Inferring expressed genes by whole-genome sequencing of plasma DNA

Liquid Biopsy Approaches for Cancer Characterization, Residual Disease Detection, and Therapy Monitoring

Liquid biopsy encompasses a variety of molecular approaches to detect circulating tumor DNA (ctDNA) and has become a powerful tool in the diagnosis and treatment of solid tumors. Current applications include comprehensive genomic profiling for identifying targetable mutations and therapeutic resistance mechanisms, with emerging applications in minimal residual disease detection and treatment response monitoring. Increasingly, the potential for liquid biopsy in guiding treatment decisions is under active investigation through prospective clinical trials using ctDNA-adaptive interventions in patients with early-stage and metastatic cancers. Limitations arise on the basis of the sensitivity and feasibility of individual liquid biopsy assays; nonetheless, emerging technologies set the stage for improving these shortcomings. As the global oncology community continues to ascertain the clinical value of liquid biopsy across the continuum of patient care, this minimally invasive approach heralds a significant advancement in the promise of precision oncology.

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.

Clinical Validation of a Noninvasive Multi-Omics Method for Multicancer Early Detection in Retrospective and Prospective Cohorts

Recent studies highlight the promise of blood-based multicancer early detection (MCED) tests for identifying asymptomatic patients with cancer. However, most focus on a single cancer hallmark, thus limiting effectiveness because of cancer's heterogeneity. Here, a blood-based multi-omics test named SeekInCare for MCED is reported. SeekInCare incorporates multiple genomic and epigenetic hallmarks, including copy number aberration, fragment size, end motif, and oncogenic virus, via shallow whole-genome sequencing from cell-free DNA, alongside seven protein tumor markers in one tube of blood. Artificial intelligence algorithms were developed to distinguish patients with cancer from individuals without cancer and to predict the likely affected organ. The retrospective study included 617 patients with cancer and 580 individuals without cancer, covering 27 cancer types. SeekInCare achieved 60.0% sensitivity at 98.3% specificity, resulting in an area under the curve of 0.899. Sensitivities were 37.7%, 50.4%, 66.7%, and 78.1% in patients with stage I, II, III, and IV disease, respectively. Additionally, SeekInCare was evaluated in a prospective cohort consisting of 1203 individuals who received the test as a laboratory-developed test (median follow-up time, 753 days) in which it achieved 70.0% sensitivity at 95.2% specificity. The performances of SeekInCare in both retrospective and prospective studies demonstrate that SeekInCare is a blood-based MCED test, showing comparable performance to the other tests currently in development. These findings support its potential clinical utility as a cancer screening test in high-risk populations.

High tissue specificity of lncRNAs maximises the prediction of tissue of origin of circulating DNA

Several studies have made it possible to envision a translational application of plasma DNA sequencing in cancer diagnosis and monitoring. However, the extremely low concentration of circulating tumour DNA (ctDNA) fragments among the total cell-free DNA (cfDNA) remains a formidable challenge to overcome and statistical models have yet to be improved enough to become of practical use. In this study, we set about appraising the predictive value of a variety of binary classification models based on cfDNA sequencing using fragmentation features extracted around transcription start sites (TSSs). We investigated (1) features summarising mapped fragment density around each TSS, (2) long non-coding RNA (lncRNA) genes versus coding genes and (3) selection criteria to generate gene classes to be assigned by the model. Given that, in healthy samples, most of the cfDNA comes from lymphomyeloid lineages, we could identify the model parametrisation with the best accuracy in those lineages using publicly available datasets of healthy patients' cfDNA. Our results show that (1) the way tissue-specific gene classes are defined matters more than what fragmentation features are included, and (2) in particular, lncRNAs are more tissue specific than coding genes and stand out in terms of both sensitivity and specificity in our results.

Genome-wide nucleosome footprints of plasma cfDNA predict preterm birth: A case-control study

BACKGROUND

Preterm birth (PTB) occurs in approximately 11% of all births worldwide, resulting in significant morbidity and mortality for both mothers and their offspring. Identifying pregnancies at risk of preterm birth during early pregnancy may help improve interventions and reduce its incidence. Plasma cell-free DNA (cfDNA), derived from placenta and other maternal tissues, serves as a dynamic indicator of biological processes and pathological changes in pregnancy. These properties establish cfDNA as a valuable biomarker for investigating pregnancy complications, including PTB.

