Cerebrospinal Fluid ctDNA-Based Molecular Assessment of Resection Extent and Prognosis in Glioma

Cerebrospinal Fluid ctDNA-Based Molecular Assessment of Resection Extent and Prognosis in Glioma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cerebrospinal Fluid ctDNA-Based Molecular Assessment of Resection Extent and Prognosis in Glioma Longbo Zhang, Jun Wu, Tianxiang Huang, Ying Wang, Jian Yu, Shifu Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5061430/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Abstract Gliomas, the most common brain tumors, present significant challenges in treatment, particularly glioblastoma multiforme (GBM), due to their infiltrative nature and difficulty in achieving gross total resection (GTR). Accurate assessment of surgical resection extent is critical for patient prognosis and survival. This study investigates the utility of cerebrospinal fluid (CSF) circulating tumor DNA (ctDNA) as a quantitative biomarker for evaluating glioma resection extent and patient prognosis. We employed next-generation sequencing (NGS) to profile genomic alterations in both tumor DNA and CSF ctDNA collected pre- and post-operatively. A high concordance rate (83.50%) between CSF ctDNA and tumor tissue was found, particularly for key somatic mutations such as TERT, TP53, PTEN and IDH1. Post-operative ctDNA analysis revealed a significant reduction in mean mutant allele frequency (MAF) and tumor mutational burden (TMB). Apart from non-GTR and multiple lesions, patients who exhibited a ≥ 90% reduction in mean MAF or in the MAF of driver mutations post-surgery demonstrated significantly improved overall survival (OS). These findings suggest that CSF ctDNA effectively represents the genetic profile of gliomas and serves as a sensitive measure for surgical resection efficacy and patient prognosis, highlighting its potential as a non-invasive biomarker for enhancing post-operative management in glioma patients. Health sciences/Biomarkers/Prognostic markers Health sciences/Oncology/Surgical oncology Gliomas Extent of Resection (EOR) Cerebrospinal Fluid (CSF) Circulating Tumor DNA (ctDNA) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Glioma is the most common brain tumor, leading to substantial mortality and morbidity 1 . Currently, the mainstay of treatment for glioma involves surgery, followed by a multidisciplinary approach incorporating radiotherapy, chemotherapy, and targeted therapies 2 , 3 . Surgical removal, particularly gross total resection (GTR), is well-established treatment that benefits patient survival. However, achieving GTR remains a significant challenge for neurosurgeons due to the difficulty in distinguishing tumor boundaries from normal brain parenchyma, attributed to the infiltrative nature of gliomas. Additionally, the unique anatomy of the brain and the risk of injuring eloquent structures, which can impair quality of life, often hinder the attainment of GTR. This is especially true for glioblastoma multiforme (GBM), the most common (50% of all gliomas) and lethal type of glioma. GBM is a diffusely infiltrating and widespread malignant neoplasm that typically invades multiple lobes and sometimes both hemispheres of the brain, even at the time of diagnosis, resulting in a median survival of only 20 months 4 – 7 . The surgical component of therapy can range from a minimally invasive biopsy to a craniotomy aimed at achieving maximum safe resection. The extent of resection varies significantly and is closely associated with the strategies of postoperative radiotherapy and chemotherapy and survival 8, 9 . However, currently, neurosurgeons and clinical oncologists assess the extent of surgical resection qualitatively, relying on intraoperative assessments and pre- and post-operative radiographic comparisons. Quantifying the extent of resection accurately and even digitally could potentially improve postoperative management and prognosis. This urgent need has compelled researchers to seek an accurate and sensitive evaluation system. Pioneers have attempted traditional imaging evaluations, imaging-based machine learning, and multi-omics approaches 10 – 13 . However, these efforts are indirect measurements, making it challenging to directly quantify the true extent of tumor resection. Given that genomic mutations serve as hallmarks for all tumors, we hypothesize whether we can longitudinally and quantitatively compare mutational changes to determine if tumor cells carrying these mutations are completely removed or persist as residual after surgery. Encouragingly, recent advances in cancer genomic profiling have greatly enhanced our understanding of gliomas' molecular landscape. Techniques such as next-generation sequencing (NGS) allow us to analyze tumor genomes to uncover mutations, copy number changes, and structural variations that potentially drive cancer formation, evolution, and impact the responses to clinical treatments 14–16 . These findings resulted in the 2016 integrated classification system of gliomas, which improves diagnostic accuracy, provides better prognostic information, and enables personalized treatment strategies 17 . So next, it is crucial to find a practical and effective tool that can represent the mutational burden carried by glioma cells before and after surgery. Rapid advancements in sequencing technologies have paralleled the significant development of liquid biopsy, particularly circulating tumor DNA (ctDNA). Numbers of laboratory and clinical studies comparing tumor tissue profiling with simultaneous ctDNA analysis have demonstrated that ctDNA effectively recapitulates the tumor's genomic profile 18 . As a result, ctDNA is now widely used to diagnose, characterize, and manage metastatic and recurrent cancers across various cancer types 19–22 . Surpassing traditional biopsies, ctDNA allows for minimally invasive sampling and early detection before clinical or radiographic evidence of metastasis or relapse, facilitating real-time assessment and rapid evaluation in clinical decision-making. In addition to identifying actionable tumor mutations, monitoring ctDNA levels over time can predict early responses or lack thereof to therapies 19,21,23–30 . Recently, FDA-approved clinical tests like FoundationOne®Liquid CDx and Guardant360®CDx to accurately detect genetic changes in cancers such as non-small cell lung cancer, breast cancer, colorectal cancer, ovarian cancer, and prostate cancer by analyzing plasma for ctDNA. However, pan-cancer studies that evaluated ctDNA tumor fraction from various technologies have revealed that gliomas are the most challenging malignancy for liquid-biopsy applications 31,32 . The blood-brain barrier (BBB) is a possible major anatomical barrier that may limit the performance of ctDNA detection in plasma since it likely prevents tumor ctDNA in the central nervous system (CNS) from reaching the peripheral circulation system. Given the direct contact with the brain and relative ease of access, cerebrospinal fluid (CSF) ctDNA sampling is the preferred method for evaluating ctDNA in patients with primary brain tumors 18,33–35 . In this study, we investigated whether comparing ctDNA levels before and after glioma surgeries can sensitively and quantitatively evaluate the extent of resection, and whether it can serve as an effective tool for assessing patient prognosis and survival. We initially determine the capacity of ctDNA in representing tumor genetic profiling. Our results showed that ctDNA was detected in all cases. Brain tumors and pre-operative CSF ctDNA (pre-ctDNA) consistently presented major somatic mutations in TERT, TP53 , and PTEN , with IDH1 mutations mirrored in pre-ctDNA, resulting in a high concordance of 83.50% between tumor lesions and pre-ctDNA. Although the mean mutant allele frequency (MAF) in pre-ctDNA was lower than that in brain tumors, a significant correlation was observed between MAF values of each alteration in pre-ctDNA and brain tumors. Additionally, tumor mutational burden (TMB) in lesions significantly correlated with pre-ctDNA. Next, the comparison of mutated genes in pre-ctDNA and post-ctDNA revealed notable decreases. Mean MAF per individual decreased between pre- and post-ctDNA, as well as the TMB. Analysis showed that high-MAF remaining (< 10% reduction) and a high detection rate of driver mutations were significant determinants of patient prognosis and survival. Furthermore, the quantitative assessment of ctDNA was not aligned with radiographic-determining evaluations of the extent of resection. CtDNA monitoring showed a strong association with survival and demonstrated superior performance in exactly predicting the prognosis of GBM patients, underscoring its potential for enhancing GBM patient outcomes. Results Patient characteristics We retrospectively profiled 96 samples (32 brain lesions and 64 parallelly matched pre- and post-operative CSF samples) from 32 histologically confirmed brain tumor patients. The cohort included 21 high-grade gliomas (HGGs), consisting of 19 glioblastomas (GBMs), 2 diffuse midline gliomas (DMGs), 10 low-grade gliomas (LGGs), including 8 astrocytomas, 2 oligodendrogliomas, and 1 medulloblastoma (similar to the prognosis of LGGs, we classify them as LGGs in certain subsequent analyses 36,37 ). The demographic, clinical, and pathological characteristics of these patients are presented in Fig. 1 A-B and Suppl. Table 1. Of the 32 patients, 16 had lesions across multiple lobes (50.0%), while the remaining had tumors predominantly in the frontal (7 cases, 21.9%) and temporal (4 cases,12.5%) lobes (Fig. 1 C and Suppl. Figure 1A-B). In addition, 9 out of 32 patients (28.1%) developed multiple lesions. The distribution of single- and multiple-lesion cases is shown in Suppl. Figure 1. The mean sequencing depth for the 32 brain tumor samples was 1150.06x. Target sequencing for ctDNA of pre- and post-operative CSF was successfully performed for all patients, with a mean depth of 1116.74x and 1912.23, respectively. Molecular assessment of tumor resection using pre- and post-ctDNA analysis Maximal resection of the tumor is central to achieving long-term disease control. Currently, the evaluation of surgical removal relies on the surgeons’ judgment during the surgery and MRI after the procedure. However, these assessments cannot determine whether total removal has been completed or the exact removal rate of the tumor. To precisely assess the accomplishment of surgical resection and link to the prognosis, we introduced the comparison between pre-ctDNA and post-ctDNA (Fig. 1 E). Generally, mutations identified in pre-ctDNA are undetectable or dramatically reduced in mutational frequency post-operatively. The comparison of the top 20 mutated genes in pre-ctDNA and post-ctDNA profiling highlighted significant changes (Fig. 3 A), For example, TERT mutation dropped from 68.8% in pre-ctDNA to 28.1% in post-ctDNA, TP53 from 40.6–18.8%, PTEN from 28.1–9.4%, and IDH1 from 18.8–6.2%. PIK3CA , ARID1A , NF1 , and NOTCH1 mutations were undetectable in post-ctDNA (Fig. 3 A). We then calculated the MAF of each mutation in both pre- and post-ctDNA. The data showed that the MAF in post-ctDNA were dramatically decreased compared to that in pre-ctDNA (17.03% vs.1.282%, P < 0.0001) (Fig. 3 B). The mean MAF in each