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.

Background and Objectives: The accurate discrimination between patients with and without cancer using their cell-free DNA (cfDNA) is crucial for early cancer diagnosis. The end-motifs of cfDNA serve as significant cancer biomarkers, offering compelling prospects for cancer diagnosis. This study proposes EM-DeepSD, a signal decomposition deep learning framework based on cfDNA end-motifs, which is aimed at improving the accuracy of cancer diagnosis and adapting to different sequencing modalities. Materials and Methods: This study included 146 patients diagnosed with cancer and 122 non-cancer controls. EM-DeepSD comprises three core modules. Initially, it utilizes a signal decomposition module to decompose and reconstruct the input end-motif profiles, thereby generating multiple regular subsequences that optimize the subsequent modeling process. Subsequently, both a machine learning module and a deep learning module are employed to improve the accuracy of cancer diagnosis. Furthermore, this paper compares the performance of EM-DeepSD with that of existing benchmarked methods to demonstrate its superiority. Based on the EM-DeepSD framework, we developed the EM-DeepSSA model and compared it with two benchmarked methods across different cfDNA sequencing datasets. Results: In the internal validation set, EM-DeepSSA outperformed the two benchmark methods for cancer diagnosis (area under the curve (AUC), 0.920; adjusted p value < 0.05). Meanwhile, EM-DeepSSA also exhibited the best performance on two independent external testing sets that were subjected to 5-hydroxymethylcytosine sequencing (5hmCS) and broad-range cell-free DNA sequencing (BR-cfDNA-Seq), respectively (test set-1: AUC = 0.933; test set-2: AUC = 0.956; adjusted p value < 0.05). Conclusions: In summary, we present a new framework which can achieve high classification performance in cancer diagnosis and which is applicable to different sequencing modalities.

Fragmentomics analysis of plasma autosomal DNA has shown promise in cancer diagnostics. Here we evaluated the clinical utility of plasma Epstein-Barr virus (EBV) DNA fragmentomics analysis for nasopharyngeal carcinoma (NPC) screening. Among our prospective cohort of approximately 20,000 subjects that underwent two rounds of screening, we analyzed the first-round blood samples of subjects who tested positive for EBV DNA via polymerase chain reaction (PCR) (n = 558). We found that those who subsequently developed NPC in the second round exhibited a distinctive mononucleosomal size pattern, an NPC-associated end motif (specifically, a depletion of CC-motif) and aberrations in methylation identified through fragmentomics-based methylation analysis (FRAGMA). Subjects with these aberrant fragmentomics features and higher quantity of EBV DNA had a relative risk of 87.1 times greater for developing NPC in the second round compared to subjects tested negative for EBV DNA on PCR. These results demonstrate plasma DNA fragmentomics could predict future cancer risk.

Urinary cell-free DNA (ucfDNA) is a valuable resource for truly non-invasive liquid biopsy. UcfDNA comprises transrenal ucfDNA passing from the bloodstream through the glomeruli and locally shed urinary-tract ucfDNA. Understanding their differences in characteristics may enable new diagnostic applications.

We analyzed 136 ucfDNA samples from healthy controls, pregnant women, patients with chronic kidney diseases (CKDs), and bladder cancer using massively parallel sequencing. Fragmentomic characteristics including fragment sizes and 5' end motifs were deduced. The relationship between ucfDNA and chromatin accessibility was examined by overlapping ucfDNA with open chromatin regions (OCRs, lacking histones) and heterochromatin regions (HCRs, tightly packed with histones).

Compared with urinary-tract ucfDNA, the transrenal ucfDNA was shorter and enriched for C-ends. The transrenal ucfDNA was over-represented in OCRs but depleted in HCRs, indicating an interplay between the glomerular filtration barrier and the effective cfDNA size. In patients with proteinuria (preeclampsia and CKDs), the amount of ucfDNA from HCRs increased, suggesting elevated glomerular permeability of histone-bound plasma DNA molecules. In oncology, the use of hypomethylation signals in HCRs enhanced bladder cancer detection, with an area under the receiver operating characteristic curve of 0.93.

