Breast cancer is one of the most prevalent cancers and has emerged as a major global challenge. Circulating tumor DNA (ctDNA), a liquid biopsy method, overcomes the accessibility limitations of tissue-based testing and is widely used for monitoring minimal residual disease and molecular relapse, predicting prognosis, evaluating the response of neoadjuvant therapy, and optimizing treatment decisions in non-metastatic breast cancer. However, the application of ctDNA still faces many challenges. Here, we survey the clinical applications of ctDNA in non-metastatic breast cancer and discuss the significant biological and technical challenges of utilizing ctDNA. Importantly, we investigate potential avenues for addressing the challenges. In addition, emerging technologies, including fragmentomics detection, methylation sequencing, and long-read sequencing, have clinical potential and could be a future direction. Proper utilization of machine learning facilitates the identification of meaningful patterns from complex fragment and methylation profiles of ctDNA. There is still a lack of clinical trials focused on the subsets of ctDNA (e.g., circulating mitochondrial DNA), ctDNA-inferred drug-resistant clonal evolution, tumor heterogeneity, and ctDNA-guided clinical decision-making in non-metastatic breast cancer. Due to regional differences in the number of registered clinical trials, it is essential to enhance communication and foster global collaboration to advance the field.

Breast cancer remains one of the most common cancers in women worldwide. Early detection is critical for improving patient outcomes, yet current screening methods have limitations. Therefore, there is a pressing need for more sensitive and specific approaches to detect breast cancer in its earliest stages. Liquid biopsy has emerged as a promising non-invasive method for early cancer detection and management. DNA methylation, an epigenetic alteration that often precedes genetic changes, has been observed in precancerous or early cancer stages, making it a valuable biomarker. This review explores the role of DNA methylation in breast cancer and its potential for developing blood-based tests. We discuss advancements in DNA methylation detection methods, recent discoveries of potential DNA methylation biomarkers from both single-omics and multi-omics integration studies, and the role of machine learning in enhancing diagnostic accuracy. Challenges and future directions are also addressed. Although challenges remain, advances in multi-omics integration and machine learning continue to enhance the clinical potential of methylation-based biomarkers. Ongoing research is crucial to further refine these approaches and improve early detection and patient outcomes.

Restrictions resulting from the COVID-19 pandemic abruptly reversed the slow decline of the diagnosis and mortality rates of gastric cancer (GC). This scenario highlights the importance of developing cost-effective methods for mass screening and evaluation of treatment response. In this study, we evaluated a non-invasive method based on the circulating methylated cell-free DNA (cfDNA) of Reprimo (RPRM), a tumor suppressor gene associated with the development of GC. Methylated RPRM cfDNA was analyzed in three de-identified cohorts: Cohort 1 comprised 81 participants with GC and 137 healthy donors (HDs); Cohort 2 comprised 27 participants with GC undergoing gastrectomy and/or chemotherapy analyzed at the beginning and after three months of treatment; and Cohort 3 comprised 1105 population-based participants in a secondary prevention program who underwent esophagogastroduodenal (EGD) endoscopy. This cohort includes 180 normal participants, 845 participants with premalignant conditions (692 with chronic atrophic gastritis [AG] and 153 with gastric intestinal metaplasia/low-grade dysplasia [GIM/LGD]), 21 with high-grade dysplasia/early GC [HGD/eGC], and 59 with advanced GC [aGC]). A nested case-control substudy was performed using a combination of methylated RPRM cfDNA and pepsinogens (PG)-I/II ratio. The dense CpG island of the promoter region of the RPRM gene was bisulfite sequenced and analyzed to develop a fluorescence-based real-time PCR assay (MethyLight). This assay allows the determination of the absolute number of copies of methylated RPRM cfDNA. A targeted sequence of PCR amplicon products confirmed the gastric origin of the plasma-isolated samples. In Cohort 1, the mean value of GCs (32,240.00 copies/mL) was higher than that of the HD controls (139.00 copies/mL) (p < 0.0001). After dividing this cohort into training-validation subcohorts, we identified an area under the curve of 0.764 (95% confidence interval (CI) = 0.683-0.845) in the training group. This resulted in a cut-off value of 87.37 copies/mL (sensitivity 70.0% and specificity 80.2%). The validation subcohort predicted a sensitivity of 66.67% and a specificity of 83.33%. In Cohort 2 (monitoring treatment response), RPRM levels significantly decreased in responders (p = 0.0042) compared to non-responders. In Cohort 3 (population-based participants), 18.9% %, 24.1%, 30.7%, 47.0%, and 71.2% of normal, AG, GIM/LGD, HGD/eGC, and aGC participants tested positive for methylated RPRM cfDNA, respectively. Overall sensitivity and specificity in distinguishing normal/premalignant conditions vs. GC were 65.0% (95% CI 53.52% to 75.33%) and 75.9% (95% CI 73.16% to 78.49%), respectively, with an accuracy of 75.11% (95% CI 72.45% to 77.64%). Logistic regression analyses revealed an OR of 1.85 (95% CI 1.11-3.07, p = 0.02) and an odds ratio (OR) of 3.9 (95% CI 1.53-9.93, p = 0.004) for the risk of developing GIM/LGD and HGD/eGC, respectively. The combined methylated RPRM cfDNA and PG-I/II ratio reached a sensitivity of 78.9% (95% CI 54.43% to 93.95%) and specificity of 63.04% (95% CI 52.34% to 72.88%) for detecting HGD/eGC vs. three to six age- and sex-matched participants with premalignant conditions. Our results demonstrate that methylated RPRM cfDNA should be considered a direct biomarker for the non-invasive detection of GC and a predictive biomarker for treatment response.

