Diagnosis of pediatric central nervous system tumors using methylation profiling of cfDNA from cerebrospinal fluid.

Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. METHODS: We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. RESULTS: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. CONCLUSIONS: In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

[Comparison of two superparamagnetic purification magnetic beads-based screening and enrichment techniques for isolating cell-free fetal DNA from maternal plasma for non-invasive prenatal screening].

OBJECTIVE: To assess the efficiency of modified enrichment method for cell-free fetal DNA (cffDNA) through purified superparamagnetic beads during non-invasive prenatal testing (NIPT). METHODS: A total of 26 252 pregnant women undergoing NIPT at the Maternal and Child Health Care Hospital of Haidian District from December 2017 to September 2022 were recruited and randomly assigned into the conventional group (n = 10 573) and the modified enrichment group (n = 15 679), who were then subjected to the screening and enrichment of the cffDNA using a conventional and a modified technique, respectively. High-risk pregnant women detected by NIPT were subjected to invasive prenatal diagnosis. All women were followed up for their pregnancy outcomes, and the detection efficacy of the two methods was compared in terms of fragment size, concentration of cffDNA, duplicate detection rate, and indices of clinical laboratory tests. RESULTS: The fragment size of the main peak of the cell-free DNA library of the modified enrichment group was significantly lower than that of the conventional group [267 (264, 269) bp vs. 294 (292, 296) bp, P < 0.01], while the concentration of cffDNA was significantly higher [21.86% (17.61%, 26.36%) vs. 9.08% (6.87%, 11.87%), P < 0.01]. In addition, the duplicate detection rate (0.740% vs. 2.02%, X2 = 83.90, P < 0.01) and detection failure rate (0.006% vs. 0.057%, P < 0.05) in the modified enrichment group were significantly lower than those of the conventional group. The combined positive predictive value (PPV) in both high-risk (64.3% vs. 76.1%) and low-risk (35.3% vs. 45.5%) pregnant women from the modified enrichment group was slightly lower than those from the conventional group, though no significant difference was detected. There was one false negative case for trisomy 21 among the high-risk pregnant women from the conventional group, and no false negative case was found in the modified enrichment group. CONCLUSION: The modified technique to screen and enrich the cffDNA has significantly enhanced the relative concentration of cffDNA and reduced the failure and duplication detection rate of NIPT, which has significantly reduced the incidence of false negative cases due to the low concentration of cffDNA, and greatly increased the overall detection efficacy of NIPT.

Cell free DNA in patients with pancreatic adenocarcinoma: clinicopathologic correlations.

Detection of circulating tumor DNA (ctDNA) from plasma cell free DNA (cfDNA) has shown promise for diagnosis, therapeutic targeting, and prognosis. This study explores ctDNA detection by next generation sequencing (NGS) and associated clinicopathologic factors in patients with pancreatic adenocarcinoma (PDAC). Patients undergoing surgical exploration or resection of pancreatic lesions were enrolled with informed consent. Plasma samples (4-6 ml) were collected prior to surgery and cfDNA was recovered from 95 plasma samples. Adequate cfDNA for NGS (20 ng) was obtained from 81 patients. NGS was performed using the Oncomine Lung cfDNA assay on the Ion Torrent S5 sequencing platform. Twenty-five patients (30.9%) had detectable mutations in KRAS and/or TP53 with allele frequencies ranging from 0.05 to 8.5%, while mutations in other genes were detected less frequently and always along with KRAS or TP53. Detectable ctDNA mutations were more frequent in patients with poorly differentiated tumors, and patients without detectable ctDNA mutations showed longer survival (medians of 10.5 months vs. 18 months, p = 0.019). The detection of circulating tumor DNA in pancreatic adenocarcinomas is correlated with worse survival outcomes.

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

BACKGROUND: Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer. METHOD: First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy. RESULTS: The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response. CONCLUSION: These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles.