METHODS AND FINDINGS

To date, there are few methods available for PTB prediction that have been developed with large sample sizes, high-throughput screening, and validated in independent cohorts. To address this gap, we established a large-scale, multi-center case-control study involving 2,590 pregnancies (2,072 full-term and 518 preterm) from three independent hospitals to develop a spontaneous preterm birth classifier. We performed whole-genome sequencing on cfDNA, focusing on promoter profiling (read depth of promoter regions spanning from -1 to +1 kb around transcriptional start sites). Using four machine learning models and two feature selection algorithms, we developed classifiers for predicting preterm birth. Among these, the classifier based on the support vector machine model, named PTerm (Promoter profiling classifier for preterm prediction), exhibited the highest area under the curve (AUC) value of 0.878 (0.852-0.904) following leave-one-out cross-validation. Additionally, PTerm exhibited strong performance in three independent validation cohorts, achieving an overall AUC of 0.849 (0.831-0.866).

CONCLUSIONS

In summary, PTerm demonstrated high accuracy in predicting preterm birth. Additionally, it can be utilized with current non-invasive prenatal test data without changing its procedures or increasing detection cost, making it easily adaptable for preclinical tests.

Preeclampsia risk prediction from prenatal cell-free DNA screening

Preeclampsia is characterized by placental dysfunction and results in significant morbidity, but reliable early prediction remains challenging. We investigated whether clinically obtained prenatal cell-free DNA (cfDNA) screening (PDNAS) using whole-genome sequencing (WGS) data can be leveraged to predict preeclampsia risk early in pregnancy (≤16 weeks). Using 1,854 routinely collected clinical PDNAS samples (median, 12.1 weeks) with low-coverage (0.5×) WGS data, we developed a framework to quantify maternal and fetal tissue signatures using nucleosome accessibility, revealing early placental and endothelial dysfunction. These signatures informed a prediction model for preeclampsia risk, which achieved a validation performance of 0.85 area under the receiver operating characteristic curve (AUC) (81% sensitivity at 80% specificity) for preterm phenotypes several months prior to disease onset in a separate cohort of 831 consecutively collected samples, and subsequently confirmed in an external cohort of 141 samples (AUC 0.84, 79% sensitivity). We demonstrate that assessment of cfDNA nucleosome accessibility from early-pregnancy cfDNA sequence data enables the detection of early placental and endothelial-tissue aberrations and may aid in the determination of preeclampsia risk.

Circulating tumor DNA to monitor treatment response in solid tumors and advance precision oncology

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

Dynamic profiling of Cell-free DNA fragmentation uncovers postprandial metabolic and immune alterations

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Maternal plasma cell-free DNA nucleosome footprints can reveal changes in gene expression profiles during pregnancy and pre-eclampsia

BACKGROUND

Differential gene expression analysis is important to understand pregnancy processes and disease development. However, no non-invasive and comprehensive methods exist to identify differentially expressed genes (DEGs) in the fetus and placenta during pregnancy or pregnancy complications. Nucleosome footprints in maternal peripheral blood plasma cell-free DNA (cfDNA) reflect the gene expression profile of the cell of origin, mainly immune cells in the maternal blood and placenta. This study aimed to validate the feasibility of detecting changes in gene expression profiles as differentially depth gene (DDGs) based on plasma nucleosome footprints as a potential biomarker for pregnancy and pre-eclampsia.

METHODS

Deep sequencing was performed on separated plasma cfDNA collected from 34 women, including eight non-pregnant women, 14 healthy pregnant women, and 12 pre-eclamptic pregnant women. The number of reads in the promoter region of each gene was extracted and normalized. Normalized depths of genes were compared between healthy pregnant vs. non-pregnant women, all pregnant women vs. non-pregnant women, and healthy pregnant women vs. pre-eclamptic pregnant women using the Wilcoxon rank-sum test to identify statistically significant DDGs. The roles of these genes were identified by functional enrichment analysis using gene ontology.

