Detection of Neoplasms by Metagenomic Next-Generation Sequencing of Cerebrospinal Fluid

Etiological stratification and prognostic assessment of haemophagocytic lymphohistiocytosis by machine learning on onco-mNGS data and clinical data

Introduction

Hemophagocytic lymphohistiocytosis (HLH) is a rare, complicated and life threatening hyperinflammatory syndrome that maybe triggered by various infectious agents, malignancies and rheumatologic disorders. Early diagnosis and identification of the cause is essential to initiate appropriate treatment and improve the quality of life and survival of patients. The recently developed Onco-mNGS technology can be successfully used for simultaneous detection of infections and tumors.

Methods

In the present study, 92 patients with clinically confirmed HLH were etiologically subtyped for infection, tumor and autoimmunity based on CNV and microbial data generated by Onco-mNGS technology, and a predictive model was developed and validated for the differential diagnosis of the underlying disease leading to secondary HLH. Furthermore, the treatment outcomes of patients with HLH triggered by EBV infection and non-EBV infection were evaluated, respectively.

Results

The current study demonstrated that the novel Onco-mNGS can identify the infection and malignancy- related triggers among patients with secondary HLH. A random forest classification model based on CNV profile, infectious pathogen spectrum and blood microbial community was developed to better identify the different HLH subtypes and determine the underlying triggers. The prognosis for treatment of HLH patients is not only associated with CNV, but also with the presence of pathogens and non- pathogens in peripheral blood. Higher CNV burden along with frequent deletions on chromosome 19, higher pathogen burden and lower non-pathogenic microbes were prognosis factors that significantly related with unfavorable treatment outcomes.

Discussion

Our study provided comprehensive knowledge in the triggers and prognostic predictors of patients with secondary HLH, which may help early diagnosis and appropriate targeted therapy, thus improving the survival and prognosis of the patients.

Liquid Biopsy Based on Cell-Free DNA and RNA

This review delves into the rapidly evolving landscape of liquid biopsy technologies based on cell-free DNA (cfDNA) and cell-free RNA (cfRNA) and their increasingly prominent role in precision medicine. With the advent of high-throughput DNA sequencing, the use of cfDNA and cfRNA has revolutionized noninvasive clinical testing. Here, we explore the physical characteristics of cfDNA and cfRNA, present an overview of the essential engineering tools used by the field, and highlight clinical applications, including noninvasive prenatal testing, cancer testing, organ transplantation surveillance, and infectious disease testing. Finally, we discuss emerging technologies and the broadening scope of liquid biopsies to new areas of diagnostic medicine.

Comparison of the diagnostic significance of cerebrospinal fluid metagenomic next-generation sequencing copy number variation analysis and cytology in leptomeningeal malignancy

BACKGROUND

Diagnosis and monitoring of leptomeningeal malignancy remain challenging, and are usually based on neurological, radiological, cerebrospinal fluid (CSF) and pathological findings. This study aimed to investigate the diagnostic performance of CSF metagenomic next-generation sequencing (mNGS) and chromosome copy number variations (CNVs) analysis in the detection of leptomeningeal malignancy.

METHODS

Of the 51 patients included in the study, 34 patients were diagnosed with leptomeningeal malignancies, and 17 patients were diagnosed with central nervous system (CNS) inflammatory diseases. The Sayk's spontaneous cell sedimentation technique was employed for CSF cytology. And a well-designed approach utilizing the CSF mNGS-CNVs technique was explored for early diagnosis of leptomeningeal malignancy.

RESULTS

In the tumor group, 28 patients were positive for CSF cytology, and 24 patients were positive for CSF mNGS-CNVs. Sensitivity and specificity of CSF cytology were 82.35% (95% CI: 66.83-92.61%) and 94.12% (95% CI: 69.24-99.69%). In comparison, sensitivity and specificity of CSF mNGS-CNV were 70.59% (95% CI: 52.33-84.29%) and 100% (95% CI: 77.08-100%). There was no significant difference in diagnostic consistency between CSF cytology and mNGS-CNVs (p = 0.18, kappa = 0.650).

CONCLUSIONS

CSF mNGS-CNVs tend to have higher specificity compared with traditional cytology and can be used as a complementary diagnostic method for patients with leptomeningeal malignancies.

Epstein-Barr virus-positive iris diffuse large B-cell lymphoma detected by metagenomic next-generation sequencing

PURPOSE

Epstein-Barr virus (EBV)-positive diffuse large B-cell lymphoma (DLBCL) is a relatively rare subtype of DLBCL. Herein, we report a case of a patient with EBV-positive iris DLBCL after undergoing penetrating keratoplasty and discuss its possible pathogenesis.

