cfDNA Fragments Research Articles

Ultrashort Cell-Free DNA Fragments and Vimentin-Positive Circulating Tumor Cells for Predicting Early Recurrence in Patients with Biliary Tract Cancer

Background/Objectives: Biliary tract cancer (BTC) is a rare but aggressive malignancy that requires surgical treatment. However, postoperative recurrence rates are high, and reliable predictors of recurrence are limited. This study aimed to investigate the effectiveness of cell-free DNA (cfDNA) and circulating tumor cells (CTCs) in predicting early recurrence after curative surgery and complete adjuvant therapy in patients with BTC. Methods: Twenty-four patients who underwent R0 and R1 resections and completed adjuvant therapy for BTC between September 2019 and March 2022 were followed up until March 2024. Patients were categorized into early recurrence (ER) and non-ER groups, using one year as the cutoff for recurrence. Results: The combination score derived from ultrashort fragments of cfDNA, vimentin-positive CTCs, and carbohydrate antigen (CA) 19-9 levels showed a statistically significant difference between the ER and non-ER groups (p-value < 0.001). The receiver operating characteristic curve from the combination score and CA 19-9 levels yielded areas under the curve of 0.891 and 0.750, respectively. Conclusions: Although further research is required, these findings suggest that cfDNA and CTCs may increase the accuracy of predicting postoperative recurrence in patients with BTC.

Evaluation of cfDNA fragmentation characteristics in plasma for the diagnosis of lung cancer: A prospective cohort study.

Lung cancer is one of the most prevalent cancers worldwide, yet only approximately 16% of patients are diagnosed in early stage, highlighting the urgent need for novel, highly accurate detection models. In our study, patients with suspected lung cancer or lung disease, as identified through radiographic imaging, along with healthy individuals, were consecutively recruited from Beijing Chest Hospital. Circulating free DNA (cfDNA) was extracted from plasma samples, and low-depth whole-genome sequencing was performed to identify fragmentomic features for model construction. A total of 265 participants were prospectively enrolled, comprising 124 lung cancer patients and 141 noncancer individuals. The model we developed was based on four cfDNA fragmentation characteristics, including 20-bp breakpoint nucleotides motif, fragmentation size coverage, fragmentation size distribution, and copy number variation. In an independent test cohort, the model achieved an area under the curve (AUC) of 0.861 (95% CI: 0.781-0.942) and demonstrated a sensitivity of 70% (95% CI: 53.5%-83.4%) at a specificity of 89.4% (95% CI: 76.9%-96.5%). Notably, the model was also effective in detecting early-stage cancer, with an AUC of 0.808 (95% CI: 0.69-0.925). In summary, our lung cancer detection model shows strong screening capabilities by leveraging four cfDNA fragmentation characteristics, exhibiting robust performance in early cancer diagnosis.

Evaluation of a multimodal ctDNA-based assay for detection of aggressive cancers lacking standard screening tests.

Aim: Cancers lacking standard screening (LSS) options account for approximately 70% of cancer-related deaths due to late-stage diagnosis. Circulating tumor DNA (ctDNA) is a promising biomarker for multi-cancer early detection. We previously developed SPOT-MAS, a multimodal ctDNA-based assay analyzing methylation and fragmentomic profiles, effective in detecting common cancers (breast, colorectal, liver, lung and gastric). This study extends the analysis to five LSS cancers: endometrial, esophageal, head and neck, ovarian and pancreatic.Methods: SPOT-MAS was applied to profile cfDNA methylation and fragmentomic patterns in 739 healthy individuals and 135 LSS cancer patients.Results: We identified 347 differentially methylated regions and observed genome-wide hypomethylation across all five LSS cancers. Esophageal and head and neck cancers showed an enrichment of short cfDNA fragments (<150bp). Eleven 4-mer end motifs were consistently altered in cfDNA fragments across all LSS cancers. Many significant signatures were consistent with previous observations in common cancers. Notably, SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity, with a lower sensitivity of 60.7% in early-stage cancers.Conclusion: This proof-of-concept study demonstrates that SPOT-MAS a non-invasive test trained on five common cancer types, could detect a number of LSS cancer cases, potentially complementing existing screening programs.

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

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

Role of Liquid Biopsy in Progressive PSA Patients after Radical Prostatectomy.