Chromatin accessibility states impact the transrenal clearance of plasma DNA, likely through the size restriction of the glomerular barrier. This realization has enabled the rational development of novel approaches for detecting or monitoring renal dysfunction and urological cancers.

The Innovation and Technology Commission of the Hong Kong SAR Government (InnoHK initiative) and the Li Ka Shing Foundation supported this study.

Food intake affects body homeostasis and significantly changes circulating cell-free DNA (cfDNA). However, the source and elimination of postprandial cfDNA is difficult to trace, and it is unknown whether these changes can be revealed by cfDNA fragmentomics based on liquid biopsy.

We performed shallow whole-genome sequencing of 30 plasma samples from 10 healthy individuals at fasting and postprandial (30-min and 2-h time points). We assessed the effect of postprandial states on cfDNA fragment size distribution and utilized deconvolutional analysis of end motifs to determine the potential roles of DNA nucleases in cfDNA fragmentation. We correlated the fragmentation index (defined as the ratio of short-to-long fragments) with gene expression to estimate the relative contribution of various cellular and tissue sources to cfDNA.

Compared to the fasting state, we observed a significant increase in short cfDNA fragments (70-150 bp) and a decrease in long fragments (151-250 bp) at the 30-minute postprandial state, followed by an inverse trend two hours later. Deconvolutional analysis of cfDNA end motifs showed that DNASE1L3 activity decreased at the 30-minute postprandial state, while DNASE1 and DFFB activities increased at the 2-hour postprandial state. We found that the expression of genes related to cellular metabolism and immune responses was upregulated at the postprandial state. Meanwhile, the contribution of cells and tissues involved in metabolic and immune progress to circulating plasma cfDNA was increased.

The fragmentation of cfDNA is considerably influenced by postprandial states, highlighting the significance of taking postprandial effects into account when evaluating cfDNA as a biomarker. Furthermore, our study reveals the potential application of cfDNA fragmentation features in monitoring metabolic and immune status changes.

Extracellular vesicles (EVs) play a key role in cell-to-cell communication and have drawn significant attention due to their potential clinical applications. However, much remains to be understood about the biology of EV-associated DNA (EV-DNA). EV-DNA is actively released by both normal and malignant cells and consists of diverse fragments with varying structures. Because EV-DNA spans the entire genome of cells from which it originates, it continues to be attractive as a biomarker for cancer diagnosis and monitoring. Further, EV-DNA delivery can alter the function of recipient cells by interfering with cytoplasmic DNA sensor pathways. This review explores the biology and significance of EV-DNA, including its topology and fragmentomics features, modality of association with EVs, packaging mechanisms, and potential functions. It also emphasizes the specificity of vesicular DNA in identifying genetic and epigenetic changes in cancer. Additionally, it delves into the impact of EV-DNA on cellular behaviour and its potential use as a therapeutic target in cancer. The review discusses new insights into EV-DNA biology and provides perspectives and alternatives to address the challenges and concerns for future EV-DNA studies.

To evaluate the functional impact of a novel DNASE1L3 variant (c.572A>G, p.Asn191Ser) in three families with SLE and hypocomplementaemic urticarial vasculitis (HUV) from the United Arab Emirates.

Whole-exome sequencing was performed on affected patients and findings were confirmed using Sanger sequencing in family members. DNASE1L3 protein expression, secretion and enzymatic activity were assessed in HEK293 cell lines. Plasma smear assay for neutrophil extracellular traps (NETs) was evaluated in patients, family members and healthy control.

A total of seven patients diagnosed with both SLE and HUV were identified from three unrelated families. All affected individuals were found to carry a homozygous c.572A>G, p.Asn191Ser (191S) variant in DNASE1L3. The variant 191S was shown to impact the secretion and activity of DNASE1L3. Patients homozygous for 191S variant had significantly higher burden (p=0.0409) of NET structure in comparison to heterozygous and healthy control.

