Non-random fragmentation patterns in circulating cell-free DNA reflect epigenetic regulation

The biological characteristics of long cell-free DNA in spent embryos culture medium as noninvasive biomarker in in-vitro embryo selection

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 nucleic acid fragmentomics: A non-invasive window into cellular epigenomes

Clinical genomic profiling of cell-free nucleic acids (e.g. cell-free DNA or cfDNA) from blood and other body fluids has ushered in a new era in non-invasive diagnostics and treatment monitoring strategies for health conditions and diseases such as cancer. Genomic analysis of cfDNAs not only identifies disease-associated mutations, but emerging findings suggest that structural, topological, and fragmentation characteristics of cfDNAs reveal crucial information about the location of source tissues, their epigenomes, and other clinically relevant characteristics, leading to the burgeoning field of fragmentomics. The field has seen rapid developments in computational and genomics methodologies for conducting large-scale studies on health conditions and diseases - that have led to fundamental, mechanistic discoveries as well as translational applications. Several recent studies have shown the clinical utilities of the cfDNA fragmentomics technique which has the potential to be effective for early disease diagnosis, determining treatment outcomes, and risk-free continuous patient monitoring in a non-invasive manner. In this article, we outline recent developments in computational genomic methodologies and analysis strategies, as well as the emerging insights from cfNA fragmentomics. We conclude by highlighting the current challenges and opportunities.

Nucleic acid liquid biopsies in cardiovascular disease: Cell-free DNA liquid biopsies in cardiovascular disease

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.

Cell-free and extrachromosomal DNA profiling of small cell lung cancer

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

Systematically Evaluating Cell-Free DNA Fragmentation Patterns for Cancer Diagnosis and Enhanced Cancer Detection via Integrating Multiple Fragmentation Patterns

Cell-free DNA (cfDNA) fragmentation patterns have immense potential for early cancer detection. However, the definition of fragmentation varies, ranging from the entire genome to specific genomic regions. These patterns have not been systematically compared, impeding broader research and practical implementation. Here, 1382 plasma cfDNA sequencing samples from 8 cancer types are collected. Considering that cfDNA within open chromatin regions is more susceptible to fragmentation, 10 fragmentation patterns within open chromatin regions as features and employed machine learning techniques to evaluate their performance are examined. All fragmentation patterns demonstrated discernible classification capabilities, with the end motif showing the highest diagnostic value for cross-validation. Combining cross and independent validation results revealed that fragmentation patterns that incorporated both fragment length and coverage information exhibited robust predictive capacities. Despite their diagnostic potential, the predictive power of these fragmentation patterns is unstable. To address this limitation, an ensemble classifier via integrating all fragmentation patterns is developed, which demonstrated notable improvements in cancer detection and tissue-of-origin determination. Further functional bioinformatics investigations on significant feature intervals in the model revealed its impressive ability to identify critical regulatory regions involved in cancer pathogenesis.

Is ctDNA ready to outpace imaging in monitoring early and advanced breast cancer?

INTRODUCTION

Circulating tumor DNA (ctDNA) and radiological imaging are increasingly recognized as crucial elements in breast cancer management. While radiology remains the cornerstone for screening and monitoring, ctDNA holds distinctive advantages in anticipating diagnosis, recurrence, or progression, providing concurrent biological insights complementary to imaging results.

AREAS COVERED

This review delves into the current evidence on the synergistic relationship between ctDNA and imaging in breast cancer. It presents data on the clinical validity and utility of ctDNA in both early and advanced settings, providing insights into emerging liquid biopsy techniques like epigenetics and fragmentomics. Simultaneously, it explores the present and future landscape of imaging methodologies, particularly focusing on radiomics.

EXPERT OPINION

Numerous are the current technical, strategic, and economic challenges preventing the clinical integration of ctDNA analysis in the breast cancer monitoring. Understanding these complexities and devising targeted strategies is pivotal to effectively embedding this methodology into personalized patient care.

Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics.

METHODS

Peripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.

RESULTS

The cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier's outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.

CONCLUSION

This study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs

Accurate discrimination between patients with and without cancer from cfDNA is crucial for early cancer diagnosis. Herein, we develop and validate a deep-learning-based model entitled end-motif inspection via transformer (EMIT) for discriminating individuals with and without cancer by learning feature representations from cfDNA end-motifs. EMIT is a self-supervised learning approach that models rankings of cfDNA end-motifs. We include 4606 samples subjected to different types of cfDNA sequencing to develop EIMIT, and subsequently evaluate classification performance of linear projections of EMIT on six datasets and an additional inhouse testing set encopassing whole-genome, whole-genome bisulfite and 5-hydroxymethylcytosine sequencing. The linear projection of representations from EMIT achieved area under the receiver operating curve (AUROC) values ranged from 0.895 (0.835-0.955) to 0.996 (0.994-0.997) across these six datasets, outperforming its baseline by significant margins. Additionally, we showed that linear projection of EMIT representations can achieve an AUROC of 0.962 (0.914-1.0) in identification of lung cancer on an independent testing set subjected to whole-exome sequencing. The findings of this study indicate that a transformer-based deep learning model can learn cancer-discrimative representations from cfDNA end-motifs. The representations of this deep learning model can be exploited for discriminating patients with and without cancer.

Early detection and stratification of colorectal cancer using plasma cell-free DNA fragmentomic profiling

Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC.

Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology

Technological advancements in cell-free DNA (cfDNA) liquid biopsy have triggered exponential growth in numerous clinical applications. While cfDNA-based liquid biopsy has made significant strides in personalizing cancer treatment, the exploration and translation of epigenetics in liquid biopsy to clinical practice is still nascent. This comprehensive review seeks to provide a broad yet in-depth narrative of the present status of epigenetics in cfDNA liquid biopsy and its associated challenges. It highlights the potential of epigenetics in cfDNA liquid biopsy technologies with the hopes of enhancing its clinical translation. The momentum of cfDNA liquid biopsy technologies in recent years has propelled epigenetics to the forefront of molecular biology. We have only begun to reveal the true potential of epigenetics in both our understanding of disease and leveraging epigenetics in the diagnostic and therapeutic domains. Recent clinical applications of epigenetics-based cfDNA liquid biopsy revolve around DNA methylation in screening and early cancer detection, leading to the development of multi-cancer early detection tests and the capability to pinpoint tissues of origin. The clinical application of epigenetics in cfDNA liquid biopsy in minimal residual disease, monitoring, and surveillance are at their initial stages. A notable advancement in fragmentation patterns analysis has created a new avenue for epigenetic biomarkers. However, the widespread application of cfDNA liquid biopsy has many challenges, including biomarker sensitivity, specificity, logistics including infrastructure and personnel, data processing, handling, results interpretation, accessibility, and cost effectiveness. Exploring and translating epigenetics in cfDNA liquid biopsy technology can transform our understanding and perception of cancer prevention and management. cfDNA liquid biopsy has great potential in precision oncology to revolutionize conventional ways of early cancer detection, monitoring residual disease, treatment response, surveillance, and drug development. Adapting the implementation of liquid biopsy workflow to the local policy worldwide and developing point-of-care testing holds great potential to overcome global cancer disparity and improve cancer outcomes.

Accurate Early Detection and EGFR Mutation Status Prediction of Lung Cancer Using Plasma cfDNA Coverage Patterns: A Proof-of-Concept Study

Lung cancer is a major global health concern with a low survival rate, often due to late-stage diagnosis. Liquid biopsy offers a non-invasive approach to cancer detection and monitoring, utilizing various features of circulating cell-free DNA (cfDNA). In this study, we established two models based on cfDNA coverage patterns at the transcription start sites (TSSs) from 6X whole-genome sequencing: an Early Cancer Screening Model and an EGFR mutation status prediction model. The Early Cancer Screening Model showed encouraging prediction ability, especially for early-stage lung cancer. The EGFR mutation status prediction model exhibited high accuracy in distinguishing between EGFR-positive and wild-type cases. Additionally, cfDNA coverage patterns at TSSs also reflect gene expression patterns at the pathway level in lung cancer patients. These findings demonstrate the potential applications of cfDNA coverage patterns at TSSs in early cancer screening and in cancer subtyping.

Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning

Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (cell-free DNA) shows relatively higher coverage at TFBS due to the protection by TF from degradation by nucleases and short fragments of cfDNA are enriched in TFBS. However, there are still great difficulties in the noninvasive identification of TFBSs from experimental techniques. In this study, we propose a deep learning-based approach that can noninvasively predict TFBSs of cfDNA by learning sequence information from known TFBSs through convolutional neural networks. Under the addition of long short-term memory, our model achieved an area under the curve of 84%. Based on this model to predict cfDNA, we found consistent motifs in cfDNA fragments and lower coverage occurred upstream and downstream of these cfDNA fragments, which is consistent with a previous study. We also found that the binding sites of the same TF differ in different cell lines. TF-specific target genes were detected from cfDNA and were enriched in cancer-related pathways. In summary, our method of locating TFBSs from plasma has the potential to reflect the intrinsic regulatory mechanism from a noninvasive perspective and provide technical guidance for dynamic monitoring of disease in clinical practice.

Isolation and Characterization of Cell-Free DNA from Cerebral Organoids

Early detection of neurological conditions is critical for timely diagnosis and treatment. Identifying cellular-level changes is essential for implementing therapeutic interventions prior to symptomatic disease onset. However, monitoring brain tissue directly through biopsies is invasive and poses a high risk. Bodily fluids such as blood or cerebrospinal fluid contain information in many forms, including proteins and nucleic acids. In particular, cell-free DNA (cfDNA) has potential as a versatile neurological biomarker. Yet, our knowledge of cfDNA released by brain tissue and how cfDNA changes in response to deleterious events within the brain is incomplete. Mapping changes in cfDNA to specific cellular events is difficult in vivo, wherein many tissues contribute to circulating cfDNA. Organoids are tractable systems for examining specific changes consistently in a human background. However, few studies have investigated cfDNA released from organoids. Here, we examined cfDNA isolated from cerebral organoids. We found that cerebral organoids release quantities of cfDNA sufficient for downstream analysis with droplet-digital PCR and whole-genome sequencing. Further, gene ontology analysis of genes aligning with sequenced cfDNA fragments revealed associations with terms related to neurodevelopment and autism spectrum disorder. We conclude that cerebral organoids hold promise as tools for the discovery of cfDNA biomarkers related to neurodevelopmental and neurological disorders.

Fragmentation patterns of cell-free DNA and somatic mutations in the urine of metastatic breast cancer patients

BACKGROUND

Urinary cell-free deoxyribonucleic acid (DNA) (ucfDNA) holds promise as a biomarker; however, its potential remains largely unexplored. We examined the fragmentation pattern of ucfDNA and identified somatic mutations within urine samples from metastatic breast cancer (MBC) patients.

METHODS

Urine and blood specimens were collected before treatment from 45 MBC patients and posttreatment urine samples from 16 of the 45 patients at the China National Cancer Center. Somatic mutations and tumor mutational burden (TMB) in the urine and plasma of 10 patients were analyzed by next-generation sequencing (NGS). Fragmentation patterns of cfDNA were displayed using electropherograms. Differences in the extracted amount of cfDNA, length of cfDNA fragments, and TMB between urine and plasma were compared using a Wilcoxon test.