BACKGROUND: Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature. METHODS: To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements. RESULTS: Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types. CONCLUSIONS: Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy. KEYPOINTS: DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.

A panel sequencing dataset of peripheral blood gene variations in pan-cancer.

Circulating cell-free DNA (cfDNA) in the peripheral blood is a promising biomarker for cancer diagnosis and prognosis. Somatic mutations identified in cancers have been used to detect therapeutic targets for clinical transformation and individualize drug selection, while germline variants can predict a patient's risk of developing cancer and drug sensitivity. However, no platform has been developed to analyze, calculate, integrate, and friendly visualize these pan-cancer cfDNA mutations deeply. In this work, we performed panel sequencing encompassing 1,115 cancer-related genes across 16,659 cancer patients, spanning 27 cancer types. We detected 496 germline variants in leukocytes and 11,232 somatic mutations in the cfDNA of all patients. CPGV (Cancer Peripheral blood Gene Variations), a database constructed from this dataset, is the first pan-cancer cfDNA database that encompasses somatic mutations, germline variants, and further comparative analyses of mutations across different cancer types. It bears great promise to serve as a valuable resource for cancer research.

Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA.

Aberrant DNA methylation patterns have been used for cancer detection. However, DNA hemi-methylation, present at about 10% CpG dinucleotides, has been less well studied. Here we show that a majority of differentially hemi-methylated regions (DHMRs) in liver tumor DNA or plasma cells free (cf) DNA do not overlap with differentially methylated regions (DMRs) of the same samples, indicating that DHMRs could serve as independent biomarkers. Furthermore, we analyzed the cfDNA methylomes of 215 samples from individuals with liver or brain cancer and individuals without cancer (controls), and trained machine learning models using DMRs, DHMRs or both. The models incorporated with both DMRs and DHMRs show a superior performance compared to models trained with DMRs or DHMRs, with AUROC being 0.978, 0.990, and 0.983 in distinguishing control, liver and brain cancer, respectively, in a validation cohort. This study supports the potential of utilizing both DMRs and DHMRs for multi-cancer detection.

Quantification method of ctDNA using cell-free DNA methylation profile for noninvasive screening and monitoring of colon cancer.

BACKGROUND: Colon cancer ranks as the second most lethal form of cancer globally. In recent years, there has been active investigation into using the methylation profile of circulating tumor DNA (ctDNA), derived from blood, as a promising indicator for diagnosing and monitoring colon cancer. RESULTS: We propose a liquid biopsy-based epigenetic method developed by utilizing 49 patients and 260 healthy controls methylation profile data to screen and monitor colon cancer. Our method initially identified 901 colon cancer-specific hypermethylated (CaSH) regions in the tissues of the 49 cancer patients. We then used these CaSH regions to accurately quantify the amount of circulating tumor DNA (ctDNA) in the blood samples of these same patients, utilizing cell-free DNA methylation profiles. Notably, the methylation profiles of ctDNA in the blood exhibited high sensitivity (82%) and specificity (93%) in distinguishing patients with colon cancer from the control group, with an area under the curve of 0.903. Furthermore, we confirm that our method for ctDNA quantification is effective for monitoring cancer patients and can serve as a valuable tool for postoperative prognosis. CONCLUSIONS: This study demonstrated a successful application of the quantification of ctDNA among cfDNA using the original cancer tissue-derived CaSH region for screening and monitoring colon cancer.

Genome-Scale Multimodal Analysis of Cell-Free DNA Whole-Methylome Sequencing for Noninvasive Esophageal Cancer Detection.