RESULTS

Plasma cfDNA revealed different nucleosome footprints in preeclampsia pregnant, healthy pregnant, and non-pregnant women. Gene annotation revealed that the functions of 629 DDGs in pregnant and non-pregnant women mainly involved immune regulation, regardless of pre-eclampsia. 1978 DDGs between healthy pregnant and pre-eclamptic pregnant women displayed differences in immune regulation, cell cycle regulation, and sensory perception. These results are consistent with prior microarray and RNA-sequencing data.

CONCLUSIONS

The depth of the cfDNA nucleosome footprint in maternal plasma can be used to reflect changes in the gene expression profile during pregnancy and pre-eclampsia. The plasma cfDNA nucleosome footprint is a potential non-invasive biomarker for pregnancy and placental-origin pregnancy complications.

Genomic alterations and transcriptional phenotypes in circulating free DNA and matched metastatic tumor

BACKGROUND

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), due to the aggressive clinical course of this cancer, which makes obtaining tumor biopsies exceedingly challenging.

METHODS

In this study, we analyzed a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC. We conducted cfDNA low-pass whole genome sequencing (0.1X coverage), comparing it with time-point matched tumor characterized using whole-exome (130X) and transcriptome sequencing.

RESULTS

A direct comparison of cfDNA and tumor biopsy revealed that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not detected in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Sequencing coverage of plasma DNA fragments around transcription start sites showed distinct treatment-related changes and captured the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors. This allowed for the prediction of SCLC neuroendocrine phenotypes and treatment responses.

CONCLUSIONS

cfDNA captures a comprehensive view of tumor heterogeneity and evolution. These findings have significant implications for the non-invasive stratification of SCLC, a disease currently treated as a single entity.

Comprehensive evaluation of the impact of whole-genome bisulfite sequencing (WGBS) on the fragmentomic characteristics of plasma cell-free DNA

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Artificial intelligence and machine learning in cell-free-DNA-based diagnostics

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

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.

Gene expression profiles based on maternal plasma cfDNA nucleosome footprints indicate fetal development and maternal immunity changes during pregnancy progress

BACKGROUND

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

METHODS

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

RESULTS

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

DISCUSSION

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

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.

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.

Genomic alterations associated with resistance and circulating tumor DNA dynamics for early detection of progression on CDK4/6 inhibitor in advanced breast cancer

Combined CDK4/6 inhibitor (CDK4/6i) and endocrine therapy significantly improves outcome for patients with estrogen receptor-positive (ER+) metastatic breast cancer, but drug resistance and thus disease progression inevitably occur. Herein, we aimed to identify genomic alterations associated with combined CDK4/6i and endocrine therapy resistance, and follow the levels of specific mutations in longitudinal circulating tumor DNA (ctDNA) for early detection of progression. From a cohort of 86 patients with ER+ metastatic breast cancer we performed whole exome sequencing or targeted sequencing of paired tumor (N = 8) or blood samples (N = 5) obtained before initiation of combined CDK4/6i and endocrine therapy and at disease progression. Mutations in oncogenic genes at progression were rare, while amplifications of growth-regulating genes were more frequent. The most frequently acquired alterations observed were PIK3CA and TP53 mutations and PDK1 amplification. Longitudinal ctDNA dynamics of mutant PIK3CA or private mutations revealed increased mutation levels at progression in 8 of 10 patients (80%). Impressively, rising levels of PIK3CA-mutated ctDNA were detected 4-17 months before imaging. Our data add to the growing evidence supporting longitudinal ctDNA analysis for real-time monitoring of CDK4/6i response and early detection of progression in advanced breast cancer. Further, our analysis suggests that amplification of growth-related genes may contribute to combined CDK4/6i and endocrine therapy resistance.

Utilization of molecular genetic approaches for colorectal cancer screening

The feasibility of population screening for colorectal cancer has been demonstrated in several studies. Most of these studies have considered individual characteristics, diagnostic approaches, epidemiological data, and socioeconomic factors. In this article, we comment on an editorial by Metaxas et al published in the recent issue of the journal. The authors emphasized the need to raise public awareness through health education programs and the possibility of using easily accessible non-invasive screening methods. Here, we focus on non-invasive molecular genetic approaches that can aid in colorectal cancer screening. On the one hand, we highlighted the use of tumor DNA/RNA markers directly for screening and, on the other hand, underline the use of polygenic risk assessment and hereditary predisposition to select individuals for more thorough cancer screening.

