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

Protocol for the analysis of cell-free DNA end characteristics for accurate cancer diagnosis

Fragmentation patterns of plasma cell-free DNA (cfDNA) are promising biomarkers in cancer diagnosis. Here, we present a protocol to enrich tumor-derived cfDNA molecules through end selection. We describe steps for installing software, aligning plasma cfDNA data to the reference genome, and performing end selection on cfDNA. We then detail procedures for building cancer diagnostic models with artificial intelligence. Overall, we provide commands to align cfDNA whole-genome sequencing data starting from raw reads, then extract fragmentomic features, and finally build diagnostic models with performance evaluations. For complete information on the generation and use of this protocol, please refer to Ju et al.1.

Analysis of the clinical application value of cfDNA methylation and fragmentation in early diagnosis of esophageal cancer

BACKGROUND

This study explores the clinical value of cfDNA methylation and fragmentation for the early diagnosis of esophageal cancer using liquid biopsy.

METHODS

Whole genome bisulfite sequencing and low-pass whole genome sequencing were utilized to detect cfDNA biomarkers, comparing 30 esophageal cancer patients with 10 healthy controls.

RESULTS

Significant differences in cfDNA methylation and fragmentation were observed between cancerous and non-cancerous samples (p < 0.05). A volcano plot identified 822 differentially methylated markers (817 upregulated, 5 downregulated), with SOX17, SOX1, ZNF382, ZNF667-AS1, and TFPI2 highly associated with esophageal cancer. Fragmentation markers (EDM, FSD, FSR, TFBS, CNV) showed 95 % specificity and sensitivity, with EDM demonstrating the best performance.

CONCLUSION

Our study highlights the clinical potential of cfDNA methylation and fragmentation biomarkers for the early diagnosis of esophageal cancer.

Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection

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.

Application of Digital Polymerase Chain Reaction (dPCR) in Non-Invasive Prenatal Testing (NIPT)

This article reviews the current applications of the digital polymerase chain reaction (dPCR) in non-invasive prenatal testing (NIPT) and explores its potential to complement or surpass the capabilities of Next-Generation Sequencing (NGS) in prenatal testing. The growing incidence of genetic disorders in maternal-fetal medicine has intensified the demand for precise and accessible NIPT options, which aim to minimize the need for invasive prenatal diagnostic procedures. Cell-free fetal DNA (cffDNA), the core analyte in NIPT, is influenced by numerous factors such as maternal DNA contamination, placental health, and fragment degradation. dPCR, with its inherent precision and ability to detect low-abundance targets, demonstrates robustness against these interferences. Although NGS remains the gold standard due to its comprehensive diagnostic capabilities, its high costs limit widespread use, particularly in resource-limited settings. In contrast, dPCR provides comparable accuracy with lower complexity and expense, making it a promising alternative for prenatal testing.

A perspective review on the systematic implementation of ctDNA in phase I clinical trial drug development

Circulating tumour DNA (ctDNA) represents an increasingly important biomarker for the screening, diagnosis and management of patients in clinical practice in advanced/metastatic disease across multiple cancer types. In this context, ctDNA-based comprehensive genomic profiling is now available for patient management decisions, and several ctDNA-based companion diagnostic assays have been approved by regulatory agencies. However, although the assessment of ctDNA levels in Phase II-III drug development is now gathering momentum, it remains somewhat surprisingly limited in the early Phase I phases in light of the potential opportunities provided by such analysis. In this perspective review, we investigate the potential and hurdles of applying ctDNA testing for the inclusion and monitoring of patients in phase 1 clinical trials. This will enable more informed decisions regarding patient inclusion, dose optimization, and proof-of-mechanism of drug biological activity and molecular response, thereby supporting the evolving oncology drug development paradigm. Furthermore, we will highlight the use of cost-efficient, agnostic genome-wide techniques (such as low-pass whole genome sequencing and fragmentomics) and methylation-based methods to facilitate a more systematic integration of ctDNA in early clinical trial settings.