DNA methylation carrying epigenetic aberrations could potentially serve as a non-invasive tool for revolutionizing cancer diagnosis and monitoring. Here, we comprehensively evaluated the diagnostic value of plasma methylated HIST1H3G, and constructed diagnostic and prognostic models aimed at facilitating early detection and improving the prognosis of hepatocellular carcinoma (HCC).

The level of HIST1H3G promoter methylation in HCC tissues was evaluated based on the UALCAN database, followed by validation through serum samples collected from HCC patients. We recruited 205 participants, encompassing 70 HCC patients, 79 liver cirrhosis (LC) patients, 46 hepatitis patients and 10 HCC patients before and after treatment with either transarterial chemoembolization (TACE) or radiofrequency ablation (RFA). Analysis of plasma HIST1H3G was performed using methylation-specific quantitative polymerase chain reaction (qPCR). Diagnostic and prognostic prediction models were formulated using the random forest algorithm, and the performance of these models was rigorously evaluated through receiver operating characteristics curve (ROC) analysis.

The methylation level of HIST1H3G was markedly elevated in both HCC tissues and plasma samples derived from HCC patients. HIST1H3G, PIVKA-II, total bilirubin (TBIL) and age were selected as the optimal markers and were included in the development of a diagnostic model. This model demonstrated superior accuracy in distinguishing HCC from high-risk populations, outperforming alpha-fetoprotein (AFP) in both the training cohort consisting of LC patients and the validation cohort comprising hepatitis patients. Additionally, HIST1H3G and albumin (Alb) were chosen to establish a prediction model for early HCC diagnosis, and this model exhibited a remarkable ability to identify early HCC. Furthermore, our prognostic prediction model proved effective in predicting the prognosis and survival outcomes of HCC patients.

Together, we identified and validated a diagnostic model that incorporated methylated HIST1H3G and clinically applicable serological indicators in HCC. The findings of our study established a pivotal foundation for the development of a non-invasive approach to identification and management in HCC.

Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement and refinement, artificial intelligence (AI) has demonstrated exceptional capabilities in processing intricate multidimensional BC-related data. AI has proven advantageous in various facets of BC management, encompassing efficient screening and diagnosis, precise prognosis assessment, and personalized treatment planning. However, the implementation of AI into precision medicine and clinical practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, and integration of multiple clinical pathways. In this review, we provide a comprehensive overview of the current research related to AI in BC, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact. Furthermore, this article emphasizes the significance of constructing patient-oriented AI algorithms. Additionally, we explore the opportunities and potential research directions within this burgeoning field.

The value of integrating clinical variables, radiomics, and tumor-derived cell-free DNA (cfDNA) for the prediction of survival and response to chemoradiation of patients with resectable esophageal adenocarcinoma is not yet known. Our aim was to investigate if radiomics and cfDNA metrics combined with clinical variables can improve personalized predictions.

A cohort of 111 patients with resectable esophageal adenocarcinoma from 2 centers treated with neoadjuvant chemoradiation therapy was used for exploratory retrospective analyses. Models combining the clinical variables of the SOURCE survival model with radiomic features and cfDNA were built using elastic net regression and internally validated using 5-fold cross-validation. Model performance for overall survival (OS) and time to progression (TTP) were evaluated with the C-index and the area under the curve for pathologic complete response.

The best-performing baseline models for OS and TTP were based on the combination of SOURCE-cfDNA that reached a C-index of 0.55 and 0.59 compared with 0.44 to 0.45 with SOURCE alone. The addition of restaging positron emission tomography radiomics to SOURCE was the most promising addition for predicting OS (C-index: 0.65) and TTP (C-index: 0.60). Baseline risk stratification was achieved for OS and TTP by combining SOURCE with radiomics or cfDNA, log-rank P < .01. The best-performing combination model for the prediction of pathologic complete response reached an area under the curve of 0.61 compared with 0.47 with SOURCE variables alone.

The addition of radiomics and cfDNA can improve the performance of an established survival model. External validity needs to be further assessed in future studies together with the optimization of radiomic pipelines.

Hepatocellular carcinoma (HCC) is a major global health concern, being the sixth most common cancer and the third leading cause of cancer deaths worldwide. Less than 30% of HCC patients are eligible for curative treatment, primarily due to diagnosis at advanced stages. This emphasizes the importance of early detection in improving survival outcomes. In this study, we investigated the methylation levels of certain genes and miRNAs in liquid biopsy and developed a methyl predictive model (MPM-8G). The AUC for MPM-8G was found to be significantly higher than that for AFP (alpha-fetoprotein) alone. When MPM-8G and AFP were combined, the AUC increased notably, indicating that the combined use of MPM-8G and AFP offers superior diagnostic performance and enhances the accuracy of HCC detection. Furthermore, the combination of MPM-8G and AFP proved to be a powerful tool for early diagnosis of HCC. This study successfully identified differences in the methylation levels of certain genes and miRNAs in liquid biopsy from HCC patients, leading to the construction of a predictive model for early diagnosis. The impressive performance of these methylation markers underscores their potential for further clinical application in the management of HCC.

Circulating tumor DNA (ctDNA) is nowadays considered a robust source to search for druggable tumoral genetic alterations, and in some specific settings liquid biopsy (LB) is already part of the diagnostics scenario and it has successfully implemented in the everyday practice. Three strengths make LB an extraordinary tool: i) to represent the complex molecular mosaicism that characterizes spatially heterogeneous malignancies; ii) to monitor in real-time the tumoral molecular landscape (i.e. to depict the longitudinal/temporal tumor evolution); iii) to ensure molecular profiling even in those cases in which tissue sampling is not feasible or not adequate. This review provides a snapshot of the current state of the art concerning ctDNA assay utility in gastric cancer (GC), testing its robustness as marker and seeking to understand the reasons for the delay in its application in clinical practice.

