Integrated epigenetic biomarkers in circulating cell-free DNA as a robust classifier for pancreatic cancer

Genome-wide 5-hydroxymethylcytosines in circulating cell-free DNA as noninvasive diagnostic markers for gastric cancer

BACKGROUND

5-Hydroxymethylcytosine-enriched gene profiles and regions show tissue-specific and tumor specific. There is a potential value to explore cell-free DNA 5-hydroxymethylcytosine feature biomarkers for early gastric cancer detection.

METHODS

A matched case‒control study design with 50 gastric cancer patients and 50 controls was performed to sequence the different 5-hydroxymethylcytosine modification features of cell free DNA. Significantly differential 5-hydroxymethylcytosine modification genes were identified to construct a gastric cancer diagnostic model. Data set from GEO was used as an external testing set to test the robustness of the diagnostic model.

RESULTS

Accounting for more than 90% of 5-hydroxymethylcytosine peaks were distributed in the gene body in both the gastric cancer and control groups. The diagnostic model was developed based on five different 5-hydroxymethylcytosine modification genes, FBXL7, PDE3A, TPO, SNTG2 and STXBP5. The model could effectively distinguish gastric cancer patients from controls in the training (AUC = 0.95, sensitivity = 88.6%, specificity = 94.3%), validation (AUC = 0.87, sensitivity = 73.3%, specificity = 93.3%) and testing (AUC = 0.90, sensitivity = 81.9%, specificity = 90.2%) sets. The risk scores of the controls from the model were significantly lower than those of gastric cancer patients in both our own data (P < 0.001) and GEO external testing data (P < 0.001), and no significant difference between different TNM stage patients (P = 0.09 and 0.66). Furthermore, there was no significant difference between the healthy control and benign gastric disease patients in the testing set from GEO (P = 0.10).

CONCLUSIONS

The characteristics of 5-hydroxymethylcytosine in cell free DNA are specific to gastric cancer patients, and the diagnostic model constructed by five genes' 5-hydroxymethylcytosine features could effectively identify gastric cancer patients.

Prospects of liquid biopsy in the prognosis and clinical management of gastrointestinal cancers

Gastrointestinal (GI) cancers account for one-fourth of the global cancer incidence and are incriminated to cause one-third of cancer-related deaths. GI cancer includes esophageal, gastric, liver, pancreatic, and colorectal cancers, mostly diagnosed at advanced stages due to a lack of accurate markers for early stages. The invasiveness of diagnostic methods like colonoscopy for solid biopsy reduces patient compliance as it cannot be frequently used to screen patients. Therefore, minimally invasive approaches like liquid biopsy may be explored for screening and early identification of gastrointestinal cancers. Liquid biopsy involves the qualitative and quantitative determination of certain cancer-specific biomarkers in body fluids such as blood, serum, saliva, and urine to predict disease progression, therapeutic tolerance, toxicities, and recurrence by evaluating minimal residual disease and its correlation with other clinical features. In this review, we deliberate upon various tumor-specific cellular and molecular entities such as circulating tumor cells (CTCs), tumor-educated platelets (TEPs), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), exosomes, and exosome-derived biomolecules and cite recent advances pertaining to their use in predicting disease progression, therapy response, or risk of relapse. We also discuss the technical challenges associated with translating liquid biopsy into clinical settings for various clinical applications in gastrointestinal cancers.

Unlocking the potential of tumor-derived DNA in urine for cancer detection: methodological challenges and opportunities

High cancer mortality rates and the rising cancer burden worldwide drive the development of innovative methods in order to advance cancer diagnostics. Urine contains a viable source of tumor material and allows for self-collection from home. Biomarker testing in this liquid biopsy represents a novel approach that is convenient for patients and can be effective in detecting cancer at a curable stage. Here, we set out to provide a detailed overview of the rationale behind urine-based cancer detection, with a focus on non-urological cancers, and its potential for cancer diagnostics. Moreover, evolving methodological challenges and untapped opportunities for urine biomarker testing are discussed, particularly emphasizing DNA methylation of tumor-derived cell-free DNA. We also provide future recommendations for technical advancements in urine-based cancer detection and elaborate on potential mechanisms involved in the transrenal transport of cell-free DNA.

Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients' blood

During cancer progression, tumorigenic and immune signals are spread through circulating molecules, such as cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in the blood. So far, they have not been comprehensively investigated in gastrointestinal cancers. Here, we profile 4 categories of cell-free omics data from patients with colorectal cancer and patients with stomach adenocarcinoma and then assay 15 types of genomic, epigenomic, and transcriptomic variations. We find that multi-omics data are more appropriate for detection of cancer genes compared with single-omics data. In particular, cfRNAs are more sensitive and informative than cfDNAs in terms of detection rate, enriched functional pathways, etc. Moreover, we identify several peripheral immune signatures that are suppressed in patients with cancer. Specifically, we establish a γδ-T cell score and a cancer-associated-fibroblast (CAF) score, providing insights into clinical statuses like cancer stage and survival. Overall, we reveal a cell-free multi-molecular landscape that is useful for blood monitoring in personalized cancer treatment.

Diagnosing and monitoring pancreatic cancer through cell-free DNA methylation: progress and prospects

Pancreatic cancer is one of the most challenging cancers due to its high mortality rates. Considering the late diagnosis and the limited survival benefit with current treatment options, it becomes imperative to optimize early detection, prognosis and prediction of treatment response. To address these challenges, significant research efforts have been undertaken in recent years to develop liquid-biopsy-based biomarkers for pancreatic cancer. In particular, an increasing number of studies point to cell-free DNA (cfDNA) methylation analysis as a promising non-invasive approach for the discovery and validation of epigenetic biomarkers with diagnostic or prognostic potential. In this review we provide an update on recent advancements in the field of cfDNA methylation analysis in pancreatic cancer. We discuss the relevance of DNA methylation in the context of pancreatic cancer, recent cfDNA methylation research, its clinical utility, and future directions for integrating cfDNA methylation analysis into routine clinical practice.

Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study

BACKGROUND

Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC.

METHODS

The Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis.

RESULTS

In total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed.

CONCLUSION

Using a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

Integrated fragmentomic profile and 5-Hydroxymethylcytosine of capture-based low-pass sequencing data enables pan-cancer detection via cfDNA

BACKGROUND

Using epigenetic markers and fragmentomics of cell-free DNA for cancer detection has been proven applicable.

METHODS

We further investigated the diagnostic potential of combining two features (epigenetic markers and fragmentomic information) of cell-free DNA for detecting various types of cancers. To do this, we extracted cfDNA fragmentomic features from 191 whole-genome sequencing data and studied them in 396 low-pass 5hmC sequencing data, which included four common cancer types and control samples.

RESULTS

In our analysis of 5hmC sequencing data from cancer samples, we observed aberrant ultra-long fragments (220-500 bp) that differed from normal samples in terms of both size and coverage profile. These fragments played a significant role in predicting cancer. Leveraging the ability to detect cfDNA hydroxymethylation and fragmentomic markers simultaneously in low-pass 5hmC sequencing data, we developed an integrated model that incorporated 63 features representing both fragmentomic features and hydroxymethylation signatures. This model achieved high sensitivity and specificity for pan-cancer detection (88.52% and 82.35%, respectively).

CONCLUSION

We showed that fragmentomic information in 5hmC sequencing data is an ideal marker for cancer detection and that it shows high performance in low-pass sequencing data.

Emerging role of non-invasive and liquid biopsy biomarkers in pancreatic cancer

A global increase in the incidence of pancreatic cancer (PanCa) presents a major concern and health burden. The traditional tissue-based diagnostic techniques provided a major way forward for molecular diagnostics; however, they face limitations based on diagnosis-associated difficulties and concerns surrounding tissue availability in the clinical setting. Late disease development with asymptomatic behavior is a drawback in the case of existing diagnostic procedures. The capability of cell free markers in discriminating PanCa from autoimmune pancreatitis and chronic pancreatitis along with other precancerous lesions can be a boon to clinicians. Early-stage diagnosis of PanCa can be achieved only if these biomarkers specifically discriminate the non-carcinogenic disease stage from malignancy with respect to tumor stages. In this review, we comprehensively described the non-invasive disease detection approaches and why these approaches are gaining popularity for their early-stage diagnostic capability and associated clinical feasibility.

Bridging biological cfDNA features and machine learning approaches

Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.

