Tissue-specific cell-free DNA degradation quantifies circulating tumor DNA burden

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.

Lateral Flow Platform for Lung Cancer Diagnosis through Simultaneous Detection of ctDNA and MicroRNA

Early cancer screening is essential for reducing cancer-related mortality and improving survival rates. Simultaneous detection of multiple tumor markers can enhance the accuracy and specificity of cancer diagnosis, helping us to mitigate false-positive results associated with single-marker analysis. Here, we have developed a lateral flow detection platform that combines recombinase polymerase amplification (RPA), CRISPR Cas9, and catalyzed hairpin assembly (CHA) for the simultaneous detection of KRAS ctDNA and miRNA-223 in lung cancer. The CRISPR Cas9 system acts as a linking element, enabling specific recognition and binding to RPA amplicons of KRAS ctDNA while facilitating the capture of Au-DNA-Bio nanoparticles (NPs), thereby producing a stronger detection signal through Au NPs aggregation. The CHA system enhances this platform by providing sensitive detection of miRNA-223. Our platform was tested on a limited number of clinical saliva samples, demonstrating feasibility but requiring further validation with larger cohorts.

A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths

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

Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEYPOINTS

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

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.

Triple quenching effect of nanozyme catalyzed precipitation combined with enzyme-free amplification for photoelectrochemical biosensing of circulating tumor DNA

In this work, a new photoelectrochemical (PEC) biosensor based on triple quenching effect of nanozyme catalyzed precipitation to PEC signal of MgIn2S4 was constructed for ultrasensitive detection of circulating tumor DNA (ctDNA). Enzyme-free amplification technology was used to convert target ctDNA into a large number of product chains (PC) to improve the detection sensitivity. Co3O4 nanozyme with excellent peroxidase (POD)-like activity was introduced to the surface of MgIn2S4 by PC. Co3O4 could oxidize chromogenic agent 3-Amino-9-ethylcarbazole (AEC) to produce red insoluble precipitation in the presence of H2O2, resulting in the PEC signal "off" of MgIn2S4 to achieve ultrasensitive detection of ctDNA. In particular, Co3O4 nanozyme showed three synergistic quenching effects on PEC signal of MgIn2S4, which contributed greatly to improving the detection sensitivity. Firstly, the light absorption range of Co3O4 could reach 1000 nm, and compete with MgIn2S4 for light absorption. Secondly, the produced red precipitation belonged to the insulating material and had large electrochemical impedance, which hindered the transmission of photogenerated carriers. Thirdly, the precipitation also prevented the electron donor ascorbic acid (AA) from transferring electrons to MgIn2S4. This biosensor provided a promising sensitive PEC detection technology for ctDNA, and further broadened the application of nanozymes in the field of PEC analysis.

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.

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.

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.

Current Challenges of Methylation-Based Liquid Biopsies in Cancer Diagnostics

In current clinical practice, effective cancer testing and screening paradigms are limited to specific types of cancer, exhibiting varying efficiency, acceptance, and adherence. Cell-free DNA (cfDNA) methylation profiling holds promise in providing information about the presence of malignity regardless of its type and location while leveraging blood-based liquid biopsies as a method to obtain analytical samples. However, technical difficulties, costs and challenges resulting from biological variations, tumor heterogeneity, and exogenous factors persist. This method exploits the mechanisms behind cfDNA release but faces issues like fragmentation, low concentrations, and high background noise. This review explores cfDNA methylation's origins, means of detection, and profiling for cancer diagnostics. The critical evaluation of currently available multi-cancer early detection methods (MCEDs) as well as tests targeting single genes, emphasizing their potential and limits to refine strategies for early cancer detection, are explained. The current methodology limitations, workflows, comparisons of clinically approved liquid biopsy-based methylation tests for cancer, their utilization in companion diagnostics as well as the biological limitations of the epigenetics approach are discussed, aiming to help healthcare providers as well as researchers to orient themselves in this increasingly complex and evolving field of diagnostics.

Confined DNA tetrahedral molecular sieve for size-selective electrochemiluminescence sensing

Size selectivity is crucial in highly accurate preparation of biosensors. Herein, we described an innovative electrochemiluminescence (ECL) sensing platform based on the confined DNA tetrahedral molecular sieve (DTMS) for size-selective recognition of nucleic acids and small biological molecule. Firstly, DNA template (T) was encapsulated into the inner cavity of DNA tetrahedral scaffold (DTS) and hybridized with quencher (Fc) labeled probe DNA to prepare DTMS, accordingly inducing Ru(bpy)32+ and Fc closely proximate, resulting the sensor in a "signal-off" state. Afterwards, target molecules entered the cavity of DTMS to realize the size-selective molecular recognition while prohibiting large molecules outside of the DTMS, resulting the sensor in a "signal-on" state due to the release of Fc. The rigid framework structure of DTS and the anchor of DNA probe inside the DTS effectively avoided the nuclease degradation of DNA probe, and nonspecific protein adsorption, making the sensor possess potential application prospect for size-selective molecular recognition in diagnostic analysis with high accuracy and specificity.