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.

Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data

Motivation

The analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation, but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns.

Results

We introduce Liquid Biopsy Feature extract (LBFextract), a comprehensive package designed to streamline feature extraction from cfDNA sequencing data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data.

Data and Code Availability and Implementation

LBFextract is freely accessible at https://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406.

A promising application of kidney-specific cell-free DNA methylation markers in real-time monitoring sepsis-induced acute kidney injury

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

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.

Non-invasive determination of gene expression in placental tissue using maternal plasma cell-free DNA fragmentation characters

BACKGROUND

The expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem.

METHODS

Peripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data.

RESULTS

From five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A "DEPICT" gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta.

CONCLUSION

The DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome.

Considering Biomarkers of Neurodegeneration in Alzheimer's Disease: The Potential of Circulating Cell-Free DNA in Precision Neurology

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

Methylation-Associated Nucleosomal Patterns of Cell-Free DNA in Cancer Patients and Pregnant Women

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Predicting ammonia emissions and global warming potential in composting by machine learning

The amounts of gases emitted from composting are key to evaluating global warming potential (GWP). However, few methods can accurately predict the quantities of relevant gas emissions. In this study, three developed machine-learning models were used to predict NH3 emissions and GWP. The extreme gradient boosting model provided the best predictions (R2 > 90 %) compared to random forest, making it a suitable method for calculating NH3 emissions and GWP. The k-nearest neighbor classification model was utilized to determined compost maturity achieving 92 % accuracy. Shapley Additive ExPlanation analysis was applied to identify key factors influencing gas emissions and maturity. Aeration rate, carbon-to-nitrogen ratio and moisture content showed high importance in decreasing order for predicting NH3 emissions, while NO3- was the most significant factor for predicting GWP. Practical applications of predictive models suggested that prediction of GWP was 792614 Mg CO2e year-1 close to annual calculation of 789000 Mg CO2e year-1 in California.

Plasma Cell-Free DNA Concentration and Fragmentomes Predict Neoadjuvant Chemotherapy Response in Cervical Cancer Patients

Cervical cancer remains one of the most lethal gynecological malignancies. However, biomarkers for more precise patient care are an unmet need. Herein, the concentration of 285 plasma cell-free DNA (cfDNA) samples are analyzed from 84 cervical patients and the clinical significance of cfDNA fragmentomic characteristics across the neoadjuvant chemotherapy (NACT) treatment. Patients with poor NACT response exhibit a significantly greater escalation in cfDNA levels following the initial cycle of treatment, in comparison to patients with a favorable response. Distinctive end motif profiles and promoter coverages of cfDNA in initial plasma are observed between patients with differing NACT responses. Notably, the DNASE1L3 analysis further demonstrates the intrinsic association between cfDNA characteristics and chemotherapy resistance. The cfDNA and motif ratios show a good discriminative capacity for predicting non-responders from responders (area under the curve (AUC) > 0.8). In addition, transcriptional start sites (TSS) coverages around promoters discern the alteration of biological processes associated with chemotherapy resistance and reflect the potential value in predicting chemotherapy response. These findings in predictive biomarkers may optimize treatment selection, minimize unnecessary treatment, and assist in establishing personalized treatment strategies for cervical cancer patients.

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

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.

Histone modifications of circulating nucleosomes are associated with changes in cell-free DNA fragmentation patterns

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

Fragmentomics of plasma mitochondrial and nuclear DNA inform prognosis in COVID-19 patients with critical symptoms

BACKGROUND

The mortality rate of COVID-19 patients with critical symptoms is reported to be 40.5%. Early identification of patients with poor progression in the critical cohort is essential to timely clinical intervention and reduction of mortality. Although older age, chronic diseases, have been recognized as risk factors for COVID-19 mortality, we still lack an accurate prediction method for every patient. This study aimed to delve into the cell-free DNA fragmentomics of critically ill patients, and develop new promising biomarkers for identifying the patients with high mortality risk.