RESULTS

The fragmentation patterns of ucfDNA were categorized as follows: (1) profile A (n = 26) containing a short peak (100-200 bp) and a long peak (>1500 bp); (2) profile B (n = 8) containing only a long peak; and (3) profile C (n = 11) containing flat pattern. For profile A patients, the short-peaked ucfDNA circulating in the bloodstream was much shorter compared with plasma cfDNA (149 vs. 171 bp, Wilcoxon test, P = 0.023). The fragmentation patterns in lung metastasis patients exhibited a higher propensity toward profile C ( P = 0.002). After treatment, 87.5% of the patients exhibited consistent fragmentation patterns. The concordance rate for somatic mutations in the plasma and urine was 30%, and the median TMB of urine and plasma was not significantly different.

CONCLUSIONS

This study established a fragmentation pattern for ucfDNA and detected somatic mutations in the urine of MBC patients. These results suggest the potential application of ucfDNA as a biomarker for MBC.

Principles of digital sequencing using unique molecular identifiers

Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA reveals clinically relevant biomarkers but requires specialized protocols such as whole-genome bisulfite sequencing. Meanwhile, millions of cell-free DNA samples are being profiled by whole-genome sequencing. Here, we develop FinaleMe, a non-homogeneous Hidden Markov Model, to predict DNA methylation of cell-free DNA and, therefore, tissues-of-origin, directly from plasma whole-genome sequencing. We validate the performance with 80 pairs of deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing data.

Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology

Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptome data as a comprehensive cellular reference set for disease-agnostic cfDNA cell-of-origin analysis. We correlate cfDNA-inferred nucleosome spacing with gene expression to rank the relative contribution of over 490 cell types to plasma cfDNA. In 744 healthy individuals and patients, we uncover cell type signatures in support of emerging disease paradigms in oncology and prenatal care. We train predictive models that can differentiate patients with colorectal cancer (84.7%), early-stage breast cancer (90.1%), multiple myeloma (AUC 95.0%), and preeclampsia (88.3%) from matched controls. Importantly, our approach performs well in ultra-low coverage cfDNA datasets and can be readily transferred to diverse clinical settings for the expansion of liquid biopsy.

Identification and In-Silico study of non-synonymous functional SNPs in the human SCN9A gene

Single nucleotide polymorphisms are the most common form of DNA alterations at the level of a single nucleotide in the genomic sequence. Genome-wide association studies (GWAS) were carried to identify potential risk genes or genomic regions by screening for SNPs associated with disease. Recent studies have shown that SCN9A comprises the NaV1.7 subunit, Na+ channels have a gene encoding of 1988 amino acids arranged into 4 domains, all with 6 transmembrane regions, and are mainly found in dorsal root ganglion (DRG) neurons and sympathetic ganglion neurons. Multiple forms of acute hypersensitivity conditions, such as primary erythermalgia, congenital analgesia, and paroxysmal pain syndrome have been linked to polymorphisms in the SCN9A gene. Under this study, we utilized a variety of computational tools to explore out nsSNPs that are potentially damaging to heath by modifying the structure or activity of the SCN9A protein. Over 14 potentially damaging and disease-causing nsSNPs (E1889D, L1802P, F1782V, D1778N, C1370Y, V1311M, Y1248H, F1237L, M936V, I929T, V877E, D743Y, C710W, D623H) were identified by a variety of algorithms, including SNPnexus, SNAP-2, PANTHER, PhD-SNP, SNP & GO, I-Mutant, and ConSurf. Homology modeling, structure validation, and protein-ligand interactions also were performed to confirm 5 notable substitutions (L1802P, F1782V, D1778N, V1311M, and M936V). Such nsSNPs may become the center of further studies into a variety of disorders brought by SCN9A dysfunction. Using in-silico strategies for assessing SCN9A genetic variations will aid in organizing large-scale investigations and developing targeted therapeutics for disorders linked to these variations.

Circulating cell-free DNA-based multi-cancer early detection

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

Language model enables end-to-end accurate detection of cancer from cell-free DNA

We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.

Circulating cell-free DNA fragmentation is a stepwise and conserved process linked to apoptosis

BACKGROUND

Circulating cell-free DNA (cfDNA) is a pool of short DNA fragments mainly released from apoptotic hematopoietic cells. Nevertheless, the precise physiological process governing the DNA fragmentation and molecular profile of cfDNA remains obscure. To dissect the DNA fragmentation process, we use a human leukemia cell line HL60 undergoing apoptosis to analyze the size distribution of DNA fragments by shallow whole-genome sequencing (sWGS). Meanwhile, we also scrutinize the size profile of plasma cfDNA in 901 healthy human subjects and 38 dogs, as well as 438 patients with six common cancer types by sWGS.

RESULTS

Distinct size distribution profiles were observed in the HL60 cell pellet and supernatant, suggesting fragmentation is a stepwise process. Meanwhile, C-end preference was seen in both intracellular and extracellular cfDNA fragments. Moreover, the cfDNA profiles are characteristic and conserved across mammals. Compared with healthy subjects, distinct cfDNA profiles with a higher proportion of short fragments and lower C-end preference were found in cancer patients.

CONCLUSIONS

Our study provides new insight into fragmentomics of circulating cfDNA processing, which will be useful for early diagnosis of cancer and surveillance during cancer progression.

Distinct Features of Plasma Ultrashort Single-Stranded Cell-Free DNA as Biomarkers for Lung Cancer Detection

BACKGROUND

Using broad range cell-free DNA sequencing (BRcfDNA-Seq), a nontargeted next-generation sequencing (NGS) methodology, we previously identified a novel class of approximately 50 nt ultrashort single-stranded cell-free DNA (uscfDNA) in plasma that is distinctly different from 167 bp mononucleosomal cell-free DNA (mncfDNA). We hypothesize that uscfDNA possesses characteristics that are useful for disease detection.

METHODS

Using BRcfDNA-Seq, we examined both cfDNA populations in the plasma of 18 noncancer controls and 14 patients with late-stage nonsmall cell lung carcinoma (NSCLC). In comparison to mncfDNA, we assessed whether functional element (FE) peaks, fragmentomics, end-motifs, and G-Quadruplex (G-Quad) signatures could be useful features of uscfDNA for NSCLC determination.