We functionally evaluated the effect of a novel DNASE1L3 (c.572A>G, p.Asn191Ser) in familial SLE with a consistent pattern of HUV across seven patients. This variant resulted in impaired secretion and enzymatic activity of DNASE1L3 along with increased NETosis in patients with homozygous genotype.

Major autoimmune rheumatic disorders, such as systemic lupus erythematosus and Sjögren's syndrome, are defined by the presence of autoantibodies. These diseases are brought on by immune system dysregulation, which can present clinically in a wide range of ways. The etiologies of these illnesses are complex and heavily impacted by a variety of genetic and environmental variables. The most powerful susceptibility element for each of these disorders is still the human leukocyte antigen (HLA) area, that was the initial locus found to be associated. This region is primarily responsible for the HLA class II genes, such as DQA1, DQB1, and DRB1, however class I genes have also been linked. Numerous genetic variants that do not pose a risk to HLA have been found as a result of intensive research into the genetic component of these diseases conducted over the last 20 years. Furthermore, it is generally acknowledged that autoimmune rheumatic illnesses have similar genetic backgrounds and share molecular pathways of disease, including the interferon (IFN) type I routes. Pleiotropic sites for autoimmune rheumatic illnesses comprise TNIP1, DNASEL13, IRF5, the HLA region, and others. It remains a challenge to determine the causative biological mechanisms beneath the genetic connections. Nonetheless, functional analyses of the loci and mouse models have produced recent advancements. With an emphasis on the HLA region, we present an updated summary of the structure of genes underpinning both of these autoimmune rheumatic illnesses here.

Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

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.

Over the past two decades, the number of genetically defined autoinflammatory interferonopathies has steadily increased. Aicardi-Goutières syndrome and proteasome-associated autoinflammatory syndromes (PRAAS, also known as CANDLE) are caused by genetic defects that impair homeostatic intracellular nucleic acid and protein processing respectively. Research into these genetic defects revealed intracellular sensors that activate type I interferon production. In SAVI and COPA syndrome, genetic defects that cause chronic activation of the dinucleotide sensor stimulator of interferon genes (STING) share features of lung inflammation and fibrosis; and selected mutations that amplify interferon-α/β receptor signalling cause central nervous system manifestations resembling Aicardi-Goutières syndrome. Research into the monogenic causes of childhood-onset systemic lupus erythematosus (SLE) demonstrates the pathogenic role of autoantibodies to particle-bound extracellular nucleic acids that distinguishes monogenic SLE from the autoinflammatory interferonopathies. This Review introduces a classification for autoinflammatory interferonopathies and discusses the divergent and shared pathomechanisms of interferon production and signalling in these diseases. Early success with drugs that block type I interferon signalling, new insights into the roles of cytoplasmic DNA or RNA sensors, pathways in type I interferon production and organ-specific pathology of the autoinflammatory interferonopathies and monogenic SLE, reveal novel drug targets that could personalize treatment approaches.

Technical advances over the past two decades have enabled robust detection of cell-free DNA (cfDNA) in biological samples. Yet, higher clinical sensitivity is required to realize the full potential of liquid biopsies. This opinion article argues that to overcome current limitations, the abundance of informative cfDNA molecules - such as circulating tumor DNA (ctDNA) - collected in a sample needs to increase. To accomplish this, new methods to modulate the biological processes that govern cfDNA production, trafficking, and clearance in the body are needed, informed by a deeper understanding of cfDNA biology. Successful development of such methods could enable a major leap in the performance of liquid biopsies and vastly expand their utility across the spectrum of clinical care.

Cell-free placental DNA (cfpDNA) is present in maternal circulation during gestation. CfpDNA carries great potential as a research and clinical tool as it provides a means to investigate the placental (epi)genome across gestation, which previously required invasive placenta sampling procedures. CfpDNA has been widely implemented in the clinical setting for noninvasive prenatal testing (NIPT). Despite this, the basic biology of cfpDNA remains poorly understood, limiting the research and clinical utility of cfpDNA. This review will examine the current knowledge of cfpDNA, including origins and molecular characteristics, highlight gaps in knowledge, and discuss future research directions.