Lung cancer is the leading cause of cancer-related mortality worldwide, highlighting the urgent need for more accurate and minimally invasive diagnostic tools to improve early detection and patient outcomes. While low-dose computed tomography (LDCT) is effective for screening in high-risk individuals, its high false-positive rate necessitates more precise diagnostic strategies. Liquid biopsy, particularly ctDNA methylation analysis, represents a promising alternative for non-invasive classification of indeterminate pulmonary nodules (IPNs). This review highlights the progress and clinical potential of liquid biopsy technologies, including traditional proteins markers, cfDNA, exosomes, metabolomics, circulating tumor cells (CTCs) and platelets, in lung cancer diagnosis. We discuss the integration of ctDNA methylation analysis with traditional imaging and clinical data to enhance the early detection of IPNs, as well as potential solutions to address the challenges of low biomarker concentration and background noise. By advancing precision diagnostics, liquid biopsy technologies could transform lung cancer management, improve survival rates, and reduce the disease burden.

Cell-free DNA (cfDNA) in plasma carries epigenetic signatures specific to tissue or cell of origin. Aberrant methylation patterns in circulating cfDNA have emerged as valuable tools for noninvasive cancer detection, prenatal diagnostics, and organ transplant assessment. Such epigenetic changes also hold significant promise for the diagnosis of neurodegenerative diseases, which often progresses slowly and has a lengthy asymptomatic period. However, genome-wide cfDNA methylation changes in neurodegenerative diseases remain poorly understood.

We used whole-genome bisulfite sequencing (WGBS) to profile age-dependent and ALS-associated methylation signatures in cfDNA from 30 individuals, including young and middle-aged controls, as well as ALS patients with matched controls. We identified 5,223 age-related differentially methylated loci (DMLs) (FDR < 0.05), with 51.6% showing hypomethylation in older individuals. Our results significantly overlapped with age-associated CpGs identified in a large blood-based epigenome-wide association study (EWAS). Comparing ALS patients to controls, we detected 1,045 differentially methylated regions (DMRs) in gene bodies, promoters, and intergenic regions. Notably, these DMRs were linked to key ALS-associated pathways, including endocytosis and cell adhesion. Integration with spinal cord transcriptomics revealed that 31% of DMR-associated genes exhibited differential expression in ALS patients compared to controls, with over 20 genes significantly correlating with disease duration. Furthermore, comparison with published single-nucleus RNA sequencing (snRNA-Seq) data of ALS demonstrated that cfDNA methylation changes reflects cell-type-specific gene dysregulation in the brain of ALS patients, particularly in excitatory neurons and astrocytes. Deconvolution of cfDNA methylation profiles suggested altered proportions of immune and liver-derived cfDNA in ALS patients.

cfDNA methylation is a powerful tool for assessing age-related changes and ALS-specific molecular dysregulation by revealing perturbed locus, genes, and the proportional contributions of different tissues/cells to the plasma. This technique holds promise for clinical application in biomarker discovery across a broad spectrum of neurodegenerative disorders.

Primary live cancer (PLC), including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). This meta-analysis was conducted to evaluate the diagnostic efficacy of blood methylated septin 9 gene (mSEPT9) for PLC and to analyze its performance across various subgroups.

We conducted a comprehensive search across PubMed, the Cochrane Library, Embase, and China National Knowledge Infrastructure (CNKI), covering research up to May 10, 2024. The pooled sensitivity, specificity, diagnostic odds ratios, and area under the summary receiver operating characteristic (AUC) were calculated for the diagnostic performance of mSEPT9 for PLC. The quality of the studies was assessed using the QUADAS-2 tool, and the meta-analysis was performed using Stata16.0 software.

Ten articles with 2,182 participants were included in the meta-analysis. The pooled sensitivity of mSEPT9 for detecting primary liver cancer was 0.51 (95% confidence interval [CI]: 0.37-0.65), and the pooled specificity was 0.93 (95% CI: 0.78-0.98). The pooled diagnostic odds ratio was 13 (95% CI: -58), and the area under the Summary Receiver Operator Characteristic Curve was 0.75 (95% CI: 0.71-0.79). Subgroup analyses showed that ICC, case-control studies, qPCR and Asian populations had higher specificities (0.99 [95% CI: 0.97-1.00], 0.93 [95% CI: 0.91-0.95], 0.90 [95% CI: 0.88-0.92] and 0.94 [95% CI: 0.92-0.96], respectively) and diagnostic odds ratios (62.04 [95% CI: 6.53-589.53], 17.62 [95% CI: 4.03-76.99], 13.03 [95% CI: 2.01-84.63] and 14.19 [95% CI: 2.42-83.11], respectively) compared to hepatocellular carcinoma, cohort Study, and Euramerican populations.

This study confirmed that mSEPT9 in blood has high specificity and moderate sensitivity for detecting primary liver cancer. The diagnostic performance of mSEPT9 varied across different subgroups, limiting its use as an independent screening tool and necessitating its use in conjunction with other methods for confirmatory diagnostics.

https://www.crd.york.ac.uk/prospero/, identifier CRD42024549669.

