Circulating tumor DNA (ctDNA) has emerged as a dynamic biomarker in cancer, as evidenced by its increasing integration into clinical practice. Carrying tumor specific characteristics, ctDNA can be used to inform treatment selection, monitor response, and identify drug resistance. In this review, we provide a comprehensive, up-to-date summary of ctDNA in monitoring treatment response with a focus on lung, colorectal, and breast cancers, and discuss current challenges and future directions.

Circulating cell-free DNA (cfDNA) has emerged as a compelling candidate of liquid biopsy markers for the diagnosis and prognosis of several cancers. We systematically reviewed data on the role of cfDNA markers in the diagnosis, prognosis and treatment of hepatocellular carcinoma (HCC). Early studies suggested that levels of circulating cfDNA, mitochondrial DNA and cfDNA integrity are higher in patients with HCC than chronic liver diseases. In subsequent studies, methylation changes in circulating tumor DNA (ctDNA) as well as cfDNA fragmentation patterns and circulating nucleosomes were found to offer high sensitivity (>60%) and excellent specificity (>90%) for HCC diagnosis. The predictive role of cfDNA markers and ctDNA has been assessed in a few studies including untreated patients with HCC providing promising results for prediction of survival. However, port-hepatectomy detection of cfDNA/ctDNA markers or copy number variation indicators of cfDNA seem to reflect minimum residual disease and thus a high risk for HCC recurrence. The same markers can be useful for prediction after transarterial chemoembolization, radiofrequency ablation, radiotherapy and even systemic therapies. In conclusion, cfDNA markers can be useful in HCC surveillance, improving early diagnosis rates, as well as for monitoring treatment effectiveness and minimal residual disease post-treatment.

Genomic analyses of cell-free DNA (cfDNA) in plasma are enabling noninvasive blood-based biomarker approaches to cancer detection and disease monitoring. Current approaches for identification of circulating tumour DNA typically use targeted tumour-specific mutations or methylation analyses. An emerging approach is based on the recognition of altered genome-wide cfDNA fragmentation in patients with cancer. Recent studies have revealed a multitude of characteristics that can affect the compendium of cfDNA fragments across the genome, collectively called the 'cfDNA fragmentome'. These changes result from genomic, epigenomic, transcriptomic and chromatin states of an individual and affect the size, position, coverage, mutation, structural and methylation characteristics of cfDNA. Identifying and monitoring these changes has the potential to improve early detection of cancer, especially using highly sensitive multi-feature machine learning approaches that would be amenable to broad use in populations at increased risk. This Review highlights the rapidly evolving field of genome-wide analyses of cfDNA characteristics, their comparison to existing cfDNA methods, and recent related innovations at the intersection of large-scale sequencing and artificial intelligence. As the breadth of clinical applications of cfDNA fragmentome methods have enormous public health implications for cancer screening and personalized approaches for clinical management of patients with cancer, we outline the challenges and opportunities ahead.

The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengths. We validated the model, which we named 'Fragle', by using low-pass whole-genome-sequencing data from multiple cancer types and healthy control cohorts. In independent cohorts, Fragle outperformed tumour-naive methods, achieving higher accuracy and lower detection limits. We also show that Fragle is compatible with targeted sequencing data. In plasma samples from patients with colorectal cancer, longitudinal analysis with Fragle revealed strong concordance between ctDNA dynamics and treatment responses. In patients with resected lung cancer, Fragle outperformed a tumour-naive gene panel in the prediction of minimal residual disease for risk stratification. The method's versatility, speed and accuracy for ctDNA quantification suggest that it may have broad clinical utility.

Background/Objectives: Circulating tumor DNA (ctDNA) analysis is a powerful tool for non-invasive monitoring of tumor burden and treatment response. Reliable quantification methods are critical for the effective use of ctDNA as a tumor biomarker. Digital PCR (dPCR) offers high sensitivity and quantification, but requires the prior knowledge of tumor-specific genomic alterations. Next-generation sequencing (NGS) provides a more comprehensive approach but is semi-quantitative, relying on variant allelic fraction (VAF), which can be influenced by non-tumor cell-free DNA. Methods: We developed a novel quantitative NGS (qNGS) method for absolute quantification of nucleotide variants, utilizing unique molecular identifiers (UMIs) and of quantification standards (QSs), short synthetic DNA sequences modified to include characteristic mutations for unique identification in sequencing data. We evaluated the performance of this method using plasma samples spiked with mutated DNA and plasma pools from cancer patients. We further applied our technique to plasma samples from four non-small cell lung cancer (NSCLC) patients enrolled in the ELUCID trial. Results: Our qNGS approach demonstrated robust linearity and correlation with dPCR in both spiked and patient-derived plasma samples. Notably, the analysis of clinical samples from the ELUCID trial revealed the ability of our method to simultaneously quantify multiple variants in a single plasma sample. Significant differences in ctDNA levels were observed between baseline and post-treatment samples collected after three weeks of front-line therapy. Conclusions: We introduce a novel qNGS method that enables the absolute quantification of ctDNA, independent of non-tumor circulating DNA variations. This technique was applied for the first time to serial samples from NSCLC patients, demonstrating its ability to simultaneously monitor multiple variants, making it a robust and versatile tool for precision oncology.

