Epigenetic analysis of cell-free DNA by fragmentomic profiling.

Cell-free DNA (cfDNA) fragmentation patterns contain important molecular information linked to tissues of origin. We explored the possibility of using fragmentation patterns to predict cytosine-phosphate-guanine (CpG) methylation of cfDNA, obviating the use of bisulfite treatment and associated risks of DNA degradation. This study investigated the cfDNA cleavage profile surrounding a CpG (i.e., within an 11-nucleotide [nt] window) to analyze cfDNA methylation. The cfDNA cleavage proportion across positions within the window appeared nonrandom and exhibited correlation with methylation status. The mean cleavage proportion was ∼twofold higher at the cytosine of methylated CpGs than unmethylated ones in healthy controls. In contrast, the mean cleavage proportion rapidly decreased at the 1-nt position immediately preceding methylated CpGs. Such differential cleavages resulted in a characteristic change in relative presentations of CGN and NCG motifs at 5' ends, where N represented any nucleotide. CGN/NCG motif ratios were correlated with methylation levels at tissue-specific methylated CpGs (e.g., placenta or liver) (Pearson's absolute r > 0.86). cfDNA cleavage profiles were thus informative for cfDNA methylation and tissue-of-origin analyses. Using CG-containing end motifs, we achieved an area under a receiver operating characteristic curve (AUC) of 0.98 in differentiating patients with and without hepatocellular carcinoma and enhanced the positive predictive value of nasopharyngeal carcinoma screening (from 19.6 to 26.8%). Furthermore, we elucidated the feasibility of using cfDNA cleavage patterns to deduce CpG methylation at single CpG resolution using a deep learning algorithm and achieved an AUC of 0.93. FRAGmentomics-based Methylation Analysis (FRAGMA) presents many possibilities for noninvasive prenatal, cancer, and organ transplantation assessment.

Individualized dynamic methylation-based analysis of cell-free DNA in postoperative monitoring of lung cancer.

BACKGROUND: The feasibility of DNA methylation-based assays in detecting minimal residual disease (MRD) and postoperative monitoring remains unestablished. We aim to investigate the dynamic characteristics of cancer-related methylation signals and the feasibility of methylation-based MRD detection in surgical lung cancer patients. METHODS: Matched tumor, tumor-adjacent tissues, and longitudinal blood samples from a cohort (MEDAL) were analyzed by ultra-deep targeted sequencing and bisulfite sequencing. A tumor-informed methylation-based MRD (timMRD) was employed to evaluate the methylation status of each blood sample. Survival analysis was performed in the MEDAL cohort (n = 195) and validated in an independent cohort (DYNAMIC, n = 36). RESULTS: Tumor-informed methylation status enabled an accurate recurrence risk assessment better than the tumor-naïve methylation approach. Baseline timMRD-scores were positively correlated with tumor burden, invasiveness, and the existence and abundance of somatic mutations. Patients with higher timMRD-scores at postoperative time-points demonstrated significantly shorter disease-free survival in the MEDAL cohort (HR: 3.08, 95% CI: 1.48-6.42; P = 0.002) and the independent DYNAMIC cohort (HR: 2.80, 95% CI: 0.96-8.20; P = 0.041). Multivariable regression analysis identified postoperative timMRD-score as an independent prognostic factor for lung cancer. Compared to tumor-informed somatic mutation status, timMRD-scores yielded better performance in identifying the relapsed patients during postoperative follow-up, including subgroups with lower tumor burden like stage I, and was more accurate among relapsed patients with baseline ctDNA-negative status. Comparing to the average lead time of ctDNA mutation, timMRD-score yielded a negative predictive value of 97.2% at 120 days prior to relapse. CONCLUSIONS: The dynamic methylation-based analysis of peripheral blood provides a promising strategy for postoperative cancer surveillance. TRIAL REGISTRATION: This study (MEDAL, MEthylation based Dynamic Analysis for Lung cancer) was registered on ClinicalTrials.gov on 08/05/2018 (NCT03634826). https://clinicaltrials.gov/ct2/show/NCT03634826 .

Multiomics Analysis Reveals Distinct Immunogenomic Features of Lung Cancer with Ground-Glass Opacity.