Rapid advancements in science and technology have allowed medical providers to treat wider ranges of diseases with safer and more effective therapies than ever before. One of the areas of health that has been consistently understudied, however, is one that affects us all: environmental health or the effects that the chemicals we are exposed to every day have on our acute and chronic health. This effect can be exacerbated during and shortly after pregnancy, as an individual exposure is often shared by both the mother and the fetus/neonate. The diagnosis and monitoring of chemical exposure can be quite challenging, and improving our understanding of the effects of exposure will therefore require effective use of an expanding set of biomarker tests and biological matrices. This review covers the background and history of neonatal biomarkers of exposure, effect, and susceptibility, focusing on the potential uses for the non-invasive matrix of exhaled breath for the detection and monitoring of chemical exposures. IMPACT: Provides a brief overview of Food and Drug Administration and National Institutes of Health Joint Leadership Council BEST (Biomarkers, EndpointS, and other Tools) Resource. Summarizes new and potential biomarkers for fetal exposure. Collates studies using breath as a matrix for environmental exposures.

Globally, breast cancer is the most frequent type of cancer, and its early diagnosis and screening can significantly improve the probability of survival and quality of life of those affected. Liquid biopsy-based targets such as circulating tumor cells, circulating tumor DNA, and exosomes have been instrumental in the early discovery of cancer, and have been found to be effective in stage therapy, recurrence monitoring, and drug selection. Biosensors based on these target related biomarkers convert the tested substances into quantifiable signals such as electrical and optical signals through signal transduction, which has the advantages of high sensitivity, simple operation, and low invasiveness. This review provides an overview of the latest progress of liquid biopsy biomarkers in the diagnosis, prognosis and treatment of breast cancer, compares the application and advantages of different biosensors based on these biomarkers in the diagnosis of breast cancer, and analyzes the limitations and solutions of biosensor based methods.

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.

Renal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease.

High-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively.

Comparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7).

Our findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications.

This review provides a comprehensive overview of the evolving role of minimal residual disease (MRD) for patients with Colon Cancer (CC). Currently, the standard of care for patients with non-metastatic CC is adjuvant chemotherapy (ACT) for all patients with stage III and high-risk stage II CC following surgical intervention. Despite a 5-20% improvement in long-term survival outcomes, this approach also results in a significant proportion of patients receiving ACT without any therapeutic benefit and being unnecessarily exposed to the risks of secondary side effects. This underscores an unmet clinical need for more precise stratification to distinguish patients who necessitate ACT from those who can be treated with surgery alone. By employing liquid biopsy, it is possible to discern MRD enabling the categorization of patients as MRD-positive or MRD-negative, potentially revolutionizing the management of ACT. This review aimed to examine the heterogeneity of methodologies currently available for MRD detection, encompassing the state-of-the-art technologies, their respective advantages, limitations, and the technological challenges and multi-omic approaches that can be utilized to enhance assay performance. Furthermore, a discussion was held regarding the clinical trials that employ an MRD assay focusing on the heterogeneity of the assays used. These differences in methodology, target selection, and performance risk producing inconsistent results that may not solely reflect biological/clinical differences but may be the consequence of the preferential use of particular products in studies conducted in different countries. Standardization and harmonization of MRD assays will be crucial to ensure the liquid revolution delivers reliable and clinically actionable outcomes for patients.

Enhancing the effectiveness of utilizing circulating cell-free DNA (cfDNA) for disease screening remains a challenge, necessitating improved sensitivity, specificity, cost-efficiency, and patient adherence. We present here LAMP-MS, an innovative technology that integrates linear amplification with single-base quantitative nucleic acid mass spectrometry on silicon chips. This approach overcomes several limitations in utilizing cfDNA 5-methylcytosine (5 mC) status for colorectal cancer (CRC) screening. LAMP-MS enables unbiased amplification of as little as 1 ng of cfDNA, site-specifically quantify methylation levels of multiple 5 mC sites, thereby facilitating cost-effective, high-resolution quantitative detection of cfDNA methylation markers. We have validated the accuracy of DNA methylation determination using DNA probes and cfDNA from patient plasma samples, confirmed by mass spectrometric peak areas. Additionally, we have further shown this Mass Array technology could be expanded to also quantify RNA m6A modification sites. Combining the ability to work with ultra-low input materials and a visually interpretable output, LAMP-MS stands out as a promising method for real-world applications in clinics and laboratories for nucleic acid methylation detection and quantification.

Small cell lung cancer (SCLC) is highly aggressive with poor prognosis. Despite a relative prevalence of circulating tumour DNA (ctDNA) in SCLC, liquid biopsies are not currently implemented, unlike non-SCLC where cell-free DNA (cfDNA) mutation profiling in the blood has utility for guiding targeted therapies and assessing minimal residual disease. cfDNA methylation profiling is highly sensitive for SCLC detection and holds promise for disease monitoring and molecular subtyping; cfDNA fragmentation profiling has also demonstrated clinical potential. Extrachromosomal DNA (ecDNA), that is often observed in SCLC, promotes tumour heterogeneity and chemotherapy resistance and can be detected in blood. We discuss how these cfDNA profiling modalities can be harnessed to expand the clinical applications of liquid biopsy in SCLC.

Pregnancy significantly alters the maternal immune system, affecting fetal development. The collection of tissues from the human placenta and fetus is not ethically or practically feasible at various gestational stages, thus limiting the study of gene expression in the fetus and placenta. Recent studies have shown that plasma cell-free DNA (cfDNA) nucleosome patterns can predict gene expression in the source tissue, offering insights into an individual's health status. This study aimed to identify pregnancy-related gene expression changes across gestational periods using cfDNA nucleosome distribution to understand fetal development and maternal immune changes.