The potential of circulating free DNA of methylated IGFBP as a biomarker for type 2 diabetes Mellitus: A Comprehensive review

T2DM detection methods are commonly used in teens and adults but are generally unsuitable to unborn fetuses in the context of non-invasive prenatal testing (NIPT). Biophysical and biochemical tests for fetuses are often invasive, carry risks, and have low sensitivity and specificity, with no direct method available to diagnose T2DM in utero. In contrast, cell-free DNA (cfDNA) is known have high sensitivity (93-98 %) and specificity (94-100 %) for cancer detection and fetal genetic disorders (trisomy 21, 8, and 13) making it applicable for fetal epigenetic and genetic analysis, including T2DM early detection. However, no study has explored its use for this purpose. Our review focuses on the potential of IGFBP methylation levels in cfDNA as biomarkers for NIPT of T2DM. Placental global hypomethylation in GDM may predict T2DM during the prenatal period, and a similar pattern potentially be detected in cfDNA. Targeted genes reliable for NIPT, such as IGFBPs are needed because their significant role in T2DM and GDM. Among these, IGFBP-1 and IGFBP-2 have shown potential as predictive genes, exhibiting hypermethylation in placental tissue from GDM cases. This hypermethylation reduces their expression and the formation of the IGF-1-IGFBP complex, leading to increased levels of free IGF-1, which is associated with T2DM in the fetus. Hypermethylation regions have longer fragment sizes in cfDNA, thus in T2DM cases, hypermethylation of IGFBP-1 and IGFBP-2 from fetus results in longer cfDNA fragments. Therefore, analyzing the methylation levels and fragment sizes of IGFBP-1 or IGFBP-2 cfDNA could be a promising biomarker for identifying fetal T2DM risk non-invasively.

Leveraging epigenetic alterations in pancreatic ductal adenocarcinoma for clinical applications

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by late detection and poor prognosis. Recent research highlights the pivotal role of epigenetic alterations in driving PDAC development and progression. These changes, in conjunction with genetic mutations, contribute to the intricate molecular landscape of the disease. Specific modifications in DNA methylation, histone marks, and non-coding RNAs are emerging as robust predictors of disease progression and patient survival, offering the potential for more precise prognostic tools compared to conventional clinical staging. Moreover, the detection of epigenetic alterations in blood and other non-invasive samples holds promise for earlier diagnosis and improved management of PDAC. This review comprehensively summarises current epigenetic research in PDAC and identifies persisting challenges. These include the complex nature of epigenetic profiles, tumour heterogeneity, limited access to early-stage samples, and the need for highly sensitive liquid biopsy technologies. Addressing these challenges requires the standardisation of methodologies, integration of multi-omics data, and leveraging advanced computational tools such as machine learning and artificial intelligence. While resource-intensive, these efforts are essential for unravelling the functional consequences of epigenetic changes and translating this knowledge into clinical applications. By overcoming these hurdles, epigenetic research has the potential to revolutionise the management of PDAC and improve patient outcomes.

FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit

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

An appropriate DNA input for bisulfite conversion reveals LINE-1 and Alu hypermethylation in tissues and circulating cell-free DNA from cancers

The autonomous and active Long-Interspersed Element-1 (LINE-1, L1) and the non-autonomous Alu retrotransposon elements, contributing to 30% of the human genome, are the most abundant repeated sequences. With more than 90% of their sequences being methylated in normal cells, these elements undeniably contribute to the global DNA methylation level and constitute a major part of circulating-cell-free DNA (cfDNA). So far, the hypomethylation status of LINE-1 and Alu in cellular and extracellular DNA has long been considered a prevailing hallmark of ageing-related diseases and cancer. This study demonstrated that errors in LINE-1 and Alu methylation level measurements were caused by an excessive input quantity of genomic DNA used for bisulfite conversion. Using the minuscule DNA amount of 0.5 ng, much less than what has been used and recommended so far (500 ng-2 μg) or 1 μL of cfDNA extracted from 1 mL of blood, we revealed hypermethylation of LINE-1 and Alu in 407 tumour samples of primary breast, colon and lung cancers when compared with the corresponding pair-matched adjacent normal tissue samples (P < 0.05-0.001), and in cfDNA from 296 samples of lung cancers as compared with 477 samples from healthy controls (P < 0.0001). More importantly, LINE-1 hypermethylation in cfDNA is associated with healthy ageing. Our results have not only contributed to the standardized bisulfite-based protocols for DNA methylation assays, particularly in applications on repeated sequences but also provided another perspective for other repetitive sequences whose epigenetic properties may have crucial impacts on genome architecture and human health.