PURPOSE: Simultaneous profiling of cell-free DNA (cfDNA) methylation and fragmentation features to improve the performance of cfDNA-based cancer detection is technically challenging. We developed a method to comprehensively analyze multimodal cfDNA genomic features for more sensitive esophageal squamous cell carcinoma (ESCC) detection. MATERIALS AND METHODS: Enzymatic conversion-mediated whole-methylome sequencing was applied to plasma cfDNA samples extracted from 168 patients with ESCC and 251 noncancer controls. ESCC characteristic cfDNA methylation, fragmentation, and copy number signatures were analyzed both across the genome and at accessible cis-regulatory DNA elements. To distinguish ESCC from noncancer samples, a first-layer classifier was developed for each feature type, the prediction results of which were incorporated to construct the second-layer ensemble model. RESULTS: ESCC plasma genome displayed global hypomethylation, altered fragmentation size, and chromosomal copy number alteration. Methylation and fragmentation changes at cancer tissue-specific accessible cis-regulatory DNA elements were also observed in ESCC plasma. By integrating multimodal genomic features for ESCC detection, the ensemble model showed improved performance over individual modalities. In the training cohort with a specificity of 99.2%, the detection sensitivity was 81.0% for all stages and 70.0% for stage 0-II. Consistent performance was observed in the test cohort with a specificity of 98.4%, an all-stage sensitivity of 79.8%, and a stage 0-II sensitivity of 69.0%. The performance of the classifier was associated with the disease stage, irrespective of clinical covariates. CONCLUSION: This study comprehensively profiles the epigenomic landscape of ESCC plasma and provides a novel noninvasive and sensitive ESCC detection approach with genome-scale multimodal analysis.

Casting a wider net: The clinical potential of EV transcriptomics in multi-analyte liquid biopsy.

Cancer cells release cell-free DNA (cfDNA) and extracellular vesicles (EVs) into the bloodstream, allowing disease non-invasive monitoring. In this issue of Cancer Cell, Casanova-Salas et al. analyze cfDNA, EV-DNA, and EV-RNA in prostate cancer longitudinal cohorts treated with androgen receptor signaling inhibitors and taxanes, identifying signals reflecting tumor adaptation processes.

A dual-gene panel of two fragments of methylated IRF4 and one of ZEB2 in plasma cell-free DNA for gastric cancer detection.

Early detection is crucial for increasing the survival rate of gastric cancer (GC). We aimed to identify a methylated cell-free DNA (cfDNA) marker panel for detecting GC. The differentially methylated CpGs (DMCs) were selected from datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The selected DMCs were validated and further selected in tissue samples (40 gastric cancer and 36 healthy white blood cell samples) and in a quarter sample volume of plasma samples (37 gastric cancer, 12 benign gastric disease, and 43 healthy individuals). The marker combination selected was then evaluated in a normal sample volume of plasma samples (35 gastric cancer, 39 control diseases, and 40 healthy individuals) using real-time methylation-specific PCR (MSP). The analysis of the results compared methods based on 2-ΔΔCt values and Ct values. In the results, 30 DMCs were selected through bioinformatics methods, and then 5 were selected for biological validation. The marker combination of two fragments of IRF4 (IRF4-1 and IRF4-2) and one of ZEB2 was selected due to its good performance. The Ct-based method was selected for its good results and practical advantages. The assay, IRF4-1 and IRF4-2 in one fluorescence channel and ZEB2 in another, obtained 74.3% sensitivity for the GC group at any stage, at 92.4% specificity. In conclusion, the panel of IRF4 and ZEB2 in plasma cfDNA demonstrates good diagnostic performance and application potential in clinical settings.

Extracellular Vesicle DNA Extraction and Sequencing in Ancient Serum Samples From Patients With Breast Cancer.