Plasma samples were collected from 150 healthy pregnant women in different trimesters (early, mid, and late) and 32 healthy nonpregnant women. The correlation between gene expression and physiological changes during pregnancy was evaluated by inferring differential expression profiles around the transcription start site (TSS) using cfDNA nucleosome distribution patterns obtained through whole-genome sequencing. We utilized Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to annotate differentially expressed genes with the mother and fetus.

We identified gene expression changes that support the regulation of fetal development and immune system function during pregnancy. Differential coverage genes were mainly enriched in pathways related to transcription and translation, organic compound metabolism, and immune regulation. In addition, differentially expressed genes with significant temporal trends were identified. Among them, the upregulated differential genes were mainly related to development, whereas those with downregulated trends were mainly related to the immune system response. This indicates that differential changes of the placenta and maternal are significantly correlated with the pregnancy status.

This study demonstrated the differential gene expression represented by the characteristic distribution of cfDNA nucleosome in maternal peripheral blood can effectively capture significant changes in maternal immunity and fetal development throughout pregnancy stages. It may help identify abnormal gene expression patterns associated with complications in pregnancy and childbirth, enhancing the quality of life and safety for both mother and fetus.

In recent years, immunotherapy, namely immune checkpoint inhibitor therapy, has significantly transformed the approach to treating various forms of cancer. Simultaneously, the adoption of clinical oncology has been sluggish due to the exorbitant expense of therapy, the adverse effects experienced by patients, and the inconsistency in treatment response among individuals. As a reaction, individualized methods utilizing predictive biomarkers have arisen as novel strategies for categorizing patients to achieve successful immunotherapy. Recently, the identification and examination of circulating tumor cells (CTCs) have gained attention as predictive indicators for the treatment of cancer patients undergoing chemotherapy and for personalized targeted therapy. CTCs have been found to exhibit immunological checkpoints in several types of solid tumors, which has contributed to our understanding of managing cancer immunotherapy. Circulating tumor cells (CTCs) present in the bloodstream have a crucial function in the formation of metastases. Nevertheless, the practical usefulness of existing CTC tests is mostly restricted by methodological limitations.

DNA methylation, as exemplified by cytosine-C5 methylation in mammals and adenine-N6 methylation in bacteria, is a key epigenetic process. Developing non-nucleoside inhibitors to cause DNA hypomethylation is crucial for treating various conditions without the toxicities associated with existing cytidine-based hypomethylating agents. This study characterized fifteen quinoline-based analogs, particularly compounds with additions like a methylamine (9) or methylpiperazine (11), which demonstrate similar low micromolar inhibitory potency against human DNMT1 and Clostridioides difficile CamA. These compounds (9 and 11) intercalate into CamA-bound DNA via the minor groove, causing a conformational shift that moves the catalytic domain away from the DNA. This study adds to the limited examples of DNA methyltransferases being inhibited by non-nucleotide compounds through DNA intercalation. Additionally, some quinoline-based analogs inhibit other DNA-interacting enzymes, such as polymerases and base excision repair glycosylases. Finally, compound 11 elicits DNA damage response via p53 activation in cancer cells.

Early detection of hepatocellular carcinoma (HCC) can greatly improve the survival rate of patients. We aimed to develop a novel marker panel based on cell-free DNA (cfDNA) methylation for the detection of HCC. The differentially methylated CpG sites (DMCs) specific for HCC blood diagnosis were selected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, then validated by the whole genome bisulphite sequencing (WGBS) of 12 paired HCC and paracancerous tissues. The clinical performance of the panel was evaluated using tissue samples [32 HCC, chronic liver disease (CLD), and healthy individuals] and plasma cohorts (173 HCC, 199 CLD, and 98 healthy individuals). The combination of G protein subunit beta 4 (GNB4) and Riplet had the optimal area under the curve (AUC) in seven candidates through TCGA, GEO, and WGBS analyses. In tissue validation, the GNB4 and Riplet showed an AUC of 100% with a sensitivity and specificity of 100% for detecting any-stage HCC. In plasma, it demonstrated a high sensitivity of 84.39% at 91.92% specificity, with an AUC of 92.51% for detecting any-stage HCC. The dual-marker panel had a higher sensitivity of 78.26% for stage I HCC than alpha-fetoprotein (AFP) of 47.83%, and a high sensitivity of 70.27% for detecting a single tumour (size ≤3 cm). In conclusion, we developed a novel dual-marker panel that demonstrates high accuracy in detecting HCC, surpassing the performance of AFP testing.

The incidence of neurodegenerative diseases is a growing concern worldwide, affecting individuals from diverse backgrounds. Although these pathologies are primarily associated with aging and genetic susceptibility, their severity varies among the affected population. Numerous studies have indicated air pollution as a significant contributor to the increasing prevalence of neurodegeneration. Cohort studies have provided compelling evidence of the association between prolonged exposure to different air toxicants and cognitive decline, behavioural deficits, memory impairment, and overall neuronal health deterioration. Furthermore, molecular research has revealed that air pollutants can disrupt the body's protective mechanisms, participate in neuroinflammatory pathways, and cause neuronal epigenetic modifications. The mitochondrial epigenome is particularly interesting to the scientific community due to its potential to significantly impact our understanding of neurodegenerative diseases' pathogenesis and their release in the peripheral circulation. While protein hallmarks have been extensively studied, the possibility of using circulating epigenetic signatures, such as methylated DNA fragments, miRNAs, and genome-associated factors, as diagnostic tools and therapeutic targets requires further groundwork. The utilization of circulating epigenetic signatures holds promise for developing novel prognostic strategies, creating paramount point-of-care devices for disease diagnosis, identifying therapeutic targets, and developing clinical data-based disease models utilizing multi-omics technologies and artificial intelligence, ultimately mitigating the threat and prevalence of neurodegeneration.