Modulating cell-free DNA biology as the next frontier in liquid biopsies

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.

Chromatin accessibility: biological functions, molecular mechanisms and therapeutic application

The dynamic regulation of chromatin accessibility is one of the prominent characteristics of eukaryotic genome. The inaccessible regions are mainly located in heterochromatin, which is multilevel compressed and access restricted. The remaining accessible loci are generally located in the euchromatin, which have less nucleosome occupancy and higher regulatory activity. The opening of chromatin is the most important prerequisite for DNA transcription, replication, and damage repair, which is regulated by genetic, epigenetic, environmental, and other factors, playing a vital role in multiple biological progresses. Currently, based on the susceptibility difference of occupied or free DNA to enzymatic cleavage, solubility, methylation, and transposition, there are many methods to detect chromatin accessibility both in bulk and single-cell level. Through combining with high-throughput sequencing, the genome-wide chromatin accessibility landscape of many tissues and cells types also have been constructed. The chromatin accessibility feature is distinct in different tissues and biological states. Research on the regulation network of chromatin accessibility is crucial for uncovering the secret of various biological processes. In this review, we comprehensively introduced the major functions and mechanisms of chromatin accessibility variation in different physiological and pathological processes, meanwhile, the targeted therapies based on chromatin dynamics regulation are also summarized.

OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions

Open chromatin regions (OCRs) play a crucial role in transcriptional regulation and gene expression. In recent years, there has been a growing interest in using plasma cell-free DNA (cfDNA) sequencing data to detect OCRs. By analyzing the characteristics of cfDNA fragments and their sequencing coverage, researchers can differentiate OCRs from non-OCRs. However, the presence of noise and variability in cfDNA-seq data poses challenges for the training data used in the noise-tolerance learning-based OCR estimation approach, as it contains numerous noisy labels that may impact the accuracy of the results. For current methods of detecting OCRs, they rely on statistical features derived from typical open and closed chromatin regions to determine whether a region is OCR or non-OCR. However, there are some atypical regions that exhibit statistical features that fall between the two categories, making it difficult to classify them definitively as either open or closed chromatin regions (CCRs). These regions should be considered as partially open chromatin regions (pOCRs). In this paper, we present OCRClassifier, a novel framework that combines control charts and machine learning to address the impact of high-proportion noisy labels in the training set and classify the chromatin open states into three classes accurately. Our method comprises two control charts. We first design a robust Hotelling T2 control chart and create new run rules to accurately identify reliable OCRs and CCRs within the initial training set. Then, we exclusively utilize the pure training set consisting of OCRs and CCRs to create and train a sensitized T2 control chart. This sensitized T2 control chart is specifically designed to accurately differentiate between the three categories of chromatin states: open, partially open, and closed. Experimental results demonstrate that under this framework, the model exhibits not only excellent performance in terms of three-class classification, but also higher accuracy and sensitivity in binary classification compared to the state-of-the-art models currently available.

Cell-free placental DNA: What do we really know?

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.

Blood biomarkers of hepatocellular carcinoma: a critical review

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

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.

Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA

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

Evaluation and integration of cell-free DNA signatures for detection of lung cancer

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

Cell-free DNA end characteristics enable accurate and sensitive cancer diagnosis

The fragmentation patterns of cell-free DNA (cfDNA) in plasma can potentially be utilized as diagnostic biomarkers in liquid biopsy. However, our knowledge of this biological process and the information encoded in fragmentation patterns remains preliminary. Here, we investigated the cfDNA fragmentomic characteristics against nucleosome positioning patterns in hematopoietic cells. cfDNA molecules with ends located within nucleosomes were relatively shorter with altered end motif patterns, demonstrating the feasibility of enriching tumor-derived cfDNA in patients with cancer through the selection of molecules possessing such ends. We then developed three cfDNA fragmentomic metrics after end selection, which showed significant alterations in patients with cancer and enabled cancer diagnosis. By incorporating machine learning, we further built high-performance diagnostic models, which achieved an overall area under the curve of 0.95 and 85.1% sensitivity at 95% specificity. Hence, our investigations explored the end characteristics of cfDNA fragmentomics and their merits in building accurate and sensitive cancer diagnostic models.