Currently, due to the invasive nature of colonoscopy and the associated pain, people avoid undergoing the procedure, making it difficult to detect the majority of potential early stage colorectal carcinoma/precancerous lesions or advanced adenoma. Advanced colorectal adenoma is the main precursor to the development of colorectal carcinoma. Therefore, improving advanced colorectal adenoma detection rate can significantly decrease the development and morbidity of colorectal carcinoma. Accordingly, a non-invasive method to screen high-risk people for colonoscopy in clinical practice is urgently needed.

With the development of medical technology, screening methods for colorectal carcinoma are emerging rapidly, and diverse non-invasive methods are being developed. Cell-free DNA (cfDNA), commonly referred to as liquid biopsy, has promising application prospects as a minimally invasive strategy for early screening of colorectal cancer. CfDNA has already been applied in the field of prenatal diagnosis, advanced carcinoma, and organ transplantation, and the application cfDNA in advanced colorectal adenoma is at the cutting-edge of current research. Thus, this review summarizes the progress in research on different biological characteristics of cfDNA and its utility in the screening of advanced colorectal adenoma, including sizes of cfDNA molecules, end signature of cfDNA (preferred ends, end motifs, jagged ends), nucleosomal footprints, cfDNA topology, cfDNA methylation, and cfDNA integrity.

We hope that this review will advance this promising research field.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional's skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Personalized medicine is an emerging field that provides novel approaches to disease's early diagnosis, prevention, treatment, and prognosis based on the patient's criteria in gene expression, environmental factors, lifestyle, and diet. To date, hepatocellular carcinoma (HCC) is a significant global health burden, with an increasing incidence and significant death rates, despite advancements in surveillance, diagnosis, and therapeutic approaches. The majority of HCC lesions develop in patients with liver cirrhosis, carrying the risks of mortality associated with both the tumor burden and the cirrhosis. New therapeutic agents involving immune checkpoint inhibitors and targeted agents have been developed for sequential or concomitant application for advanced HCC but only a tiny percentage of patients benefit from each approach. Moreover, clinicians encounter difficulties determining the most appropriate regimen for each patient. This emphasizes the need for a personalized treatment approach. In other words, patients should no longer undergo treatment based on their tumor's histology but depending on the distinct molecular targets specific to their tumor biology. However, the utilization of precision medicine in managing HCC is still challenging. This review aims to discuss the role of personalized medicine in diagnosing, managing, and defining the prognosis of HCC. We also discuss the role of liquid biopsy and their clinical applications for immunotherapies in HCC. More clinical studies are still necessary to improve the precision of biomarkers used in the treatment decision for patients with HCC.

This article reviews surveillance for the detection of early stage hepatocellular carcinoma, covering the rationale for surveillance, optimal selection of persons needing surveillance, methods and frequency of screening, strategies for addressing barriers to surveillance, and trends for future improvement in surveillance leading to more effective cancer control and improved patient outcomes. The importance of integrating liver cancer surveillance as a core component of national public health programs is emphasized. The impact of emerging technologies for identifying persons at risk, stratifying individual risk to improve the cost-effectiveness of surveillance programs, and improving the performance, accessibility, and convenience of surveillance are discussed.

Cell-free DNA (cfDNA) is non-randomly fragmented in human body fluids. Analyzing such fragmentation patterns of cfDNA holds great promise for liquid biopsy. Whole-genome bisulfite sequencing (WGBS) is widely used for cfDNA methylation profiling. However, its applicability for studying fragmentomic characteristics remains largely unexplored.

We performed paired WGBS and whole-genome sequencing (WGS) on 66 peripheral plasma samples from 58 pregnant women. Then, we systematically compared the fragmentation patterns of cell-free nuclear DNA and mitochondrial DNA (mtDNA) sequenced from these two approaches. Additionally, we evaluated the extent of the size shortening in fetal-derived cfDNA and estimated the fetal DNA fraction in maternal plasma using both sequencing methods.