Alterations in DNA methylation profiles are typically found in cancer cells, combining genome-wide hypomethylation with hypermethylation of specific regions, such as CpG islands, which are normally unmethylated. Driving effects in cancer development have been associated with alteration of DNA methylation in certain regions, inducing, for example, the repression of tumor suppressor genes or the activation of oncogenes and retrotransposons. These alterations represent prime candidates for the development of specific markers for the detection, diagnosis and prognosis of cancer. In particular, these markers, distributed along the genome, provide a wealth of information that offers potential for innovation in the field of liquid biopsy, in particular thanks to the emergence of artificial intelligence for diagnostic purposes. This could overcome the limitations related to sensitivities and specificities, which remain too low for the most difficult applications in oncology: the detection of cancers at an early stage, the monitoring of residual disease and the analysis of brain tumors. In addition, targeting the enzymatic processes that control the epigenome offers new therapeutic strategies that could reverse the regulatory anomalies of these altered epigenomes.

Liquid biopsies, in particular, analysis of cell-free DNA, are expected to revolutionize the current landscape of cancer diagnostics and treatment. However, the existing methods for cfDNA-based liquid biopsies for cancer have certain limitations, such as fragment interruption and GC bias, which are likely to be resolved by the emerging Oxford Nanopore Technologies (ONT), characterized by long read-length, fast read-times, high throughput, and polymerase chain reaction-free. In this review, we summarized the current literatures regarding the feasibility and applications of cfDNA-based liquid biopsies using ONT for cancer management, a possible game-changer that we believe is promising in detecting multimodal biomarkers and can be applied in a wide range of oncology utilities including early screening, diagnosis, and treatment monitoring.

While nanopore sequencing is increasingly used for mapping DNA modifications, it is important to recognize false positive calls as they can mislead biological interpretations. To assist biologists and methods developers, we describe a framework for rigorous evaluation that highlights the use of false discovery rate with rationally designed negative controls capturing both general background and confounding modifications. Our critical assessment across multiple forms of DNA modifications highlights that while nanopore sequencing performs reliably for high-abundance modifications, including 5-methylcytosine (5mC) at CpG sites in mammalian cells and 5-hydroxymethylcytosine (5hmC) in mammalian brain cells, it makes a significant proportion of false positive detections for low-abundance modifications, such as 5mC at CpH sites, 5hmC and N6-methyldeoxyadenine (6mA) in most mammal cell types. This study highlights the urgent need to incorporate this framework in future methods development and biological studies, and advocates prioritizing nanopore sequencing for mapping abundant over rare modifications in biomedical applications.

Neurodegenerative diseases, such as Alzheimer's disease (AD), are a growing public health crisis, exacerbated by an aging global population and the lack of effective early disease-modifying therapies. Early detection of neurodegenerative disorders is critical to delaying symptom onset and mitigating disease progression, but current diagnostic tools often rely on detecting pathology once clinical symptoms have emerged and significant neuronal damage has already occurred. While disease-specific biomarkers, such as amyloid-beta and tau in AD, offer precise insights, they are too limited in scope for broader neurodegeneration screening for these conditions. Conversely, general biomarkers like neurofilament light chain (NfL) provide valuable staging information but lack targeted insights. Circulating cell-free DNA (cfDNA), released during cell death, is emerging as a promising biomarker for early detection. Derived from dying cells, cfDNA can capture both general neurodegenerative signals and disease-specific insights, offering multi-layered genomic and epigenomic information. Though its clinical potential remains under investigation, advances in cfDNA detection sensitivity, standardized protocols, and reference ranges could establish cfDNA as a valuable tool for early screening. cfDNA methylation signatures, in particular, show great promise for identifying tissue-of-origin and disease-specific changes, offering a minimally invasive biomarker that could transform precision neurology. However, further research is required to address technological challenges and validate cfDNA's utility in clinical settings. Here, we review recent work assessing cfDNA as a potential early biomarker in AD. With continued advances, cfDNA could play a pivotal role in shifting care from reactive to proactive, improving diagnostic timelines and patient outcomes.

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915-0.981), with a sensitivity of 79.3 % (95 % CI: 71.5-87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6-100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3-85.8 %) in stage I patients, 100 % (95 % CI: 100.0-100.0 %) in stage II patients, 100 % (95 % CI: 100.0-100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%-100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.

Circulating tumor DNA (ctDNA) methylation, an innovative liquid biopsy biomarker, has emerged as a promising tool in early cancer diagnosis, monitoring, and prognosis prediction. As a noninvasive approach, liquid biopsy overcomes the limitations of traditional tissue biopsy. Among various biomarkers, ctDNA methylation has garnered significant attention due to its high specificity and early detection capability across diverse cancer types. Despite its immense potential, the clinical application of ctDNA methylation faces substantial challenges pertaining to sensitivity, specificity, and standardization. In this review, we begin by introducing the basic biology and common detection techniques of ctDNA methylation. We then explore recent advancements and the challenges faced in the clinical application of ctDNA methylation in liquid biopsies. This includes progress in early screening and diagnosis, identification of clinical molecular subtypes, monitoring of recurrence and minimal residual disease (MRD), prediction of treatment response and prognosis, assessment of tumor burden, and determination of tissue origin. Finally, we discuss the future perspectives and challenges of ctDNA methylation detection in clinical applications. This comprehensive overview underscores the vital role of ctDNA methylation in enhancing cancer diagnostic accuracy, personalizing treatments, and effectively monitoring disease progression, providing valuable insights for future research and clinical practice.