A Novel CRISPR/Cas12a-Mediated Ratiometric Dual-Signal Electrochemical Biosensor for Ultrasensitive and Reliable Detection of Circulating Tumor Deoxyribonucleic Acid

Circulating tumor DNA (ctDNA) holds great promise as a noninvasive biomarker for cancer diagnosis, treatment, and prognosis. However, the accurate and specific quantification of low-abundance ctDNA in serum remains a significant challenge. This study introduced, for the first time, a novel exponential amplification reaction (EXPAR)-assisted CRISPR/Cas12a-mediated ratiometric dual-signal electrochemical biosensor for ultrasensitive and reliable detection of ctDNA. To implement the dual-signal strategy, a signal unit (ssDNA-MB@Fc/UiO-66-NH2) was prepared, consisting of methylene blue-modified ssDNA as the biogate to encapsulate ferrocene signal molecules within UiO-66-NH2 nanocarriers. The presence of target ctDNA KRAS triggered EXPAR amplification, generating numerous activators for Cas12a activation, resulting in the cleavage of ssDNA-P fully complementary to the ssDNA-MB biogate. Due to the inability to form a rigid structure dsDNA (ssDNA-MB/ssDNA-P), the separation of ssDNA-MB biogate from the UiO-66-NH2 surface was hindered by electrostatic interactions. Consequently, the supernatant collected after centrifugation exhibited either no or only a weak presence of Fc and MB signal molecules. Conversely, in the absence of the target ctDNA, the ssDNA-MB biogate was open, leading to the leakage of Fc signal molecules. This clever ratiometric strategy with Cas12a as the "connector", reflecting the concentration of ctDNA KRAS based on the ratio of the current intensities of the two electroactive signal molecules, enhanced detection sensitivity by at least 60-300 times compared to single-signal strategies. Moreover, this strategy demonstrated satisfactory performance in ctDNA detection in complex human serum, highlighting its potential for cancer diagnosis.

Molecular phenotyping of small cell lung cancer using targeted cfDNA profiling of transcriptional regulatory regions

We report an approach for cancer phenotyping based on targeted sequencing of cell-free DNA (cfDNA) for small cell lung cancer (SCLC). In SCLC, differential activation of transcription factors (TFs), such as ASCL1, NEUROD1, POU2F3, and REST defines molecular subtypes. We designed a targeted capture panel that identifies chromatin organization signatures at 1535 TF binding sites and 13,240 gene transcription start sites and detects exonic mutations in 842 genes. Sequencing of cfDNA from SCLC patient-derived xenograft models captured TF activity and gene expression and revealed individual highly informative loci. Prediction models of ASCL1 and NEUROD1 activity using informative loci achieved areas under the receiver operating characteristic curve (AUCs) from 0.84 to 0.88 in patients with SCLC. As non-SCLC (NSCLC) often transforms to SCLC following targeted therapy, we applied our framework to distinguish NSCLC from SCLC and achieved an AUC of 0.99. Our approach shows promising utility for SCLC subtyping and transformation monitoring, with potential applicability to diverse tumor types.

Cell‐free DNA‐associated multi‐feature applications in cancer diagnosis and treatment

Malignant tumours pose significant challenges in terms of high morbidity and mortality rates, primarily due to the lack of large‐scale applicable screening methods and efficient treatment strategies. However, the development of liquid biopsies, particularly circulating cell‐free DNA (cfDNA), offers promising solutions characterised by their non‐invasiveness and cost‐effectiveness, providing comprehensive tumour information on a global scale. The release of cfDNA is predominantly associated with cell death and turnover, while its elimination occurs through nuclease digestion, renal excretion into the urine and uptake by the liver and spleen. Extensive research into the biological properties of cfDNA has led to the identification of novel applications, including non‐invasive cancer screening, cancer subtype classification, tissue‐of‐origin detection and monitoring of treatment efficacy. Additionally, emerging fields such as methylation‐omics, fragment‐omics and nucleosome‐omics show immense potential as tissue‐ and disease‐specific markers. Therefore, this review aims to comprehensively introduce the latest detection techniques of cfDNA, along with detailed information on its characteristics and applications, providing valuable insights for cancer diagnosis and monitoring, which will assist us in purposefully enhancing relevant features for a more comprehensive application in clinical practice.

cfOmics: a cell-free multi-Omics database for diseases

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

Real-time analysis of the cancer genome and fragmentome from plasma and urine cell-free DNA using nanopore sequencing