An improved understanding of the cfDNA fragmentomics has proved it as a promising biomarker in clinical applications. However, biological characteristics of cfDNA in spent embryos culture medium (SECM) remain unsolved obstacles before the application in non-invasive in-vitro embryo selection. In this study, we developed a Tn5 transposase and ligase integrated dual-library construction sequencing strategy (TDual-Seq) and revealed the fragmentomic profile of cfDNA of all sizes in early embryonic development. The detected ratio of long cfDNA (>500 bp) was improved from 4.23 % by traditional NGS to 12.80 % by TDual-Seq. End motif analysis showed long cfDNA molecules have a more dominance of fragmentation intracellularly in apoptotic cells with higher predominance of G-end, while shorter cfDNA undergo fragmentation process both intracellularly and extracellularly. Moreover, the mutational pattern of cfDNA and the correlated GO biological process were well differentiated in cleavage and blastocyst embryos. Finally, we developed a multiparametric index (TQI) that employs the fragmentomic profiles of cfDNA, and achieved an area under the ROC curve of 0.927 in screening top quality embryos. TDual-Seq strategy has facilitated characterizing the fragmentomic profile of cfDNA of all sizes in SECM, which are served as a class of non-invasive biomarkers in the evaluation of embryo quality in in-vitro fertilization. And this improved strategy has opened up potential clinical utilities of long cfDNA analysis.

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.

Cardiovascular disease (CVD) is the leading cause of death worldwide, and despite treatment efforts, cardiovascular function cannot always be restored, and progression of disease be prevented. Critical insights are oftentimes based on tissue samples. Current knowledge of tissue pathology typically relies on invasive biopsies or postmortem samples. Liquid biopsies, which assess circulating mediators to deduce the histology and pathology of distant tissues, have been advancing rapidly in cancer research and offer a promising approach to be translated to the understanding and treatment of CVD. The widely understood elevations in cell-free DNA during acute and chronic cardiovascular conditions, associate with disease, severity, and offer prognostic value. The role of neutrophil extracellular traps (NETs) and circulating nucleases in thrombosis provide a solid rationale for liquid biopsies in CVD. cfDNA originates from various tissue types and cellular sources, including mitochondria and nuclei, and can be used to trace cell and tissue type lineage, as well as to gain insight into the activation status of cells. This article discusses the origin, structure, and potential utility of cfDNA, offering a deeper and less invasive approach for the understanding of the complexities of CVD.

The properties of cell-free DNA (cfDNA) are intensely studied for their potential as non-invasive biomarkers. We explored the effect of common genetic variants on the concentration and fragmentation properties of cfDNA using a genome-wide association study (GWAS) based on low-coverage whole-genome sequencing data of 140,000 Dutch non-invasive prenatal tests (NIPTs). Our GWAS detects many genome-wide significant loci, functional enrichments for phagocytes, liver, adipose tissue, and macrophages, and genetic correlations with autoimmune and cardiovascular disease. A common (7%) missense variant in DNASE1L3 (p.Arg206Cys) strongly affects all cfDNA properties. It increases the size of fragments, lowers cfDNA concentrations, affects the distribution of cleave-site motifs, and increases the fraction of circulating fetal DNA during pregnancy. For the application of NIPT, and potentially other cfDNA-based tests, this variant has direct clinical consequences, as it increases the odds of inconclusive results and impairs the sensitivity of NIPT by causing predictors to overestimate the fetal fraction.