individual also significantly dropped between pre- and post-ctDNA (16.44% vs.1.915%, P < 0.0001) (Fig. 3 C-D, Suppl. Figure 3A-B). Next, we compared the TMB between pre- and post-ctDNA. The results indicated that TMB was dramatically decreased in post-ctDNA (6.733 vs. 1.506, P < 0.0001) with an average decrease rate of 78.47% (median: 100%, 95% CI: 66.67%-100%) (Fig. 3 E-F, Suppl. Figure 3C-D). The detectable driver mutations (such as PTEN, ATRX, IDH1, ARID1B, PIK3CA, TP53, BRAF , and EGFR ) also greatly dropped in post-ctDNA (Suppl. Figure 4E) ( Fisher's exact test, P = 0.0211). There was no significant difference in the TMB decreasing rate between LGG and HGG patients (70.93% vs 82.42%, P = 0.4716) (Fig. 3 G). We analyzed the population that remained mutations in post-ctDNA (red dashed circle in Fig. 3 F-G). The data indicated that patients with remaining mutations in post-ctDNA predominantly suffered from multiple lesions (Fig. 4 G-H, red solid dots) ( Fisher's exact test, P = 0.0055). Consistent with these data, HGG patients with multiple lesions, who had lower mutation clearance rates, had a worse overall survival rate (median survival: 13.50m vs. 19.00m, P = 0.0473) (Suppl. Figure 3F-G). Impact of ctDNA dynamics on prognosis in HGGs Numbers of previous studies have demonstrated that GTR could significantly benefit the OS of HGG patients. We first validated this finding in our HGG cohort. The data showed that GTR of lesions based on T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) was associated with better OS (Log-rank test, T1WI: median survival: 10.00m vs. 18.50m, P = 0.0831; T2WI: median survival: 11.50m vs. 21.00m, P = 0.0516) (Fig. 4 A-B). Although statistical significance was not achieved, likely due to the low sample size in our cohort. Next, we explored whether molecularly tracing the dynamic changes of CSF ctDNA could determine the prognosis of HGG patients. Previous studies suggested that the MAF was tightly associated the prognosis and acted as an independent predictive factor 38–40 . We found that patients with 90% or greater decrease in mean MAF had significantly better OS compared to those with less reduction (19.00m vs. 10.00m, P = 0.0126) (Fig. 4 C). Furthermore, we explored the clearance rate of driver mutations, which strongly contribute to tumorigenesis. The results revealed that a high detection rate of driver mutations in post-ctDNA was linked to significantly better OS (17.50m vs. 8.00m) (Fig. 4 D). Additionally, we analyzed tumor size, Ki67 expression, pre-ctDNA MAF and TMB, and found that none of these factors had a definitive relationship with OS (Suppl. Figure 4A-D). Summarizing the various factors mentioned above (multiple lesions, non-GTR, high remaining MAF, and high burden of detectable driver mutations) (Fig. 4 E), we analyzed all HGG patients and identified two patients (14 and 29) who were positive for all risk factors and had short OS of 6 months and 8 months, respectively (Fig. 4 F-G). CSF ctDNA enhances HGG prognosis assessment To evaluate whether MAF decreasing in post-ctDNA paralleled the extent of surgical resections, we explored the GTR rate in groups with detectable and undetectable mutations in post-ctDNA. The results indicated no significant association between radiographically indicated extent of resection and mutation clearance rate (Suppl. Figure 5A-B). Patient (Pt) 27 underwent non-GTR surgery, with a residual lesion located near the frontal horn of the lateral ventricle (arrowed). However, the CSF ctDNA profile showed no detectable DNA repair gene PMS2 in post-ctDNA, whereas it had a 13.04% MAF in pre-ctDNA (Fig. 5 A). This patient achieved a long OS of 29 month, significantly higher than the average OS (9.8 months 41,42 , similar to ours) in non-GTR HGGs. Pt 28 had the highest mean MAF (37.10%) in pre-ctDNA and underwent T2WI GTR. In post-ctDNA, the MAF dropped to 0, including undetectable driver mutations PTEN and TP53 . This patient had a favorable prognosis with an OS of 33 months (Fig. 5 B). Pt 12 had the highest TMB in pre-ctDNA (22.73) and received T2WI GTR. In post-ctDNA, the MAF decreased to 0, including undetectable driver mutations ATRX, ARID1B and PIK3CA . The patient also had a favorable prognosis with an OS of 48 months and remained in good condition up to our follow-up endpoint (Fig. 5 C). However, in Pt 16, despite underwent T1WI GTR, the post-ctDNA profile, which did not parallel the surgical resection, indicated a MAF reduction of 86.83% with remaining mutations in TERT and AURKB . This patient eventually had a much shorter OS (12 months) compared to those with T1WI GTR (15.2 months 41 , ours 18.5 months) (Fig. 6D). Together, pre- and post-operative ctDNA comparison serves as a potential digital and precise metric that can independently and strongly correlate with patient prognosis and guide post-operative treatment strategies. Discussion Maximal safe resection is paramount for improving survival outcomes in glioma patients, particularly those with GBM 43 . It directly impacts the intensity of postoperative treatment 44,45 . However, due to the infiltrative growth pattern of gliomas and the delicate nature of brain tissue, achieving complete or extended resection poses significant clinical challenges. Therefore, accurately determining the extent of tumor removal is critical. Currently, resection extent is assessed by comparing pre- and post-operative neuroimaging, which provides anatomical details but does not capture cellular or molecular changes 46,47 . Developing a precise, digitalized system for assessing the extent of surgical resection is thus crucial. Such advancements could