Background/Objectives: Currently, the prediction of disease recurrence after radical prostatectomy (RP) in localized prostate cancer (PCa) relies on clinicopathological parameters, which lack accuracy in predicting clinical outcomes. This study focused on evaluating the utility of cfDNA levels and fragmentation patterns as prognostic biomarkers in progressive prostate-specific antigen (PSA) patients, including those with persistent PSA and biochemical recurrence (BR), after primary treatment in localized PCa patients. Methods: Twenty-nine high-risk localized PCa patients were enrolled in the study between February 2022 and May 2023. Blood samples were obtained before robotic RP. cfDNA concentration and fragment size were quantified using the Quant-it PicoGreen dsDNA Assay kit and Agilent 2200 TapeStation System, respectively. Results: The mean PSA value at diagnosis was 9.4 ng/mL. Seven patients (24.1%) had stage pT2 and 22 (75.9%) pT3. Nine patients (31%) had detectable PSA at the first PSA control six weeks after surgery, and four patients (20%) had BR during a mean follow-up of 18.4 months. No associations were found between cfDNA levels or fragmentation patterns and clinicopathological data. Although not statistically significant, patients with detectable PSA levels post-surgery exhibited higher cfDNA levels and shorter fragments compared with those with undetectable PSA. Conclusions: Our study indicated a tendency toward more fragmented cfDNA levels in PCa patients with persistent PSA. Strikingly, biochemical recurrent PCa patients exhibited similar cfDNA levels and fragmentation patterns compared to non-recurrent patients. Further studies exploring liquid biopsy-derived biomarkers in localized PCa patients are needed to elucidate their clinical utility in predicting PSA persistence.

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

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

8414 Utilization of Cell Free DNA Integrity Index to Differentiate Benign from Malignant Thyroid Nodules

Abstract Disclosure: S. Tarafdar: None. S. Dutta: None. P. Mukhopadhyay: None. N.P. Bhattacharya: None. S. Ghosh: None. Introduction: Recent advances in molecular genetics have evaluated a potential role for plasma cell-free DNA (cfDNA) integrity index as a non-invasive cancer marker. Data on cfDNA integrity in differentiated thyroid cancer is sparse and needs to be investigated further. Aim of the study: The purpose of this study is to investigate the hypothesis that longer DNA strands in plasma may serve as a marker for thyroid cancer. Methods: Patients presenting with thyroid nodules underwent ultrasonography with Thyroid Image Reporting and Data Systems (TIRADS) scoring and FNAC (Bethesda classification).Patients with Bethesda 3, 4, 5, and 6 had surgery and confirmation by histopathology. Patients with Bethesda 2 presenting with compressive symptoms and cosmetic concerns underwent surgery; the remaining patients were considered benign based on clinical follow-up, USG, and FNAC characteristics. Blood sample collected prior to FNAC was used to extract and quantify cfDNA. To assess the integrity index 180/67, 306/67, and 476/67 for the APP gene, we used a quantitative real-time PCR (qPCR) method based on the quantification of four amplicons of varying lengths (67, 180, 306, and 476 bp, respectively). Because culture cells had extremely intact genomic DNA, the genomic DNA obtained from the MDA-T32 papillary thyroid cancer cell line was utilised as a reference to estimate the relative DNA strand integrity in plasma DNA. To determine the (ΔCt) value for 67,180, 306, and 476 bp, the Ct value of the sample was subtracted from that of the MDA-T32 control. Results: A total of 161 subjects were recruited, of these 92 were benign and 69 had differentiated thyroid cancer(confirmed by histopathology). Using the 180/67 bp integrity index benign thyroid nodule patients had significant lower value (median 0.39, 95% CI: 0.33-0.46) compared to patients with differentiated thyroid cancer (median 0.62, 95% CI: 0.56-0.68). A cfDNA integrity index of 180/67 bp could significantly differentiate between benign and malignant thyroid nodules (P&amp;lt;0.001). However, there was no significant difference between malignant and benign thyroid nodules based on the cell free DNA integrity indexes of 306/67 and 476/67. Conclusions: The cfDNA integrity score of 180/67 exhibits potential as a circulating marker for the detection of thyroid nodules and as a means of monitoring cfDNA fragmentation in patients with thyroid cancer. However, further confirmation through prospective trials is necessary. Presentation: 6/3/2024