Pancreatic Cancer Biomarkers: Oncogenic Mutations, Tissue and Liquid Biopsies, and Radiomics-A Review

Pancreatic cancer is one of the most fatal malignancies, as approximately 80% of patients are at advanced stages by the time of diagnosis. The main reason for the poor overall survival is late diagnosis that is partially due to the lack of tools for early-stage detection. In addition, there are several challenges in evaluating response to treatment and predicting prognosis. In this article, we do a review of the most common pancreatic cancer biomarkers with emphasis in new and promising approaches. Liquid biopsies seem to have important clinical applications in early detection, screening, prognosis, and longitudinal monitoring of on-treatment patients. Together with biomarkers in imaging, can represent valuable alternative non-invasive tools in order to achieve a more effective management of pancreatic cancer patients.

Blood-based DNA methylation profiling for the detection of ovarian cancer

OBJECTIVES

Ovarian cancer is a fatal gynecological cancer due to the lack of effective screening strategies at early stage. This study explored the utility of DNA methylation profiling of blood samples for the detection of ovarian cancer.

METHODS

Targeted bisulfite sequencing was performed on tissue (n = 152) and blood samples (n = 373) obtained from healthy women, women with benign ovarian tumors, or malignant epithelial ovarian tumors. Based on the tissue-derived differentially-methylated regions, a supervised machine learning algorithm was implemented and cross-validated using the blood-derived DNA methylation profiles of the training cohort (n = 178) to predict and classify each blood sample as malignant or non-malignant. The model was further evaluated using an independent test cohort (n = 184).

RESULTS

Comparison of the DNA methylation profiles of normal/benign and malignant tumor samples identified 1272 differentially-methylated regions, with 49.4% hypermethylated regions and 50.6% hypomethylated regions. Five-fold cross-validation of the model using the training dataset yielded an area under the curve of 0.94. Using the test dataset, the model accurately predicted non-malignancy in 96.2% of healthy women (n = 53) and 93.5% of women with benign tumors (n = 46). For patients with malignant tumors, the model accurately predicted malignancy in 44.4% of stage I-II (n = 9), 86.4% of stage III (n = 59), 100.0% of stage IV tumors (n = 6), and 81.8% of tumors with unknown stage (n = 11). Overall, the model yielded a predictive accuracy of 89.5%.

CONCLUSIONS

Our study demonstrates the potential clinical application of blood-based DNA methylation profiling for the detection of ovarian cancer.

Hydroxymethylation profile of cell-free DNA is a biomarker for early colorectal cancer

Early detection of cancer will improve survival rates. The blood biomarker 5-hydroxymethylcytosine has been shown to discriminate cancer. In a large covariate-controlled study of over two thousand individual blood samples, we created, tested and explored the properties of a 5-hydroxymethylcytosine-based classifier to detect colorectal cancer (CRC). In an independent validation sample set, the classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (95% CI [87, 93]). Sensitivity was 55% at 95% specificity. Performance was similar for early stage 1 (AUC 89%; 95% CI [83, 94]) and late stage 4 CRC (AUC 94%; 95% CI [89, 98]). The classifier could detect CRC even when the proportion of tumor DNA in blood was undetectable by other methods. Expanding the classifier to include information about cell-free DNA fragment size and abundance across the genome led to gains in sensitivity (63% at 95% specificity), with similar overall performance (AUC 91%; 95% CI [89, 94]). We confirm that 5-hydroxymethylcytosine can be used to detect CRC, even in early-stage disease. Therefore, the inclusion of 5-hydroxymethylcytosine in multianalyte testing could improve sensitivity for the detection of early-stage cancer.

Early diagnosis of pancreatic cancer: What strategies to avoid a foretold catastrophe

While great strides in improving survival rates have been made for most cancers in recent years, pancreatic ductal adenocarcinoma (PDAC) remains one of the solid tumors with the worst prognosis. PDAC mortality often overlaps with incidence. Surgical resection is the only potentially curative treatment, but it can be performed in a very limited number of cases. In order to improve the prognosis of PDAC, there are ideally two possible ways: the discovery of new strategies or drugs that will make it possible to treat the tumor more successfully or an earlier diagnosis that will allow patients to be operated on at a less advanced stage. The aim of this review was to summarize all the possible strategies available today for the early diagnosis of PDAC and the paths that research needs to take to make this goal ever closer. All the most recent studies on risk factors and screening modalities, new laboratory tests including liquid biopsy, new imaging methods and possible applications of artificial intelligence and machine learning were reviewed and commented on. Unfortunately, in 2022 the results for this type of cancer still remain discouraging, while a catastrophic increase in cases is expected in the coming years. The article was also written with the aim of highlighting the urgency of devoting more attention and resources to this pathology in order to reach a solution that seems more and more unreachable every day.