Rationale: Ground-glass opacity (GGO)-associated lung cancers are common and radiologically distinct clinical entities known to have an indolent clinical course and superior survival, implying a unique underlying biology. However, the molecular and immune characteristics of GGO-associated lung nodules have not been systemically studied. Objectives: To provide mechanistic insights for the treatment of these radiologically distinct clinical entities. Methods: We initiated a prospective cohort study to collect and characterize pulmonary nodules with GGO components (nonsolid and part-solid nodules) or without GGO components, as precisely quantified by using three-dimensional image reconstruction to delineate the molecular and immune features associated with GGO. Multiomics assessment conducted by using targeted gene panel sequencing, RNA sequencing, TCR (T-cell receptor) sequencing, and circulating tumor DNA detection was performed. Measurements and Main Results: GGO-associated lung cancers exhibited a lower tumor mutation burden than solid nodules. Transcriptomic analysis revealed a less active immune environment in GGO components and immune pathways, decreased expression of immune activation markers, and less infiltration of most immune-cell subsets, which was confirmed by using multiplex immunofluorescence. Furthermore, T-cell repertoire sequencing revealed lower T-cell expansion in GGO-associated lung cancers. HLA loss of heterozygosity was significantly less common in lung adenocarcinomas with GGO components than in those without. Circulating tumor DNA analysis suggested that the release of tumor DNA to the peripheral blood was correlated with the tumor size of non-GGO components. Conclusions: Compared with lung cancers presenting with solid lung nodules, GGO-associated lung cancers are characterized by a less active metabolism and a less active immune microenvironment, which may be the mechanisms underlying their indolent clinical course. Clinical trial registered with www.clinicaltrials.gov (NCT03320044).

Monitoring of circulating tumor DNA and its aberrant methylation in the surveillance of surgical lung Cancer patients: protocol for a prospective observational study.

BACKGROUND: Detection of circulating tumor DNA (ctDNA) is a promising method for postoperative surveillance of lung cancer. However, relatively low positive rate in early stage patients restricts its application. Aberrant methylation of ctDNA can be detected in blood samples, and may provide a more sensitive method. This study is designed to systematically evaluate and compare the detection of aberrant methylation and mutations in ctDNA among surgical non-small cell lung cancer (NSCLC) patients, aiming to investigate the feasibility of ctDNA detection as a means of lung cancer surveillance. METHODS: This is a prospective observational study. Consecutive surgical NSCLC patients will be recruited. Blood samples will be collected both before and after surgery (during the follow-up period), while matching tumor tissues and tumor-adjacent normal tissues will be collected during surgery. Quantitative analysis of aberrant methylation and mutations of ctDNA will be conducted in combination with a three-year follow-up data. DISCUSSION: This is the first registered prospective study designed to investigate the feasibility of ctDNA methylation detection as a means of postoperative lung cancer surveillance. We will systematically evaluate and compare the quantitative detection of ctDNA mutations and ctDNA methylation in surgical NSCLC patients, combining with the follow-up information. By integrating genetic and epigenetic information of ctDNA, more effective strategies for postoperative surveillance may be defined. TRIAL REGISTRATION: This study (MEDAL, MEthylation based Dynamic Analysis for Lung cancer) was registered on ClinicalTrials.gov on 08/05/2018 (NCT03634826; Pre-results).

Construction of Immune-Related Diagnostic Model for Latent Tuberculosis Infection and Active Tuberculosis.

Background: Tuberculosis (TB) is one of the most infectious diseases caused by Mycobacterium tuberculosis (M. tb), and the diagnosis of active tuberculosis (TB) and latent TB infection (LTBI) remains challenging. Methods: Gene expression files were downloaded from the GEO database to identify the differentially expressed genes (DEGs). The ssGSEA algorithm was applied to assess the immunological characteristics of patients with LTBI and TB. Weighted gene co-expression network analysis, protein-protein interaction network, and the cytoHubba plug-in of Cytoscape were used to identify the real hub genes. Finally, a diagnostic model was constructed using real hub genes and validated using a validation set. Results: Macrophages and natural killer cells were identified as important immune cells strongly associated with TB. In total, 726 mRNAs were identified as DEGs. MX1, STAT1, IFIH1, DDX58, and IRF7 were identified as real hub immune-related genes. The diagnostic model generated by the five real hub genes could distinguish active TB from healthy controls or patients with LTBI. Conclusion: Our study may provide implications for the diagnosis and drug development of M. tb infections.

Development and Validation of Machine Learning-based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts.

