylcytosine in bisulfite sequencing. PLoS ONE. 2010;5(1):e8888. https://doi.org/10.1371/journal.pone.0008888.View ArticlePubMedPubMed CentralGoogle Scholar
  69. Jin SG, Kadam S, Pfeifer GP. Examination of the specificity of DNA methylation profiling techniques towards 5-methylcytosine and 5-hydroxymethylcytosine. Nucleic Acids Res. 2010;38(11):e125. https://doi.org/10.1093/nar/gkq223.View ArticlePubMedPubMed CentralGoogle Scholar
  70. Yu M, Hon GC, Szulwach KE, Song CX, Jin P, Ren B, et al. Tet-assisted bisulfite sequencing of 5-hydroxymethylcytosine. Nat Protoc. 2012;7(12):2159–70. https://doi.org/10.1038/nprot.2012.137.View ArticlePubMedPubMed CentralGoogle Scholar
  71. Booth MJ, Ost TW, Beraldi D, Bell NM, Branco MR, Reik W, et al. Oxidative bisulfite sequencing of 5-methylcytosine and 5-hydroxymethylcytosine. Nat Protoc. 2013;8(10):1841–51. https://doi.org/10.1038/nprot.2013.115.View ArticlePubMedPubMed CentralGoogle Scholar
  72. Plongthongkum N, Diep DH, Zhang K. Advances in the profiling of DNA modifications: cytosine methylation and beyond. Nat Rev Genet. 2014;15(10):647–61. https://doi.org/10.1038/nrg3772.View ArticlePubMedGoogle Scholar
  73. Petterson A, Chung TH, Tan D, Sun X, Jia XY. RRHP: a tag-based approach for 5-hydroxymethylcytosine mapping at single-site resolution. Genome Biol. 2014;15(9):456. https://doi.org/10.1186/s13059-014-0456-5.View ArticlePubMedPubMed CentralGoogle Scholar
  74. Booth MJ, Branco MR, Ficz G, Oxley D, Krueger F, Reik W, et al. Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution. Science. 2012;336(6083):934–7. https://doi.org/10.1126/science.1220671.View ArticlePubMedGoogle Scholar
  75. Cui L, Chung TH, Tan D, Sun X, Jia XY. JBP1-seq: a fast and efficient method for genome-wide profiling of 5hmC. Genomics. 2014;104(5):368–75. https://doi.org/10.1016/j.ygeno.2014.08.023.View ArticlePubMedGoogle Scholar
  76. Bock C. Analysing and interpreting DNA methylation data. Nat Rev Genet. 2012;13(10):705–19. https://doi.org/10.1038/nrg3273.View ArticlePubMedGoogle Scholar
  77. Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66(4):683–91. https://doi.org/10.1136/gutjnl-2015-310912.View ArticlePubMedGoogle Scholar
  78. Mojtabanezhad Shariatpanahi A, Yassi M, Nouraie M, Sahebkar A, Varshoee Tabrizi F, Kerachian MA. The importance of stool DNA methylation in colorectal cancer diagnosis: a meta-analysis. PLoS ONE. 2018;13(7):e0200735. https://doi.org/10.1371/journal.pone.0200735.View ArticlePubMedPubMed CentralGoogle Scholar
  79. Church TR, Wandell M, Lofton-Day C, Mongin SJ, Burger M, Payne SR, et al. Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut. 2014;63(2):317–25. https://doi.org/10.1136/gutjnl-2012-304149.View ArticlePubMedGoogle Scholar
  80. Battaglin F, Naseem M, Puccini A, Lenz HJ. Molecular biomarkers in gastro-esophageal cancer: recent developments, current trends and future directions. Cancer Cell Int. 2018;18:99. https://doi.org/10.1186/s12935-018-0594-z.View ArticlePubMedPubMed CentralGoogle Scholar
  81. Pectasides E, Stachler MD, Derks S, Liu Y, Maron S, Islam M, et al. Genomic heterogeneity as a barrier to precision medicine in gastroesophageal adenocarcinoma. Cancer Discov. 2018;8(1):37–48. https://doi.org/10.1158/2159-8290.Cd-17-0395.View ArticlePubMedGoogle Scholar
  82. Gao J, Wang H, Zang W, Li B, Rao G, Li L, et al. Circulating tumor DNA functions as an alternative for tissue to overcome tumor heterogeneity in advanced gastric cancer. Cancer Sci. 2017;108(9):1881–7. https://doi.org/10.1111/cas.13314.View ArticlePubMedPubMed CentralGoogle Scholar
  83. Wang H, Li B, Liu Z, Gong J, Shao L, Ren J, et al. HER2 copy number of circulating tumour DNA functions as a biomarker to predict and monitor trastuzumab efficacy in advanced gastric cancer. Eur J Cancer. 2018;88:92–100. https://doi.org/10.1016/j.ejca.2017.10.032.View ArticlePubMedGoogle Scholar
  84. Bennett CW, Berchem G, Kim YJ, El-Khoury V. Cell-free DNA and next-generation sequencing in the service of personalized medicine for lung cancer. Oncotarget. 2016;7(43):71013–35. https://doi.org/10.18632/oncotarget.11717.View ArticlePubMedPubMed CentralGoogle Scholar
  85. Zhang J, Han X, Gao C, Xing Y, Qi Z, Liu R, et al. 5-Hydroxymethylome in circulating cell-free DNA as A potential biomarker for non-small-cell lung cancer. Genom Proteom Bioinf. 2018. https://doi.org/10.1016/j.gpb.2018.06.002.View ArticleGoogle Scholar
  86. Chiu BYQZZ, Stepniak L, Zhang X, Chernoff M, Zimmerman TL, He C, Zhang W, editors. 5-Hydroxymethylcytosine of circulating cell-free DNA in plasma: a novel non-invasive marker for progression and prognosis in multiple myeloma. ASH: Atlanta; 2017.Google Scholar
  87. Tang W, Wan S, Yang Z, Teschendorff AE, Zou Q. Tumor origin detection with tissue-specific miRNA and DNA methylation markers. Bioinformatics. 2018;34(3):398–406. https://doi.org/10.1093/bioinformatics/btx622.View ArticlePubMedGoogle Scholar
  88. Yada E, Wada S, Yoshida S, Sasada T. Use of patient-derived xenograft mouse models in cancer research and treatment. Future sci OA. 2017;4(3):FSO271. https://doi.org/10.4155/fsoa-2017-0136.View ArticlePubMedPubMed CentralGoogle Scholar
  89. Karemaker ID, Vermeulen M. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol. 2018. https://doi.org/10.1016/j.tibtech.2018.04.002.View ArticlePubMedGoogle Scholar
  90. Shen SY, Singhania R, Fehringer G, Chakravarthy A, Roehrl MHA, Chadwick D, et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature. 2018. https://doi.org/10.1038/s41586-018-0703-0.View ArticlePubMedPubMed CentralGoogle Scholar
  91. Hahn MA, Li AX, Wu X, Pfeifer GP. Single base resolution analysis of 5-methylcytosine and 5-hydroxymethylcytosine by RRBS and TAB-RRBS. Methods Mol Biol. 2015;1238:273–87. https://doi.org/10.1007/978-1-4939-1804-1_14.View ArticlePubMedPubMed CentralGoogle Scholar

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