Is Cell-Free DNA Testing in Pancreatic Ductal Adenocarcinoma Ready for Prime Time?

Simple Summary

Pancreatic cancer is a deadly cancer with limited treatment options. It is often detected in most people at stages where cure is not possible. There is no good test to know if a person will respond to treatment or if there is any disease beyond what can be seen by available imaging tests. Genetic material from the tumor is expected to float in the blood. Studying the alterations in the genetic material could help detect the tumor early, give an idea about its aggressiveness and response to available treatments, and facilitate the discovery of newer therapies. The focus of the studies so far has been on only one kind of genetic aberration, mutations, which has not given us great results. There is a need to explore another type of change known as methylation that could hold answers for managing pancreatic cancers better.

Abstract

Cell-free DNA (cfDNA) testing currently does not have a significant role in PDA management: it is insufficient to diagnose PDA, and its use is primarily restricted to identifying targetable mutations (if tissue is insufficient or unavailable). cfDNA testing has the potential to address critical needs in PDA management, such as pre-operative risk stratification (POR), prognostication, and predicting (and monitoring) treatment response. Prior studies have focused primarily on somatic mutations, specifically KRAS variants, and have shown limited success in addressing prognosis and POR. Recent studies have demonstrated the importance of other less prevalent mutations (ERBB2 and TP53), but no studies have provided reliable mutation panels for clinical use. Methylation aberrations in cfDNA (epigenetic markers) in PDA have been relatively less explored. However, early evidence has suggested they offer diagnostic and, to some extent, prognostic value. The inclusion of epigenetic markers of cfDNA adds another dimension to genomic testing and may open new therapeutic avenues beyond addressing critical areas of need in PDA treatment. For cfDNA to substantially influence PDA management, concerted efforts are required to include less frequent mutations and epigenetic markers. Furthermore, relying on KRAS mutations for PDA management will always be inadequate.

Liquid biopsies to predict CDK4/6 inhibitor efficacy and resistance in breast cancer

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors combined with endocrine therapy have transformed the treatment of estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2 negative (HER2-) metastatic breast cancer. However, some patients do not respond to this treatment, and patients inevitably develop resistance, such that novel biomarkers are needed to predict primary resistance, monitor treatment response for acquired resistance, and personalize treatment strategies. Circumventing the spatial and temporal limitations of tissue biopsy, newly developed liquid biopsy approaches have the potential to uncover biomarkers that can predict CDK4/6 inhibitor efficacy and resistance in breast cancer patients through a simple blood test. Studies on circulating tumor DNA (ctDNA)-based liquid biopsy biomarkers of CDK4/6 inhibitor resistance have focused primarily on genomic alterations and have failed thus far to identify clear and clinically validated predictive biomarkers, but emerging epigenetic ctDNA methodologies hold promise for further discovery. The present review outlines recent advances and future directions in ctDNA-based biomarkers of CDK4/6 inhibitor treatment response.

Probabilistic modeling methods for cell-free DNA methylation based cancer classification

Background

cfMeDIP-seq is a low-cost method for determining the DNA methylation status of cell-free DNA and it has been successfully combined with statistical methods for accurate cancer diagnostics. We investigate the diagnostic classification aspect by applying statistical tests and dimension reduction techniques for feature selection and probabilistic modeling for the cancer type classification, and we also study the effect of sequencing depth.

Methods

We experiment with a variety of statistical methods that use different feature selection and feature extraction methods as well as probabilistic classifiers for diagnostic decision making. We test the (moderated) t-tests and the Fisher’s exact test for feature selection, principal component analysis (PCA) as well as iterative supervised PCA (ISPCA) for feature generation, and GLMnet and logistic regression methods with sparsity promoting priors for classification. Probabilistic programming language Stan is used to implement Bayesian inference for the probabilistic models.

Results and conclusions

We compare overlaps of differentially methylated genomic regions as chosen by different feature selection methods, and evaluate probabilistic classifiers by evaluating the area under the receiver operating characteristic scores on discovery and validation cohorts. While we observe that many methods perform equally well as, and occasionally considerably better than, GLMnet that was originally proposed for cfMeDIP-seq based cancer classification, we also observed that performance of different methods vary across sequencing depths, cancer types and study cohorts. Overall, methods that seem robust and promising include Fisher’s exact test and ISPCA for feature selection as well as a simple logistic regression model with the number of hyper and hypo-methylated regions as features.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04651-9.