Multi-Omics Profiling Unveils the Complexity and Dynamics of Immune Infiltrates in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a highly heterogeneous malignancy. The reasons behind the global rise in the incidence of ICC remain unclear, and there exists limited knowledge regarding the immune cells within the tumor microenvironment (TME). In this study, a more comprehensive analysis of multi-omics data was performed using machine learning methods. The study found that the immunoactivity of B cells, macrophages, and T cells in the infiltrating immune cells of ICC exhibits a significantly higher level of immunoactivity in comparison to other immune cells. During the immune sensing and response, the effect of antigen-presenting cells (APCs) such as B cells and macrophages on activating NK cells was weakened, while the effect of activating T cells became stronger. Simultaneously, four distinct subpopulations, namely BLp, MacrophagesLp, BHn, and THn, have been identified from the infiltrating immune cells, and their corresponding immune-related marker genes have been identified. The immune sensing and response model of ICC has been revised and constructed based on our current comprehension. This study not only helps to deepen the understanding the heterogeneity of infiltrating immune cells in ICC, but also may provide valuable insights into the diagnosis, evaluation, treatment, and prognosis of ICC.

DNA demethylation triggers cell free DNA release in colorectal cancer cells

BACKGROUND

Liquid biopsy based on cell-free DNA (cfDNA) analysis holds significant promise as a minimally invasive approach for the diagnosis, genotyping, and monitoring of solid malignancies. Human tumors release cfDNA in the bloodstream through a combination of events, including cell death, active and passive release. However, the precise mechanisms leading to cfDNA shedding remain to be characterized. Addressing this question in patients is confounded by several factors, such as tumor burden extent, anatomical and vasculature barriers, and release of nucleic acids from normal cells. In this work, we exploited cancer models to dissect basic mechanisms of DNA release.

METHODS

We measured cell loss ratio, doubling time, and cfDNA release in the supernatant of a colorectal cancer (CRC) cell line collection (N = 76) representative of the molecular subtypes previously identified in cancer patients. Association analyses between quantitative parameters of cfDNA release, cell proliferation, and molecular features were evaluated. Functional experiments were performed to test the impact of modulating DNA methylation on cfDNA release.

RESULTS

Higher levels of supernatant cfDNA were significantly associated with slower cell cycling and increased cell death. In addition, a higher cfDNA shedding was found in non-CpG Island Methylator Phenotype (CIMP) models. These results indicate a positive correlation between lower methylation and increased cfDNA levels. To explore this further, we exploited methylation microarrays to identify a subset of probes significantly associated with cfDNA shedding and derive a methylation signature capable of discriminating high from low cfDNA releasers. We applied this signature to an independent set of 176 CRC cell lines and patient derived organoids to select 14 models predicted to be low or high releasers. The methylation profile successfully predicted the amount of cfDNA released in the supernatant. At the functional level, genetic ablation of DNA methyl-transferases increased chromatin accessibility and DNA fragmentation, leading to increased cfDNA release in isogenic CRC cell lines. Furthermore, in vitro treatment of five low releaser CRC cells with a demethylating agent was able to induce a significant increase in cfDNA shedding.

CONCLUSIONS

Methylation status of cancer cell lines contributes to the variability of cfDNA shedding in vitro. Changes in methylation pattern are associated with cfDNA release levels and might be exploited to increase sensitivity of liquid biopsy assays.

Early Detection of Malignant and Premalignant Peripheral Nerve Tumors Using Cell-Free DNA Fragmentomics

PURPOSE

Early detection of neurofibromatosis type 1 (NF1)-associated peripheral nerve sheath tumors (PNST) informs clinical decision-making, enabling early definitive treatment and potentially averting deadly outcomes. In this study, we describe a cell-free DNA (cfDNA) fragmentomic approach that distinguishes nonmalignant, premalignant, and malignant forms of PNST in the cancer predisposition syndrome, NF1.

