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

doi:10.21203/rs.3.rs-3216228/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-3216228/v1

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Purpose

The early diagnosis of leptomeningeal malignancy remains a formidable challenge in clinical practice. This study aimed to investigate the diagnostic potential of 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. However, mNGS-CNVs exhibited a higher specificity than cytology.

Conclusion

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.

Copy number variations

mNGS

cytology

leptomeningeal malignancy diagnosis

Leptomeningeal malignancy, affecting approximately 5–15% of individuals with tumors [1], primarily arises from tumors originating in various sites such as the lung, breast, and hematological malignancies like lymphoma and leukemia. The most common tumors involving leptomeninges are lung cancer, breast cancer, lymphoma and leukemia; Additionally, these malignancies can be observed in primary brain tumors such as gliomas, medulloblastomas, and ependymomas [1–3]. The presence of leptomeningeal malignancies is frequently associated with exceptional morbidity and mortality [4, 5]. However, diagnosing and effectively managing these conditions pose significant challenges.

Cerebrospinal fluid (CSF) analysis offers valuable diagnostic insights into leptomeningeal lesions. As a conventional morphological test, CSF cytology is instrumental in identifying tumor cells in individuals with leptomeningeal malignancy [6–8]. In recent years, metagenomic next-generation sequencing (mNGS) of CSF has emerged as a frontline diagnostic test for patients with meningitis with unknown etiology, enabling the identification of infectious pathogens. The majority of sequences obtained from mNGS correspond to human DNA, providing clinicians with an opportunity to utilize this wealth of sequencing information [9, 10].

Copy number variations (CNVs) refer to the deletion or amplification of DNA fragments at least 1 kb in length compared to the reference genome [11, 12]. Previous studies have highlighted the association between CNVs and tumor risk [13–15]. The diverse range of CNVs, in terms of their number and genomic distribution, reflects early genomic variations during tumorigenesis. Moreover, these variations may be linked to selection pressures on the tumor genome, representing distinct evolutionary processes and pathways within the tumor genome [16, 17].

Tumor DNA detected in CSF results from tumor necrosis and apoptosis [18, 19]. Therefore, leveraging CSF sequencing analysis allows for more robust tumor diagnosis, particularly in cases with clinical suspicion but insufficient evidence. In this study, we employed mNGS-CNVs to investigate pathogenic microorganisms and structural variants within the human genome using CSF samples. Our aim was to explore an integrated diagnostic approach for leptomeningeal malignancy and CNS inflammatory diseases based on a single CSF mNGS-CNVs procedure. We assessed the utility of mNGS-CNVs in characterizing and monitoring leptomeningeal malignancy while comparing its performance with CSF cytology. The findings of this study provide valuable diagnostic references for implementing this technique in clinical practice.

Patient enrollment

From March 2022 to February 2023, a total of 51 patients in the Department of Neurology, Peking Union Medical College Hospital, were enrolled in this study (Fig. 1). Among these patients, 34 patients were diagnosed with CNS tumors (tumor group), and 17 patients were diagnosed with CNS inflammatory diseases (non-tumor group).

Diagnosis of CNS tumors

The diagnosis of CNS tumors was primarily based on the fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS) guidelines and the Chinese expert consensus on the diagnosis of meningeal carcinomatosis. To establish the diagnosis, the following criteria were considered: (1) Identification of malignant cells through pathological specimens obtained from surgery, biopsy, or CSF cytology; (2) Presence of typical neurological symptoms or signs of the disease, accompanied by MRI enhancement scans indicating the presence of lesions or meningeal enhancement; and (3) Reasonable exclusion of other diseases.

CSF cytology

CSF samples were collected by lumbar puncture. The Sayk’s spontaneous cell sedimentation technique was employed for further analysis. Briefly, 0.5 mL of CSF was placed into the spontaneous sedimentation chamber and stored overnight at 4°C. The cells precipitated onto the slides. Then, the slides were air-dried and stained with May–Grunwald–Giemsa staining [7]. The morphological characteristics of tumor cells in CSF cytology were assessed, including cell morphology, cytoplasmic features, nucleus characteristics, presence of pigment granules or vacuoles in the cytoplasm, regularly or clustered arrangement of cells, and distinct morphological variations observed in tumor cells from different sources.