RESULTS

In noncancer participants, compared to mncfDNA, uscfDNA fragments showed a 45.2-fold increased tendency to form FE peaks (enriched in promoter, intronic, and exonic regions), demonstrated a distinct end-motif-frequency profile, and presented with a 4.9-fold increase in G-Quad signatures. Within NSCLC participants, only the uscfDNA population had discoverable FE peak candidates. Additionally, uscfDNA showcased different end-motif-frequency candidates distinct from mncfDNA. Although both cfDNA populations showed increased fragmentation in NSCLC, the G-Quad signatures were more discriminatory in uscfDNA. Compilation of cfDNA features using principal component analysis revealed that the first 5 principal components of both cfDNA subtypes had a cumulative explained variance of >80%.

CONCLUSIONS

These observations indicate that the distinct biological processes of uscfDNA and that FE peaks, fragmentomics, end-motifs, and G-Quad signatures are uscfDNA features with promising biomarker potential. These findings further justify its exploration as a distinct class of biomarker to augment pre-existing liquid biopsy approaches.

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

Multi-faceted attributes of salivary cell-free DNA as liquid biopsy biomarkers for gastric cancer detection

BACKGROUND

Recent advances in circulating cell-free DNA (cfDNA) analysis from biofluids have opened new avenues for liquid biopsy (LB). However, current cfDNA LB assays are limited by the availability of existing information on established genotypes associated with tumor tissues. Certain cancers present with a limited list of established mutated cfDNA biomarkers, and thus, nonmutated cfDNA characteristics along with alternative biofluids are needed to broaden the available cfDNA targets for cancer detection. Saliva is an intriguing and accessible biofluid that has yet to be fully explored for its clinical utility for cancer detection.

METHODS

In this report, we employed a low-coverage single stranded (ss) library NGS pipeline "Broad-Range cell-free DNA-Seq" (BRcfDNA-Seq) using saliva to comprehensively investigate the characteristics of salivary cfDNA (ScfDNA). The identification of cfDNA features has been made possible by applying novel cfDNA processing techniques that permit the incorporation of ultrashort, ss, and jagged DNA fragments. As a proof of concept using 10 gastric cancer (GC) and 10 noncancer samples, we examined whether ScfDNA characteristics, including fragmentomics, end motif profiles, microbial contribution, and human chromosomal mapping, could differentiate between these two groups.

RESULTS

Individual and integrative analysis of these ScfDNA features demonstrated significant differences between the two cohorts, suggesting that disease state may affect the ScfDNA population by altering nuclear cleavage or the profile of contributory organism cfDNA to total ScfDNA. We report that principal component analysis integration of several aspects of salivary cell-free DNA fragmentomic profiles, genomic element profiles, end-motif sequence patterns, and distinct oral microbiome populations can differentiate the two populations with a p value of < 0.0001 (PC1).

CONCLUSION

These novel features of ScfDNA characteristics could be clinically useful for improving saliva-based LB detection and the eventual monitoring of local or systemic diseases.

The Clinical Significance of Circulating Tumor DNA for Minimal Residual Disease Identification in Early-Stage Non-Small Cell Lung Cancer

Lung cancer (LC) is the deadliest malignancy worldwide. In an operable stage I-III patient setting, the detection of minimal residual disease (MRD) after curative treatment could identify patients at higher risk of relapse. In this context, the study of circulating tumor DNA (ctDNA) is emerging as a useful tool to identify patients who could benefit from an adjuvant treatment, and patients who could avoid adverse events related to a more aggressive clinical management. On the other hand, ctDNA profiling presents technical, biological and standardization challenges before entering clinical practice as a decisional tool. In this paper, we review the latest advances regarding the role of ctDNA in identifying MRD and in predicting patients' prognosis, with a particular focus on clinical trials investigating the potential of ctDNA, the technical challenges to address and the biological parameters that influence the MRD detection.

Fragmentomic analysis of circulating tumor DNA-targeted cancer panels

BACKGROUND

The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner.

PATIENTS AND METHODS

We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n = 320). Each cohort was split 70%/30% into training and validation sets.

RESULTS

In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99.

CONCLUSIONS

To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.

Impact of ramucirumab plus erlotinib on circulating cell-free DNA from patients with untreated metastatic non-small cell lung cancer with EGFR-activating mutations (RELAY phase 3 randomized study)

Background

An exploratory, proof-of-concept, liquid biopsy addendum to examine biomarkers within cell-free DNA (cfDNA) in the RELAY phase 3, randomized, double-blind, placebo-controlled study was conducted. RELAY showed improved progression-free survival (PFS) with ramucirumab (RAM), a human immunoglobulin G1 vascular endothelial growth factor receptor 2 antagonist, plus erlotinib (ERL), a tyrosine kinase inhibitor, compared with placebo (PL) plus ERL.

Methods

Treatment-naïve patients with endothelial growth factor receptor (EGFR)-mutated metastatic non-small cell lung cancer were randomized (1:1) to RAM + ERL or PL + ERL. Plasma samples were collected at baseline, on treatment, and at 30-day post-study treatment discontinuation follow-up. Baseline and treatment-emergent gene alterations and EGFR-activating mutation allele counts were investigated by next-generation sequencing (NGS) and droplet digital polymerase chain reaction (ddPCR), respectively. cfDNA concentration and fragment size were evaluated by real-time polymerase chain reaction and the BioAnalyzer. Patients with a valid baseline plasma sample were included (70 RAM + ERL, 61 PL + ERL).