PURPOSE: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning-based model to estimate the malignant probability of MPNs to guide decision-making. EXPERIMENTAL DESIGN: A boosted ensemble algorithm (XGBoost) was used to predict malignancy using the clinicoradiologic variables of 1,739 nodules from 520 patients with MPNs at a Chinese center. The model (PKU-M model) was trained using 10-fold cross-validation in which hyperparameters were selected and fine-tuned. The model was validated and compared with solitary pulmonary nodule (SPN) models, clinicians, and a computer-aided diagnosis (CADx) system in an independent transnational cohort and a prospective multicentric cohort. RESULTS: The PKU-M model showed excellent discrimination [area under the curve; AUC (95% confidence interval (95% CI)), 0.909 (0.854-0.946)] and calibration (Brier score, 0.122) in the development cohort. External validation (583 nodules) revealed that the AUC of the PKU-M model was 0.890 (0.859-0.916), higher than those of the Brock model [0.806 (0.771-0.838)], PKU model [0.780 (0.743-0.817)], Mayo model [0.739 (0.697-0.776)], and VA model [0.682 (0.640-0.722)]. Prospective comparison (200 nodules) showed that the AUC of the PKU-M model [0.871 (0.815-0.915)] was higher than that of surgeons [0.790 (0.711-0.852), 0.741 (0.662-0.804), and 0.727 (0.650-0.788)], radiologist [0.748 (0.671-0.814)], and the CADx system [0.757 (0.682-0.818)]. Furthermore, the model outperformed the clinicians with an increase of 14.3% in sensitivity and 7.8% in specificity. CONCLUSIONS: After its development using machine learning algorithms, validation using transnational multicentric cohorts, and prospective comparison with clinicians and the CADx system, this novel prediction model for MPNs presented solid performance as a convenient reference to help decision-making.

Intelectin is required for IL-13-induced monocyte chemotactic protein-1 and -3 expression in lung epithelial cells and promotes allergic airway inflammation.

Asthma is characterized by airway inflammation, mucus overproduction, airway hyperreactivity, and peribronchial fibrosis. Intelectin has been shown to be increased in airway epithelium of asthmatics. However, the role of intelectin in the pathogenesis of asthma is unknown. Airway epithelial cells can secrete chemokines such as monocyte chemotactic protein (MCP)-1 and -3 that play crucial roles in asthmatic airway inflammation. We hypothesized that intelectin plays a role in allergic airway inflammation by regulating chemokine expression. In a mouse allergic asthma model, we found that mRNA expression of intelectin-2 as well as MCP-1 and -3 in mouse lung was increased very early (within 2 h) after allergen challenge. Expression of intelectin protein was localized to mucous cells in airway epithelium. Treatment of MLE12 mouse lung epithelial cells with interleukin IL-13, a critical mediator of allergic airway disease, induced expression of intelectin-1 and -2 as well as MCP-1 and -3. When IL-13-induced intelectin-1 and -2 expression was inhibited by RNA interference, IL-13-induced extracellular signal-regulated kinase 1/2 phosphorylation and MCP-1 and -3 production by MLE12 cells was inhibited. Furthermore, inhibition of intelectin expression by airway transfection with shRNA targeting intelectin-1 and -2 attenuated allergen-induced airway inflammation. We conclude that intelectin, a molecule expressed by airway epithelial cells and upregulated in asthma, is required for IL-13-induced MCP-1 and -3 production in mouse lung epithelial cells and contributes to allergic airway inflammation.

Liquid biopsy in newly diagnosed patients with locoregional (I-IIIA) non-small cell lung cancer.

INTRODUCTION: Liquid biopsy is a promising method for the management of lung cancer, but previous studies focused mainly on patients with advanced-stage disease. As the methodology has progressed for the detection of circulating tumor DNA (ctDNA) and its aberrant methylation, researchers are gradually investigating the utility of liquid biopsy in early-stage patients. As a result, liquid biopsy has shown its potential for the application in patients with early- and locally advanced-stage non-small cell lung cancer (NSCLC). Areas covered: This review summarizes the utility of liquid biopsy in NSCLC and provide an outlook for future development. We focus on the role of ctDNA and its aberrant methylation in patients with stage IA to stageⅢA NSCLC, in the field of early detection and screening, perioperative management, and postoperative surveillance. Expert opinion: Liquid biopsy has shown the potential for clinical application of early-stage patients but has not been routinely applied yet. The utilization of liquid biopsy will be promoted by improved detection methods and data from well-designed clinical trials. With the development of precision medicine, liquid biopsy will likely play an increasingly important clinical role.

Perioperative Dynamic Changes in Circulating Tumor DNA in Patients with Lung Cancer (DYNAMIC).