Combinatorial Power of cfDNA, CTCs and EVs in Oncology

Liquid biopsy is a promising technique for clinical management of oncological patients. The diversity of analytes circulating in the blood useable for liquid biopsy testing is enormous. Circulating tumor cells (CTCs), cell-free DNA (cfDNA) and extracellular vesicles (EVs), as well as blood cells and other soluble components in the plasma, were shown as liquid biopsy analytes. A few studies directly comparing two liquid biopsy analytes showed a benefit of one analyte over the other, while most authors concluded the benefit of the additional analyte. Only three years ago, the first studies to examine the value of a characterization of more than two liquid biopsy analytes from the same sample were conducted. We attempt to reflect on the recent development of multimodal liquid biopsy testing in this review. Although the analytes and clinical purposes of the published multimodal studies differed significantly, the additive value of the analytes was concluded in almost all projects. Thus, the blood components, as liquid biopsy reservoirs, are complementary rather than competitive, and orthogonal data sets were even shown to harbor synergistic effects. The unmistakable potential of multimodal liquid biopsy testing, however, is dampened by its clinical utility, which is yet to be proven, the lack of methodical standardization and insufficiently mature reimbursement, logistics and data handling.

Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays

Aim: Tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion on DNA followed by hybridization to Infinium HumanMethylation BeadChips allows nucleotide resolution of 5-hydroxymethylcytosine genome-wide. Here, the authors compared data quality acquired from BS-treated and oxBS-treated samples. Materials & methods: Raw BeadArray data from 417 pairs of samples across 12 independent datasets were included in the study. Probe call rates were compared between paired BS and oxBS treatments controlling for technical variables. Results: oxBS-treated samples had a significantly lower call rate. Among technical variables, DNA-specific extraction kits performed better with higher call rates after oxBS conversion. Conclusion: The authors emphasize the importance of quality control during oxBS conversion to minimize information loss and recommend using a DNA-specific extraction kit for DNA extraction and an oxBSQC package for data preprocessing.

Circulating Cell-Free Tumor DNA in Advanced Pancreatic Adenocarcinoma Identifies Patients With Worse Overall Survival

Background

Plasma-based circulating cell-free tumor DNA (ctDNA) genomic profiling by next-generation sequencing (NGS)is an emerging diagnostic tool for pancreatic cancer (PC). The impact of detected genomic alterations and variant allele fraction (VAF) in tumor response to systemic treatments and outcomes is under investigation.

Methods

Patients with advanced PC who had ctDNA profiled at time of initial diagnosis were retrospectively evaluated. We considered the somatic alteration with the highest VAF as the dominant clone allele frequency (DCAF). ctDNA NGS results were related to clinical demographics, progression-free survival (PFS) and overall survival (OS).

Results

A total of 104 patients were evaluated. Somatic alterations were detected in 84.6% of the patients. Patients with ≥ 2 detectable genomic alterations had worse median PFS (p < 0.001) and worse median OS (p = 0.001). KRAS was associated with disease progression to systemic treatments (80.4% vs 19.6%, p = 0.006), worse median PFS (p < 0.001) and worse median OS (p = 0.002). TP53 was associated with worse median PFS (p = 0.02) and worse median OS (p = 0.001). The median DCAF was 0.45% (range 0-55%). DCAF >0.45% was associated with worse median PFS (p<0.0001) and median OS (p=0.0003). Patients that achieved clearance of KRAS had better PFS (p=0.047), while patients that achieved clearance of TP53 had better PFS (p=0.0056) and OS (p=0.037).

Conclusions

Initial detection of ctDNA in advanced PC can identify somatic alterations that may help predict clinical outcomes. The dynamics of ctDNA are prognostic of outcomes and should be evaluated in prospective studies.

Integrated 5-hydroxymethylcytosine and fragmentation signatures as enhanced biomarkers in lung cancer

Background

Lung cancer is one of most common cancers worldwide, with a 5-year survival rate of less than 20%, which is mainly due to late-stage diagnosis. Noninvasive methods using 5-hydroxymethylation of cytosine (5hmC) modifications and fragmentation profiles from 5hmC cell-free DNA (cfDNA) sequencing provide an opportunity for lung cancer detection and management.