The placenta, a temporary but essential organ for gestational support, undergoes intricate morphological and functional transformations throughout gestation. However, the spatiotemporal patterns of gene expression underlying placentation remain poorly understood. Utilizing Stereo-seq, we constructed a Mouse Placentation Spatiotemporal Transcriptomic Atlas (MPSTA) spanning from embryonic day (E) 7.5 to E14.5, which includes the transcriptomes of large trophoblast cells that were not captured in previous single-cell atlases. We defined four distinct strata of the ectoplacental cone, an early heterogeneous trophectoderm structure, and elucidated the spatial trajectory of trophoblast differentiation during early postimplantation stages before E9.5. Focusing on the labyrinth region, the interface of nutrient exchange in the mouse placenta, our spatiotemporal ligand-receptor interaction analysis unveiled pivotal modulators essential for trophoblast development and placental angiogenesis. We also found that paternally expressed genes are exclusively enriched in the placenta rather than in the decidual regions, including a cluster of genes enriched in endothelial cells that may function in placental angiogenesis. At the invasion front, we identified interface-specific transcription factor regulons, such as Atf3, Jun, Junb, Stat6, Mxd1, Maff, Fos, and Irf7, involved in gestational maintenance. Additionally, we revealed that maternal high-fat diet exposure preferentially affects this interface, exacerbating inflammatory responses and disrupting angiogenic homeostasis. Collectively, our findings furnish a comprehensive, spatially resolved atlas that offers valuable insights and benchmarks for future explorations into placental morphogenesis and pathology.

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.

Plasma cell-free DNA (cfDNA) fragmentation patterns are emerging directions in cancer liquid biopsy with high translational significance. Conventionally, the cfDNA sequencing reads are aligned to a reference genome to extract their fragmentomic features. In this study, through cfDNA fragmentomics profiling using different reference genomes on the same datasets in parallel, we report systematic biases in such conventional reference-based approaches. The biases in cfDNA fragmentomic features vary among races in a sample-dependent manner and therefore might adversely affect the performances of cancer diagnosis assays across multiple clinical centers. In addition, to circumvent the analytical biases, we develop Freefly, a reference-free approach for cfDNA fragmentomics profiling. Freefly runs ∼60-fold faster than the conventional reference-based approach while generating highly consistent results. Moreover, cfDNA fragmentomic features reported by Freefly can be directly used for cancer diagnosis. Hence, Freefly possesses translational merit toward the rapid and unbiased measurement of cfDNA fragmentomics.

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes.

We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X.

Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring.

Recent studies have revealed an unexplored population of long cell-free DNA (cfDNA) molecules in human plasma using long-read sequencing technologies. However, the biological properties of long cfDNA molecules (>500 bp) remain largely unknown. To this end, we have investigated the origins of long cfDNA molecules from different genomic elements. Analysis of plasma cfDNA using long-read sequencing reveals an uneven distribution of long molecules from across the genome. Long cfDNA molecules show overrepresentation in euchromatic regions of the genome, in sharp contrast to short DNA molecules. We observe a stronger relationship between the abundance of long molecules and mRNA gene expression levels, compared with short molecules (Pearson's r = 0.71 vs. -0.14). Moreover, long and short molecules show distinct fragmentation patterns surrounding CpG sites. Leveraging the cleavage preferences surrounding CpG sites, the combined cleavage ratios of long and short molecules can differentiate patients with hepatocellular carcinoma (HCC) from non-HCC subjects (AUC = 0.87). We also investigated knockout mice in which selected nuclease genes had been inactivated in comparison with wild-type mice. The proportion of long molecules originating from transcription start sites are lower in Dffb-deficient mice but higher in Dnase1l3-deficient mice compared with that of wild-type mice. This work thus provides new insights into the biological properties and potential clinical applications of long cfDNA molecules.