significantly enhance comprehensive glioma treatment and potentially improve survival outcomes. CtDNA comprises small DNA fragments found in body fluids, originating from apoptotic and necrotic cancer cells 48–50 . With the advancement of sequencing technology, ctDNA analysis as a liquid biopsy has become instrumental for cancer screening, molecular profiling, detection of minimal residual disease (MRD), and dynamic monitoring of genomic alterations over time. This capability enables early tumor diagnosis and real-time assessment of tumor progression and sensitivity to clinical management 19 . The sensitivity and accuracy of ctDNA analysis depend on the appropriate selection of the source biological fluid. For example, plasma samples are preferred over serum samples due to higher overall quantity of ctDNA 51,52 . Furthermore, the selection of the body fluid for ctDNA analysis is also influenced by the types and locations of different cancers. In the central nervous system, both primary and metastatic cancers, CSF is the optimum choice for ctDNA analysis compared to plasma 32,33,35 . This is likely because brain tumors are shielded by the BBB, a significant anatomical barrier that may prevent tumor-derived ctDNA from entering the peripheral circulation. Recent research also indicates that increased BBB permeability can elevate plasma ctDNA levels 53 . Given its direct contact with the brain and easier accessibility, CSF offers substantial advantages over plasma for evaluating ctDNA in patients with brain tumors 54 . Here, we aimed to develop a system for digitally quantifying the extent of brain tumor resection by comparing CSF ctDNA levels before and after surgery. We initially evaluated ctDNA's capacity to reflect the genetic profile of tumors. Our findings indicated that consistent major somatic mutations in TERT , TP53 , PTEN , and IDH1 were found in both brain tumors and pre-ctDNA, resulting in a high concordance between tumor tissue and pre-ctDNA. Additionally, the MAF and TMB in tumor lesions showed a significant correlation with those in pre-ctDNA. Previous studies have indicated that tumor progression, tumor mass, and the spread of tumors toward the ventricular system or subarachnoid space are associated with the shedding of tumor DNA into the CSF 18 . In our study, we found a positive association between the TMB of CSF ctDNA and tumor Ki67 expression. This correlation may be explained by the fact that high TMB contributes to genetic instability, which can drive tumor growth and proliferation, as evidenced by elevated Ki67 levels 33,55 . After validating the capacity of CSF ctDNA to recapitulate tumor genetic profiling, we assessed changes in CSF ctDNA levels before and after tumor resection to evaluate tumor removal. Mutations in pre-ctDNA were either undetectable or exhibited a dramatic reduction in mutational frequency post-operatively. In individuals, mean MAF decreased from 16.44–1.915% and the TMB decreased from 6.733 to 1.506, resulting in an average reduction of 78.47%. Patients with a persistent high mutational burden in post-ctDNA predominantly had multiple lesions, correlating with significantly worse overall survival rates, which aligns with the challenge of achieving complete tumor removal with multiple lesions. MAF is crucial for cancer prognosis as it reflects tumor size, genetic stability, and treatment response, which are linked to poor outcomes across different types of tumors. MAF thus guides clinicians in understanding tumor dynamics and optimizing treatment strategies for improved patient outcomes 40,56–58 . To determine whether quantifying dynamic changes in CSF ctDNA could independently predict survival in glioma patients. We applied the comparison of MAF pre- and post-tumor resection. The results revealed that patients who experienced greater reductions in MAF (90%) showed better outcomes compared to those with smaller reductions, independent of achieving GTR. We also investigated driver mutations, which directly contribute to cancer development and progression by providing selective growth advantages to cancer cells 59 . The results indicated that a higher clearance rate of driver mutations in post-CSF ctDNA predicts better future survival outcomes. Our research has demonstrated that CSF ctDNA effectively represents the mutational profile of gliomas. By comparing pre- and post-operative CSF ctDNA, we can digitally and accurately assess the extent of surgical resection. This is pivotal in guiding post-operative treatment decisions for these patients. Beyond post-surgical management, ctDNA potentially serves diverse functions in patients with CNS tumors. For instance, in gliomas, particularly GBMs prone to recurrence, longitudinal monitoring of CSF ctDNA changes can provide valuable insights into tumor recurrence and real-time sensitivity assessment to corresponding treatments 19 . Additionally, distinguishing recurrence from pseudo-progression following radiotherapy in gliomas poses significant challenges that could profoundly impact treatment strategies. CSF ctDNA dynamic tracking shows potential in effectively tackling these concerns 60 . We acknowledge several limitations in this study. Firstly, the sample size was relatively small, and larger-scale clinical trials would be appreciated. Additionally, there may be a sensitivity threshold for ctDNA's effectiveness in indicating the extent of tumor resection, guiding post-operative treatment, and predicting prognosis. This sensitivity range was not identified in our study and warrants further investigation with a larger sample size. Methods Study Design and Patients Patients newly diagnosed