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

Rauschecker, Andreas M.; Hsu, Elaine; Zorn, Kelsey C.; Sucu, Yasemin D.; Federman, Scot; Gopez, Allan; Arevalo, Shaun; Talevich, Eric; Nguyen, Éric; Gottschall, Marc; Nourbakhsh, Bardia; Gold, Carl A.; Cree, Bruce A.C.; Douglas, Vanja C.; Richie, Megan B.; Shah, Maulik P.; Josephson, S. Andrew; Gelfand, Jeffrey M.; Miller, Steve; Wang, Linlin; Tihan, Tarık; DeRisi, Joseph L.; Chiu, Charles Y.; Wilson, Michael R.

doi:10.1001/jamaneurol.2021.3088

Cited by 23 publications

(21 citation statements)

References 29 publications

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“…On the other hand, there is no consensus on the setting of criteria for CNV detection threshold. In our study, the sensitivity of CSF CNVs was 70.59% when using 10 Mb as the threshold, which is similar to Gu et al [10] with the same threshold and Surrey et al [37] with an unde ned threshold. In previous studies,…”

Section: Discussionsupporting

confidence: 89%

Comparison of the Diagnostic Significance of Cerebrospinal fluid Metagenomic Next-generation Sequencing Copy Number Variation Analysis and Cytology in Leptomeningeal Malignancy

Zhang,

Fang,

Ren

et al. 2023

Preprint

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Background The early diagnosis of leptomeningeal malignancy remains a formidable challenge in clinical practice. This study aimed to investigate the diagnostic potential of cerebrospinal fluid (CSF) metagenomic next-generation sequencing (mNGS) and chromosome copy number variations (CNVs) analysis in the detection of leptomeningeal malignancy. The diagnostic significance of mNGS-CNVs was compared with that of cytology. Methods A total of 51 patients were enrolled. 34 patients were diagnosed with central nervous system (CNS) leptomeningeal malignancy (tumor group), and 17 patients were diagnosed with CNS inflammatory diseases (nontumor group). We explored a well-designed approach utilizing the CSF mNGS-CNVs technique for the early diagnosis of leptomeningeal malignancy. The diagnostic performance of CSF cytology and mNGS-CNVs was evaluated. Results CSF cytology displayed a sensitivity of 82.35% (95% CI: 66.83%-92.61%) and a specificity of 94.12% (95% CI: 69.24%-99.69%). In comparison, CSF mNGS-CNVs exhibited a slightly lower sensitivity of 70.59% (95% CI: 52.33%-84.29%), but an impressive specificity of 100% (95% CI: 77.08%-100%). Notably, comparative analysis revealed no significant difference in diagnostic consistency between cytology and mNGS-CNVs. Conclusions Our study highlighted the advantage of CSF mNGS-CNVs as a diagnostic tool for leptomeningeal malignancy when compared to traditional cytology. This comprehensive approach provides a promising strategy for utilizing CSF mNGS in the detection of CNS tumors.

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Section: Discussionsupporting

confidence: 89%

Comparison of the Diagnostic Significance of Cerebrospinal fluid Metagenomic Next-generation Sequencing Copy Number Variation Analysis and Cytology in Leptomeningeal Malignancy

Zhang,

Fang,

Ren

et al. 2023

Preprint

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“…There are also ongoing efforts in the field of metagenomics to leverage host data generated through mNGS for host transcriptomics and genomics. 7,12,13 The process of host transcriptomics analyses RNA transcripts to identify which host genes are differentially upregulated or downregulated in certain disease states. These endeavours aim to enhance our understanding of the host response in various disease states, leading to improved diagnostics and the development of novel therapeutics.…”

Section: Metagenomics and Clinical Diagnosticsmentioning

confidence: 99%

“…mNGS, therefore, represents a comprehensive assay to identify any potential pathogens in clinical samples. There are also ongoing efforts in the field of metagenomics to leverage host data generated through mNGS for host transcriptomics and genomics 7,12,13 . The process of host transcriptomics analyses RNA transcripts to identify which host genes are differentially upregulated or downregulated in certain disease states.…”

Section: Metagenomics and Clinical Diagnosticsmentioning

confidence: 99%

The transformative potential of metagenomics in microbiology: advancements and implications

Ramachandran,

Williamson

2023

Internal Medicine Journal

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“…Excitingly, recent studies have con rmed that analyzing transcriptomic data derived from human sequences of mNGS testing can aid in distinguishing infectious diseases such as sepsis, acute respiratory infections, tuberculous meningitis, and non-infectious diseases [13][14][15] . Developing intelligent algorithms based on chromosomal instability and tumor-related copy number variations generated by mNGS data is useful to diagnose malignant tumors [16][17][18] . These studies prompt us to further contemplate whether it is possible to utilize mNGS data from respiratory tract samples to establish an integrative genomic diagnostic method that combines microbial and host response characteristics of the patients.…”

Section: Introductionmentioning

confidence: 99%

Metagenomic Analysis of Bronchoalveolar Lavage Fluid Enables Differential Diagnosis Between Lung Cancer and Pulmonary Infections

Chen,

Han,

et al. 2024

Preprint

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Recent advances in unbiased metagenomic next-generation sequencing (mNGS) have enabled the simultaneous examination of both microbial and host genetic material in a single test. This study harnesses cost-effective bronchoalveolar lavage fluid (BALF) mNGS data from patients with lung cancer (n=123) and pulmonary infections (n=279). We developed a machine learning-based diagnostic approach to differentiate between these two conditions, which are often misdiagnosed in clinical settings. To ensure independence between model construction and validation, we divided the cohorts based on the collection dates of the samples. The training cohort (lung cancer, n=87; pulmonary infection, n=197) revealed distinct differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction determined by copy number variation (CNV). These features, blinded to the validation cohort, were integrated into a host/microbe metagenomics-driven machine learning model (Model VI). The model demonstrated an Area Under the Curve (AUC) of 0.87 (95% CI = 0.857-0.883) in the training cohort and 0.831 (95% CI = 0.819-0.843) in the validation cohort for differentiating between patients with lung cancer and pulmonary infections. Applying a composite predictive model based on a rule-in and rule-out strategy significantly increased accuracy in distinguishing lung cancer from tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These results underscore the potential of mNGS-based analysis as a valuable, cost-effective tool for the early differentiation of lung cancer from pulmonary infections, offering a comprehensive testing solution in a clinical context.

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

Cited by 23 publications

References 29 publications

Comparison of the Diagnostic Significance of Cerebrospinal fluid Metagenomic Next-generation Sequencing Copy Number Variation Analysis and Cytology in Leptomeningeal Malignancy

Comparison of the Diagnostic Significance of Cerebrospinal fluid Metagenomic Next-generation Sequencing Copy Number Variation Analysis and Cytology in Leptomeningeal Malignancy

The transformative potential of metagenomics in microbiology: advancements and implications

Metagenomic Analysis of Bronchoalveolar Lavage Fluid Enables Differential Diagnosis Between Lung Cancer and Pulmonary Infections

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