PURPOSE: No study has investigated the precise perioperative dynamic changes in circulating tumor DNA (ctDNA) in any patients with early-stage cancer. This study (DYNAMIC) investigated perioperative dynamic changes in ctDNA and determined the appropriate detection time of ctDNA-based surveillance for surgical patients with lung cancer.Experimental Design: Consecutive patients who underwent curative-intent lung resections were enrolled prospectively (NCT02965391). Plasma samples were obtained at multiple prespecified time points including before surgery (time A), during surgery after tumor resection (time B-time D), and after surgery (time P1-time P3). Next-generation sequencing-based detection platform was performed to calculate the plasma mutation allele frequency. The primary endpoint was ctDNA half-life after radical tumor resection. RESULTS: Thirty-six patients showed detectable mutations in time A. The plasma ctDNA concentration showed a rapid decreasing trend after radical tumor resection, with the average mutant allele fraction at times A, B, C, and D being 2.72%, 2.11%, 1.14%, and 0.17%, respectively. The median ctDNA half-life was 35.0 minutes. Patients with minimal residual disease (MRD) detection had a significant slower ctDNA half-life than those with negative MRD (103.2 minutes vs. 29.7 minutes, P = 0.001). The recurrence-free survival of patients with detectable and undetectable ctDNA concentrations at time P1 was 528 days and 543 days, respectively (P = 0.657), whereas at time P2 was 278 days and 637 days, respectively (P = 0.002). CONCLUSIONS: ctDNA decays rapidly after radical tumor resection. The ctDNA detection on the third day after R0 resection can be used as the baseline value for postoperative lung cancer surveillance.

[Analysis of characteristics of bacteria in respiratory tract infection in 2013-2016 in Heibei 3A hospital: a single-center report of 7 497 patients].

OBJECTIVE: To analyze the changes and characteristics of respiratory tract bacteria in Hebei 3A Hospital, and to provide new rationale for clinical diagnosis and treatment. METHODS: A single-center retrospective analysis was conducted. 7 497 patients with respiratory tract infection admitted to Hebei Chest Hospital from January 2013 to December 2016 were enrolled. Deep sputum was collected, and the bacterial cultures and susceptibility analysis was conducted in sputum and upper respiratory secretions were collected by fiberoptic bronchoscopy. RESULTS: A total of 7 497 patients with respiratory tract infection were enrolled in the study, and 11 909 strains of 13 kinds of dominant pathogens were isolated. The dominant pathogens for respiratory tract infection were Monilia albican (23.7%), Klebsiella pneumoniae (12.9%), Pseudomonas aeruginosa (11.6%), Escherichia coli (9.5%), Candida glabrata (9.1%), Acinetobacter baumanii (7.9%), Aspergillus (6.7%), Stenotrophomonas maltophilia (4.5%), coagulase negative Staphylococcus (3.7%) and some species of Pseudomonas (3.7%), Staphylococcus aureus (3.0%), Aerobacter cloacae (1.9%), and Candida tropicalis (1.8%). A total of 6 198 strains of 7 kinds of Gram negative (G-) bacilli infection dominant pathogens accounts for 52.0% of all infections, Klebsiella pneumonia (24.8%), Pseudomonas aeruginosa (22.3%), Escherichia coli (18.2%) and Acinetobacter baumanii (15.3%) were the main pathogens, and increased year by year. Susceptibility analysis showed that the preferred antibiotics for G- bacteria were carbapenems, followed by risperidone, sulbactam, cefepime, amikacin, and the third generation of cephalosporins. A total of 798 strains of 2 kinds of Gram positive (G+) bacilli infection dominant pathogens accounted for 6.7% of all infections, were coagulase negative Staphylococcus (54.8%) and Staphylococcus aureus (45.2%), each had changed little by year. Susceptibility analysis showed that G+ bacteria were sensitive to glycopeptides, followed by cefoxitin, cotrimoxazole, the tetracyclines, quinolones, azithromycin, erythromycin and so on. The advantages of 4 species of fungi were 4 913 strains, accounted for all of the 41.3% strains, with 57.5% of Candida albicans, and the trend was increasing year by year. Susceptibility analysis results showed that the antifungal susceptibility of dominant fungi were higher. CONCLUSIONS: G- bacilli is still the main source of infection, and showed an upward trend year by year. Fungal infection rate cannot be ignored, and we must pay attention to fungal infection incentives. We should strengthen the rational use of antibiotics.