Cell-free DNA (cfDNA) can be isolated and sequenced from blood and/or urine of cancer patients. Conventional short-read sequencing lacks deployability and speed and can be biased for short cfDNA fragments. Here, we demonstrate that with Oxford Nanopore Technologies (ONT) sequencing we can achieve delivery of genomic and fragmentomic data from liquid biopsies. Copy number aberrations and cfDNA fragmentation patterns can be determined in less than 24 h from sample collection. The tumor-derived cfDNA fraction calculated from plasma of lung cancer patients and urine of bladder cancer patients was highly correlated (R = 0.98) with the tumor fraction calculated from short-read sequencing of the same samples. cfDNA size profile, fragmentation patterns, fragment-end composition, and nucleosome profiling near transcription start sites in plasma and urine exhibited the typical cfDNA features. Additionally, a high proportion of long tumor-derived cfDNA fragments (> 300 bp) are recovered in plasma and urine using ONT sequencing. ONT sequencing is a cost-effective, fast, and deployable approach for obtaining genomic and fragmentomic results from liquid biopsies, allowing the analysis of previously understudied cfDNA populations.

Circulating tumor DNA-based copy-number profiles enable monitoring treatment effects during therapy in high-grade serous carcinoma

Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for detecting and profiling longitudinal genomics changes in cancer. While copy-number alterations (CNAs) play a major role in cancers, treatment effect monitoring using copy-number profiles has received limited attention as compared to mutations. A major reason for this is the insensitivity of CNA analysis for the real-life tumor-fraction ctDNA samples. We performed copy-number analysis on 152 plasma samples obtained from 29 patients with high-grade serous ovarian cancer (HGSC) using a sequencing panel targeting over 500 genes. Twenty-one patients had temporally matched tissue and plasma sample pairs, which enabled assessing concordance with tissues sequenced with the same panel or whole-genome sequencing and to evaluate sensitivity. Our approach could detect concordant CNA profiles in most plasma samples with as low as 5% tumor content and highly amplified regions in samples with ∼1% of tumor content. Longitudinal profiles showed changes in the CNA profiles in seven out of 11 patients with high tumor-content plasma samples at relapse. These changes included focal acquired or lost copy-numbers, even though most of the genome remained stable. Two patients displayed major copy-number profile changes during therapy. Our analysis revealed ctDNA-detectable subclonal selection resulting from both surgical operations and chemotherapy. Overall, longitudinal ctDNA data showed acquired and diminished CNAs at relapse when compared to pre-treatment samples. These results highlight the importance of genomic profiling during treatment as well as underline the usability of ctDNA.

Liquid biopsy for early detection of lung cancer

Lung cancer is the leading cause of cancer-related mortality worldwide. Early cancer detection plays an important role in improving treatment success and patient prognosis. In the past decade, liquid biopsy became an important tool for cancer diagnosis, as well as for treatment selection and response monitoring. Liquid biopsy is a broad term that defines a non-invasive test done on a sample of blood or other body fluid to look for cancer cells or other analytes that can include DNA, RNA, or other molecules released by tumor cells. Liquid biopsies mainly include circulating tumor DNA, circulating RNA, microRNA, proteins, circulating tumor cells, exosomes, and tumor-educated platelets. This review summarizes the progress and clinical application potential of liquid biopsy for early detection of lung cancer.

Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients' blood

During cancer progression, tumorigenic and immune signals are spread through circulating molecules, such as cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in the blood. So far, they have not been comprehensively investigated in gastrointestinal cancers. Here, we profile 4 categories of cell-free omics data from patients with colorectal cancer and patients with stomach adenocarcinoma and then assay 15 types of genomic, epigenomic, and transcriptomic variations. We find that multi-omics data are more appropriate for detection of cancer genes compared with single-omics data. In particular, cfRNAs are more sensitive and informative than cfDNAs in terms of detection rate, enriched functional pathways, etc. Moreover, we identify several peripheral immune signatures that are suppressed in patients with cancer. Specifically, we establish a γδ-T cell score and a cancer-associated-fibroblast (CAF) score, providing insights into clinical statuses like cancer stage and survival. Overall, we reveal a cell-free multi-molecular landscape that is useful for blood monitoring in personalized cancer treatment.

Electrochemical nucleic acid sensors: Competent pathways for mobile molecular diagnostics

Electrochemical nucleic acid biosensor has demonstrated great promise in clinical diagnostic tests, mainly because of its flexibility, high efficiency, low cost, and easy integration for analytical applications. Numerous nucleic acid hybridization-based strategies have been developed for the design and construction of novel electrochemical biosensors for diagnosing genetic-related diseases. This review describes the advances, challenges, and prospects of electrochemical nucleic acid biosensors for mobile molecular diagnosis. Specifically, the basic principles, sensing elements, applications in diagnosis of cancer and infectious diseases, integration with microfluidic technology and commercialization are mainly included in this review, aiming to provide new insights and directions for the future development of electrochemical nucleic acid biosensors.