EXPERIMENTAL DESIGN

cfDNA was isolated from plasma samples of a novel cohort of 101 patients with NF1 and 21 healthy controls and underwent whole-genome sequencing. We investigated diagnosis-specific signatures of copy-number alterations with in silico size selection as well as fragment profiles. Fragmentomics were analyzed using complementary feature types: bin-wise fragment size ratios, end motifs, and fragment non-negative matrix factorization signatures.

RESULTS

The novel cohort of patients with NF1 validated that our previous cfDNA copy-number alteration-based approach identifies malignant PNST (MPNST) but cannot distinguish between benign and premalignant states. Fragmentomic methods were able to differentiate premalignant states including atypical neurofibromas (AN). Fragmentomics also adjudicated AN cases suspicious for MPNST, correctly diagnosing samples noninvasively, which could have informed clinical management.

CONCLUSIONS

Novel cfDNA fragmentomic signatures distinguish AN from benign plexiform neurofibromas and MPNST, enabling more precise clinical diagnosis and management. This study pioneers the early detection of malignant and premalignant PNST in NF1 and provides a blueprint for decentralizing noninvasive cancer surveillance in hereditary cancer predisposition syndromes.

Extracting regulatory active chromatin footprint from cell-free DNA

Cell-free DNA (cfDNA) has emerged as a pivotal player in precision medicine, revolutionizing the diagnostic and therapeutic landscape. While its clinical applications have significantly increased in recent years, current cfDNA assays have limited ability to identify the active transcriptional programs that govern complex disease phenotypes and capture the heterogeneity of the disease. To address these limitations, we have developed a non-invasive platform to enrich and examine the active chromatin fragments (cfDNAac) in peripheral blood. The deconvolution of the cfDNAac signal from traditional nucleosomal chromatin fragments (cfDNAnuc) yields a catalog of features linking these circulating chromatin signals in blood to specific regulatory elements across the genome, including enhancers, promoters, and highly transcribed genes, mirroring the epigenetic data from the ENCODE project. Notably, these cfDNAac counts correlate strongly with RNA polymerase II activity and exhibit distinct expression patterns for known circadian genes. Additionally, cfDNAac signals across gene bodies and promoters show strong correlations with whole blood gene expression levels defined by GTEx. This study illustrates the utility of cfDNAac analysis for investigating epigenomics and gene expression, underscoring its potential for a wide range of clinical applications in precision medicine.

DNA methylation and gene expression as determinants of genome-wide cell-free DNA fragmentation

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.

Chromosome-level genome assembly of Aquilaria yunnanensis

Aquilaria yunnanensis is an endangered agarwood-producing tree currently listed on the IUCN Red List of Threatened Species. The agarwood it produces has important medicinal and economic value, but its population has sharply declined due to human destruction and habitat reduction. Therefore, obtaining genomic information on A. yunnanensis is beneficial for its protection work. We assembled a chromosome-level reference genome of A. yunnanensis by using BGI short reads, PacBio HiFi long reads, coupled with Hi-C technology. The final genome assembly of A. yunnanensis is 847.04 Mb, with N50 size of 99.68 Mb, in which 805.49 Mb of the bases were anchored on eight pseudo-chromosomes. Two gapless pseudo-chromosomes were detected in the assembly. A total of 27,955 protein-coding genes as well as 74.65% repetitive elements were annotated. These findings may provide valuable resources in conservation, functional genomics, and molecular breeding of A. yunnanensis, as well as the molecular phylogenetics and evolutionary patterns in Aquilaria.