Procedure and analysis of mNGS-CNVs

DNA was extracted from the CSF using commercially available automated nucleic acid extraction kits. Following the extraction, library preparation was conducted using end-repair, ligation adaptor and other necessary procedures. Subsequently, CSF metagenomics next-generation DNA sequencing was performed on the Illumina Nextseq platform (Illumina Nextseq CN550) at the V-Medical laboratory in China. To ensure the accuracy of the sequencing data, a rigorous quality control process was implemented.

The obtained sequencing reads were aligned to the human reference genome (hg19) using BWA-MeM software (version: 0.7.17-r1188) to identify both human and nonhuman sequences present in the samples. The nonhuman sequences were subjected to analysis using MiTopia-PAI, a self-built metagenomic data analysis software. This analysis aimed to identify pathogenic microorganisms and recognize clinical pathogenic microorganisms with high confidence.

For CNV analysis, the human sequences within the samples were analyzed using CNVkit (version: 0.9.9). To establish the baseline for CNV analysis, a dataset of tumor-negative samples was used. These baseline samples consisted of CSF samples obtained from patients diagnosed with autoimmune encephalitis. The results of the CNV analysis were visualized by setting the window size and applying a filtering threshold [20]. CNV segments exceeding 10 Mb in size were considered positive results for the specimens.

Statistical analysis

SPSS 29 (Statistical Package for the Social Sciences, version 29.0, IBM, New York, USA) was applied to the statistical analysis of the data. Sensitivity and specificity, along with their corresponding 95% confidence intervals (CIs), were calculated using the chi-square test (or Fisher’s exact test when applicable).

Basic information

In total, 51 patients were enrolled in the study (Fig. 1). The baseline characteristics are shown in Table 1.

Table 1

Clinical manifestations and auxiliary examinations of patients.
Number (%)	Tumor group (n = 34)	Non-tumor group (n = 17)
Characteristic
Age (y); median (range)	47 (10–79)	57 (2–76)
Sex
Male	14 (41.18%)	9 (52.94%)
Female	20 (58.82%)	8 (47.06%)
Clinical manifestations
Headache	18 (52.94%)	9 (52.94%)
Fever	0	7 (41.18%)
Diminution of vision	3 (8.82%)	2 (11.76%)
Cognitive impairment	5 (14.71%)	1 (5.89%)
Consciousness disorder	5 (14.71%)	5 (29.41%)
Limb numbness	3 (8.82%)	2 (11.76%)
Limb weakness	5 (14.71%)	2 (11.76%)
Diplopia	4 (11.76%)	3 (17.65%)
CSF characteristics
Increased pressure	7 (20.59%)	6 (35.29%)
Increased protein	22 (64.71%)	13 (76.47%)
Radiographic features (MRI)	n = 20	n = 7
Enhanced lesions	19 (95.00%)	6 (85.71%)

CSF pressure (> 180 mmH₂O) was increased in 20.59% of the tumor group and 35.29% of the nontumor group. Increased total protein was present in 64.71% of the tumor group and 76.47% of the non-tumor group. In the tumor group, 20 patients completed enhanced MRI examinations, with 19 (95.00%) patients displaying enhanced lesions or meningeal involvement. In the non-tumor group, 7 patients completed MRI, and 6 (85.71%) patients had abnormal signals.

CSF cytology and CNVs analysis

In the tumor group, 28 (82.35%) patients had positive CSF cytology, and representative positive results are presented in Fig. 2. Among the 6 (17.65%) patients with negative cytology, one patient was diagnosed with meningeal carcinomatosis (MC), 3 patients with CNS lymphoma, and 1 patient with a primitive neuroectodermal tumor. Additionally, 24 (70.59%) patients had positive CSF CNV results, and some examples are presented in Fig. 3. The median CNV segment size was 10.5 (range: 1 ~ 49). No CNV segments larger than 10 Mb were detected in the remaining 10 (29.41%) patients with negative CNV results. Among those with negative CNV results, 3 patients were diagnosed with MC, 6 patients with CNS lymphoma, and 1 patient with glioma (Table 2).