Fragmentomic analysis of circulating tumor DNA-targeted cancer panels

BACKGROUND

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy

Circulating cell-free DNA (ccfDNA) is a liquid biopsy biomaterial attracting significant attention for the implementation of precision medicine diagnostics. Deeper knowledge related to its structure and biology would enable the development of such applications. In this study, we employed Raman spectroscopy to unravel the biomolecular profile of human ccfDNA in health and disease. We established reference Raman spectra of ccfDNA samples from healthy males and females with different conditions, including cancer and diabetes, extracting information about their chemical composition. Comparative observations showed a distinct spectral pattern in ccfDNA from breast cancer patients taking neoadjuvant therapy. Raman analysis of ccfDNA from healthy, prediabetic, and diabetic males uncovered some differences in their biomolecular fingerprints. We also studied ccfDNA released from human benign and cancer cell lines and compared it to their respective gDNA, confirming it mirrors its cellular origin. Overall, we explored for the first time Raman spectroscopy in the study of ccfDNA and provided spectra of samples from different sources. Our findings introduce Raman spectroscopy as a new approach to implementing liquid biopsy diagnostics worthy of further elaboration.

Efficacy of Immune Checkpoint Blockade and Biomarkers of Response in Lymphoma: A Narrative Review

Immune checkpoint blockade (ICB) has revolutionized the prognosis of several advanced-stage solid tumors. However, its success has been far more limited in hematological malignancies and is mostly restricted to classical Hodgkin lymphoma (cHL) and primary mediastinal B cell lymphoma (PMBCL). In patients with non-Hodgkin lymphoma (NHL), response to PD-1/PD-L1 ICB monotherapy has been relatively limited, although some subtypes are more sensitive than others. Numerous predictive biomarkers have been investigated in solid malignancies, such as PD-L1 expression, tumor mutational burden (TMB) and microsatellite instability (MSI), among others. This review aims to appraise the current knowledge on PD-1/PD-L1 ICB efficacy in lymphoma when used either as monotherapy or combined with other agents, and describes potential biomarkers of response in this specific setting.

Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA

Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer detection and tissue-of-origin localization is achieved using our algorithm, which incorporates cancer type-specific profiles of mutation distribution and chromatin organization in tumor tissues as model references. Our integrative model detects early-stage cancers, including those of pancreatic origin, with high sensitivity that is comparable to that of late-stage detection. Model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. Our methodologies may lay the groundwork for accurate cfDNA-based cancer diagnosis, especially at early stages.

Bridging biological cfDNA features and machine learning approaches

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

Nucleosome Patterns in Circulating Tumor DNA Reveal Transcriptional Regulation of Advanced Prostate Cancer Phenotypes

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology.

SIGNIFICANCE

This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology. This article is highlighted in the In This Issue feature, p. 517.

Integrated, Longitudinal Analysis of Cell-free DNA in Uveal Melanoma

Uveal melanomas are rare tumors arising from melanocytes that reside in the eye. Despite surgical or radiation treatment, approximately 50% of patients with uveal melanoma will progress to metastatic disease, most often to the liver. Cell-free DNA (cfDNA) sequencing is a promising technology due to the minimally invasive sample collection and ability to infer multiple aspects of tumor response. We analyzed 46 serial cfDNA samples from 11 patients with uveal melanoma over a 1-year period following enucleation or brachytherapy (n = ∼4/patient) using targeted panel, shallow whole genome, and cell-free methylated DNA immunoprecipitation sequencing. We found detection of relapse was highly variable using independent analyses (P = 0.06-0.46), whereas a logistic regression model integrating all cfDNA profiles significantly improved relapse detection (P = 0.02), with greatest power derived from fragmentomic profiles. This work provides support for the use of integrated analyses to improve the sensitivity of circulating tumor DNA detection using multi-modal cfDNA sequencing.

Significance

Here, we demonstrate integrated, longitudinal cfDNA sequencing using multi-omic approaches is more effective than unimodal analysis. This approach supports the use of frequent blood testing using comprehensive genomic, fragmentomic, and epigenomic techniques.

Integrative analysis of multi-omics data for liquid biopsy

The innovation of liquid biopsy holds great potential to revolutionise cancer management through early diagnosis and timely treatment of cancer. Integrative analysis of different tumour-derived omics data (such as genomics, epigenetics, fragmentomics, and proteomics) from body fluids for cancer detection and monitoring could outperform the analysis of single modality data alone. In this review, we focussed on the discussion of early cancer detection and molecular residual disease surveillance based on multi-omics data of blood. We summarised diverse types of tumour-derived components, current popular platforms for profiling cancer-associated signals, machine learning approaches for joint analysis of liquid biopsy data, as well as multi-omics-based early detection of cancers, molecular residual disease monitoring, and treatment response surveillance. We also discussed the challenges and future directions of multi-omics-based liquid biopsy. With the development of both experimental protocols and computational methods dedicated to liquid biopsy, the implementation of multi-omics strategies into the clinical workflow will likely benefit the clinical management of cancers including decision-making guidance and patient outcome improvement.