METHODS

A 72-year-old male patient presented to our hospital with progressive blurring of vision in the left eye for the past 4 months. Small white nodular lesions were observed on the iris and retinal surface of the left eye, with a white cloud-like opacity in the vitreous cavity.

RESULTS

The patient was eventually diagnosed with EBV-positive iris DLBCL after undergoing pathological and metagenomic tests. After injecting methotrexate in the left vitreous cavity and administering systemic and local antiviral treatments, the ocular lesions disappeared.

CONCLUSION

EBV infection, drug immunosuppression, and aging-related immune deterioration may play significant roles in the pathogenesis of EBV-positive iris DLBCL.

SYNOPSIS

Epstein-Barr virus (EBV)-positive diffuse large B-cell lymphoma (DLBCL) is a new subtype of DLBCL, which rarely occurs. Herein, we report a case of a patient with EBV-positive iris DLBCL after undergoing penetrating keratoplasty and discuss its possible pathogenesis.

Utility of metagenomic Next-Generation Sequencing for simultaneously detecting pathogens and neoplasms

Objectives

Clinicians often face the challenge of differentially diagnosing febrile patients who are suspected of infectious diseases, since the clinical manifestations of infection and cancer may overlap. A single test that can detect both pathogens and tumor could provide timely and accurate diagnostic clues to aid the treatment and management of these patients.

Methods

We enrolled eight patients to evaluate the utility of metagenomic Next-Generation Sequencing for simultaneously detecting pathogens and neoplasms using body fluids and tissue samples. Patients were selected by the following criteria: 1) Tumor was not considered upon hospitalization, but mNGS testing indicated neoplasm; 2) Tumor was not excluded, but microbial infection was primarily suspected according to initial clinical assessment.

Results

We detected potential pathogens in five patients, three of whom had progressed into critical infections. Moreover, abnormal chromosomal copy numbers were identified in all patients that indicated presence of neoplasms, which were pathologically confirmed.

Conclusions

Although copy number variations do not render a definitive cancer diagnosis, it can prompt clinicians to conduct more focused diagnostic testing for cancer, potentially saving time and cost. As a result, integrating copy number analysis with pathogen detection in mNGS may help establish rapid and accurate diagnosis for febrile patients.

Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning

BACKGROUND

Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level.

METHODS

The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier.

RESULTS

We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model's performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model.

CONCLUSIONS

We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.

Evaluation of the diagnostic utility of metagenomic next-generation sequencing testing for pathogen identification in infected hosts: a retrospective cohort study

Background

Metagenomic next-generation sequencing (mNGS) testing identifies thousands of potential pathogens in a single blood test, though data on its real-world diagnostic utility are lacking.

Objectives

Determine the diagnostic utility of mNGS testing in practice and factors associated with high clinical utility.

Design

Retrospective cohort study of mNGS tests ordered from June 2018 through May 2020 at a community teaching hospital.

Methods

Tests were included if ordered for diagnostic purposes in patients with probable or high clinical suspicion of infection. Exclusions included patient expiration, hospice care, or transfer outside of the institution. Utility criteria were established a priori by the research team. Two investigators independently reviewed each test and categorized it to either high or low diagnostic utility. Reviewer discordance was referred to a third investigator. The stepwise multiple regression method was used to identify clinical factors associated with high diagnostic utility.

Results

Among 96 individual tests from 82 unique patients, 80 tests met the inclusion criteria for analysis. At least one potential pathogen was identified in 58% of tests. Among 112 pathogens identified, there were 74 bacteria, 25 viruses, 12 fungi, and 1 protozoon. In all, 46 tests (57.5%) were determined to be of high diagnostic utility. Positive mNGS tests were identified in 36 (78.3%) and 11 (32.4%) of high and low diagnostic utility tests, respectively (p < 0.001). Antimicrobials were changed after receiving test results in 31 (67.4%) of high utility tests and 4 (11.8%) of low utility tests (p < 0.0001). In the multiple regression model, a positive test [odds ratio (OR) = 10.9; 95% confidence interval (CI), 3.2-44.4] and consultation with the company medical director (OR = 3.6; 95% CI, 1.1-13.7) remained significantly associated with high diagnostic utility.

Conclusion

mNGS testing resulted in high clinical utility in most cases. Positive mNGS tests were associated with high diagnostic utility. Consultation with the Karius® medical director is recommended to maximize utility.