Results

TP53 mutation was the most frequently co-occurring baseline gene alteration (43%). Post-study treatment discontinuation EGFR T790M mutation rates were 54.5% (6/11) and 41.2% (7/17) by ddPCR, and 22.2% (2/9) and 29.4% (5/17) by NGS, in the RAM + ERL and PL + ERL arms, respectively. EGFR-activating mutation allele count decreased at Cycle 4 in both treatment arms and was sustained at follow-up with RAM + ERL. PFS improved for patients with no detectable EGFR-activating mutation at Cycle 4 vs. those with detectable EGFR-activating mutation. Total cfDNA concentration increased from baseline at Cycle 4 and through to follow-up with RAM + ERL. cfDNA fragment size was similar between treatment arms at baseline [mean (standard deviation) base pairs: RAM + ERL, 173.4 (2.6); PL + ERL, 172.9 (3.2)] and was shorter at Cycle 4 with RAM + ERL vs. PL + ERL [169.5 (2.8) vs. 174.1 (3.3), respectively; P<0.0001]. Baseline vs. Cycle 4 paired analysis showed a decrease in cfDNA fragment size for 84% (48/57) and 23% (11/47) of patient samples in the RAM + ERL and PL + ERL arms, respectively.

Conclusions

EGFR-activating mutation allele count was suppressed, total cfDNA concentration increased, and short fragment-sized cfDNA increased with RAM + ERL, suggesting the additional anti-tumor effect of RAM may contribute to the PFS benefit observed in RELAY with RAM + ERL vs. PL + ERL.

Trial Registration

ClinicalTrials.gov; identifier: NCT02411448.

Multi-Faceted Attributes of Salivary Cell-free DNA as Liquid Biopsy Biomarkers for Gastric Cancer Detection

Background

Recent advances in circulating cell-free DNA (cfDNA) analysis from biofluids have opened new avenues for liquid biopsy (LB). However, current cfDNA LB assays are limited by the availability of existing information on established genotypes associated with tumor tissues. Certain cancers present with a limited list of established mutated cfDNA biomarkers, and thus, nonmutated cfDNA characteristics along with alternative biofluids are needed to broaden the available cfDNA targets for cancer detection. Saliva is an intriguing and accessible biofluid that has yet to be fully explored for its clinical utility for cancer detection.

Methods

In this report, we employed a low-coverage single stranded (ss) library NGS pipeline "Broad-Range cell-free DNA-Seq" (BRcfDNA-Seq) using saliva to comprehensively investigate the characteristics of salivary cfDNA (ScfDNA). The identification of cfDNA features has been made possible by applying novel cfDNA processing techniques that permit the incorporation of ultrashort, ss, and jagged DNA fragments. As a proof of concept using 10 gastric cancer (GC) and 10 noncancer samples, we examined whether ScfDNA characteristics, including fragmentomics, end motif profiles, microbial contribution, and human chromosomal mapping, could differentiate between these two groups.

Results

Individual and integrative analysis of these ScfDNA features demonstrated significant differences between the two cohorts, suggesting that disease state may affect the ScfDNA population by altering nuclear cleavage or the profile of contributory organism cfDNA to total ScfDNA. We report that principal component analysis integration of several aspects of salivary cell-free DNA fragmentomic profiles, genomic element profiles, end-motif sequence patterns, and distinct oral microbiome populations can differentiate the two populations with a p value of < 0.0001 (PC1).

Conclusion

These novel features of ScfDNA characteristics could be clinically useful for improving saliva-based LB detection and the eventual monitoring of local or systemic diseases.

Blood has never been thicker: Cell-free DNA fragmentomics in the liquid biopsy toolbox of B-cell lymphomas

Liquid biopsies utilizing plasma circulating tumor DNA (ctDNA) are anticipated to revolutionize decision-making in cancer care. In the field of lymphomas, ctDNA-based blood tests represent the forefront of clinically applicable tools to harness decades of genomic research for disease profiling, quantification, and detection. More recently, the discovery of nonrandom fragmentation patterns in cell-free DNA (cfDNA) has opened another avenue of liquid biopsy research beyond mutational interrogation of ctDNA. Through examination of structural features, nucleotide content, and genomic distribution of massive numbers of plasma cfDNA molecules, the study of fragmentomics aims at identifying new tools that augment existing ctDNA-based analyses and discover new ways to profile cancer from blood tests. Indeed, the characterization of aberrant lymphoma ctDNA fragment patterns and harnessing them with powerful machine-learning techniques are expected to unleash the potential of nonmutant molecules for liquid biopsy purposes. In this article, we review cfDNA fragmentomics as an emerging approach in the ctDNA research of B-cell lymphomas. We summarize the biology behind the formation of cfDNA fragment patterns and discuss the preanalytical and technical limitations faced with current methodologies. Then we go through the advances in the field of lymphomas and envision what other noninvasive tools based on fragment characteristics could be explored. Last, we place fragmentomics as one of the facets of ctDNA analyses in emerging multiview and multiomics liquid biopsies. We pay attention to the unknowns in the field of cfDNA fragmentation biology that warrant further mechanistic investigation to provide rational background for the development of these precision oncology tools and understanding of their limitations.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer

Introduction

Breast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.

Methods

Here, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.

Results

We identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.

Discussion

Our findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

Liquid biopsy in hematological malignancies: current and future applications

The assessment of the cancer mutational profile is crucial for patient management, stratification, and therapeutic decisions. At present, in hematological malignancies with a solid mass, such as lymphomas, tumor genomic profiling is generally performed on the tissue biopsy, but the tumor may harbor genetic lesions that are unique to other anatomical compartments. The analysis of circulating tumor DNA (ctDNA) on the liquid biopsy is an emerging approach that allows genotyping and monitoring of the disease during therapy and follow-up. This review presents the different methods for ctDNA analysis and describes the application of liquid biopsy in different hematological malignancies. In diffuse large B-cell lymphoma (DLBCL) and Hodgkin lymphoma (HL), ctDNA analysis on the liquid biopsy recapitulates the mutational profile of the tissue biopsy and can identify mutations otherwise absent on the tissue biopsy. In addition, changes in the ctDNA amount after one or two courses of chemotherapy significantly predict patient outcomes. ctDNA analysis has also been tested in myeloid neoplasms with promising results. In addition to mutational analysis, liquid biopsy also carries potential future applications of ctDNA, including the analysis of ctDNA fragmentation and epigenetic patterns. On these grounds, several clinical trials aiming at incorporating ctDNA analysis for treatment tailoring are currently ongoing in hematological malignancies.