with brain tumors were included in this study. A total of 32 cases were analyzed, comprising 19 glioblastomas (GBMs), 2 diffuse midline gliomas (DMGs), 8 astrocytomas, 2 oligodendrogliomas, and 1 medulloblastoma. MRI scans were conducted for all patients to assess the tumors. Tumor DNA was collected from surgical resections, and tissue DNA extraction was performed from five spatially distinct regions of the tumor. CSF samples (5 ml via lumbar puncture) were collected before and after brain lesion resection for ctDNA analysis. Before CSF extraction, all patients underwent evaluations of their general health condition and the risk of brain herniation. The study protocol was approved by the ethical committee at Xiangya Hospital. Histopathology and Ki67 immunohistochemistry Histopathological diagnosis: digital images of diagnostic tumor sections from all cases were reviewed by at least two pathologists. Ki67 analysis: immunohistochemistry was performed using anti-Ki67 (Cell Signaling Technology, #9449). The percentage of Ki67 + cells were averaged across three tumor sections for each case. DNA extraction and quantification DNA of tumor FFPE and Peripheral blood lymphocytes (PBLs) were extracted using the DNeasy Tissue and Blood Kit (Qiagen). CfDNA was isolated from plasma and cerebrospinal fluid supernatant using QIAamp Circulating Nucleic Acid Kits (Qiagen). The quality control of extracted cfDNA was performed using LabChip nucleic acid analysis (total amount ≥ 1.8ng). The end repair was then carried out by adding adapters at both ends of the DNA fragment and introducing an index tag to build the ctDNA library. Somatic mutations were obtained by using PBLs as the control sample to filter out germline mutations. DNA concentration was assessed using a Qubit fluorometer (Invitrogen) and the Qubit dsDNA HS Assay Kit (high sensitivity). The size distribution of circulating tumor DNA (ctDNA) was assessed using Agilent 2100 Bioanalyzer and DNA HS kit (Agilent Technologies). Library preparation and sequencing Sequencing library construction for ctDNA was performed using the KAPA DNA Library Preparation Kit (Kapa Biosystems). Genomic DNA libraries were constructed according to the instruction manual of the Illumina TruSeq DNA Library Preparation Kit (Illumina). The libraries were hybridized to custom-designed biotinylated oligonucleotide probes (Roche NimbleGen) covering ~ 1.1 Mbp of 1021 genes as previously described 61,62 . DNA sequencing was carried out using GeneSeq2000 with 100-bp paired-end reads. Data analysis and somatic mutations calling Following sequencing data collection, clean reads were obtained by removing the terminal adaptor sequences, filtering out low-quality and short sequences. These clean reads were aligned to the reference human genome (hg19) using bwa mem. Somatic single nucleotide variants (SNVs) and small insertions and deletions (InDel) were identified using MuTect and GATK software, respectively, with strict filtering to exclude germline mutations. Candidate variants were selected following criteria: (i) Germline mutations with < 30% AFs in ctDNA, (ii) non-synonymous variants with ≥ 1 reads and VAF ≥ 1% in tissue. Non-synonymous variants with ≥ 2 reads in cerebrospinal fluid 28 , (iii) Variants < 1% samples found in single nucleotide polymorphism (SNP) databases (dbSNP, 1000G, ExAC). Clonal analysis and driver mutation identification PyClone software was employed to calculate cancer cell fraction (CCF) and cluster all non-synonymous somatic mutations into putative clonal clusters base on Bayesian clustering method. Somatic mutations located within the cluster with maximum CCF were defined as clonal and the rest were subclonal. Volumetric analyses Volumetric analyses of brain lesions, peritumoral edema and T2 abnormal signals were determined based on enhanced T1-weighted and T2-weighted MRI scans. Statistical analysis Statistical analyses and graphic visualization were performed using R (version 4.3.0.) and GraphPad Prism 10. Statistical significance was determined using Wilcoxon matched-pairs signed-rank test, Mann-Whitney U test (two-tailed), Log-rank test, Spearman test (two-tailed) and Fisher’s exact test, with P < 0.05 for significance for all comparisons. Data are presented as means ± SEM. Declarations Data availability Data that support the findings of this study are available from the corresponding authors upon reasonable request. Conflict of interests The authors declare no conflict of interests. Author contributions L.Z and J.W. conceived and designed experiments. Surgeries were performed by J.W., Z.L., T.H., Y.W., T.S. and L.H.; data acquiring and analyzing were performed by L.Z., J.Y., S.L. and C.L.; L.Z. wrote the manuscript; all authors contribute to reviewing and editing. Acknowledgments This work was supported by the National Clinical Research Fund of Geriatric Disorders, Science Fund for Distinguished Young Scholars of Hunan Province 2024JJ2091 (L.Z.). References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71 , 209-249 (2021). https://doi.org:10.3322/caac.21660 Schaff, L. R. & Mellinghoff, I. K. Glioblastoma and Other Primary Brain Malignancies in Adults: A Review. Jama-J Am Med Assoc 329 , 574-587 (2023). https://doi.org:10.1001/jama.2023.0023 Weller, M. et al. The Glioma. Nat Rev Dis Primers 10 (2024). https://doi.org:ARTN 33 10.1038/s41572-024-00516-y Wen, P. Y. & Kesari, S. Malignant gliomas in adults. 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S. Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data. Frontiers in Immunology 14 (2023). https://doi.org:ARTN 1090838 10.3389/fimmu.2023.1090838 Pairawan, S. et al. Cell-free Circulating Tumor DNA Variant Allele Frequency Associates with Survival in Metastatic Cancer. Clin Cancer Res 26 , 1924-1931 (2020). https://doi.org:10.1158/1078-0432.Ccr-19-0306 Boscolo Bielo, L. et al. Variant allele frequency: a decision-making tool in precision oncology? Trends Cancer 9 , 1058-1068 (2023). https://doi.org:10.1016/j.trecan.2023.08.011 Bagley, S. J. et al. Clinical Utility of Plasma Cell-Free DNA in Adult Patients with Newly Diagnosed Glioblastoma: A Pilot Prospective Study. Clin Cancer Res 26 , 397-407 (2020). https://doi.org:10.1158/1078-0432.Ccr-19-2533 Vogelstein, B. et al. Cancer Genome Landscapes. Science 339 , 1546-1558 (2013). https://doi.org:10.1126/science.1235122 Yekula, A. et al. Liquid Biopsy Strategies to Distinguish Progression from Pseudoprogression and Radiation Necrosis in Glioblastomas. Adv Biosyst 4 , e2000029 (2020). https://doi.org:10.1002/adbi.202000029 Chen, Y. et al. Association of Tumor Protein p53 and Ataxia-Telangiectasia Mutated Comutation With Response to Immune Checkpoint Inhibitors and Mortality in Patients With Non-Small Cell Lung Cancer. JAMA Netw Open 2 , e1911895 (2019). https://doi.org:10.1001/jamanetworkopen.2019.11895 Shi, J. et al. Genomic Landscape and Tumor Mutational Burden Determination of Circulating Tumor DNA in Over 5,000 Chinese Patients with Lung Cancer. Clin Cancer Res 27 , 6184-6196 (2021). https://doi.org:10.1158/1078-0432.CCR-21-1537 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterial.docx Suppl. Table S1: Patient Demographic and Clinical Characteristics. Suppl. Fig. S1: Number and regions of brain lesions. Suppl. Fig. S2: Comparative analysis of genetic metrics in brain tumors and pre-ctDNA and lesion characteristics. Suppl. Fig. S3: Analysis of changes in ctDNA levels before and after treatment. Suppl. Fig. S4: Correlation analysis of genetic and lesion metrics with survival. Suppl. Fig. S5: Relationship between ctDNA assessment and imaging-based extent of resection. Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5061430","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":355182901,"identity":"4bed4324-71c3-4313-9a16-924a7d8f2f7e","order_by":0,"name":"Longbo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3OMQuCQBTA8ZODJsP1iZFf4YVQQ1/Glm45wUkag6ApcD2wD1FLs3HQJM4ODrU4N0S4BCU167UF3X843sH7wSNEp/vFoHnCdPj+9ZQJpt73ZLZUJm6yqi43LFkcZ0iukSRWsmwnxvY08QZYBaLgaIhcEijTdkLBHzuAMtiBibS/lgTBbyc9YPeGMLQypA8VYgIf21eUPhKO1FAhADxyCFYjUczD4yZnJhQdxBXsYNeL0rViuT/X0XRoiQ7SRM3PkDaXdu+/MmqlNZ1Op/vbnuX9P68cyHUaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9077-3239","institution":"Department of Neurosurgery, Xiangya Hospital, Central South University","correspondingAuthor":true,"prefix":"","firstName":"Longbo","middleName":"","lastName":"Zhang","suffix":""},{"id":355182902,"identity":"7239b79b-c200-4ad0-b94a-48a9886e70ea","order_by":1,"name":"Jun Wu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wu","suffix":""},{"id":355182903,"identity":"1d01836e-6217-41a8-82d7-935e74e1d580","order_by":2,"name":"Tianxiang Huang","email":"","orcid":"","institution":"Yale School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tianxiang","middleName":"","lastName":"Huang","suffix":""},{"id":355182904,"identity":"ca134b27-1f42-4a1c-95fb-e0ae6409177a","order_by":3,"name":"Ying Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""},{"id":355182905,"identity":"f9bdd151-bb3e-4b46-bfe2-0c13410d9a9e","order_by":4,"name":"Jian Yu","email":"","orcid":"","institution":"Department of Neurosurgery, Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Yu","suffix":""},{"id":355182906,"identity":"abd3ae1b-0888-4711-8006-59e54286ac39","order_by":5,"name":"Shifu Li","email":"","orcid":"","institution":"Department of Neurosurgery, Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Shifu","middleName":"","lastName":"Li","suffix":""},{"id":355182907,"identity":"10ec27a4-bd28-4119-92b6-4fdb29ce8901","order_by":6,"name":"Chao Liu","email":"","orcid":"","institution":"Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Liu","suffix":""},{"id":355182908,"identity":"f2d26a59-7cbc-446a-8c01-dcea19616811","order_by":7,"name":"Zhiqiang Liu","email":"","orcid":"https://orcid.org/0000-0002-7579-6280","institution":"Erasmus Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-09-10 03:40:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5061430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5061430/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43856-026-01386-z","type":"published","date":"2026-02-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71800748,"identity":"1425ea1a-52c1-4fa3-9c4a-d11b5bd805da","added_by":"auto","created_at":"2024-12-18 16:49:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2021716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient characteristics. A:\u003c/strong\u003e Heat maps illustrateskey patient characteristics, including sex, age, histology, IDH mutation status, 1p/19q co-deletion, MGMT methylation, Ki-67 index, tumor size, tumor extent, cystic changes, hydrocephalus, epilepsy, severe postoperative complications, and extent of resection. \u003cstrong\u003eB:\u003c/strong\u003e Pie chart depicts the pathological distribution of tumors. \u003cstrong\u003eC:\u003c/strong\u003e Pie chart shows the distribution of tumor locations within the brain. \u003cstrong\u003eD:\u003c/strong\u003e Bar graph details the number of patients with single versus multiple lesions. \u003cstrong\u003eE:\u003c/strong\u003e Illustration of the study workflow. Abbreviations: DMG: Diffuse Midline Glioma; GBM: Glioblastoma; IDH: Isocitrate Dehydrogenase; EOR: Extent of Resection; MGMT: O(6)-Methylguanine-DNA Methyltransferase; GTR: Gross Total Resection; SPC: Severe Postoperative Complications. Data are presented as means±SEM.