Simultaneous diagnosis of tuberculous pleurisy and malignant pleural effusion using metagenomic next-generation sequencing (mNGS)

BACKGROUND

Metagenomic next-generation sequencing (mNGS) has become a powerful tool for pathogen detection, but the value of human sequencing reads generated from it is underestimated.

METHODS

A total of 138 patients with pleural effusion (PE) were diagnosed with tuberculous pleurisy (TBP, N = 82), malignant pleural effusion (MPE, N = 35), or non-TB infection (N = 21), whose PE samples all underwent mNGS analysis. Clinical TB tests including culture, Acid-Fast Bacillus (AFB) test, Xpert, and T-SPOT, were performed. To utilize mNGS for MPE identification, 25 non-MPE samples (20 TBP and 5 non-TB infection) were randomly selected to set human chromosome copy number baseline and generalized linear modeling was performed using copy number variant (CNV) features of the rest 113 samples (35 MPE and 78 non-MPE).

RESULTS

The performance of TB detection was compared among five methods. T-SPOT demonstrated the highest sensitivity (61% vs. culture 32%, AFB 12%, Xpert 35%, and mNGS 49%) but with the highest false-positive rate (10%) as well. In contrast, mNGS was able to detect TB-genome in nearly half (40/82) of the PE samples from TBP subgroup, with 100% specificity. To evaluate the performance of using CNV features of the human genome for MPE prediction, we performed the leave-one-out cross-validation (LOOCV) in the subcohort excluding the 25 non-MPE samples for setting copy number standards, which demonstrated 54.1% sensitivity, 80.8% specificity, 71.7% accuracy, and an AUC of 0.851.

CONCLUSION

In summary, we exploited the value of human and non-human sequencing reads generated from mNGS, which showed promising ability in simultaneously detecting TBP and MPE.

Utility of clinical metagenomics in diagnosing malignancies in a cohort of patients with Epstein-Barr virus positivity

Backgrounds

Differentiation between benign and malignant diseases in EBV-positive patients poses a significant challenge due to the lack of efficient diagnostic tools. Metagenomic Next-Generation Sequencing (mNGS) is commonly used to identify pathogens of patients with fevers of unknown-origin (FUO). Recent studies have extended the application of Next-Generation Sequencing (NGS) in identifying tumors in body fluids and cerebrospinal fluids. In light of these, we conducted this study to develop and apply metagenomic methods to validate their role in identifying EBV-associated malignant disease.

Methods

We enrolled 29 patients with positive EBV results in the cohort of FUO in the Department of Infectious Diseases of Huashan Hospital affiliated with Fudan University from 2018 to 2019. Upon enrollment, these patients were grouped for benign diseases, CAEBV, and malignant diseases according to their final diagnosis, and CNV analysis was retrospectively performed in 2022 using samples from 2018 to 2019.

Results

Among the 29 patients. 16 of them were diagnosed with benign diseases, 3 patients were diagnosed with CAEBV and 10 patients were with malignant diseases. 29 blood samples from 29 patients were tested for mNGS. Among all 10 patients with malignant diagnosis, CNV analysis suggested neoplasms in 9 patients. Of all 19 patients with benign or CAEBV diagnosis, 2 patients showed abnormal CNV results. The sensitivity and specificity of CNV analysis for the identification for tumors were 90% and 89.5%, separately.

Conclusions

The application of mNGS could assist in the identification of microbial infection and malignancies in EBV-related diseases. Our results demonstrate that CNV detection through mNGS is faster compared to conventional oncology tests. Moreover, the convenient collection of peripheral blood samples adds to the advantages of this approach.

Seq-ing the SINEs of central nervous system tumors in cerebrospinal fluid

It is often challenging to distinguish cancerous from non-cancerous lesions in the brain using conventional diagnostic approaches. We introduce an analytic technique called Real-CSF (repetitive element aneuploidy sequencing in CSF) to detect cancers of the central nervous system from evaluation of DNA in the cerebrospinal fluid (CSF). Short interspersed nuclear elements (SINEs) are PCR amplified with a single primer pair, and the PCR products are evaluated by next-generation sequencing. Real-CSF assesses genome-wide copy-number alterations as well as focal amplifications of selected oncogenes. Real-CSF was applied to 280 CSF samples and correctly identified 67% of 184 cancerous and 96% of 96 non-cancerous brain lesions. CSF analysis was considerably more sensitive than standard-of-care cytology and plasma cell-free DNA analysis in the same patients. Real-CSF therefore has the capacity to be used in combination with other clinical, radiologic, and laboratory-based data to inform the diagnosis and management of patients with suspected cancers of the brain.