Plasma cell-free DNA promise monitoring and tissue injury assessment of COVID-19

Coronavirus 2019 (COVID-19) is a complex disease that affects billions of people worldwide. Currently, effective etiological treatment of COVID-19 is still lacking; COVID-19 also causes damages to various organs that affects therapeutics and mortality of the patients. Surveillance of the treatment responses and organ injury assessment of COVID-19 patients are of high clinical value. In this study, we investigated the characteristic fragmentation patterns and explored the potential in tissue injury assessment of plasma cell-free DNA in COVID-19 patients. Through recruitment of 37 COVID-19 patients, 32 controls and analysis of 208 blood samples upon diagnosis and during treatment, we report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA fragmentation characteristics reflect patient-specific physiological changes during treatment. Further analysis on cfDNA tissue-of-origin tracing reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, our work demonstrates and extends the translational merit of cfDNA fragmentation pattern as valuable analyte for effective treatment monitoring, as well as tissue injury assessment in COVID-19.

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

Bridging biological cfDNA features and machine learning approaches

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

Epigenetic liquid biopsies: a novel putative biomarker in immunology and inflammation

Immune and inflammatory processes occurring within tissues are often undetectable by blood cell counts, standard circulating biomarkers, or imaging, representing an unmet biomedical need. Here, we outline recent advances indicating that liquid biopsies can broadly inform human immune system dynamics. Nucleosome-size fragments of cell-free DNA (cfDNA) released from dying cells into blood contain rich epigenetic information such as methylation, fragmentation, and histone mark patterns. This information allows to infer the cfDNA cell of origin, as well as pre-cell death gene expression patterns. We propose that the analysis of epigenetic features of immune cell-derived cfDNA can shed light on immune cell turnover dynamics in healthy people, and inform the study and diagnosis of cancer, local inflammation, infectious or autoimmune diseases, as well as responses to vaccination.

Longitudinal ctDNA profiling in precision oncology and immunο-oncology

Serial analysis of circulating tumor DNA (ctDNA) over the disease course is emerging as a prognostic, predictive and patient-monitoring biomarker. In the metastatic setting, several multigene ctDNA assays have been approved or recommended by regulatory organizations for personalized targeted therapy, especially for lung cancer. By contrast, in nonmetastatic disease, detection of ctDNA resulting from minimal residual disease (MRD) following multimodal treatment with curative intent presents major technical challenges. Several studies using tumor genotyping-informed serial ctDNA profiling have provided promising findings on the sensitivity and specificity of ctDNA in predicting the risk of recurrence. We discuss progress, limitations and future perspectives relating to the use of ctDNA as a biomarker to guide targeted therapy in metastatic disease, as well as the use of ctDNA MRD detection to guide adjuvant treatment in the nonmetastatic setting.

Application of PCR-based approaches for evaluation of cell-free DNA fragmentation in colorectal cancer

Cell-free DNA (cfDNA) testing is the core of most liquid biopsy assays. In particular, cfDNA fragmentation features could facilitate non-invasive cancer detection due to their interconnection with tumor-specific epigenetic alterations. However, the final cfDNA fragmentation profile in a purified sample is the result of a complex interplay between informative biological and artificial technical factors. In this work, we use ddPCR to study cfDNA lengths in colorectal cancer patients and observe shorter and more variable cfDNA fragments in accessible chromatin loci compared to the densely packed pericentromeric region. We also report a convenient qPCR system suitable for screening cfDNA samples for artificial high molecular weight DNA contamination.

Molecular Monitoring of Lymphomas

Molecular monitoring of tumor-derived alterations has an established role in the surveillance of leukemias, and emerging nucleic acid sequencing technologies are likely to similarly transform the clinical management of lymphomas. Lymphomas are well suited for molecular surveillance due to relatively high cell-free DNA and circulating tumor DNA concentrations, high somatic mutational burden, and the existence of stereotyped variants enabling focused interrogation of recurrently altered regions. Here, we review the clinical scenarios and key technologies applicable for the molecular monitoring of lymphomas, summarizing current evidence in the literature regarding molecular subtyping and classification, evaluation of treatment response, the surveillance of active cellular therapies, and emerging clinical trial strategies.

DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation

Plasma cell-free DNA (cfDNA) are small molecules generated through a non-random fragmentation procedure. Despite commendable translational values in cancer liquid biopsy, however, the biology of cfDNA, especially the principles of cfDNA fragmentation, remains largely elusive. Through orientation-aware analyses of cfDNA fragmentation patterns against the nucleosome structure and integration with multidimensional functional genomics data, here we report a DNA methylation - nuclease preference - cutting end - size distribution axis, demonstrating the role of DNA methylation as a functional molecular regulator of cfDNA fragmentation. Hence, low-level DNA methylation could increase nucleosome accessibility and alter the cutting activities of nucleases during DNA fragmentation, which further leads to variation in cutting sites and size distribution of cfDNA. We further develop a cfDNA ending preference-based metric for cancer diagnosis, whose performance has been validated by multiple pan-cancer datasets. Our work sheds light on the molecular basis of cfDNA fragmentation towards broader applications in cancer liquid biopsy.