Compared to WGS samples, WGBS samples demonstrated a significantly lower genome coverage and higher GC content in cfDNA. They also showed a significant decrease in the size of cell-free nuclear DNA, along with alterations in the end motif pattern that were specifically associated with CpG and "CC" sites. While there was a slight shift in the inferred nucleosome footprint from cfDNA coverages in WGBS samples, the cfDNA coverage patterns in CTCF and TSS regions remained highly consistent between these two sequencing methods. Both methods accurately reflected gene expression levels through their TSS coverages. Additionally, WGBS samples exhibited an increased abundance and longer length of mtDNA in plasma. Furthermore, we observed the size shortening of fetal cfDNA in plasma consistently, with a highly correlated fetal DNA fraction inferred by cfDNA coverage between WGBS and WGS samples (r = 0.996). However, the estimated fetal cfDNA fraction in WGBS samples was approximately 7 % lower than in WGS samples.

We confirmed that WGBS can introduce artificial breakages to cfDNA, leading to altered fragmentomic patterns in both nuclear and mitochondrial DNA. However, WGBS cfDNA remains suitable for analyzing certain cfDNA fragmentomic characteristics, such as coverage in genome regulation regions and the essential characteristics of fetal DNA in maternal plasma.

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.

The concentration of circulating cell-free DNA (cfDNA) in plasma is an important determinant of the robustness of liquid biopsies. However, biological mechanisms that lead to inter-individual differences in cfDNA concentrations remain unexplored. The concentration of plasma cfDNA is governed by an interplay between its release and clearance. We hypothesized that cfDNA clearance by nucleases might be one mechanism that contributes toward inter-individual variations in cfDNA concentrations. We performed fragmentomic analysis of the plasma cfDNA from 862 healthy individuals, with a cfDNA concentration range of 1.61-41.01 ng/mL. We observed an increase in large DNA fragments (231-600 bp), a decreased frequencies of shorter DNA fragments (20-160 bp), and an increased frequency of G-end motifs with increasing cfDNA concentrations. End motif deconvolution analysis revealed a decreased contribution of DNASE1L3 and DFFB in subjects with higher cfDNA concentration. The five subjects with the highest plasma DNA concentration (top 0.58%) had aberrantly decreased levels of DNASE1L3 protein in plasma. The cfDNA concentration could be inferred from the fragmentomic profile through machine learning and was well correlated to the measured cfDNA concentration. Such an approach could infer the fractional DNA concentration from particular tissue types, such as the fetal and tumor fraction. This work shows that individuals with different cfDNA concentrations are associated with characteristic fragmentomic patterns of the cfDNA pool and that nuclease-mediated clearance of DNA is a key parameter that affects cfDNA concentration. Understanding these mechanisms has facilitated the enhanced measurement of cfDNA species of clinical interest, including circulating fetal and tumor DNA.

Hemangiosarcoma is a highly malignant tumor commonly affecting canines, originating from endothelial cells that line blood vessels, underscoring the importance of early detection. This canine cancer is analogous to human angiosarcoma, and the development of liquid biopsies leveraging cell-free DNA (cfDNA) represents a promising step forward in early cancer diagnosis. In this study, we utilized Whole Genome Sequencing (WGS) to analyze fragment sizes and copy number alterations (CNAs) in cfDNA from 21 hemangiosarcoma-affected and 36 healthy dogs, aiming to enhance early cancer detection accuracy through machine learning models. Our findings reveal that similar to trends in human oncology, hemangiosarcoma samples exhibited shorter DNA fragment sizes compared to healthy controls, with a notable leftward shift in the primary peak. Interestingly, canine hemangiosarcoma DNA fragment sizes demonstrated eight distinct periodic patterns diverging from those typically observed in human angiosarcoma. Additionally, we identified seven novel genomic gains and nine losses in the hemangiosarcoma samples. Applying machine learning to the cfDNA fragment size distribution, we achieved an impressive average Area Under the Curve (AUC) of 0.93 in 10-fold cross-validation, underscoring the potential of this approach for precise early-stage cancer classification. This study confirms distinctive cfDNA fragment size and CNA patterns in hemangiosarcoma-affected vs. healthy dogs and demonstrates the promise of these biomarkers in canine cancer screening, early detection, and monitoring via liquid biopsies. These findings establish a foundation for broader research on cfDNA analysis in various canine cancers, integrating methodologies from human oncology to enhance early detection and diagnostic precision in veterinary medicine.