Results

A total of 157 lung cancer patients were recruited to generate the largest lung cancer cfDNA 5hmC dataset, which mainly consisted of 62 lung adenocarcinoma (LUAD), 48 lung squamous cell carcinoma (LUSC) and 25 small cell lung cancer (SCLC) patients, with most patients (131, 83.44%) at advanced tumor stages. A 37-feature 5hmC model was constructed and validated to distinguish lung cancer patients from healthy controls, with areas under the curve (AUCs) of 0.8938 and 0.8476 (sensitivity = 87.50% and 72.73%, specificity = 83.87% and 80.60%) in two distinct validation sets. Furthermore, fragment profiles of cfDNA 5hmC datasets were first explored to develop a 48-feature fragmentation model with good performance (AUC = 0.9257 and 0.822, sensitivity = 87.50% and 78.79%, specificity = 80.65% and 76.12%) in the two validation sets. Another diagnostic model integrating 5hmC signals and fragment profiles improved AUC to 0.9432 and 0.8639 (sensitivity = 87.50% and 83.33%, specificity = 90.30% and 77.61%) in the two validation sets, better than models based on either of them alone and performing well in different stages and lung cancer subtypes. Several 5hmC markers were found to be associated with overall survival (OS) and disease-free survival (DFS) based on gene expression data from The Cancer Genome Atlas (TCGA).

Conclusions

Both the 5hmC signal and fragmentation profiles in 5hmC cfDNA data are sensitive and effective in lung cancer detection and could be incorporated into the diagnostic model to achieve good performance, promoting research focused on clinical diagnostic models based on cfDNA 5hmC data.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01233-7.

Emerging Role of Epigenetic Alterations as Biomarkers and Novel Targets for Treatments in Pancreatic Ductal Adenocarcinoma

Simple Summary

Epigenetic alterations cause changes in gene expression without affecting the DNA sequence and are found to affect several molecular pathways in pancreatic tumors. Such changes are reversible, making them potential drug targets. Furthermore, epigenetic alterations occur early in the disease course and may thus be explored for early detection. Hence, a deeper understanding of epigenetics in pancreatic cancer may lead to improved diagnostics, treatments, and prognostication.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with limited treatment options. Emerging evidence shows that epigenetic alterations are present in PDAC. The changes are potentially reversible and therefore promising therapeutic targets. Epigenetic aberrations also influence the tumor microenvironment with the potential to modulate and possibly enhance immune-based treatments. Epigenetic marks can also serve as diagnostic screening tools, as epigenetic changes occur at early stages of the disease. Further, epigenetics can be used in prognostication. The field is evolving, and this review seeks to provide an updated overview of the emerging role of epigenetics in the diagnosis, treatment, and prognostication of PDAC.

Liquid Biopsies beyond Mutation Calling: Genomic and Epigenomic Features of Cell-Free DNA in Cancer

Cell-free DNA (cfDNA) analysis using liquid biopsies is a non-invasive method to gain insights into the biology, therapy response, mechanisms of acquired resistance and therapy escape of various tumors. While it is well established that individual cancer treatment options can be adjusted by panel next-generation sequencing (NGS)-based evaluation of driver mutations in cfDNA, emerging research additionally explores the value of deep characterization of tumor cfDNA genomics and fragmentomics as well as nucleosome modifications (chromatin structure), and methylation patterns (epigenomics) for comprehensive and multi-modal assessment of cfDNA. These tools have the potential to improve disease monitoring, increase the sensitivity of minimal residual disease identification, and detection of cancers at earlier stages. Recent progress in emerging technologies of cfDNA analysis is summarized, the added potential clinical value is highlighted, strengths and limitations are identified and compared with conventional targeted NGS analysis, and current challenges and future directions are discussed.

Liquid biopsy approach to pancreatic cancer

Pancreatic cancer (PC) continues to pose a major clinical challenge. There has been little improvement in patient survival over the past few decades, and it is projected to become the second leading cause of cancer mortality by 2030. The dismal 5-year survival rate of less than 10% after the diagnosis is attributable to the lack of early symptoms, the absence of specific biomarkers for an early diagnosis, and the inadequacy of available chemotherapies. Most patients are diagnosed when the disease has already metastasized and cannot be treated. Cancer interception is vital, actively intervening in the malignization process before the development of a full-blown advanced tumor. An early diagnosis of PC has a dramatic impact on the survival of patients, and improved techniques are urgently needed to detect and evaluate this disease at an early stage. It is difficult to obtain tissue biopsies from the pancreas due to its anatomical position; however, liquid biopsies are readily available and can provide useful information for the diagnosis, prognosis, stratification, and follow-up of patients with PC and for the design of individually tailored treatments. The aim of this review was to provide an update of the latest advances in knowledge on the application of carbohydrates, proteins, cell-free nucleic acids, circulating tumor cells, metabolome compounds, exosomes, and platelets in blood as potential biomarkers for PC, focusing on their clinical relevance and potential for improving patient outcomes.