From shells to sequences: A proof-of-concept study for on-site analysis of hemolymphatic circulating cell-free DNA from sentinel mussels using Nanopore technology

Blue mussels are often abundant and widely distributed in polar marine coastal ecosystems. Because of their wide distribution, ecological importance, and relatively stationary lifestyle, bivalves have long been considered suitable indicators of ecosystem health and changes. Monitoring the population dynamics of blue mussels can provide information on the overall biodiversity, species interactions, and ecosystem functioning. In the present work, we combined the concept of liquid biopsy (LB), an emerging concept in medicine based on the sequencing of free circulating DNA, with the Oxford Nanopore Technologies (ONT) platform using a portable laboratory in a remote area. Our results demonstrate that this platform is ideally suited for sequencing hemolymphatic circulating cell-free DNA (ccfDNA) fragments found in blue mussels. The percentage of non-self ccfDNA accounted for >50 % of ccfDNA at certain sampling Sites, allowing the quick, on-site acquisition of a global view of the biodiversity of a coastal marine ecosystem. These ccfDNA fragments originated from viruses, bacteria, plants, arthropods, algae, and multiple Chordata. Aside from non-self ccfDNA, we found DNA fragments from all 14 blue mussel chromosomes, as well as those originating from the mitochondrial genomes. However, the distribution of nuclear and mitochondrial DNA was significantly different between Sites. Similarly, analyses between various sampling Sites showed that the biodiversity varied significantly within microhabitats. Our work shows that the ONT platform is well-suited for LB in sentinel blue mussels in remote and challenging conditions, enabling faster fieldwork for conservation strategies and resource management in diverse settings.

Liquid Biopsy Based on Cell-Free DNA and RNA

This review delves into the rapidly evolving landscape of liquid biopsy technologies based on cell-free DNA (cfDNA) and cell-free RNA (cfRNA) and their increasingly prominent role in precision medicine. With the advent of high-throughput DNA sequencing, the use of cfDNA and cfRNA has revolutionized noninvasive clinical testing. Here, we explore the physical characteristics of cfDNA and cfRNA, present an overview of the essential engineering tools used by the field, and highlight clinical applications, including noninvasive prenatal testing, cancer testing, organ transplantation surveillance, and infectious disease testing. Finally, we discuss emerging technologies and the broadening scope of liquid biopsies to new areas of diagnostic medicine.

Systematic biases in reference-based plasma cell-free DNA fragmentomic profiling

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.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

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.

Diagnostic value of combined detection of plasma cfDNA concentration and integrity in NSCLC

Aim: To evaluate the value of combined detection of plasma cfDNA concentration and integrity in the early diagnosis of NSCLC. Methods: Real-time fluorescence quantitative PCR was used to determine the concentration and integrity of plasma cfDNA in 71 NSCLC patients and 53 healthy people. Results: Combined detection of plasma cfDNA concentration and integrity had higher diagnostic power in differentiating NSCLC patients with stage I/II from healthy people than detection of plasma cfDNA concentration alone or integrity alone. The AUC, sensitivity and specificity of the combined detection of plasma cfDNA concentration and integrity were 0.781, 0.62 and 0.85. Conclusion: Combined detection of plasma cfDNA concentration and integrity could improve the diagnostic value in NSCLC detection.

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.

Early detection of malignant and pre-malignant peripheral nerve tumors using cell-free DNA fragmentomics

Early detection of neurofibromatosis type 1 (NF1) associated peripheral nerve sheath tumors (PNST) informs clinical decision-making, potentially averting deadly outcomes. Here, we describe a cell-free DNA (cfDNA) fragmentomic approach which distinguishes non-malignant, pre-malignant and malignant forms of NF1 PNST. Using plasma samples from a novel cohort of 101 NF1 patients and 21 healthy controls, we validated that our previous cfDNA copy number alteration (CNA)-based approach identifies malignant peripheral nerve sheath tumor (MPNST) but cannot distinguish among benign and premalignant states. We therefore investigated the ability of fragment-based cfDNA features to differentiate NF1-associated tumors including binned genome-wide fragment length ratios, end motif analysis, and non-negative matrix factorization deconvolution of fragment lengths. Fragmentomic methods were able to differentiate pre-malignant states including atypical neurofibromas (AN). Fragmentomics also adjudicated AN cases suspicious for MPNST, correctly diagnosing samples noninvasively, which could have informed clinical management. Overall, this study pioneers the early detection of malignant and premalignant peripheral nerve sheath tumors in NF1 patients using plasma cfDNA fragmentomics. In addition to screening applications, this novel approach distinguishes atypical neurofibromas from benign plexiform neurofibromas and malignant peripheral nerve sheath tumors, enabling more precise clinical diagnosis and management.

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.

Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications

DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology.