In the non-tumor group, the CSF cytology of 16 (94.12%) patients showed inflammatory changes, and the cytology of 1 (5.89%) patient reported atypical-abnormal cells, which were considered tumor cells. All 17 patients exhibited negative CSF CNV results.

CSF cytology of tumor cells (spontaneous cell sedimentation technique, May-Gruwald-Giemsa staining ×200). (A) Patient 8: breast cancer brain metastasis. (B) Patient 23: diffuse midline glioma. (C) Patient 30: CNS B-cell lymphoma. (D) Patient 34: rhabdomyosarcoma.

CNV segments in patients with leptomeningeal malignancies. (A) Patient 8: breast cancer brain metastasis, 32 CNV segments. (B) Patient 23: diffuse midline glioma, 12 CNV segments. (C) Patient 30: CNS B-cell lymphoma, 5 CNV segments. (D) Patient 34: Rhabdomyosarcoma, 5 CNV segments.

Table 2

Comparison of CSF cytology and mNGS-CNVs.
		Cytology		CNV
		Positive (n = 28)	Negative (n = 6)	Positive (n = 24)	Negative (n = 10)
Meningeal carcinomatosis (n = 21)	Lung cancer	9	1	9	1
	Breast cancer	6	0	5	1
	Leukemia	1	0	1	0
	Digestive system malignancy	1	0	1	0
	Primary tumor unknown	3	0	2	1
Lymphoma (n = 7)	B-cell lymphoma	4	2	1	5
Lymphoma (n = 7)	T-cell lymphoma	0	1	0	1
Glioma (n = 2)	Diffuse midline glioma	1	0	1	0
Glioma (n = 2)	Pleomorphic xanthoma astrocytoma	0	1	0	1
Other tumors (n = 4)	Rhabdomyosarcoma	1	0	1	0
	Primitive neurotodermal tumor	1	1	2	0
	Spinal tumor	1	0	1	0

Sensitivity and specificity

The results demonstrated that for the diagnosis of leptomeningeal malignancy, CSF cytology exhibited a sensitivity of 82.35% (95% CI: 66.83%-92.61%) and specificity of 94.12% (95% CI: 69.24%-99.69%). Similarly, CSF mNGS-CNVs displayed a sensitivity of 70.59% (95% CI: 52.33%-84.29%) and a specificity of 100% (95% CI: 77.08%-100%) (Fig. 3). Moreover, two patients exhibited CNV segments larger than 3 Mb but smaller than 10 Mb. Adjusting the CNV length reference to 3 Mb resulted in a sensitivity of 76.47% (95% CI: 58.43%-88.62%) for CSF mNGS-CNVs. Importantly, no significant difference in diagnostic consistency was observed between CSF cytology and CNVs (p = 0.18).

In this study, we applied CSF mNGS-CNVs analysis and cytological examinations in a cohort of 51 patients diagnosed with leptomeningeal malignancy and inflammatory diseases. The main findings are summarized as follows. Firstly, we established a well-designed approach for the analysis of the CSF mNGS-CNV technique, which facilitated the earlier diagnosis of leptomeningeal malignancy. Secondly, among the 34 patients with leptomeningeal malignancy, positive results were detected in 28 cases using CSF cytology and 24 cases using CNVs analysis, demonstrating a high degree of diagnostic consistency between the two methods. Lastly, the advantage of mNGS-CNVs analysis was its ability to provide highly specific results without the need for repeated lumbar puncture.