Role of blood mSEPT9 in evaluating tumor burden and disease monitoring in colorectal cancer patients

Purpose

This study aimed to investigate the correlation between mSEPT9 and tumor burden as well as the role of mSEPT9 in monitoring colorectal cancer (CRC) patients.

Methods

A total of 309 patients were recruited and received mSEPT9 detection in this retrospective study. Clinicopathologic characteristics were collected, including age, gender, differentiation, gene mutation, stage, and tumor markers. The correlation between mSEPT9 and clinical tumor burden was analyzed. A relative mSEPT9 value was determined using the ΔΔC_t method.

Results

The overall positivity rate of mSEPT9 was 39.8% in CRC patients. mSEPT9 status was significantly associated with disease status and tumor markers (CEA and CA19‐9). The mSEPT9 positivity rates were 15.6%, 50.0%, 64.4%, and 70.0% for P0M0, P1M0, P0M1, and P1M1 patients, respectively (p < 0.001). Among 137 CRC patients who received mSEPT9 assay before surgery, the pre‐operation mSEPT9 positivity rate increased significantly from stage I to stage IV (Stage I vs. II vs. III vs. IV 25% vs. 59.1% vs. 57.1% vs. 70.0%, respectively). Consecutive blood samples were obtained from 26 patients during therapy. The patients with increased mSEPT9 levels showed a higher progression rate.

Conclusions

mSEPT9 was a biomarker reflecting tumor burden, and serial detections of mSEPT9 could be a promising strategy for disease monitoring in CRC patients.

Downregulation of 5-hydroxymethylcytosine is an early event in pancreatic tumorigenesis

Epigenetic alterations are increasingly recognized as important contributors to the development and progression of pancreatic ductal adenocarcinoma. 5-hydroxymethylcytosine (5hmC) is an epigenetic DNA mark generated through the ten-eleven translocation (TET) enzyme-mediated pathway and is closely linked to gene activation. However, the timing of alterations in epigenetic regulation in the progression of pancreatic neoplasia is not well understood. In this study, we hypothesized that aberrant expression of ten-eleven translocation methylcytosine dioxygenase 1 (TET1) and subsequent global 5hmC alteration are linked to early tumorigenesis in the pancreas. Therefore, we evaluated alterations of 5hmC and TET1 levels using immunohistochemistry in pancreatic neoplasms (n = 380) and normal ducts (n = 118). The study cohort included representation of the full spectrum of precancerous lesions from low- and high-grade pancreatic intraepithelial neoplasia (n = 95), intraductal papillary mucinous neoplasms (all subtypes, n = 129), intraductal oncocytic papillary neoplasms (n = 12), and mucinous cystic neoplasms (n = 144). 5hmC and TET1 were significantly downregulated in all types of precancerous lesion and associated invasive pancreatic ductal adenocarcinomas compared with normal ductal epithelium (all p < 0.001), and expression of 5hmC positively correlated with expression of TET1. Importantly, downregulation of both 5hmC and TET1 was observed in most low-grade precancerous lesions. There were no clear associations between 5hmC levels and clinicopathological factors, thereby suggesting a common epigenetic abnormality across precancerous lesions. We conclude that downregulation of 5hmC and TET1 is an early event in pancreatic tumorigenesis. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

The Detection of Cancer Epigenetic Traces in Cell-Free DNA

Nucleic acid fragments found in blood circulation originate mostly from dying cells and carry signs pointing to specific features of the parental cell types. Deciphering these clues may be transformative for numerous research and clinical applications but strongly depends on the development and implementation of robust analytical methods. Remarkable progress has been achieved in the reliable detection of sequence alterations in cell-free DNA while decoding epigenetic information from methylation and fragmentation patterns requires more sophisticated approaches. This review discusses the currently available strategies for detecting and analyzing the epigenetic marks in the liquid biopsies.

Liquid Biopsy in Pancreatic Cancer: Are We Ready to Apply It in the Clinical Practice?