The most well-studied epigenetic marker in humans is the 5-methyl modification of cytosine in DNA, which has great potential as a disease biomarker. Currently, quantification of DNA methylation relies heavily on bisulfite conversion followed by PCR amplification and NGS or microarray analysis. PCR is subject to potential bias in differential amplification of bisulfite-converted methylated versus unmethylated sequences. Here, we combine bisulfite conversion with single-molecule kinetic fingerprinting to develop an amplification-free assay for DNA methylation at the branched-chain amino acid transaminase 1 (BCAT1) promoter. Our assay selectively responds to methylated sequences with a limit of detection below 1 fM and a specificity of 99.9999%. Evaluating complex genomic DNA matrices, we reliably distinguish <5% DNA methylation at the BCAT1 promoter in whole blood DNA from completely unmethylated whole-genome amplified DNA. Taken together, these results demonstrate the feasibility and sensitivity of our amplification-free, single-molecule quantification approach to improve the early detection of methylated cancer DNA biomarkers.

Cancer continues to pose significant challenges to the medical community. Early detection, accurate molecular profiling, and adequate assessment of treatment response are critical factors in improving the quality of life and survival of cancer patients. Accumulating evidence shows that circulating tumor DNA (ctDNA) shed by tumors into the peripheral blood preserves the genetic and epigenetic information of primary tumors. Notably, DNA methylation, an essential and stable epigenetic modification, exhibits both cancer- and tissue-specific patterns. As a result, ctDNA methylation has emerged as a promising molecular marker for noninvasive testing in cancer clinics. In this review, we summarize the existing techniques for ctDNA methylation detection, describe the current research status of ctDNA methylation, and present the potential applications of ctDNA-based assays in the clinic. The insights presented in this article could serve as a roadmap for future research and clinical applications of ctDNA methylation.

Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence (AI) techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and AI applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and AI at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment.

Recent studies have highlighted the importance of the cell-free DNA (cfDNA) methylation profile in detecting breast cancer (BC) and its different subtypes. We investigated whether plasma cfDNA methylation, using cell-free Methylated DNA Immunoprecipitation and High-Throughput Sequencing (cfMeDIP-seq), may be informative in characterizing breast cancer in patients with BRCA1/2 germline mutations for early cancer detection and response to therapy.

We enrolled 23 BC patients with germline mutation of BRCA1 and BRCA2 genes, 19 healthy controls without BRCA1/2 mutation, and two healthy individuals who carried BRCA1/2 mutations. Blood samples were collected for all study subjects at the diagnosis, and plasma was isolated by centrifugation. Cell-free DNA was extracted from 1 mL of plasma, and cfMeDIP-seq was performed for each sample. Shallow whole genome sequencing was performed on the immuno-precipitated samples. Then, the differentially methylated 300-bp regions (DMRs) between 25 BRCA germline mutation carriers and 19 non-carriers were identified. DMRs were compared with tumor-specific regions from public datasets to perform an unbiased analysis. Finally, two statistical classifiers were trained based on the GLMnet and random forest model to evaluate if the identified DMRs could discriminate BRCA-positive from healthy samples.

We identified 7,095 hypermethylated and 212 hypomethylated regions in 25 BRCA germline mutation carriers compared to 19 controls. These regions discriminate tumors from healthy samples with high accuracy and sensitivity. We show that the circulating tumor DNA of BRCA1/2 mutant breast cancers is characterized by the hypomethylation of genes involved in DNA repair and cell cycle. We uncovered the TFs associated with these DRMs and identified that proteins of the Erythroblast Transformation Specific (ETS) family are particularly active in the hypermethylated regions. Finally, we assessed that these regions could discriminate between BRCA positives from healthy samples with an AUC of 0.95, a sensitivity of 88%, and a specificity of 94.74%.

Our study emphasizes the importance of tumor cell-derived DNA methylation in BC, reporting a different methylation profile between patients carrying mutations in BRCA1, BRCA2, and wild-type controls. Our minimally invasive approach could allow early cancer diagnosis, assessment of minimal residual disease, and monitoring of response to therapy.

A blood-based diagnostic test is a promising strategy for colorectal cancer (CRC). The MethyDT test (IColohunter), which detects methylation levels of NTMT1 and MAP3K14-AS1, exhibited potential in discriminating CRC, but its clinical performance needs to be validated in large-scale populations.

A multicenter, double-blinded, cross-sectional study that enrolled 1194 participants was performed. Plasma samples were collected to detect methylation levels of NTMT1 and MAP3K14-AS1 using quantitative methylation-specific PCR with the MethyDT test, and the accuracy was further evaluated by Sanger sequencing.

The sensitivities of the MethyDT test for detecting CRC, early stages of CRC (I and II), advanced adenoma (AA), and high-grade intraepithelial neoplasia (HGIN) were 91.2% (95% confidence interval [CI], 88.4-94.0), 87.4% (95% CI, 82.5-92.2), 43.5% (95% CI, 35.7-51.4), and 72.7% (95% CI, 57.5-87.9), respectively. The specificities for participants with non-AA, interfering diseases (ID), and no evidence of disease (NED) were 85.0% (95% CI, 78.8-91.3), 93.7% (95% CI, 91.4-95.9) and 97.3% (95% CI, 90.5-99.7), respectively, and its overall specificity for all-controls was 92.4% (95% CI, 90.3-94.4). The consistency of the MethyDT test with pathology for CRC was high with a kappa value of 0.830 (95% CI, 0.795-0.865). Additionally, the MethyDT test was comparable to Sanger sequencing for detecting methylation with kappa values > 0.97.