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

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.

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.

Circulating DNA reveals a specific and higher fragmentation of the Y chromosome

Chromosome stability is a key point in genome evolution, particularly that of the Y chromosome. The Y chromosome loss in blood and tumor cells is well established. Through processes that are common to other chromosomes too, the Y chromosome undergoes degradation and fragmentation in the blood stream before elimination. This process gives rise to circulating DNA (cirDNA) fragments, whose examination may provide potential insight into the role of DNA fragmentation in blood for the Y chromosome elimination. In this study, we employed shallow whole genome sequencing (sWGS) to comprehensively assess the total cirDNA and the individual chromosome fragment size profiles in the plasma of healthy male individuals. Here, we show that (i) the fragment size profiles of total circulating DNA (cirDNA) and DNA fragments originating from autosomes and the X chromosome in blood plasma are homogeneous, and have a remarkably low variability (mean CV = 7%) among healthy individuals, (ii) the Y chromosome has a distinct fragment size profile with the accumulation of the fragment < 145 bp and depletion of the dinucleosome-associated fragments (290-390 bp), and its fragment fraction in blood decreases with age. These results indicate a higher fragmentation of the Y chromosome compared to other chromosomes and this in turn might be due to its increased susceptibility to degradation. Our findings pave the way for an elucidation of the impact of chromosomal origin on DNA degradation and the Y chromosome biology.

Altered cfDNA fragmentation profile in hypomethylated regions as diagnostic markers in breast cancer

BACKGROUND

Breast cancer, the most common malignancy in women worldwide, has been proven to have both altered plasma cell-free DNA (cfDNA) methylation and fragmentation profiles. Nevertheless, simultaneously detecting both of them for breast cancer diagnosis has never been reported. Moreover, although fragmentation pattern of cfDNA is determined by nuclease digestion of chromatin, structure of which may be affected by DNA methylation, whether cfDNA methylation and fragmentation are biologically related or not still remains unclear.

METHODS

Improved cfMeDIP-seq were utilized to characterize both cfDNA methylation and fragmentation profiles in 49 plasma samples from both healthy individuals and patients with breast cancer. The feasibility of using cfDNA fragmentation profile in hypo- and hypermethylated regions as diagnostic markers for breast cancer was evaluated.

RESULTS

Mean size of cfDNA fragments (100-220 bp) mapped to hypomethylated regions decreased more in patients with breast cancer (4.60 bp, 172.33 to 167.73 bp) than in healthy individuals (2.87 bp, 174.54 to 171.67 bp). Furthermore, proportion of short cfDNA fragments (100-150 bp) in hypomethylated regions when compared with it in hypermethylated regions was found to increase more in patients with breast cancer in two independent discovery cohort. The feasibility of using abnormality of short cfDNA fragments ratio in hypomethylated genomic regions for breast cancer diagnosis in validation cohort was evaluated. 7 out of 11 patients were detected as having breast cancer (63.6% sensitivity), whereas no healthy individuals were mis-detected (100% specificity).

CONCLUSION

We identified enriched short cfDNA fragments after 5mC-immunoprecipitation (IP) in patients with breast cancer, and demonstrated the enriched short cfDNA fragments might originated from hypomethylated genomic regions. Furthermore, we proved the feasibility of using differentially methylated regions (DMRs)-dependent cfDNA fragmentation profile for breast cancer diagnosis.

Circulating cell-free DNA sequencing for early detection of lung cancer

INTRODUCTION

Lung cancer is a leading cause of death in patients with cancer. Early diagnosis is crucial to improve the prognosis of patients with lung cancer. Plasma circulating cell-free DNA (cfDNA) contains comprehensive genetic and epigenetic information from tissues throughout the body, suggesting that early detection of lung cancer can be done non-invasively, conveniently, and cost-effectively using high-sensitivity techniques such as sequencing.

AREAS COVERED

In this review, we summarize the latest technological innovations, coupled with next-generation sequencing (NGS), regarding genomic alterations, methylation, and fragmentomic features of cfDNA for the early detection of lung cancer, as well as their clinical advances. Additionally, we discuss the suitability of study designs for diagnostic accuracy evaluation for different target populations and clinical questions.