Pancreatic ductal adenocarcinoma (PDAC) exhibits the poorest prognosis of all solid tumors, with a 5-year survival of less than 10%. To improve the prognosis, it is necessary to advance in the development of tools that help us in the early diagnosis, treatment selection, disease monitoring, evaluation of the response and prognosis. Liquid biopsy (LB), in its different modalities, represents a particularly interesting tool for these purposes, since it is a minimally invasive and risk-free procedure that can detect both the presence of genetic material from the tumor and circulating tumor cells (CTCs) in the blood and therefore distantly reflect the global status of the disease. In this work we review the current status of the main LB modalities (ctDNA, exosomes, CTCs and cfRNAs) for detecting and monitoring PDAC.

Emerging noninvasive methylation biomarkers of cancer prognosis and drug response prediction

Cancer is the second leading cause of death worldwide being responsible for 9.6 million deaths in 2018. Epigenetic alterations are key in directing the aberrant expression of tumor-associated genes that drive cellular malignant transformation and cancer progression. Among epigenetic alterations, DNA methylation is the most deeply studied one in relation to environmental exposure. Tissue biopsies have traditionally been the main procedure by which a small sample of body tissue is excised to confirm cancer diagnosis or to indicate the primary site when cancer has spread. In contrast, the analysis of circulating tumor-derived material, or tumor circulome, by means of liquid biopsy of peripheral blood, urine, saliva or sputum is a noninvasive, fast and reproducible alternative to tissue biopsy. Recently, the assessment of epigenetic alterations such as DNA methylation and hydroxymethylation in circulating free DNA has been proved possible. These marks can be associated to prognosis and response to a variety of treatments including chemotherapy, hormonotherapy or immunotherapy. Epigenetic biomarkers may offer some advantages over RNA or genetic biomarkers given their stability in bodily fluids and their high tissue-specificity. While many challenges are still ahead, the unique advantages of these types of biomarkers is urging the scientific community to persevere in their clinical validation and integration into reliable prediction models. This review aims at recapitulating the emerging noninvasive DNA methylated biomarkers of importance for prediction of prognosis and drug response in cancer.

Discovery and validation of methylation signatures in blood-based circulating tumor cell-free DNA in early detection of colorectal carcinoma: a case-control study

BACKGROUND

Early detection of colorectal carcinoma (CRC) would help to identify tumors when curative treatments are available and beneficial. However, current screening methods for CRC, e.g., colonoscopy, may affect patients' compliance due to the uncomfortable, invasive and time-consuming process. In recent decades, methylation profiles of blood-based circulating tumor DNA (ctDNA) have shown promising results in the early detection of multiple tumors. Here we conducted a study to investigate the performance of ctDNA methylation markers in early detection of CRC.

RESULTS

In total, 742 participants were enrolled in the study including CRC (n = 332), healthy control (n = 333), benign colorectal disease (n = 65) and advanced adenoma (n = 12). After age-matched and randomization, 298 participants (149 cancer and 149 healthy control) were included in training set and 141 (67 cancer and 74 healthy control) were in test set. In the training set, the specificity was 89.3% (83.2-93.7%) and the sensitivity was 88.6% (82.4-93.2%). In terms of different stages, the sensitivities were 79.4% (62.1-91.2%) in patients with stage I, 88.9% (77.3-95.8%) in patients with stage II, 91.4% (76.9-98.2%) in patients with stage III and 96.2% (80.3-99.9%) in patients with stage IV. Similar results were validated in the test set with the specificity of 91.9% (83.1-97.0%) and sensitivity of 83.6% (72.5-91.6%). Sensitivities for stage I-III were 87.0% (79.7-92.4%) in the training set and 82.5% (70.2-91.3%) in the test set, respectively. In the unmatched total population, the positive ratios were 7.8% (5.2-11.2%) in healthy control, 30.8% (19.9-43.5%) in benign colorectal disease and 58.3% (27.5-84.7%) in advanced adenoma, while the sensitivities of stage I-IV were similar with training and test sets. Compared with methylated SEPT9 model, the present model had higher sensitivity (87.0% [81.8-91.2%] versus 41.2% [34.6-48.1%], P < 0.001) under comparable specificity (90.1% [85.4-93.7%] versus 90.6% [86.0-94.1%]).

CONCLUSIONS

Together our findings showed that ctDNA methylation markers were promising in the early detection of CRC. Further validation of this model is warranted in prospective studies.