Background: Cell-free DNA (cfDNA) is present in healthy individuals but is elevated in those undergoing physical exertion, trauma, sepsis, and certain cancers. Maintaining cfDNA concentrations is vital for immune homeostasis and preventing inflammatory responses. Understanding cfDNA release and clearance is essential for using cfDNA as a biomarker in clinical diagnostics. We focused on the fragment size of cfDNA and investigated cfDNA dynamics and half-life, particularly the 100-250 base pair fragments. Methods: Healthy, adult men (n = 5; age 40 ± 4.1 years) were subjected to a 30 min treadmill exercise. Blood samples were collected at 0, 5, 10, 15, 30, and 60 min post-exercise using PAXgene® Blood ccfDNA tubes to stabilize and prevent nuclease-mediated cfDNA degradation and minimize genomic DNA contamination risk. The cfDNA concentration was measured using an electrophoresis-based technique (4150 TapeStation system) to quantify the concentration based on cfDNA fragment size. Results: The results showed a cfDNA half-life of 24.2 min, with a transient increase in 100-250 base pair cfDNA fragments post-exercise, likely due to nuclease activity. These levels rapidly reverted to the baseline within an hour. Conclusions: The rapid clearance of cfDNA underscores its potential as a biomarker for real-time disease monitoring and the evaluation of treatment efficacy. This study is expected to standardize cfDNA investigations, enhancing diagnosis and treatment monitoring across various disease conditions.

Extrachromosomal circular DNA (eccDNA), a chromosome-independent circular DNA, has garnered significant attention due to its widespread distribution and intricate biogenesis in carcinoma. Existing research findings propose that multiple eccDNAs contribute to drug resistance in cancer treatments through complex and interrelated regulatory mechanisms. The unique structure and genetic properties of eccDNA increase tumor heterogeneity. This increased diversity is a result of eccDNA's ability to stimulate oncogene remodeling and participate in anomalous splicing processes through chimeric cyclization and the reintegration of loop DNA back into the linear genome. Such actions promote oncogene amplification and silencing. eccDNA orchestrates protein interactions and modulates protein degradation by acting as a regulatory messenger. Moreover, it plays a pivotal role in modeling the tumor microenvironment and intensifying the stemness characteristics of tumor cells. This review presented detailed information about the biogenesis, distinguishing features, and functions of eccDNA, emphasized the role and mechanisms of eccDNA during cancer treatment, and further proposed the great potential of eccDNA in inspiring novel strategies for precision cancer therapy and facilitating the discovery of prognostic biomarkers for cancer.

Heart failure (HF) remains a major global health challenge, and more effective and comprehensive plasma biomarkers are needed to effectively treat HF patients. Multiomics studies have shown that DNA fragments, noncoding RNAs, proteins, and metabolites may be potential plasma biomarkers for HF. However, comprehensive reviews that focus on research on plasma biomarkers for HF from an omics perspective are lacking. This review summarizes the applications of various omics approaches in the exploration of biomarkers related to the risk assessment, diagnosis, subtype classification, medical management, and prognosis prediction of HF. Moreover, as heart transplantation and left ventricular assistant device (LVAD) implantation are terminal therapies for end-stage HF patients, this review also discusses the role of cell-free DNA as a biomarker for cardiac transplant rejection and omics studies of plasma biomarkers in patients who respond to LVAD therapy. Our findings suggest that future omics research on HF biomarkers should employ integrated multiomics methods and expand the sample size to increase the robustness of the results and that the identified biomarkers should be further validated in large cohorts.

Cell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory-efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ~100X cfDNA whole-genome sequencing (WGS) dataset with over 1 billion fragments in 1.2 hours, offering up to a ~50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original approaches or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community: https://github.com/epifluidlab/FinaleToolkit.

Due to current challenges in the early detection, less than 40% of individuals diagnosed with hepatocellular carcinoma (HCC) are viable candidates for surgical intervention. Therefore, validating and launching of a novel precise diagnostic approach is essential for early diagnosis. Based on developing evidence using circulating tumor cells and their derivatives, circulating miRNAs, and extracellular vesicles (EVs), liquid biopsy may offer a reliable platform for the HCC's early diagnosis. Each liquid biopsy analyte may provide significant areas for diagnosis, prognostic assessment, and treatment monitoring of HCC patients depending on its kind, sensitivity, and specificity. The current review addresses potential clinical applications, current research, and future developments for liquid biopsy in HCC management.

cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need.

We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool's diagnostic capability.

Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape.

Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.

Sepsis-induced acute kidney injury (SI-AKI) is a common clinical syndrome that is associated with high mortality and morbidity. Effective timely detection may improve the outcome of SI-AKI. Kidney-derived cell-free DNA (cfDNA) may provide new insight into understanding and identifying SI-AKI. Plasma cfDNA from 82 healthy individuals, 7 patients with sepsis non-acute kidney injury (SN-AKI), and 9 patients with SI-AKI was subjected to genomic methylation sequencing. We deconstructed the relative contribution of cfDNA from different cell types based on cell-specific methylation markers and focused on exploring the association between kidney-derived cfDNA and SI-AKI.Based on the deconvolution of the cfDNA methylome: SI-AKI patients displayed the elevated cfDNA concentrations with an increased contribution of kidney epithelial cells (kidney-Ep) DNA; kidney-Ep derived cfDNA achieved high accuracy in distinguishing SI-AKI from SN-AKI (AUC = 0.92, 95% CI 0.7801-1); the higher kidney-ep cfDNA concentrations tended to correlate with more advanced stages of SI-AKI; strikingly, SN-AKI patients with potential kidney damage unmet by SI-AKI criteria showed higher levels of kidney-Ep derived cfDNA than healthy individuals. The autonomous screening of kidney-Ep (n = 24) and kidney endothelial (kidney-Endo, n = 12) specific methylation markers indicated the unique identity of kidney-Ep/kidney-Endo compared with other cell types, and its targeted assessment reproduced the main findings of the deconvolution of the cfDNA methylome. Our study first demonstrates that kidney-Ep- and kidney-Endo-specific methylation markers can serve as a novel marker for SI-AKI emergence, supporting further exploration of the utility of kidney-specific cfDNA methylation markers in the study of SI-AKI.

Fragmentomic features of circulating cell free mitochondrial DNA (ccf-mtDNA) including fragmentation profile, 5' end base preference and motif diversity are poorly understood. Here, we generated ccf-mtDNA sequencing data of 1607 plasma samples using capture-based next generation sequencing. We firstly found that fragmentomic features of ccf-mtDNA were remarkably different from those of circulating cell free nuclear DNA. Furthermore, region-specific fragmentomic features of ccf-mtDNA were observed, which was associated with protein binding, base composition and special structure of mitochondrial DNA. When comparing to non-cancer controls, six types of cancer patients exhibited aberrant fragmentomic features. Then, cancer detection models were built based on the fragmentomic features. Both internal and external validation cohorts demonstrated the excellent capacity of our model in distinguishing cancer patients from non-cancer control, with all area under curve higher than 0.9322. The overall accuracy of tissue-of-origin was 89.24% and 87.92% for six cancer types in two validation cohort, respectively. Altogether, our study comprehensively describes cancer-specific fragmentomic features of ccf-mtDNA and provides a proof-of-principle for the ccf-mtDNA fragmentomics-based multi-cancer detection and tissue-of-origin classification.

Hepatocellular carcinoma (HCC) presents substantial genetic and phenotypic diversity, making it challenging to predict patient outcomes. There is a clear need for novel biomarkers to better identify high-risk individuals. Long non-coding RNAs (lncRNAs) are known to play key roles in cell cycle regulation and genomic stability, and their dysregulation has been closely linked to HCC progression. Developing a prognostic model based on cell cycle-related lncRNAs could open up new possibilities for immunotherapy in HCC patients.

Transcriptomic data and clinical samples were obtained from the TCGA-HCC dataset. Cell cycle-related gene sets were sourced from existing studies, and coexpression analysis identified relevant lncRNAs (correlation coefficient >0.4, P<0.001). Univariate analysis identified prognostic lncRNAs, which were then used in a LASSO regression model to create a risk score. This model was validated via cross-validation. HCC samples were classified on the basis of their risk scores. Correlations between the risk score and tumor mutational burden (TMB), tumor immune infiltration, immune checkpoint gene expression, and immunotherapy response were evaluated via R packages and various methods (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-COUNTER, XCELL, and EPIC).

Four cell cycle-related lncRNAs (AC009549.1, AC090018.2, PKD1P6-NPIPP1, and TMCC1-AS1) were significantly upregulated in HCC. These lncRNAs were used to create a risk score (risk score=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1). This risk score had superior predictive value compared to traditional clinical factors (AUC=0.738). A nomogram was developed to illustrate the 1-year, 3-year, and 5-year overall survival (OS) rates for individual HCC patients. Significant differences in TMB, immune response, immune cell infiltration, immune checkpoint gene expression, and drug responsiveness were observed between the high-risk and low-risk groups.