BACKGROUND/AIM: Extracellular vesicle DNA (EV-DNA) has emerged as a novel biomarker for tumor mutation detection using liquid biopsies, exhibiting biological advantages compared to cell-free DNA (cfDNA). This study assessed the feasibility of EV-DNA and cfDNA extraction and sequencing in old serum samples of patients with breast cancer (BC). PATIENTS AND METHODS: A total of 28 serum samples of 27 patients with corresponding clinical information were collected between 1983 and 1991. EV-DNA was extracted using Exo-GAG kit (Nasabiotech) and cfDNA using QIAsymphony DSP Virus/Pathogen Midi Kit (Qiagen), respectively. Subsequently, 10 matched samples (EV-DNA n=5, cfDNA n=5) of five patients were subjected to sequencing using the Oncomine™ Breast cfDNA Research Assay v2 (Thermo Fisher Scientific). RESULTS: Samples were collected on median 1.9 years after primary diagnosis [interquartile range (IQR)=0.2-7.2]. Median follow-up was 9.5 years (IQR=5.2-14.2). Median age of serum samples was 36.1 years (IQR=34.5-37.3). EV-DNA and cfDNA were extracted from 100% (28/28) of the included samples. Both, DNA quantity and concentration were comparable between EV-DNA and cfDNA. Sequencing was successfully performed in 100% (10/10) of the included samples. Two matched analyses yielded equivalent results in EV-DNA and cfDNA (no mutations, n=1; PIK3CA mutation, n=1), whilst in two analyses, PIK3CA mutation was only found in cfDNA, and in one analysis, a TP53 mutation was only found in EV-DNA. CONCLUSION: EV-DNA extraction and sequencing in old serum samples of patients with BC is feasible and has the potential to address clinically relevant questions in longitudinal studies.

Serial Cell-Free DNA Sequencing in ROS1 Fusion-Positive Lung Cancers During Treatment With Entrectinib.

PURPOSE: Patients with metastatic ROS1 fusion-positive non-small cell lung cancer (NSCLC) are effectively treated with entrectinib, a multikinase inhibitor. Whether serial targeted gene panel sequencing of cell-free DNA (cfDNA) can identify response and progression along with mechanisms of acquired resistance to entrectinib is underexplored. METHODS: In patients with ROS1 fusion-positive NSCLC, coclinical trial plasma samples were collected before treatment, after two cycles, and after progression on entrectinib (global phase II clinical trial, ClinicalTrials.gov identifier: NCT02568267). Samples underwent cfDNA analysis using MSK-ACCESS. Variant allele frequencies of detectable alterations were correlated with objective response per RECIST v1.1 criteria. RESULTS: Twelve patients were included, with best response as partial response (n = 9, 75%), stable disease (n = 2, 17%), and progressive disease (PD; n = 1, 8%). A ROS1 fusion was variably detected in cfDNA; however, patients without a ROS1 fusion in cfDNA had no other somatic alterations detected, indicative of possible low cfDNA shedding. Clearance of the enrolling ROS1 fusion or concurrent non-ROS1 alterations (TP53, CDH1, NF1, or ARID1A mutations) was observed in response to entrectinib therapy. Radiologic PD was accompanied by redemonstration of a ROS1 fusion or non-ROS1 alterations. On-target resistance was rare; only one patient acquired ROS1 G2032R at the time of progression. Several patients acquired new off-target likely oncogenic alterations, including a truncating alteration in NF1. CONCLUSION: Serial cfDNA monitoring may complement radiographic assessments as determinants of response and resistance to entrectinib in ROS1 fusion-positive lung cancers in addition to detecting putative resistance mechanisms on progression.

Noninvasive prenatal screening in a pregnant woman with a history of stem cell transplant from a male donor: A case report and literature review.

BACKGROUND: As a screening method, inaccuracies in noninvasive prenatal screening (NIPS) exist, which are often attributable to biological factors. One such factor is the history of transplantation. However, there are still limited reports on such NIPS cases. METHODS: We report an NIPS case of a pregnant woman who had received a stem cell transplant from a male donor. To determine the karyotype in the woman's original cell, we performed chromosome microarray analysis (CMA) on her postnatal blood and oral mucosa. To comprehensively estimate the cell-free DNA (cfDNA) composition, we further performed standard NIPS procedures on the postnatal plasma. Moreover, we reviewed all published relevant NIPS case reports about pregnant women with transplantation history. RESULTS: NIPS showed a low-risk result for common trisomies with a fetal fraction of 65.80%. CMA on maternal white blood cells showed a nonmosaic male karyotype, while the oral mucosa showed a nonmosaic female karyotype. The proportion of donor's cfDNA in postnatal plasma was 94.73% based on the Y-chromosome reads ratio. The composition of cfDNA in maternal plasma was estimated as follows: prenatally, 13.60% maternal, 65.80% donor, and 20.60% fetal/placental, whereas postnatally, 5.27% maternal and 94.73% donor. CONCLUSIONS: This study expanded our understanding of the influence of stem cell transplantation on NIPS, allowing us to optimize NIPS management for these women.