Cell-Free DNA Fragmentomics: The Novel Promising Biomarker

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics' characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

Circulating DNA fragmentomics and cancer screening

The high fragmentation of nuclear circulating DNA (cirDNA) relies on chromatin organization and protection or packaging within mononucleosomes, the smallest and the most stabilized structure in the bloodstream. The detection of differing size patterns, termed fragmentomics, exploits information about the nucleosomal packing of DNA. Fragmentomics not only implies size pattern characterization but also considers the positioning and occupancy of nucleosomes, which result in cirDNA fragments being protected and persisting in the circulation. Fragmentomics can determine tissue of origin and distinguish cancer-derived cirDNA. The screening power of fragmentomics has been considerably strengthened in the omics era, as shown in the ongoing development of sophisticated technologies assisted by machine learning. Fragmentomics can thus be regarded as a strategy for characterizing cancer within individuals and offers an alternative or a synergistic supplement to mutation searches, methylation, or nucleosome positioning. As such, it offers potential for improving diagnostics and cancer screening.

Comparison of the structures and topologies of plasma extracted circulating nuclear and mitochondrial cell-free DNA

Introduction: The function, origin and structural features of circulating nuclear DNA (cir-nDNA) and mitochondrial DNA (cir-mtDNA) are poorly known, even though they have been investigated in numerous clinical studies, and are involved in a number of routine clinical applications. Based on our previous report disproving the conventional plasma isolation used for cirDNA analysis, this work enables a direct topological comparison of the circulating structures associated with nuclear DNA and mitochondrial cell-free DNA. Materials and methods: We used a Q-PCR and low-pass whole genome sequencing (LP-WGS) combination approach of cir-nDNA and cir-mtDNA, extracted using a procedure that eliminates platelet activation during the plasma isolation process to prevent mitochondria release in the extracellular milieu. Various physical procedures, such as filtration and differential centrifugation, were employed to infer their circulating structures. Results: DSP-S cir-mtDNA mean size profiles distributed on a slightly shorter range than SSP-S. SSP-S detected 40-fold more low-sized cir-mtDNA fragments (<90 bp/nt) and three-fold less long-sized fragments (>200 bp/nt) than DSP-S. The ratio of the fragment number below 90 bp over the fragment number above 200 bp was very homogenous among both DSP-S and SSP-S profiles, being 134-fold lower with DSP-S than with SSP-S. Cir-mtDNA and cir-nDNA DSP-S and SSP-S mean size profiles of healthy individuals ranged in different intervals with periodic sub-peaks only detectable with cir-nDNA. The very low amount of cir-mtDNA fragments of short size observed suggested that most of the cir-mtDNA is poorly fragmented and appearing longer than ∼1,000 bp, the readout limit of this LP-WGS method. Data suggested that cir-nDNA is, among DNA extracted in plasma, associated with ∼8.6% of large structures (apoptotic bodies, large extracellular vesicles (EVs), cell debris…), ∼27.7% in chromatin and small EVs and ∼63.7% mainly in oligo- and mono-nucleosomes. By contrast, cir-mtDNA appeared to be preponderantly (75.7%) associated with extracellular mitochondria, either in its free form or with large EVs; to a lesser extent, it was also associated with other structures: small EVs (∼18.4%), and exosomes or protein complexes (∼5.9%). Conclusion: This is the first study to directly compare the structural features of cir-nDNA and cir-mtDNA. The significant differences revealed between both are due to the DNA topological structure contained in the nucleus (chromatin) and in the mitochondria (plasmid) that determine their biological stability in blood. Although cir-nDNA and cir-mtDNA are principally associated with mono-nucleosomes and cell-free mitochondria, our study highlights the diversity of the circulating structures associated with cell-free DNA. They consequently have different pharmacokinetics as well as physiological functions. Thus, any accurate evaluation of their biological or diagnostic individual properties must relies on appropriate pre-analytics, and optimally on the isolation or enrichment of one category of their cirDNA associated structures.

Epigenetic liquid biopsies for minimal residual disease, what's around the corner?

Liquid biopsy assays for minimal residual disease (MRD) are used to monitor and inform oncological treatment and predict the risk of relapse in cancer patients. To-date, most MRD assay development has focused on targeting somatic mutations. However, epigenetic changes are more frequent and universal than genetic alterations in cancer and circulating tumor DNA (ctDNA) retains much of these changes. Here, we review the epigenetic signals that can be used to detect MRD, including DNA methylation alterations and fragmentation patterns that differentiate ctDNA from noncancerous circulating cell-free DNA (ccfDNA). We then summarize the current state of MRD monitoring; highlight the advantages of epigenetics over genetics-based approaches; and discuss the emerging paradigm of assaying both genetic and epigenetic targets to monitor treatment response, detect disease recurrence, and inform adjuvant therapy.

Serological, fragmentomic, and epigenetic characteristics of cell-free DNA in patients with lupus nephritis

Objectives

The biological characteristics of plasma circulating cell-free DNA (cfDNA) are related to the pathogenesis of lupus nephritis (LN). The aim of this study was to explore the biological characteristics of cfDNA in patients with LN in terms of serology, fragment omics, and epigenetics, and to discuss the possibility of liquid biopsy for cfDNA as an alternative to conventional tissue biopsy.

Methods

cfDNA was extracted from plasma samples of 127 patients with systemic lupus erythematosus (64 with LN, 63 without LN). The cfDNA concentration was determined using the Qubit method. Next-generation sequencing cfDNA methylation profiling was performed for three LN patients and six non-LN patients. The methylation panel was designed based on data from The Cancer Genome Atlas cohort. The fragmentation index, motif score, and DELFI score were calculated to explore the fragmentation profile of cfDNA in patients with LN. Statistical and machine learning methods were used to select features to calculate the methylation scores of the samples.

Results

Patients with LN had significantly lower cfDNA concentrations (P = 0.0347) than those without LN. This may be associated with the presence of anti-double-stranded DNA antibodies (r = -0.4189; P = 0.0296). The mean DELFI score (proportion of short fragments of cfDNA) in patients with LN was significantly higher than that in patients without LN (P = 0.0238). Based on the pan-cancer data, 73, 66, 8, and 10 features were selected and used to calculate the methylation scores. The mean methylation scores of these features in patients with LN differed significantly from those in patients without LN (P = 0.0238).