The MethyDT test demonstrates excellent sensitivity and specificity for CRC and high consistency with Sanger sequencing for methylation, suggesting it may serve as a potential noninvasive diagnostic tool for the detection of CRC.

This clinical trial has been registered in ClinicalTrials.gov (NCT05508503).

In metastatic hormone-sensitive prostate cancer (mHSPC), new first-line combination therapies have enhanced overall survival (OS), but clinical outcomes for individual patients vary greatly and are difficult to predict. Peripheral blood circulating tumor cell (CTC) count is the most extensively validated prognostic liquid biomarker in metastatic castration-resistant prostate cancer (mCRPC), and recent studies have suggested that it may also be informative in mHSPC.

To examine the prognostic value of CTC count in men with mHSPC.

In this prognostic study, peripheral blood was drawn at registration (baseline) and at progression to mCRPC in the S1216 study (March 1, 2013, to July 15, 2017), a phase 3, prospective, randomized clinical trial in men with mHSPC. The CTCs were enumerated using a US Food and Drug Administration-cleared isolation platform. Counts were categorized as 0, 1 to 4, or 5 or more CTCs per 7.5 mL based on the prognostic value of these cut points in prior studies. The data analysis was performed between October 28, 2022, and June 15, 2023.

Metastatic hormone-sensitive prostate cancer.

Circulating tumor cell count was evaluated for an association with 3 prespecified trial end points: OS, progression-free survival, and 7-month prostate-specific antigen, after adjusting for other baseline covariates using proportional hazards and logistic regression models.

Of 1313 S1216 participants (median [IQR] age, 68 [44-92] years), evaluable samples from 503 (median [IQR] age, 69 [46-90] years) with newly diagnosed mHSPC were collected at baseline, and 93 samples were collected at progression. Baseline counts were 5 or more CTCs per 7.5 mL in 60 samples (11.9%), 1 to 4 CTCs per 7.5 mL in 107 samples (21.3%), and 0 CTCs per 7.5 mL in 336 samples (66.8%). Median OS for men with 5 or more CTCs per 7.5 mL was 27.9 months (95% CI, 24.1-31.2 months) compared with 56.2 months (95% CI, 45.7-69.8 months) for men with 1 to 4 CTCs per 7.5 mL and not reached at 78.0 months follow-up for men with 0 CTCs per 7.5 mL. After adjusting for baseline clinical covariates, men with 5 or more CTCs per 7.5 mL at baseline had a significantly higher hazard of death (hazard ratio, 3.22; 95% CI, 2.22-4.68) and disease progression (hazard ratio, 2.46; 95% CI, 1.76-3.43) and a lower likelihood of prostate-specific antigen complete response (odds ratio, 0.26; 95% CI, 0.12-0.54) compared with men with 0 CTCs per 7.5 mL at baseline. Adding baseline CTC count to other known prognostic factors (covariates only: area under the curve, 0.73; 95% CI, 0.67-0.79) resulted in an increased prognostic value for 3-year survival (area under the curve, 0.79; 95% CI, 0.73-0.84).

In this prognostic study, the findings validate CTC count as a prognostic biomarker that improved upon existing prognostic factors and estimated vastly divergent survival outcomes regardless of subsequent lines of therapy. As such, baseline CTC count in mHSPC may serve as a valuable noninvasive biomarker to identify men likely to have poor survival who may benefit from clinical trials of intensified or novel regimens.

Moyamoya disease (MMD) is a progressive steno-occlusive cerebrovascular disorder that predominantly affecting East Asian populations. The intricate interplay of distinct and overlapping mechanisms, including genetic associations such as the RNF213-p.R4810K variant, contributes to the steno-occlusive lesions and moyamoya vessels. However, genetic mutations alone do not fully elucidate the occurrence of MMD, suggesting a potential role for epigenetic factors. Accruing evidence has unveiled the regulatory role of epigenetic markers, including DNA methylation, histone modifications, and non-coding RNAs (ncRNAs), in regulating pivotal cellular and molecular processes implicated in the pathogenesis of MMD by modulating endothelial cells and smooth muscle cells. The profile of these epigenetic markers in cerebral vasculatures and circulation has been determined to identify potential diagnostic biomarkers and therapeutic targets. Furthermore, in vitro studies have demonstrated the multifaceted effects of modulating specific epigenetic markers on MMD pathogenesis. These findings hold great potential for the discovery of novel therapeutic targets, translational studies, and clinical applications. In this review, we comprehensively summarize the current understanding of epigenetic mechanisms, including DNA methylation, histone modifications, and ncRNAs, in the context of MMD. Furthermore, we discuss the potential challenges and opportunities that lie ahead in this rapidly evolving field.

The sampling of circulating biomarkers provides an opportunity for non-invasive evaluation and monitoring of cancer activity. In modern day practice, this has typically been in the form of circulating tumor DNA (ctDNA) detected in plasma. The field of ctDNA has been a burgeoning technology, with prominent applications for blood-based cancer screening and in disease status assessment, especially after curative-intent surgery to evaluate for minimal residual disease (MRD). Clinical applications for the latter show an incredibly high sensitivity in certain cancer types with a need for additional studies to determine how much clinical decision-making should be adapted based on ctDNA results and which cancer types, stages, and treatments are best informed by ctDNA results. This chapter provides an overview of ctDNA detection as tool for cancer screening, detecting MRD, and/or molecularly characterizing a cancer, highlighting the rapidly amassing research as a prognostic biomarker and emerging data on ctDNA as a predictive biomarker.