The timely diagnosis of leptomeningeal malignancy remains challenging, often requiring prompt CSF cytology, tumor tissue pathology, gene mutation detection, and other auxiliary examinations [21–23]. While cytology is a classical method in the diagnosis of leptomeningeal malignancy, its diagnostic accuracy may vary and heavily relies on experienced cytopathologists [22–24]. Moreover, the positive rate of cytology from a single lumbar puncture has its limitations. In certain centers, standardized procedures for CSF cytology may not be uniformly implemented. Therefore, there is a need to explore more suggestive diagnostic techniques that offer greater diagnostic accuracy for leptomeningeal malignancy.

Previous studies have demonstrated that gene profiles in CSF can provide valuable insights into CNS tumors [19, 25, 26]. Recently, researchers have found that mNGS testing of human peripheral blood, bronchoalveolar lavage fluid (BALF), CSF or other body fluids can offer clues for distinguishing between [27–29], malignancies and infectious/inflammatory diseases [30–32]. This approach was mainly based on CNV analysis, which exhibits relatively high specificity. For instance, Gu et al. [10] reported that the mNGS-CNVs had a sensitivity of 75% and a specificity of 100% in detecting malignant tumors in patients with negative results in CSF cytology and flow cytometry.

In our study, we found a moderate sensitivity and high specificity of the CSF mNGS-CNVs technique when compared with cytology, providing evidence for its feasibility and potential as an adjunctive method in the differential diagnosis of CNS tumors. Additionally, CSF mNGS analysis excluded the presence of suspicious microorganisms, aiding in the exclusion of infectious encephalitis or meningitis. In addition, mNGS-CNVs analysis required a smaller CSF volume and did not rely on the integrity of cells and repeated lumber punctures.

However, there are still some problems that need to be considered and explored seriously. There is currently no consensus on the cutoff value of CSF CNVs. In our study, we used 10 Mb as the boundary line, taking into account previous studies by Gu et al. [10, 32], resulting in a sensitivity of 70.59% under this arbitrary standard. Lowering the length boundary line to 3 Mb increased the sensitivity of CNVs detection to 76.47%. However, the determination of the CNVs length thresholds based on tumor characteristics remains unclear, necessitating further data analysis to the selection of appropriate cutoff values.

Furthermore, there exists a strong correlation between tumor purity, the aneuploidy of the genome, and CNVs [33]. Different tumor types may have different CNV patterns [34], and the detection of CNVs in certain tumor types may present challenges. In our study, the accuracy of CNVs in detecting lymphoma was low, and the underlying mechanism for this observation remain uncertain. Thus, future investigations should focus on uncovering the relationship between tumor cell DNA content, the number of chromosome CNV fragments, tumor types, and CSF CNV detection.

Accurate detection of CNVs in the human genome and subsequent analysis of the biological implications of these variations hold significant importance for tumor diagnosis. Overall, our study employed a well-designed and comprehensive approach to analyze CSF samples, which can contribute to the differential diagnosis of CNS malignancies and infectious diseases. Nevertheless, further research with larger sample sizes is warranted to validate and expand upon these findings, ensuring a more robust understanding of the diagnostic capabilities of mNGS-CNVs analysis in various types of CNS tumors.

Statements & Declarations

Funding This study was supported by Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS: 2021-I2M-1-003).

Conflict of interest The authors have no relevant financial or non-financial interests to disclose.

Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Le Zhang, Kechi Fang, Haitao Ren, Siyuan Fan, Jing Wang and Hongzhi Guan. The first draft of the manuscript was written by Le Zhang and Kechi Fang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Peking Union Medical College Hospital (JS-890). Informed consent was obtained from all patients.

Consent to participate Informed consent was obtained from all individual participants included in the study.

Consent to publish The authors affirm that human research participants provided informed consent for publication of the images in Figure 2 and Figure 3.

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

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Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Materials and Methods

Patient enrollment

Diagnosis of CNS tumors

CSF cytology

Procedure and analysis of mNGS-CNVs

Statistical analysis

Results

Basic information

CSF cytology and CNVs analysis

Sensitivity and specificity

Discussion

Conclusions

Declarations

References

Additional Declarations

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