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5061430/v1/90aa44395c1d76a817fe5836.png"},{"id":71800753,"identity":"7232e31c-39c7-4f56-a68e-8099b94f91a9","added_by":"auto","created_at":"2024-12-18 16:49:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2493680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCSF ctDNA represents the genetic profile of brain tumors. A:\u003c/strong\u003e The mutational landscape of brain tumor lesions and pre-operative CSF ctDNA (pre-ctDNA). The top bar graphs display the tumor mutational burden (TMB). The middle-stacked bar graphs indicate the proportion of mutational types. The bottom heat maps present the mutational landscape of the top 20 alterations. The right bar graph depicts the mutational rate. \u003cstrong\u003eB:\u003c/strong\u003eBar graph shows the mutant allele frequency (MAF) in brain tumor lesions and pre-ctDNA (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003eC:\u003c/strong\u003eThe MAF in pre-ctDNA is correlated with that in tumor tissue DNA (each dot represents a shared mutation in both tissue and pre-ctDNA; Spearman \u003cem\u003er\u003c/em\u003e= 0.5635, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003eD:\u003c/strong\u003e Mean MAF in individuals in pre-ctDNA is correlated with that in tumor tissue DNA (each dot represents a mean MAF in individuals; all the patients: Spearman \u003cem\u003er\u003c/em\u003e = 0.3926, \u003cem\u003eP\u003c/em\u003e= 0.0262; HGGs (red): Spearman \u003cem\u003er\u003c/em\u003e = 0.5870, \u003cem\u003eP\u003c/em\u003e = 0.0051). \u003cstrong\u003eE:\u003c/strong\u003eScatter plot shows the TMB in tissue DNA and pre-ctDNA (Wilcoxon signed-rank test, \u003cem\u003eP\u003c/em\u003e = 0.0551). \u003cstrong\u003eF:\u003c/strong\u003e The TMB in pre-ctDNA is correlated with that in tumor tissue DNA (Spearman \u003cem\u003er\u003c/em\u003e = 0.5517, \u003cem\u003eP\u003c/em\u003e = 0.0011). \u003cstrong\u003eG:\u003c/strong\u003eThe TMB in pre-ctDNA positively associates with Ki67 expressions in tumor lesions (Spearman \u003cem\u003er\u003c/em\u003e = 0.6471, \u003cem\u003eP\u003c/em\u003e = 0.0027). \u003cstrong\u003eH:\u003c/strong\u003eQuantification shows that high Ki67 (≥20%) expression tumors have significantly higher TMB in pre-ctDNA (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). I: Stacked bar graphs depict pre-ctDNA-private, shared, and tissue-private mutations in each individual patient. \u003cstrong\u003eJ:\u003c/strong\u003eScatter plot shows a mean concordance of 83.50% between brain tumor and CSF ctDNA (each circle represents the concordance in each individual). Data are presented as means±SEM.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-5061430/v1/f0b5c1be9008a0bd548a8800.png"},{"id":71800749,"identity":"4628bed6-6097-4dda-a903-e3c20a3a6968","added_by":"auto","created_at":"2024-12-18 16:49:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1983016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular assessment of tumor resection using pre- and post-ctDNA analysis. A:\u003c/strong\u003eThe mutational landscape of pre- and post-ctDNA. The top bar graphs display the TMB. The middle-stacked bar graphs indicate the proportion of mutational types. The bottom heat maps present the mutational landscape of the top 20 alterations. The right heat maps and bar graph depict the mutational rate changes before and after surgery. \u003cstrong\u003eB:\u003c/strong\u003eComparison of the MAF in pre- and post-ctDNA (Wilcoxon signed-rank test, ****: \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001). \u003cstrong\u003eC-D:\u003c/strong\u003e The mean MAF in individuals significantly drops in post-ctDNA (C: Wilcoxon signed-rank test, ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003eE:\u003c/strong\u003eQuantification shows that the TMB in post-ctDNA is significantly decreased compared to pre-ctDNA (Wilcoxon signed-rank test, ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003eF:\u003c/strong\u003eScatter plot shows a mean reduction of 78.47% comparing post-ctDNA to pre-ctDNA (red circle indicates patients who do not reach 100% reduction, meaning detectable mutational remaining). \u003cstrong\u003eG:\u003c/strong\u003e Quantification of the TMB decrease rate in LGGs and HGGs (red circles indicate patients who do not reach 100% reduction, meaning detectable mutational remaining; red solid dots indicate patients with multiple lesions; Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, \u003cem\u003eP\u003c/em\u003e = 0.4716). \u003cstrong\u003eH:\u003c/strong\u003eBar graph shows that the population with remaining mutations detected predominantly suffered from multiple tumor lesions in HGGs (Fisher exact test, **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). Data are presented as means±SEM.