8414 Utilization Of Cell Free DNA Integrity Index To Differentiate Benign From Malignant Thyroid Nodules

Abstract Disclosure: S. Tarafdar: None. S. Dutta: None. P. Mukhopadhyay: None. N.P. Bhattacharya: None. S. Ghosh: None. Introduction: Recent advances in molecular genetics have evaluated a potential role for plasma cell-free DNA (cfDNA) integrity index as a non-invasive cancer marker. Data on cfDNA integrity in differentiated thyroid cancer is sparse and needs to be investigated further. Aim of the study: The purpose of this study is to investigate the hypothesis that longer DNA strands in plasma may serve as a marker for thyroid cancer. Methods: Patients presenting with thyroid nodules underwent ultrasonography with Thyroid Image Reporting and Data Systems (TIRADS) scoring and FNAC (Bethesda classification).Patients with Bethesda 3, 4, 5, and 6 had surgery and confirmation by histopathology. Patients with Bethesda 2 presenting with compressive symptoms and cosmetic concerns underwent surgery; the remaining patients were considered benign based on clinical follow-up, USG, and FNAC characteristics. Blood sample collected prior to FNAC was used to extract and quantify cfDNA. To assess the integrity index 180/67, 306/67, and 476/67 for the APP gene, we used a quantitative real-time PCR (qPCR) method based on the quantification of four amplicons of varying lengths (67, 180, 306, and 476 bp, respectively). Because culture cells had extremely intact genomic DNA, the genomic DNA obtained from the MDA-T32 papillary thyroid cancer cell line was utilised as a reference to estimate the relative DNA strand integrity in plasma DNA. To determine the (ΔCt) value for 67,180, 306, and 476 bp, the Ct value of the sample was subtracted from that of the MDA-T32 control. Results: A total of 161 subjects were recruited, of these 92 were benign and 69 had differentiated thyroid cancer(confirmed by histopathology). Using the 180/67 bp integrity index benign thyroid nodule patients had significant lower value (median 0.39, 95% CI: 0.33-0.46) compared to patients with differentiated thyroid cancer (median 0.62, 95% CI: 0.56-0.68). A cfDNA integrity index of 180/67 bp could significantly differentiate between benign and malignant thyroid nodules (P&amp;lt;0.001). However, there was no significant difference between malignant and benign thyroid nodules based on the cell free DNA integrity indexes of 306/67 and 476/67. Conclusions: The cfDNA integrity score of 180/67 exhibits potential as a circulating marker for the detection of thyroid nodules and as a means of monitoring cfDNA fragmentation in patients with thyroid cancer. However, further confirmation through prospective trials is necessary. Presentation: 6/3/2024

Multidimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early colorectal cancer detection and broad patient benefit. Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128.

Extracellular DNA from the blood plasma of patients with schizophrenia stimulates the TLR9-NF-kB signaling pathway in cultured human lymphocytes

Schizophrenia is a mental illness of complex etiology. Recently, there has been increased interest in the role of the immune system in the pathophysiology of mental disorders. The concept of neuroinflammation in neurodevelopmental disorders is gaining widespread interest, including the role of Toll-like receptors.Schizophrenia is associated with an increase in the concentration of cfDNA in human blood, and the composition of cfDNA fragments changes significantly compared to cellular DNA: GC-rich fragments of the ribosomal repeat accumulate and base oxidation occurs. Similar changes, but less pronounced, also occur for cfDNA from healthy donors.To confirm the hypothesis about the possible participation of cfDNA in the inflammation induction, we studied the effect of cfDNA samples on cultured mononuclear cells.Unlike cellular DNA, cfDNA(SZ) and cfDNA(K) stimulate transcription of the TLR9 gene in mononuclear cells. After 1 hour the amount of TLR9 RNA increases by 2.9 and 3.3 times compared to the control. After 24 hours, the TLR9 RNA level decreases slightly, but is still 2-3 times higher than the control level. After 1 hour, TLR9 protein increases by 1.5 and 1.7 times, respectively, and further increased after 24h of culture.An increase TLR9 protein expression correlates with an increase of the transcription factor NF-kB in lymphocytes and is accompanied by an increase in proinflammatory cytokine IL8 RNA, the transcription of IL8 is controlled by the NF-kB factor.Thus, cfDNA(SZ) and cfDNA(K) stimulate the TLR9-NF-kB-proinflammatory cytokine signaling pathway in lymphocytes. The effect of cfDNA also depends on the concentration of these fragments in the extracellular environment. Since the concentrations of cfDNA in the blood of patients with schizophrenia are significantly increased compared to healthy donors, we should expect a much higher level of activation of the TLR9-NF-kB signaling pathway in the body cells of sick people.Samples of cfDNA from patients with schizophrenia have a pronounced biological effect on cells of the immune system, stimulating the synthesis of pro-inflammatory cytokines by activating the TLR9-NF-kB-proinflammatory cytokines signaling pathway. High levels of cfDNA in blood plasma may be one of the reasons for the induction and maintenance of low-level inflammation in schizophrenia.