Conclusions

The specificity of cfDNA in patients with LN was identified using serological, fragmentomic, and epigenetic analyses. The findings may have implications for the development of new molecular markers of LN.

CRAG: de novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing

The fine-scale cell-free DNA fragmentation patterns in early-stage cancers are poorly understood. We developed a de novo approach to characterize the cell-free DNA fragmentation hotspots from plasma whole-genome sequencing. Hotspots are enriched in open chromatin regions, and, interestingly, 3'end of transposons. Hotspots showed global hypo-fragmentation in early-stage liver cancers and are associated with genes involved in the initiation of hepatocellular carcinoma and associated with cancer stem cells. The hotspots varied across multiple early-stage cancers and demonstrated high performance for the diagnosis and identification of tissue-of-origin in early-stage cancers. We further validated the performance with a small number of independent case-control-matched early-stage cancer samples.

Neutrophil extracellular traps have auto-catabolic activity and produce mononucleosome-associated circulating DNA

BACKGROUND

As circulating DNA (cirDNA) is mainly detected as mononucleosome-associated circulating DNA (mono-N cirDNA) in blood, apoptosis has until now been considered as the main source of cirDNA. The mechanism of cirDNA release into the circulation, however, is still not fully understood. This work addresses that knowledge gap, working from the postulate that neutrophil extracellular traps (NET) may be a source of cirDNA, and by investigating whether NET may directly produce mono-N cirDNA.

METHODS

We studied (1) the in vitro kinetics of cell derived genomic high molecular weight (gHMW) DNA degradation in serum; (2) the production of extracellular DNA and NET markers such as neutrophil elastase (NE) and myeloperoxidase (MPO) by ex vivo activated neutrophils; and (3) the in vitro NET degradation in serum; for this, we exploited the synergistic analytical information provided by specifically quantifying DNA by qPCR, and used shallow WGS and capillary electrophoresis to perform fragment size analysis. We also performed an in vivo study in knockout mice, and an in vitro study of gHMW DNA degradation, to elucidate the role of NE and MPO in effecting DNA degradation and fragmentation. We then compared the NET-associated markers and fragmentation size profiles of cirDNA in plasma obtained from patients with inflammatory diseases found to be associated with NET formation and high levels of cirDNA (COVID-19, N = 28; systemic lupus erythematosus, N = 10; metastatic colorectal cancer, N = 10; and from healthy individuals, N = 114).

RESULTS

Our studies reveal that gHMW DNA degradation in serum results in the accumulation of mono-N DNA (81.3% of the remaining DNA following 24 h incubation in serum corresponded to mono-N DNA); "ex vivo" NET formation, as demonstrated by a concurrent 5-, 5-, and 35-fold increase of NE, MPO, and cell-free DNA (cfDNA) concentration in PMA-activated neutrophil culture supernatant, leads to the release of high molecular weight DNA that degrades down to mono-N in serum; NET mainly in the form of gHMW DNA generate mono-N cirDNA (2 and 41% of the remaining DNA after 2 h in serum corresponded to 1-10 kbp fragments and mono-N, respectively) independent of any cellular process when degraded in serum; NE and MPO may contribute synergistically to NET autocatabolism, resulting in a 25-fold decrease in total DNA concentration and a DNA fragment size profile similar to that observed from cirDNA following 8 h incubation with both NE and MPO; the cirDNA size profile of NE KO mice significantly differed from that of the WT, suggesting NE involvement in DNA degradation; and a significant increase in the levels of NE, MPO, and cirDNA was detected in plasma samples from lupus, COVID-19, and mCRC, showing a high correlation with these inflammatory diseases, while no correlation of NE and MPO with cirDNA was found in HI.

CONCLUSIONS

Our work describes the mechanisms by which NET and cirDNA are linked. In doing so, we demonstrate that NET are a major source of mono-N cirDNA independent of apoptosis and establish a new paradigm of the mechanisms of cirDNA release in normal and pathological conditions. We also demonstrate a link between immune response and cirDNA.

A hitchhiker's guide to cell-free DNA biology

Liquid biopsy provides a noninvasive window to the cancer genome and physiology. In particular, cell-free DNA (cfDNA) is a versatile analyte for guiding treatment, monitoring treatment response and resistance, tracking minimal residual disease, and detecting cancer earlier. Despite certain successes, brain cancer diagnosis is amongst those applications that has so far resisted clinical implementation. Recent approaches have highlighted the clinical gain achievable by exploiting cfDNA biological signatures to boost liquid biopsy or unlock new applications. However, the biology of cfDNA is complex, still partially understood, and affected by a range of intrinsic and extrinsic factors. This guide will provide the keys to read, decode, and harness cfDNA biology: the diverse sources of cfDNA in the bloodstream, the mechanism of cfDNA release from cells, the cfDNA structure, topology, and why accounting for cfDNA biology matters for clinical applications of liquid biopsy.

Multimodal analysis of ctDNA methylation and fragmentomic profiles enhances detection of nonmetastatic colorectal cancer

Aims: Early detection of colorectal cancer (CRC) provides substantially better survival rates. This study aimed to develop a blood-based screening assay named SPOT-MAS ('screen for the presence of tumor by DNA methylation and size') for early CRC detection with high accuracy. Methods: Plasma cell-free DNA samples from 159 patients with nonmetastatic CRC and 158 healthy controls were simultaneously analyzed for fragment length and methylation profiles. We then employed a deep neural network with fragment length and methylation signatures to build a classification model. Results: The model achieved an area under the curve of 0.989 and a sensitivity of 96.8% at 97% specificity in detecting CRC. External validation of our model showed comparable performance, with an area under the curve of 0.96. Conclusion: SPOT-MAS based on integration of cancer-specific methylation and fragmentomic signatures could provide high accuracy for early-stage CRC detection.