In clinical oncology, cell-free DNA (cfDNA) has shown immense potential in its ability to noninvasively detect cancer at various stages and monitor the progression of therapy. Despite the rapid improvements in cfDNA liquid biopsy approaches, achieving the required sensitivity to detect rare tumor-derived cfDNA still remains a challenge. For next-generation sequencing, the perceived presentation of cfDNA is strongly linked to the extraction and library preparation protocols. Conventional double-stranded DNA library preparation (dsDNA-LP) focuses on assessing ~167bp double-stranded mononucleosomal (mncfDNA) and its other oligonucleosomal cell-free DNA counterparts in plasma. However, dsDNA-LP methods fail to include short, single-stranded, or nicked DNA in the final library preparation, biasing the representation of the actual cfDNA populations in plasma. The emergence of single-stranded library preparation (ssDNA-LP) strategies over the past decade has now allowed these other populations of cfDNA to be studied from plasma. With the use of ssDNA-LP, single-stranded, nicked, and ultrashort cfDNA can be comprehensively assessed for its molecular characteristics and clinical potential. In this review, we overview the current literature on applications of ssDNA-LP on plasma cfDNA from a potential cancer liquid biopsy perspective. To this end, we discuss the molecular principles of single-stranded DNA adapter ligation, how library preparation contributes to the understanding of native cfDNA characteristics, and the potential for ssDNA-LP to improve the sensitivity of circulating tumor DNA detection. Additionally, we review the current literature on the newly reported species of plasma ultrashort single-stranded cell-free DNA plasma, which appear biologically distinct from mncfDNA. We conclude with a discussion of future perspectives of ssDNA-LP for liquid biopsy endeavors.

Prenatal cell-free DNA (cfDNA) screening uses extracellular fetal DNA circulating in the peripheral blood of pregnant women to detect prevalent fetal chromosomal anomalies. However, numerous severe conditions with underlying single-gene defects are not included in current prenatal cfDNA screening. In this prospective, multicenter and observational study, pregnant women at elevated risk for fetal genetic conditions were enrolled for a cfDNA screening test based on coordinative allele-aware target enrichment sequencing. This test encompasses the following three of the most frequent pathogenic genetic variations: aneuploidies, microdeletions and monogenic variants. The cfDNA screening results were compared to invasive prenatal or postnatal diagnostic test results for 1,090 qualified participants. The comprehensive cfDNA screening detected a genetic alteration in 135 pregnancies with 98.5% sensitivity and 99.3% specificity relative to standard diagnostics. Of 876 fetuses with suspected structural anomalies on ultrasound examination, comprehensive cfDNA screening identified 55 (56.1%) aneuploidies, 6 (6.1%) microdeletions and 37 (37.8%) single-gene pathogenic variants. The inclusion of targeted monogenic conditions alongside chromosomal aberrations led to a 60.7% increase (from 61 to 98) in the detection rate. Overall, these data provide preliminary evidence that a comprehensive cfDNA screening test can accurately identify fetal pathogenic variants at both the chromosome and single-gene levels in high-risk pregnancies through a noninvasive approach, which has the potential to improve prenatal evaluation of fetal risks for severe genetic conditions arising from heterogenous molecular etiologies. ClinicalTrials.gov registration: ChiCTR2100045739 .

Multiple studies have shown that cell-free DNA (cfDNA) from cancer patients differ in both fragment length and fragment end motif (FEM) from healthy individuals, yet there is a lack of understanding of how the two factors combined are associated with cancer and gene transcription. In this study, we conducted cfDNA fragmentomics evaluations using plasma from lung cancer patients (n = 12) and healthy individuals (n = 7). A personal gene expression profile was established from plasma using H3K36me3 cell-free chromatin immunoprecipitation sequencing (cfChIP-seq). The genes with the highest expression displayed an enrichment of short cfDNA fragments (median = 19.99%, IQR: 16.94-27.13%, p < 0.0001) compared to the genes with low expression. Furthermore, distinct GC-rich FEMs were enriched after cfChIP. Combining the frequency of short cfDNA fragments with the presence of distinct FEMs resulted in an even further enrichment of the most expressed genes (median = 37.85%, IQR: 30.10-39.49%, p < 0.0001). An in vitro size selection of <150 bp cfDNA could isolate cfDNA representing active genes and the size-selection enrichment correlated with the cfChIP-seq enrichment (Spearman r range: 0.499-0.882, p < 0.0001). This study expands the knowledge regarding cfDNA fragmentomics and sheds new light on how gene activity is associated with both cfDNA fragment lengths and distinct FEMs.