METHODS

We utilized whole genome sequencing on the plasma cell-free DNA (cfDNA) from 33 COVID-19 patients with critical symptoms, whose outcomes were classified as survival (n = 16) and death (n = 17). Mitochondrial DNA (mtDNA) abundance and fragmentomic properties of cfDNA, including size profiles, ends motif and promoter coverages were interrogated and compared between survival and death groups.

RESULTS

Significantly decreased abundance (~ 76% reduction) and dramatically shorter fragment size of cell-free mtDNA were observed in deceased patients. Likewise, the deceased patients exhibited distinct end-motif patterns of cfDNA with an enhanced preference for "CC" started motifs, which are related to the activity of nuclease DNASE1L3. Several dysregulated genes involved in the COVID-19 progression-related pathways were further inferred from promoter coverages. These informative cfDNA features enabled a high PPV of 83.3% in predicting deceased patients in the critical cohort.

CONCLUSION

The dysregulated biological processes observed in COVID-19 patients with fatal outcomes may contribute to abnormal release and modifications of plasma cfDNA. Our findings provided the feasibility of plasma cfDNA as a promising biomarker in the prognosis prediction in critically ill COVID-19 patients in clinical practice.

Mining nucleic acid "omics" to boost liquid biopsy in cancer

Treatments for cancer patients are becoming increasingly complex, and there is a growing desire from clinicians and patients for biomarkers that can account for this complexity to support informed decisions about clinical care. To achieve precision medicine, the new generation of biomarkers must reflect the spatial and temporal heterogeneity of cancer biology both between patients and within an individual patient. Mining the different layers of 'omics in a multi-modal way from a minimally invasive, easily repeatable, liquid biopsy has increasing potential in a range of clinical applications, and for improving our understanding of treatment response and resistance. Here, we detail the recent developments and methods allowing exploration of genomic, epigenomic, transcriptomic, and fragmentomic layers of 'omics from liquid biopsy, and their integration in a range of applications. We also consider the specific challenges that are posed by the clinical implementation of multi-omic liquid biopsies.

Analysis of the primary factors influencing donor derived cell-free DNA testing in kidney transplantation

The donor-derived cell-free DNA (ddcfDNA) is found in the plasma and urine of kidney transplant recipients and displays notable potential in diagnosing rejection, specifically antibody-mediated rejection (ABMR). Nonetheless, the quantitative methods of ddcfDNA lacking standardization and diverse detection techniques can impact the test outcomes. Besides, both the fraction and absolute values of ddcfDNA have been reported as valuable markers for rejection diagnosis, but they carry distinct meanings and are special in various pathological conditions. Additionally, ddcfDNA is highly sensitive to kidney transplant injury. The various sampling times and combination with other diseases can indeed impact ddcfDNA detection values. This review comprehensively analyses the various factors affecting ddcfDNA detection in kidney transplantation, including the number of SNPs and sequencing depths. Furthermore, different pathological conditions, distinct sampling time points, and the presence of complex heterologous signals can influence ddcfDNA testing results in kidney transplantation. The review also provides insights into ddcfDNA testing on different platforms along with key considerations.

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.

Cell-free DNA from germline TP53 mutation carriers reflect cancer-like fragmentation patterns

Germline pathogenic TP53 variants predispose individuals to a high lifetime risk of developing multiple cancers and are the hallmark feature of Li-Fraumeni syndrome (LFS). Our group has previously shown that LFS patients harbor shorter plasma cell-free DNA fragmentation; independent of cancer status. To understand the functional underpinning of cfDNA fragmentation in LFS, we conducted a fragmentomic analysis of 199 cfDNA samples from 82 TP53 mutation carriers and 30 healthy TP53-wildtype controls. We find that LFS individuals exhibit an increased prevalence of A/T nucleotides at fragment ends, dysregulated nucleosome positioning at p53 binding sites, and loci-specific changes in chromatin accessibility at development-associated transcription factor binding sites and at cancer-associated open chromatin regions. Machine learning classification resulted in robust differentiation between TP53 mutant versus wildtype cfDNA samples (AUC-ROC = 0.710-1.000) and intra-patient longitudinal analysis of ctDNA fragmentation signal enabled early cancer detection. These results suggest that cfDNA fragmentation may be a useful diagnostic tool in LFS patients and provides an important baseline for cancer early detection.