The risk score model we developed enhances the prognostication of HCC patients by identifying those at high risk for poor outcomes. This model could lead to new immunotherapy strategies for HCC patients.

Hepatocellular Carcinoma (HCC) is a malignant tumor with high morbidity and mortality worldwide, which represents a serious threat to human life, health and quality of life. Blood-based detection is essential for HCC screening, early diagnosis, prognosis evaluation, and surveillance. Current non-invasive detection strategy including serum alpha-fetoprotein (AFP), ultrasound, computerized tomography, and magnetic resonance imaging. The limited specificity of an AFP and the dependence on operator experience and diagnostic personnel for ultrasound have constrained their utility in early HCC diagnosis. In recent years, with the development of various detection technologies, there has been an increasing focus on exploring blood-based detection markers for HCC. The types of markers include protein markers, DNA mutation, DNA epigenetic modification, mRNA, miRNA, and so on. However, numerous methodological and biological factors limit the clinical sensitivity and generalization performance of these new biomarkers. In this review, we describe the state-of-the-art technologies for cfDNA analysis, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve HCC diagnostics and patient care.

Cell-free DNA (cfDNA) analysis offers an attractive noninvasive means of detecting and monitoring diseases. cfDNA cleavage patterns within a short range (e.g., 11 nucleotides) have been reported to correlate with cytosine-phosphate-guanine (CpG) methylation, allowing fragmentomics-based methylation analysis (FRAGMA). Here, we adopted FRAGMA to the extended region harboring multiple nucleosomes, termed FRAGMAXR.

We profiled cfDNA nucleosomal patterns over the genomic regions from -800 to 800 bp surrounding differentially methylated CpG sites, harboring approximately 8 nucleosomes, referred to as CpG-associated cfDNA nucleosomal patterns. Such nucleosomal patterns were analyzed by FRAGMAXR in cancer patients and pregnant women.

We identified distinct cfDNA nucleosomal patterns around differentially methylated CpG sites. Compared with subjects without cancer, patients with hepatocellular carcinoma (HCC) showed reduced amplitude of nucleosomal patterns, with a gradual decrease over tumor stages. Nucleosomal patterns associated with differentially methylated CpG sites could be used to train a machine learning model, resulting in the detection of HCC patients with an area under the receiver operating characteristic curve of 0.93. We further demonstrated the feasibility of multicancer detection using a dataset comprising lung, breast, and ovarian cancers. The tissue-of-origin analysis of plasma cfDNA from pregnant women and cancer patients revealed that the placental DNA and tumoral DNA contributions deduced by FRAGMAXR correlated well with values measured using genetic variants (Pearson r: 0.85 and 0.94, respectively).

CpG-associated cfDNA nucleosomal patterns of cfDNA molecules are influenced by DNA methylation and might be useful for biomarker developments for cancer liquid biopsy and noninvasive prenatal testing.

Liquid biopsy techniques using cell-free DNA (cfDNA) play an increasingly important role in the characterization and surveillance of solid tumors. For blood cancers, molecular response assessment techniques using circulating malignant cells or bone marrow aspirates are well established in clinical care. However, cfDNA has an emerging role in hematology as well, with the opportunity for disease assessment and quantification independent of circulating disease burden or invasive biopsies. In this review, we discuss key technologies and clinical data for the utilization of cfDNA in lymphomas, myeloma, and leukemias.

The analysis of tissues of origin of cell-free DNA (cfDNA) is of research and diagnostic interest. Many studies focused on bisulfite treatment or immunoprecipitation protocols to assess the tissues of origin of cfDNA. DNA loss often occurs during such processes. Fragmentomics of cfDNA molecules has uncovered a wealth of information related to tissues of origin of cfDNA. There is still much room for the development of tools for assessing contributions from various tissues into plasma using fragmentomic features. Hence, we developed an approach to analyze the relative contributions of DNA from different tissues into plasma, by identifying characteristic fragmentation patterns associated with selected histone modifications. We named this technique as FRAGmentomics-based Histone modification Analysis (FRAGHA). Deduced placenta-specific histone H3 lysine 27 acetylation (H3K27ac)-associated signal correlated well with the fetal DNA fraction in maternal plasma (Pearson's r = 0.96). The deduced liver-specific H3K27ac-associated signal correlated with the donor-derived DNA fraction in liver transplantation recipients (Pearson's r = 0.92) and was significantly increased in patients with hepatocellular carcinoma (HCC) (P < 0.01, Wilcoxon rank-sum test). Significant elevations of erythroblasts-specific and colon-specific H3K27ac-associated signals were observed in patients with β-thalassemia major and colorectal cancer, respectively. Furthermore, using the fragmentation patterns from tissue-specific H3K27ac regions, a machine learning algorithm was developed to enhance HCC detection, with an area under the curve (AUC) of up to 0.97. Finally, genomic regions with H3K27ac or histone H3 lysine 4 trimethylation (H3K4me3) were found to exhibit different fragmentomic patterns of cfDNA. This study has shed light on the relationship between cfDNA fragmentomics and histone modifications, thus expanding the armamentarium of liquid biopsy.