Circulating DNA fragmentomics and cancer screening

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

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

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

A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA

Cell-free DNA (cfDNA) has the potential to inform tumor subtype classification and help guide clinical precision oncology. Here we develop Griffin, a framework for profiling nucleosome protection and accessibility from cfDNA to study the phenotype of tumors using as low as 0.1x coverage whole genome sequencing data. Griffin employs a GC correction procedure tailored to variable cfDNA fragment sizes, which generates a better representation of chromatin accessibility and improves the accuracy of cancer detection and tumor subtype classification. We demonstrate estrogen receptor subtyping from cfDNA in metastatic breast cancer. We predict estrogen receptor subtype in 139 patients with at least 5% detectable circulating tumor DNA with an area under the receive operator characteristic curve (AUC) of 0.89 and validate performance in independent cohorts (AUC = 0.96). In summary, Griffin is a framework for accurate tumor subtyping and can be generalizable to other cancer types for precision oncology applications.

A hitchhiker's guide to cell-free DNA biology

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

Liquid biopsies to predict CDK4/6 inhibitor efficacy and resistance in breast cancer

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors combined with endocrine therapy have transformed the treatment of estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2 negative (HER2-) metastatic breast cancer. However, some patients do not respond to this treatment, and patients inevitably develop resistance, such that novel biomarkers are needed to predict primary resistance, monitor treatment response for acquired resistance, and personalize treatment strategies. Circumventing the spatial and temporal limitations of tissue biopsy, newly developed liquid biopsy approaches have the potential to uncover biomarkers that can predict CDK4/6 inhibitor efficacy and resistance in breast cancer patients through a simple blood test. Studies on circulating tumor DNA (ctDNA)-based liquid biopsy biomarkers of CDK4/6 inhibitor resistance have focused primarily on genomic alterations and have failed thus far to identify clear and clinically validated predictive biomarkers, but emerging epigenetic ctDNA methodologies hold promise for further discovery. The present review outlines recent advances and future directions in ctDNA-based biomarkers of CDK4/6 inhibitor treatment response.

Integrating chromatin accessibility states in the design of targeted sequencing panels for liquid biopsy

Dying tumor cells shed DNA fragments into the circulation that are known as circulating tumor DNA (ctDNA). Liquid biopsy tests aim to detect cancer using known markers, including genetic alterations and epigenetic profiles of ctDNA. Despite various advantages, the major limitation remains the low fraction of tumor-originating DNA fragments in a high background of normal blood-cell originating fragments in the cell-free DNA (cfDNA) pool in plasma. Deep targeted sequencing of cfDNA allows for enrichment of fragments in known cancer marker-associated regions of the genome, thus increasing the chances of detecting the low fraction variant harboring fragments. Most targeted sequencing panels are designed to include known recurrent mutations or methylation markers of cancer. Here, we propose the integration of cancer-specific chromatin accessibility states into panel designs for liquid biopsy. Using machine learning approaches, we first identify accessible and inaccessible chromatin regions specific to each major human cancer type. We then introduce a score that quantifies local chromatin accessibility in tumor relative to blood cells and show that this metric can be useful for prioritizing marker regions with higher chances of being detected in cfDNA for inclusion in future panel designs.

ESMO recommendations on the use of circulating tumour DNA assays for patients with cancer: a report from the ESMO Precision Medicine Working Group

Circulating tumour DNA (ctDNA) assays conducted on plasma are rapidly developing a strong evidence base for use in patients with cancer. The European Society for Medical Oncology convened an expert working group to review the analytical and clinical validity and utility of ctDNA assays. For patients with advanced cancer, validated and adequately sensitive ctDNA assays have utility in identifying actionable mutations to direct targeted therapy, and may be used in routine clinical practice, provided the limitations of the assays are taken into account. Tissue based testing remains the preferred test for many cancer patients, due to limitations of ctDNA assays detecting fusion events and copy number changes, although ctDNA assays may be routinely used when faster results will be clinically important, or when tissue biopsies are not possible or inappropriate. Reflex tumour testing should be considered following a non-informative ctDNA result, due to false negative results with ctDNA testing. In patients treated for early-stage cancers, detection of molecular residual disease (MRD) or molecular relapse (MR), has high evidence of clinical validity in anticipating future relapse in many cancers. MRD/MR detection cannot be recommended in routine clinical practice, as currently there is no evidence for clinical utility in directing treatment. Additional potential applications of ctDNA assays, under research development and not recommended for routine practice, include identifying patients not responding to therapy with early dynamic changes in ctDNA levels, monitoring therapy for the development of resistance mutations prior to clinical progression, and in screening asymptomatic people for cancer. Recommendation for reporting of results, future development of ctDNA assays, and future clinical research are made.