EXPERT OPINION

Currently, cfDNA-based early screening and diagnosis of lung cancer faces many challenges, such as unsatisfactory performance, lack of quality control standards, and poor repeatability. However, the progress of several large prospective studies employing epigenetic features has shown promising predictive performance, which has inspired cfDNA sequencing for future clinical applications. Furthermore, the development of multi-omics markers for lung cancer, including genome-wide methylation and fragmentomics, is expected to play an increasingly important role in the future.

Clinical application of liquid biopsy based on circulating tumor DNA in non-small cell lung cancer

Lung cancer is a widely occurring and deadly malignancy, with high prevalence rates in China and across the globe. Specifically, non-small cell lung cancer (NSCLC) represents about 85% of all lung cancer cases. The 5-year disease-free survival rate after surgery for stage IB-IIIB NSCLC patients (disease-free survival, DFS) has notably declined from 73% to 13%. Early detection of abnormal cancer molecules and subsequent personalized treatment plans are the most effective ways to address this problem. Liquid biopsy, surprisingly, enables safe, accurate, non-invasive, and dynamic tracking of disease progression. Among the various modalities, circulating tumor DNA (ctDNA) is the most commonly used liquid biopsy modality. ctDNA serves as a credible "liquid biopsy" diagnostic tool that, to a certain extent, overcomes tumor heterogeneity and harbors genetic mutations in malignancies, thereby providing early information on tumor genetic alterations. Despite considerable academic interest in the clinical significance of ctDNA, consensus on its utility remains lacking. In this review, we assess the role of ctDNA testing in the diagnosis and management of NSCLC as a reference for clinical intervention in this disease. Lastly, we examine future directions to optimize ctDNA for personalized therapy.

Genomic approaches to cancer and minimal residual disease detection using circulating tumor DNA

Liquid biopsies using cell-free circulating tumor DNA (ctDNA) are being used frequently in both research and clinical settings. ctDNA can be used to identify actionable mutations to personalize systemic therapy, detect post-treatment minimal residual disease (MRD), and predict responses to immunotherapy. ctDNA can also be isolated from a range of different biofluids, with the possibility of detecting locoregional MRD with increased sensitivity if sampling more proximally than blood plasma. However, ctDNA detection remains challenging in early-stage and post-treatment MRD settings where ctDNA levels are minuscule giving a high risk for false negative results, which is balanced with the risk of false positive results from clonal hematopoiesis. To address these challenges, researchers have developed ever-more elegant approaches to lower the limit of detection (LOD) of ctDNA assays toward the part-per-million range and boost assay sensitivity and specificity by reducing sources of low-level technical and biological noise, and by harnessing specific genomic and epigenomic features of ctDNA. In this review, we highlight a range of modern assays for ctDNA analysis, including advancements made to improve the signal-to-noise ratio. We further highlight the challenge of detecting ultra-rare tumor-associated variants, overcoming which will improve the sensitivity of post-treatment MRD detection and open a new frontier of personalized adjuvant treatment decision-making.

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.

The functional and clinical roles of liquid biopsy in patient-derived models

The liquid biopsy includes the detection of circulating tumor cells (CTCs) and CTC clusters in blood, as well as the detection of, cell-free DNA (cfDNA)/circulating tumor DNA (ctDNA) and extracellular vesicles (EVs) in the patient's body fluid. Liquid biopsy has important roles in translational research. But its clinical utility is still under investigation. Newly emerged patient-derived xenograft (PDX) and CTC-derived xenograft (CDX) faithfully recapitulate the genetic and morphological features of the donor patients' tumor and patient-derived organoid (PDO) can mostly mimic tumor growth, tumor microenvironment and its response to drugs. In this review, we describe how the development of these patient-derived models has assisted the studies of CTCs and CTC clusters in terms of tumor biological behavior exploration, genomic analysis, and drug testing, with the help of the latest technology. We then summarize the studies of EVs and cfDNA/ctDNA in PDX and PDO models in early cancer diagnosis, tumor burden monitoring, drug test and response monitoring, and molecular profiling. The challenges faced and future perspectives of research related to liquid biopsy using patient-derived models are also discussed.