Pancreatic ductal adenocarcinoma (PDAC) remains a malignancy with one of the highest mortality rates. One limitation in the diagnosis and treatment of PDAC is the lack of an early and universal biomarker. Extensive research performed recently to develop new assays which could fit this role is available. In this review, we will discuss the current landscape of liquid biopsy in patients with PDAC. Specifically, we will review the various methods of liquid biopsy, focusing on circulating tumor DNA (ctDNA) and exosomes and future opportunities for improvement using artificial intelligence or machine learning to analyze results from a multi-omic approach. We will also consider applications which have been evaluated, including the utility of liquid biopsy for screening and staging patients at diagnosis as well as before and after surgery. We will also examine the potential for liquid biopsy to monitor patient treatment response in the setting of clinical trial development.

We studied the poorly-known dynamics of circulating DNA (cir-nDNA), as monitored prospectively over an extended post-surgery period, in patients with cancer.

On patients with stage III colon cancer (N = 120), using personalised molecular tags we carried out the prospective, multicenter, blinded cohort study of the post-surgery serial analysis of cir-nDNA concentration. 74 patients were included and 357 plasma samples tested.

During post-operative follow-up, the patients' median cir-nDNA concentration was greater (P < 0.0001 in the [43-364 days range]) than both the median value in healthy individuals and the pre-surgery value. These cir-nDNA levels were highly associated with NETs markers (P-value associating MPO and cir-nDNA, and NE and cir-nDNA are 6.6 x 10-17, and 1.9 x 10-7), in accordance with previous reports which indicate that cir-nDNA are NETs by-products. Unexpectedly, in 34 out of 50 patients we found that NETs continued to be formed for an extended duration post-surgery, even in patients without disease progression. Given that this phenomenon was observed in patients without adjuvant CT, and in patients >18 months post-surgery, the data suggest that the persistence of NETs formation is not due to the adjuvant CT.

(1), Given the inter-patient heterogeneity, the post-surgery cir-nDNA level cannot be considered a reliable value, and caution must be exercised when determining mutation allele frequency or the mutation status; and (2), specific studies must be undertaken to investigate the possible clinical impact of the persistent, low-grade inflammation resulting from elevated NETs levels, such as observed in these post-surgery patients, given that such levels are known to potentially induce adverse cardiovascular or thrombotic events.

This work was supported by the H2020 European ERA-NET grant on Translational Cancer Research (TRANSCAN-2).

The advent of liquid biopsies has brought significant changes to the diagnosis and monitoring of non-small cell lung cancer (NSCLC), presenting both promise and challenges. Molecularly targeted drugs, capable of enhancing survival rates, are now available to around a quarter of NSCLC patients. However, to ensure their effectiveness, precision diagnosis is essential. Circulating tumor DNA (ctDNA) analysis as the most advanced liquid biopsy modality to date offers a non-invasive method for tracking genomic changes in NSCLC. The potential of ctDNA is particularly rooted in its ability to furnish comprehensive (epi-)genetic insights into the tumor, thereby aiding personalized treatment strategies. One of the key advantages of ctDNA-based liquid biopsies in NSCLC is their ability to capture tumor heterogeneity. This capability ensures a more precise depiction of the tumor's (epi-)genomic landscape compared to conventional tissue biopsies. Consequently, it facilitates the identification of (epi-)genetic alterations, enabling informed treatment decisions, disease progression monitoring, and early detection of resistance-causing mutations for timely therapeutic interventions. Here we review the current state-of-the-art in ctDNA-based liquid biopsy technologies for NSCLC, exploring their potential to revolutionize clinical practice. Key advancements in ctDNA detection methods, including PCR-based assays, next-generation sequencing (NGS), and digital PCR (dPCR), are discussed, along with their respective strengths and limitations. Additionally, the clinical utility of ctDNA analysis in guiding treatment decisions, monitoring treatment response, detecting minimal residual disease, and identifying emerging resistance mechanisms is examined. Liquid biopsy analysis bears the potential of transforming NSCLC management by enabling non-invasive monitoring of Minimal Residual Disease and providing early indicators for response to targeted treatments including immunotherapy. Furthermore, considerations regarding sample collection, processing, and data interpretation are highlighted as crucial factors influencing the reliability and reproducibility of ctDNA-based assays. Addressing these challenges will be essential for the widespread adoption of ctDNA-based liquid biopsies in routine clinical practice, ultimately paving the way toward personalized medicine and improved outcomes for patients with NSCLC.