DNA Hairpins and Dumbbell-Wheel Transitions Amplified Walking Nanomachine for Ultrasensitive Nucleic Acid Detection

Nucleic acids, including circulating tumor DNA (ctDNA), microRNA, and virus DNA/RNA, have been widely applied as potential disease biomarkers for early clinical diagnosis. In this study, we present a concept of DNA nanostructures transitions for the construction of DNA bipedal walking nanomachine, which integrates dual signal amplification for direct nucleic acid assay. DNA hairpins transition is developed to facilitate the generation of multiple target sequences; meanwhile, the subsequent DNA dumbbell-wheel transition is controlled to achieve the bipedal walker, which cleaves multiple tracks around electrode surface. Through combination of strand displacement reaction and digestion cycles, DNA monolayer at the electrode interface could be engineered and target-induced signal variation is realized. In addition, pH-assisted detachable intermolecular DNA triplex design is utilized for the regeneration of electrochemical biosensor. The high consistency between this work and standard quantitative polymerase chain reaction is validated. Moreover, the feasibilities of this biosensor to detect ctDNA and SARS-CoV-2 RNA in clinical samples are demonstrated with satisfactory accuracy and reliability. Therefore, the proposed approach has great potential applications for nucleic acid based clinical diagnostics.

A clinician’s handbook for using ctDNA throughout the patient journey

Background

The promise of precision cancer medicine presently centers around the genomic sequence of a patient’s tumor being translated into timely, actionable information to inform clinical care. The analysis of cell-free DNA from liquid biopsy, which contains circulating tumor DNA (ctDNA) in patients with cancer, has proven to be amenable to various settings in oncology. However, open questions surrounding the clinical validity and utility of plasma-based analyses have hindered widespread clinical adoption.

Main body

Owing to the rapid evolution of the field, studies supporting the use of ctDNA as a biomarker throughout a patient’s journey with cancer have accumulated in the last few years, warranting a review of the latest status for clinicians who may employ ctDNA in their precision oncology programs. In this work, we take a step back from the intricate coverage of detection approaches described extensively elsewhere and cover basic concepts around the practical implementation of next generation sequencing (NGS)-guided liquid biopsy. We compare relevant targeted and untargeted approaches to plasma DNA analysis, describe the latest evidence for clinical validity and utility, and highlight the value of genome-wide ctDNA analysis, particularly as it relates to early detection strategies and discovery applications harnessing the non-coding genome.

Conclusions

The maturation of liquid biopsy for clinical application will require interdisciplinary efforts to address current challenges. However, patients and clinicians alike may greatly benefit in the future from its incorporation into routine oncology care.

Dynamics of RAS/BRAF Mutations in cfDNA from Metastatic Colorectal Carcinoma Patients Treated with Polychemotherapy and Anti-EGFR Monoclonal Antibodies

Analysis of plasma-derived cell-free DNA (cfDNA) might allow for the early identification of resistance in metastatic colorectal carcinoma (mCRC) patients receiving anti-EGFR monoclonal antibodies. We tested plasma samples from the Erbitux Metastatic Colorectal Cancer Strategy (ERMES) phase III trial of FOLFIRI+Cetuximab in first-line treatment of RAS/BRAF wild-type mCRC. Samples were collected at baseline (n = 37), at 8 weeks of treatment (n = 32), progressive disease (PD; n = 36) and 3 months after PD (n = 21). cfDNA testing was performed using the Idylla™ ctKRAS and ctNRAS-BRAF tests and the Oncomine Pan-Cancer Cell-Free Assay. Analysis of basal samples revealed RAS/BRAF mutations in 6/37 cases. A transient RAS positivity not associated with PD was observed at 8 weeks in five cases that showed no mutations at baseline and PD. The frequency of mutant cases increased at PD (33.3%) and decreased again at 3 months after PD (9.5%). The median progression-free survival (mPFS) of patients RAS/BRAF mutant at PD was 7.13 months versus 7.71 months in wild-type patients (p = 0.3892). These data confirm that the occurrence of RAS/BRAF mutations in mCRC patients receiving anti-EGFR agents is relatively frequent. However, the cfDNA dynamics of RAS mutations in patients treated with anti-EGFR agents plus polychemotherapy are complex and might not be directly associated with resistance to treatment.