Genomic profiling of cell-free DNA from dogs with benign and malignant tumors

ObjectiveCancer is currently the most common cause of death in adult dogs. Like humans, dogs have a one-third chance of developing cancer in their lifetime. We used shallow whole-genome sequencing (sWGS) to analyze blood cell-free DNA (cfDNA) from four tumor-bearing dogs (one with benign and three with malignant tumors) and 38 healthy dogs.ResultsSimilar to the results observed in the healthy dogs, no copy number aberration (CNA) was detected in the dog with benign lipomas, and the distribution of cfDNA fragment size (FS) closely resembled that of the healthy dogs. However, among the three dogs diagnosed with malignant tumors, two dogs exhibited varying degrees and quantities of CNAs. Compared to the distribution of FS in the healthy dogs, the cancer dogs exhibited a noticeable shift towards shorter lengths. These findings indicated that CNA and FS profiles derived from sWGS data can be used for non-invasive cancer detection in dogs.

Partitioning behavior of short DNA fragments in polymer/salt aqueous two-phase systems.

The development of liquid biopsy as a minimally invasive technique for tumor profiling has created a need for efficient biomarker extraction systems from body fluids. The analysis of circulating cell-free DNA (cfDNA) is especially promising, but the low amounts and high fragmentation of cfDNA found in plasma pose challenges to its isolation. While the potential of aqueous two-phase systems (ATPS) for the extraction and purification of various biomolecules has already been successfully established, there is limited literature on the applicability of these findings to short cfDNA-like fragments. This study presents the partitioning behavior of a 160 bp DNA fragment in polyethylene glycol (PEG)/salt ATPS at pH 7.4. The effect of PEG molecular weight, tie-line length, neutral salt additives, and phase volume ratio is evaluated to maximize DNA recovery. Selected ATPS containing a synthetic plasma solution spiked with human serum albumin and immunoglobulin G are tested to determine the separation of DNA fragments from the main plasma protein fraction. By adding 1.5% (w/w) NaCl to a 17.7% (w/w) PEG 400/17.3% (w/w) phosphate ATPS, 88% DNA recovery was achieved in the salt-rich bottom phase while over 99% of the protein was removed.

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.

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data

MotivationThe 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. ResultsWe 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 ImplementationLBFextract 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.

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.

An ultrasensitive DNA-enhanced amplification method for detecting cfDNA drug-resistant mutations in non-small cell lung cancer with selective FEN-assisted degradation of dominant somatic fragments.

Blood cell-free DNA (cfDNA) can be a new reliable tool for detecting epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) patients. However, the currently reported cfDNA assays have a limited role in detecting drug-resistant mutations due to their deficiencies in sensitivity, stability, or mutation detection rate. We developed an Archaeoglobus fulgidus-derived flap endonuclease (Afu FEN)-based DNA-enhanced amplification system of mutated cfDNA by designing a pair of hairpin probes to anneal with wild-type cfDNA to form two 5'-flaps, allowing for the specific cleavage of wild-type cfDNA by Afu FEN. When the dominant wild-type somatic cfDNA fragments were cleaved by structure-recognition-specific Afu FEN, the proportion of mutated cfDNA in the reaction system was greatly enriched. As the amount of mutated cfDNA in the system was further increased by PCR amplification, the mutation status could be easily detected through first-generation sequencing. In a mixture of synthetic wild-type and T790M EGFR DNA fragments, our new assay still could detect T790M mutation at the fg level with remarkably high sensitivity. We also tested its performance in detecting low variant allele frequency (VAF) mutations in clinical samples from NSCLC patients. The plasma cfDNA samples with low VAF (0.1 and 0.5 %) could be easily detected by DNA-enhanced amplification. This system with enhanced amplification of mutated cfDNA is an effective tool used for the early screening and individualized targeted therapy of NSCLC by providing arapid, sensitive, and economical way for the detection of drug-resistant mutations in tumors.

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.

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

BackgroundThe 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. MethodsPeripheral 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. ResultsFrom 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. ConclusionThe 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.

Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis

Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we sought to develop a deep learning model incorporating cfDNA methylation and fragment size profiles. To achieve this, we utilized methylation data collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Then we generated methylated DNA immunoprecipitation sequencing and genome-wide Enzymatic Methyl-seq (EM-seq) form lung cancer tissue and plasma. Using these data, we selected 366 methylation markers. A targeted EM-seq panel was designed using the selected markers, and 142 lung cancer and 56 healthy samples were produced with the panel. Additionally, cfDNA samples from healthy individuals and lung cancer patients were diluted to evaluate sensitivity. Its lung cancer detection performance reached an accuracy of 81.5% and an area under the receiver operating characteristic curve of 0.87. In the serial dilution experiment, we achieved tumor fraction detection of 1% at 98% specificity and 0.1% at 80% specificity. In conclusion, we successfully developed and validated a combination of methylation panel and a deep learning model that can distinguish between patients with lung cancer and healthy individuals.

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.