Cancer is currently the most common cause of death in adult dogs. Like humans, dogs have a one-third chance of developing cancer in their lifetime. We used shallow whole-genome sequencing (sWGS) to analyze blood cell-free DNA (cfDNA) from four tumor-bearing dogs (one with benign and three with malignant tumors) and 38 healthy dogs.

Similar to the results observed in the healthy dogs, no copy number aberration (CNA) was detected in the dog with benign lipomas, and the distribution of cfDNA fragment size (FS) closely resembled that of the healthy dogs. However, among the three dogs diagnosed with malignant tumors, two dogs exhibited varying degrees and quantities of CNAs. Compared to the distribution of FS in the healthy dogs, the cancer dogs exhibited a noticeable shift towards shorter lengths. These findings indicated that CNA and FS profiles derived from sWGS data can be used for non-invasive cancer detection in dogs.

Liver cancer is a global health challenge, causing a significant social-economic burden. Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer, which is highly heterogeneous in terms of molecular and cellular signatures. Early-stage or small tumors are typically treated with surgery or ablation. Currently, chemotherapies and immunotherapies are the best treatments for unresectable tumors or advanced HCC. However, drug response and acquired resistance are not predictable with the existing systematic guidelines regarding mutation patterns and molecular biomarkers, resulting in sub-optimal treatment outcomes for many patients with atypical molecular profiles. With advanced technological platforms, valuable information such as tumor genetic alterations, epigenetic data, and tumor microenvironments can be obtained from liquid biopsy. The inter- and intra-tumoral heterogeneity of HCC are illustrated, and these collective data provide solid evidence in the decision-making process of treatment regimens. This article reviews the current understanding of HCC detection methods and aims to update the development of HCC surveillance using liquid biopsy. Recent critical findings on the molecular basis, epigenetic profiles, circulating tumor cells, circulating DNAs, and omics studies are elaborated for HCC diagnosis. Besides, biomarkers related to the choice of therapeutic options are discussed. Some notable recent clinical trials working on targeted therapies are also highlighted. Insights are provided to translate the knowledge into potential biomarkers for detection and diagnosis, prognosis, treatment response, and drug resistance indicators in clinical practice.

Circulating cell-free DNA (cfDNA) is emerging as an avenue for cancer detection, but the characteristics of cfDNA fragmentation in the blood are poorly understood. We evaluate the effect of DNA methylation and gene expression on genome-wide cfDNA fragmentation through analysis of 969 individuals. cfDNA fragment ends more frequently contained CCs or CGs, and fragments ending with CGs or CCGs are enriched or depleted, respectively, at methylated CpG positions. Higher levels and larger sizes of cfDNA fragments are associated with CpG methylation and reduced gene expression. These effects are validated in mice with isogenic tumors with or without the mutant IDH1, and are associated with genome-wide changes in cfDNA fragmentation in patients with cancer. Tumor-related hypomethylation and increased gene expression are associated with decrease in cfDNA fragment size that may explain smaller cfDNA fragments in human cancers. These results provide a connection between epigenetic changes and cfDNA fragmentation with implications for disease detection.

Cell-free DNA (cfDNA) from the bloodstream has been studied for cancer biomarker discovery, and chromatin-derived epigenetic features have come into the spotlight for their potential to expand clinical applications. Methylation, fragmentation, and nucleosome positioning patterns of cfDNA have previously been shown to reveal epigenomic and inferred transcriptomic information. More recently, histone modifications have emerged as a tool to further identify tumor-specific chromatin variants in plasma. A number of sequencing methods have been developed to analyze these epigenetic markers, offering new insights into tumor biology. Features within cfDNA allow for cancer detection, subtype and tissue of origin classification, and inference of gene expression. These methods provide a window into the complexity of cancer and the dynamic nature of its progression. In this review, we highlight the array of epigenetic features in cfDNA that can be extracted from chromatin- and nucleosome-associated organization and outline potential use cases in cancer management.