Longitudinal integrative cell-free DNA analysis in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.

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.

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.

Chromatin- and nucleosome-associated features in liquid biopsy: implications for cancer biomarker discovery

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

Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer.

METHOD

First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy.

RESULTS

The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response.

CONCLUSION

These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

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.

Single-stranded pre-methylated 5mC adapters uncover the methylation profile of plasma ultrashort Single-stranded cell-free DNA

Whole-genome bisulfite sequencing (BS-Seq) measures cytosine methylation changes at single-base resolution and can be used to profile cell-free DNA (cfDNA). In plasma, ultrashort single-stranded cfDNA (uscfDNA, ∼50 nt) has been identified together with 167 bp double-stranded mononucleosomal cell-free DNA (mncfDNA). However, the methylation profile of uscfDNA has not been described. Conventional BS-Seq workflows may not be helpful because bisulfite conversion degrades larger DNA into smaller fragments, leading to erroneous categorization as uscfDNA. We describe the '5mCAdpBS-Seq' workflow in which pre-methylated 5mC (5-methylcytosine) single-stranded adapters are ligated to heat-denatured cfDNA before bisulfite conversion. This method retains only DNA fragments that are unaltered by bisulfite treatment, resulting in less biased uscfDNA methylation analysis. Using 5mCAdpBS-Seq, uscfDNA had lower levels of DNA methylation (∼15%) compared to mncfDNA and was enriched in promoters and CpG islands. Hypomethylated uscfDNA fragments were enriched in upstream transcription start sites (TSSs), and the intensity of enrichment was correlated with expressed genes of hemopoietic cells. Using tissue-of-origin deconvolution, we inferred that uscfDNA is derived primarily from eosinophils, neutrophils, and monocytes. As proof-of-principle, we show that characteristics of the methylation profile of uscfDNA can distinguish non-small cell lung carcinoma from non-cancer samples. The 5mCAdpBS-Seq workflow is recommended for any cfDNA methylation-based investigations.

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.

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.

Effects of blood-processing protocols on cell-free DNA fragmentomics in plasma: Comparisons of one- and two-step centrifugations

BACKGROUND

Cell-free DNA (cfDNA) fragmentomic characteristics are promising analytes with abundant physiological signals for non-invasive disease diagnosis and monitoring. Previous studies on plasma cfDNA fragmentomics commonly employed a two-step centrifugation process for removing cell debris, involving a low-speed centrifugation followed by a high-speed centrifugation. However, the effects of centrifugation conditions on the analysis of cfDNA fragmentome remain uncertain.

METHODS

We collected blood samples from 10 healthy individuals and divided each sample into two aliquots for plasma preparation with one- and two-step centrifugation processes. We performed whole genome sequencing (WGS) of the plasma cfDNA in the two groups and comprehensively compared the cfDNA fragmentomic features. Additionally, we reanalyzed the fragmentomic features of cfDNA from 16 healthy individuals and 16 COVID-19 patients, processed through one- and two-step centrifugation in our previous study, to investigate the impact of centrifugation on disease signals.

RESULTS

Our results showed that there were no significant differences observed in the characteristics of nuclear cfDNA, including size, motif diversity score (MDS) of end motifs, and genome distribution, between plasma samples treated with one- and two-step centrifugation. The cfDNA size shortening in COVID-19 patients was observed in plasma samples with one- and two-step centrifugation methods. However, we observed a significantly higher relative abundance and longer size of cell-free mitochondrial DNA (mtDNA) in the one-step samples compared to the two-step samples. This difference in mtDNA caused by the one- and two-step centrifugation methods surpasses the pathological difference between COVID-19 patients and healthy individuals.

CONCLUSIONS

Our findings indicate that one-step low-speed centrifugation is a simple and potentially suitable method for analyzing nuclear cfDNA fragmentation characteristics. These results offer valuable guidance for cfDNA research in various clinical scenarios.

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor

Background

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies.

Methods

Here, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing.

Results

Direct comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses.

Conclusions

These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.

Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors

Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) in 204 plasma samples from 87 patients before and during treatment with pembrolizumab from a pan-cancer phase II investigator-initiated trial (INSPIRE). We trained a pan-cancer methylation signature using independent methylation array data from The Cancer Genome Atlas to quantify cancer-specific methylation (CSM) and fragment-length score (FLS) for each sample. CSM and FLS are strongly correlated with tumor-informed ctDNA levels. Early kinetics of CSM predict overall survival and progression-free survival, independently of tumor type, PD-L1, and tumor mutation burden. Early kinetics of FLS are associated with overall survival independently of CSM. Our tumor-naïve mutation-agnostic ctDNA approach integrating methylomics and fragmentomics could predict outcomes in patients treated with pembrolizumab.

SIGNIFICANCE

Analysis of methylation and fragment length in plasma using cfMeDIP-seq provides a tumor-naive approach to measure ctDNA with results comparable with a tumor-informed bespoke ctDNA. Early kinetics within the first weeks of treatment in methylation and fragment quantity can predict outcomes with pembrolizumab in patients with various advanced solid tumors. This article is featured in Selected Articles from This Issue, p. 897.

Dynamic bTMB combined with residual ctDNA improves survival prediction in locally advanced NSCLC patients with chemoradiotherapy and consolidation immunotherapy

Background

Liquid biopsy-based biomarkers, including circulating tumor DNA (ctDNA) and blood tumor mutational burden (bTMB), are recognized as promising predictors of prognoses and responses to immune checkpoint inhibitors (ICIs), despite insufficient sensitivity of single biomarker detection. This research aims to determine whether the combinatorial utility of longitudinal ctDNA with bTMB analysis could improve the prognostic and predictive effects.

Methods

This prospective two-center cohort trial, consisting of discovery and validation datasets, enrolled unresectable locally advanced non-small-cell lung cancer (LA-NSCLC) patients and assigned them to chemoradiotherapy (CRT) or CRT + consolidation ICI cohorts from 2018 to 2022. Blood specimens were collected pretreatment, 4 weeks post-CRT, and at progression to assess bTMB and ctDNA using 486-gene next-generation sequencing. Dynamic ∆bTMB was calculated as post-CRT bTMB minus baseline bTMB levels. Decision curve analyses were performed to calculate Concordance index (C-index).

Results

One hundred twenty-eight patients were enrolled. In the discovery dataset (n = 73), patients treated with CRT and consolidation ICI had significantly longer overall survival (OS; median not reached [NR] vs 20.2 months; P < 0.001) and progression-free survival (PFS; median 25.2 vs 11.4 months; P = 0.011) than those without ICI. Longitudinal analysis demonstrated a significant decrease in ctDNA abundance post-CRT (P < 0.001) but a relative increase with disease progression. Post-CRT detectable residual ctDNA correlated with significantly shorter OS (median 18.3 months vs NR; P = 0.001) and PFS (median 7.3 vs 25.2 months; P < 0.001). For patients with residual ctDNA, consolidation ICI brought significantly greater OS (median NR vs 14.8 months; P = 0.005) and PFS (median 13.8 vs 6.2 months; P = 0.028) benefit, but no significant difference for patients with ctDNA clearance. Dynamic ∆bTMB was predictive of prognosis. Patients with residual ctDNA and increased ∆bTMB (∆bTMB > 0) had significantly worse OS (median 9.0 vs 23.0 months vs NR; P < 0.001) and PFS (median 3.4 vs 7.3 vs 25.2 months; P < 0.001). The combinatorial model integrating post-CRT ctDNA with ∆bTMB had optimal predictive effects on OS (C-index = 0.723) and PFS (C-index = 0.693), outperforming individual features. In the independent validation set, we confirmed residual ctDNA predicted poorer PFS (median 50.8 vs 14.3 months; P = 0.026) but identified more consolidation ICI benefit (median NR vs 8.3 months; P = 0.039). The combined model exhibited a stable predictive advantage (C-index = 0.742 for PFS).

Conclusions

The multiparameter assay integrating qualitative residual ctDNA testing with quantitative ∆bTMB dynamics improves patient prognostic risk stratification and efficacy predictions, allowing for personalized consolidation therapy for LA-NSCLC.