Cell-free DNA (cfDNA) is a promising analyte for non-invasive liquid biopsy, carrying abundant signatures for disease diagnosis and monitoring. In infectious disease researches, blood plasma samples are routinely heat-inactivated before proceeding with downstream analyses. However, the effects of heat inactivation on cfDNA fragmentomic analysis remain largely unclear, potentially introducing biases or altering the characteristics of cfDNA.

We performed a comprehensive investigation of cfDNA concentrations and fragmentomics in 21 plasma samples from 7 healthy individuals, by comparing the sample group without the heat inactivation to those exposed to once or twice heat-inactivation at 56 °C for 30 min and following freeze-thaw.

Plasma samples with once and twice heat inactivation displayed no significant deviations in primary characteristics, including cfDNA concentrations, size profiles, end motif features, and genome-wide distributions, compared to samples without heat treatment.

Heat-inactivated cfDNA can be utilized for liquid biopsy in infectious disease researches, without substantial impact on cfDNA concentrations and fragmentomic properties. This study provides essential insights into the effects of heat inactivation on cfDNA properties and will contribute to the development of reliable non-invasive biomarkers for infectious disease.

Cell-free DNA (cfDNA) has shown promise in detecting various cancers, but the diagnostic performance of cfDNA end motifs for multiple cancer types requires verification. This study aimed to assess the utility of cfDNA end motifs for multi-cancer detection.

This study included 206 participants: 106 individuals with cancer, representing 20 cancer types, and 100 healthy individuals. The participants were divided into training and testing cohorts. All plasma cfDNA samples were profiled by whole-genome sequencing. A random forest model was constructed using cfDNA 4 bp-end-motif profiles to predict cancer in the training cohort, and its performance was evaluated in the testing cohort. Additionally, a separate random forest model was developed to predict immunotherapy responses.

In the training cohort, the model based on 4 bp-end-motif profiles achieved an AUC of 0.962 (95% CI 0.936-0.987). The AUC in the testing cohort was 0.983 (95% CI 0.960-1.000). The model also maintained excellent predictive ability in different tumor sub-cohorts, including lung cancer (AUC 0.918, 95% CI 0.862-0.974), gastrointestinal cancer (AUC 0.966, 95% CI 0.938-0.993), and other cancer cohort (AUC 0.859, 95% CI 0.776-0.942). Moreover, the model utilizing 4 bp-end-motif profiles exhibited sensitivity in identifying responders to immunotherapy (AUC 0.784, 95% CI 0.609-0.960).

The model based on 4 bp-end-motif profiles demonstrates superior sensitivity in multi-cancer detection. Detection of 4 bp-end-motif profiles may serve as potential predictive biomarkers for cancer immunotherapy.

The scarcity of malignant Hodgkin and Reed-Sternberg cells hampers tissue-based comprehensive genomic profiling of classic Hodgkin lymphoma (cHL). By contrast, liquid biopsies show promise for molecular profiling of cHL due to relatively high circulating tumour DNA (ctDNA) levels1-4. Here we show that the plasma representation of mutations exceeds the bulk tumour representation in most cases, making cHL particularly amenable to noninvasive profiling. Leveraging single-cell transcriptional profiles of cHL tumours, we demonstrate Hodgkin and Reed-Sternberg ctDNA shedding to be shaped by DNASE1L3, whose increased tumour microenvironment-derived expression drives high ctDNA concentrations. Using this insight, we comprehensively profile 366 patients, revealing two distinct cHL genomic subtypes with characteristic clinical and prognostic correlates, as well as distinct transcriptional and immunological profiles. Furthermore, we identify a novel class of truncating IL4R mutations that are dependent on IL-13 signalling and therapeutically targetable with IL-4Rα-blocking antibodies. Finally, using PhasED-seq5, we demonstrate the clinical value of pretreatment and on-treatment ctDNA levels for longitudinally refining cHL risk prediction and for detection of radiographically occult minimal residual disease. Collectively, these results support the utility of noninvasive strategies for genotyping and dynamic monitoring of cHL, as well as capturing molecularly distinct subtypes with diagnostic, prognostic and therapeutic potential.