Quantitative Analysis of Plasma Cell-Free DNA and Its DNA Integrity and Hypomethylation Status as Biomarkers for Tumor Burden and Disease Progression in Patients with Metastatic Neuroendocrine Neoplasias

Simple Summary

Neuroendocrine neoplasias (NEN) are a heterogeneous group of frequent slow-progressing malignant tumors for which a reliable marker for tumor relapse and progression is still lacking. Previously, circulating cell-free DNA and its global methylation status and fragmentation rate have been proposed to be valuable prognostic tumor markers in a variety of malignancies. In the current study, we compared plasma cell-free DNA (cfDNA) properties of NEN patients with a healthy control group and a group of surgically cured patients. Our results revealed significantly higher plasma cfDNA concentrations with increased fragmentation and hypomethylation in patients with advanced metastatic NEN, which was strongly associated with tumor load and could help to differentiate between metastasized disease and presumably cured patients. This suggests that the combined analysis of plasma cfDNA characteristics is a potent and sensitive prognostic and therapeutic biomarker for tumor burden and disease progression in patients with neuroendocrine neoplasias.

Abstract

Background: Neuroendocrine neoplasia (NEN) encompasses a diverse group of malignancies marked by histological heterogeneity and highly variable clinical outcomes. Apart from Chromogranin A, specific biomarkers predicting residual tumor disease, tumor burden, and disease progression in NEN are scant. Thus, there is a strong clinical need for new and minimally invasive biomarkers that allow for an evaluation of the prognosis, clinical course, and response to treatment of NEN patients, thereby helping implement individualized treatment decisions in this heterogeneous group of patients. In the current prospective study, we evaluated the role of plasma cell-free DNA concentration and its global hypomethylation and fragmentation as possible diagnostic and prognostic biomarkers in patients with neuroendocrine neoplasias. Methods: The plasma cfDNA concentration, cfDNA Alu hypomethylation, and LINE-1 cfDNA integrity were evaluated prospectively in 63 NEN patients with presumably cured or advanced metastatic disease. The cfDNA characteristics in NEN patients were compared to the results of a group of 29 healthy controls and correlated with clinical and histopathological data of the patients. Results: Patients with advanced NEN showed a significantly higher cfDNA concentration and percentage of Alu hypomethylation and a reduced LINE-1 cfDNA integrity as compared to the surgically cured NET patients and the healthy control group. The increased hypomethylation and concentration of cfDNA and the reduced cfDNA integrity in NEN patients were strongly associated with tumor burden and poor prognosis, while no correlation with tumor grading, differentiation, localization, or hormonal activity could be found. Multiparametric ROC analysis of plasma cfDNA characteristics was able to distinguish NEN patients with metastatic disease from the control group and the cured NEN patients with AUC values of 0.694 and 0.908, respectively. This was significant even for the group with only a low tumor burden. Conclusions: The present study, for the first time, demonstrates that the combination of plasma cfDNA concentration, global hypomethylation, and fragment length pattern has the potential to serve as a potent and sensitive prognostic and therapeutic “liquid biopsy” biomarker for tumor burden and disease progression in patients with neuroendocrine neoplasias.

Ratiometric Electrochemical Switch for Circulating Tumor DNA through Recycling Activation of Blocked DNAzymes

Circulating tumor DNA (ctDNA) serves as a powerful noninvasive and viable biomarker for the diagnosis of cancers. The abundance of ctDNA in patients with advanced stages is significantly higher than that in patients with early stages. Herein, a ratiometric electrochemical biosensor for the detection of ctDNA is developed by smart design of DNA probes and recycles of DNAzyme activation. The conformational variation of DNA structures leads to the changes of two types of electrochemical species. This enzyme-free sensing strategy promotes excellent amplification efficiency upon target recognition. The obtained results assure good analytical performances and a limit of detection as low as 25 aM is achieved. Additionally, this method exhibits outstanding selectivity and great application prospects in biological sample analysis.

The Role of Circulating Biomarkers in Lung Cancer

Lung cancer is the leading cause of cancer morbidity and mortality worldwide and early diagnosis is crucial for the management and treatment of this disease. Non-invasive means of determining tumour information is an appealing diagnostic approach for lung cancers as often accessing and removing tumour tissue can be a limiting factor. In recent years, liquid biopsies have been developed to explore potential circulating tumour biomarkers which are considered reliable surrogates for understanding tumour biology in a non-invasive manner. Most common components assessed in liquid biopsy include circulating tumour cells (CTCs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), microRNA and exosomes. This review explores the clinical use of circulating tumour biomarkers found in liquid biopsy for screening, early diagnosis and prognostication of lung cancer patients.