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.

Exploring the translational landscape of the long noncoding RNA transcriptome in acute respiratory distress syndrome: it is a long way to the top.

Acute respiratory distress syndrome (ARDS) poses a significant and widespread public health challenge. Extensive research conducted in recent decades has considerably improved our understanding of the disease pathophysiology. Nevertheless, ARDS continues to rank among the leading causes of mortality in intensive care units and its management remains a formidable task, primarily due to its remarkable heterogeneity. As a consequence, the syndrome is underdiagnosed, prognostication has important gaps and selection of the appropriate therapeutic approach is laborious. In recent years, the noncoding transcriptome has emerged as a new area of attention for researchers interested in biomarker development. Numerous studies have confirmed the potential of long noncoding RNAs (lncRNAs), transcripts with little or no coding information, as noninvasive tools for diagnosis, prognosis and prediction of the therapeutic response across a broad spectrum of ailments, including respiratory conditions. This article aims to provide a comprehensive overview of lncRNAs with specific emphasis on their role as biomarkers. We review current knowledge on the circulating lncRNAs as potential markers that can be used to enhance decision making in ARDS management. Additionally, we address the primary limitations and outline the steps that will be essential for integration of the use of lncRNAs in clinical laboratories. Our ultimate objective is to provide a framework for the implementation of lncRNAs in the management of ARDS.

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

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

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

Plasma cell-free DNA (cfDNA) fragmentation patterns are emerging directions in cancer liquid biopsy with high translational significance. Conventionally, the cfDNA sequencing reads are aligned to a reference genome to extract their fragmentomic features. In this study, through cfDNA fragmentomics profiling using different reference genomes on the same datasets in parallel, we report systematic biases in such conventional reference-based approaches. The biases in cfDNA fragmentomic features vary among races in a sample-dependent manner and therefore might adversely affect the performances of cancer diagnosis assays across multiple clinical centers. In addition, to circumvent the analytical biases, we develop Freefly, a reference-free approach for cfDNA fragmentomics profiling. Freefly runs ∼60-fold faster than the conventional reference-based approach while generating highly consistent results. Moreover, cfDNA fragmentomic features reported by Freefly can be directly used for cancer diagnosis. Hence, Freefly possesses translational merit toward the rapid and unbiased measurement of cfDNA fragmentomics.

Noninvasive Lung Cancer Subtype Classification Using Tumor-Derived Signatures and cfDNA Methylome.

Accurate diagnosis of lung cancer is important for treatment decision-making. Tumor biopsy and histologic examination are the standard for determining histologic lung cancer subtypes. Liquid biopsy, particularly cell-free DNA (cfDNA), has recently shown promising results in cancer detection and classification. In this study, we investigate the potential of cfDNA methylome for the noninvasive classification of lung cancer histologic subtypes. We focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcriptional activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in cross-validation and an AUC of 0.747 in the independent validation. Tumor copy-number alterations inferred from cfDNA methylome analysis revealed potential for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasive lung cancer subtyping, offering insights for cancer monitoring and early detection. SIGNIFICANCE: This study explores the use of cfDNA methylomes for the classification of lung cancer subtypes, vital for effective treatment. By identifying specific methylation markers in tumor tissues, we developed a robust classification model achieving high accuracy for noninvasive subtype detection. This cfDNA methylome approach offers promising avenues for early detection and monitoring.