NucPosDB: a database of nucleosome positioning in vivo and nucleosomics of cell-free DNA

Nucleosome positioning is involved in many gene regulatory processes happening in the cell, and it may change as cells differentiate or respond to the changing microenvironment in a healthy or diseased organism. One important implication of nucleosome positioning in clinical epigenetics is its use in the “nucleosomics” analysis of cell-free DNA (cfDNA) for the purpose of patient diagnostics in liquid biopsies. The rationale for this is that the apoptotic nucleases that digest chromatin of the dying cells mostly cut DNA between nucleosomes. Thus, the short pieces of DNA in body fluids reflect the positions of nucleosomes in the cells of origin. Here, we report a systematic nucleosomics database — NucPosDB — curating published nucleosome positioning datasets in vivo as well as datasets of sequenced cell-free DNA (cfDNA) that reflect nucleosome positioning in situ in the cells of origin. Users can select subsets of the database by a number of criteria and then obtain raw or processed data. NucPosDB also reports the originally determined regions with stable nucleosome occupancy across several individuals with a given condition. An additional section provides a catalogue of computational tools for the analysis of nucleosome positioning or cfDNA experiments and theoretical algorithms for the prediction of nucleosome positioning preferences from DNA sequence. We provide an overview of the field, describe the structure of the database in this context, and demonstrate data variability using examples of different medical conditions. NucPosDB is useful both for the analysis of fundamental gene regulation processes and the training of computational models for patient diagnostics based on cfDNA. The database currently curates ~ 400 publications on nucleosome positioning in cell lines and in situ as well as cfDNA from > 10,000 patients and healthy volunteers. For open-access cfDNA datasets as well as key MNase-seq datasets in human cells, NucPosDB allows downloading processed mapped data in addition to the regions with stable nucleosome occupancy. NucPosDB is available at https://generegulation.org/nucposdb/.

Comprehensive Approach to Distinguish Patients with Solid Tumors from Healthy Controls by Combining Androgen Receptor Mutation p.H875Y with Cell-Free DNA Methylation and Circulating miRNAs

Liquid biopsy-based tests emerge progressively as an important tool for cancer diagnostics and management. Currently, researchers focus on a single biomarker type and one tumor entity. This study aimed to create a multi-analyte liquid biopsy test for the simultaneous detection of several solid cancers. For this purpose, we analyzed cell-free DNA (cfDNA) mutations and methylation, as well as circulating miRNAs (miRNAs) in plasma samples from 97 patients with cancer (20 bladder, 9 brain, 30 breast, 28 colorectal, 29 lung, 19 ovarian, 12 pancreas, 27 prostate, 23 stomach) and 15 healthy controls via real-time qPCR. Androgen receptor p.H875Y mutation (AR) was detected for the first time in bladder, lung, stomach, ovarian, brain, and pancreas cancer, all together in 51.3% of all cancer samples and in none of the healthy controls. A discriminant function model, comprising cfDNA mutations (COSM10758, COSM18561), cfDNA methylation markers (MLH1, MDR1, GATA5, SFN) and miRNAs (miR-17-5p, miR-20a-5p, miR-21-5p, miR-26a-5p, miR-27a-3p, miR-29c-3p, miR-92a-3p, miR-101-3p, miR-133a-3p, miR-148b-3p, miR-155-5p, miR-195-5p) could further classify healthy and tumor samples with 95.4% accuracy, 97.9% sensitivity, 80% specificity. This multi-analyte liquid biopsy-based test may help improve the simultaneous detection of several cancer types and underlines the importance of combining genetic and epigenetic biomarkers.

The Role of Dielectrophoresis for Cancer Diagnosis and Prognosis

Liquid biopsy is emerging as a potential diagnostic tool for prostate cancer (PC) prognosis and diagnosis. Unfortunately, most circulating tumor cells (CTC) technologies, such as AdnaTest or Cellsearch®, critically rely on the epithelial cell adhesion molecule (EpCAM) marker, limiting the possibility of detecting cancer stem-like cells (CSCs) and mesenchymal-like cells (EMT-CTCs) that are present during PC progression. In this context, dielectrophoresis (DEP) is an epCAM independent, label-free enrichment system that separates rare cells simply on the basis of their specific electrical properties. As compared to other technologies, DEP may represent a superior technique in terms of running costs, cell yield and specificity. However, because of its higher complexity, it still requires further technical as well as clinical development. DEP can be improved by the use of microfluid, nanostructured materials and fluoro-imaging to increase its potential applications. In the context of cancer, the usefulness of DEP lies in its capacity to detect CTCs in the bloodstream in their epithelial, mesenchymal, or epithelial-mesenchymal phenotype forms, which should be taken into account when choosing CTC enrichment and analysis methods for PC prognosis and diagnosis.

Size distribution of cell-free DNA in oncology

Tumor-specific, circulating cell-free DNA (cfDNA) in liquid biopsy test is a novel promising biomarker in the advancement of cancer management, including early diagnosis, screening, prognosis, identification of actionable targets, and serial tumor monitoring. The specific size pattern of DNA fragments derived from cancer cells is observed to differ from that of cfDNA fragments shed by non-cancer cells. Research into the physiological and biological properties of cfDNA reveals the molecular signature carried by each cfDNA fragments, which can reflect their tissue origins, as well as the mutational profiles with significant genetic alterations. Understanding the fragmentation and size distribution of cfDNA might be a valuable hotspot in liquid biopsy research, with the potential to drive innovation in oncology.