\u003c/p\u003e","description":"","filename":"Figure33.png","url":"https://assets-eu.researchsquare.com/files/rs-5061430/v1/fa8fba2cad5568904f54ed2a.png"},{"id":71800752,"identity":"f5ae0a7c-98d3-40c9-8ad2-d8a666c6e56d","added_by":"auto","created_at":"2024-12-18 16:49:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1498378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of ctDNA dynamics on prognosis in HGGs. A:\u003c/strong\u003e T1WI-based gross total resection (GTR) and non-GTR, and associated survival comparison. Left: T1WI contrast-enhanced and T2WI images (arrow: tumor lesion); right: survival analysis (Log-rank test, median survival of non-GTR vs GTR: 10.00 months vs. 18.50 months, \u003cem\u003eP\u003c/em\u003e = 0.0831). \u003cstrong\u003eB:\u003c/strong\u003eT2WI-based GTR and non-GTR, and associated survival comparison. Left: T2WI images (arrow: tumor lesion); right: survival analysis (Log-rank test, median survival of non-GTR vs GTR: 11.50 months vs. 21.00 months, \u003cem\u003eP\u003c/em\u003e = 0.0516). \u003cstrong\u003eC:\u003c/strong\u003eSurvival analysis of the mean MAF with a 90% reduction in post-ctDNA compared to pre-ctDNA (Log-rank test, median survival: 10.00 months vs. 19.00 months, \u003cem\u003eP\u003c/em\u003e= 0.0126). \u003cstrong\u003eD:\u003c/strong\u003e Survival analysis of the driver mutations with a 90% reduction in post-ctDNA compared to pre-ctDNA (Log-rank test, median survival: 8.00 months vs. 17.50 months, \u003cem\u003eP\u003c/em\u003e = 0.0294). \u003cstrong\u003eE:\u003c/strong\u003eList of four risk factors impacting survival in HGGs: multiple lesions, non-GTR, high remaining MAF, and high burden of detectable driver mutations. Patients 14 and 29 exhibit all four high-risk factors. \u003cstrong\u003eF:\u003c/strong\u003e Patient (Pt) 14, with multiple tumor lesions, underwent a non-GTR tumor surgery. Left top: T1WI contrast-enhanced images before and after surgery (arrow indicates contrast-enhanced lesions). Left bottom: heat maps showing detected genomic alterations, including \u003cem\u003ePTEN, WT1,\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e, identified in pre-ctDNA, with \u003cem\u003ePTEN\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e persisting in post-ctDNA. Right: MAF in each sample (Wilcoxon signed-rank test, P = 0.2500). \u003cstrong\u003eG:\u003c/strong\u003e Pt 29, who had multiple tumor lesions, received a non-GTR tumor surgery. Left top: T1WI contrast-enhanced scans are shown before and after the surgery (arrow points to contrast-enhanced lesions). Left bottom: heat maps reveal mutations detected in pre-ctDNA, including \u003cem\u003ePTEN, SERPINB3, TERT\u003c/em\u003e and \u003cem\u003eLRP1B\u003c/em\u003e, with only LRP1B not detected in post-ctDNA. Right: MAF in each sample (Wilcoxon signed-rank test, \u003cem\u003eP\u003c/em\u003e = 0.1250). Driver mutations indicated in red.\u003c/p\u003e","description":"","filename":"Figure43.png","url":"https://assets-eu.researchsquare.com/files/rs-5061430/v1/7d60e0bc4842cf8f55934fe8.png"},{"id":71800751,"identity":"618cf96b-3cac-4555-9f70-09d5376f5f32","added_by":"auto","created_at":"2024-12-18 16:49:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1529321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCSF ctDNA enhances HGG prognosis assessment. A: \u003c/strong\u003ePt 27, with non-GTR surgery and a residual lesion near the frontal horn of the lateral ventricle (top left: MRI images; arrow indicates T2WI lesion located near the frontal horn of the lateral ventricle), has no detectable alterations in post-ctDNA (bottom left), despite \u003cem\u003ePMS2\u003c/em\u003e mutation in pre-ctDNA. \u003cstrong\u003eB: \u003c/strong\u003ePt 28, with the highest pre-ctDNA MAF (37.10%), underwent T2WI GTR (left). Post-surgery, no mutations are detectable (right, Wilcoxon signed-rank test, ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), resulting in a favorable OS of 33 months. \u003cstrong\u003eC:\u003c/strong\u003e Pt 12, with the highest TMB in pre-ctDNA (22.73) (left), had T2WI GTR (top right), resulting in no mutations detected post-operatiely (bottom right, Wilcoxon signed-rank test, ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). The patient has a favorable prognosis with an OS of 48 months and remained in good condition up to our follow-up endpoint. \u003cstrong\u003eD:\u003c/strong\u003e Pt 16, despite T1WI GTR (top), showed residual mutations in \u003cem\u003eTERT\u003c/em\u003e and \u003cem\u003eAURKB\u003c/em\u003e in post-ctDNA (bottom, the MAF in pre- vs. post-ctDNA: 10.52% vs.1.39%, Wilcoxon signed-rank test, ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), leading to a shorter OS of 12 months. Driver mutations indicated in red.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5061430/v1/2cf8d59717a14c42cc2a7a1f.png"},{"id":106583813,"identity":"1f957d45-638b-4cca-b87e-a3c0a28b4de5","added_by":"auto","created_at":"2026-04-10 07:12:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11786033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5061430/v1/8c134151-0b46-46a0-bb12-b23263be36af.pdf"},{"id":71802182,"identity":"6d87fad7-6f48-4ef1-a524-dd660def5308","added_by":"auto","created_at":"2024-12-18 16:57:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":968405,"visible":true,"origin":"","legend":"\u003cp\u003eSuppl. Table S1: Patient Demographic and Clinical Characteristics.\u003c/p\u003e\n\u003cp\u003eSuppl. Fig. S1: Number and regions of brain lesions.\u003c/p\u003e\n\u003cp\u003eSuppl. Fig. S2: Comparative analysis of genetic metrics in brain tumors and pre-ctDNA and lesion characteristics.\u003c/p\u003e\n\u003cp\u003eSuppl. Fig. S3: Analysis of changes in ctDNA levels before and after treatment.\u003c/p\u003e\n\u003cp\u003eSuppl. Fig. S4: Correlation analysis of genetic and lesion metrics with survival.\u003c/p\u003e\n\u003cp\u003eSuppl. Fig. S5: Relationship between ctDNA assessment and imaging-based extent of resection.\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5061430/v1/dcccf5450fe7318363234242.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cerebrospinal Fluid ctDNA-Based Molecular Assessment of Resection Extent and Prognosis in Glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioma is the most common brain tumor, leading to substantial mortality and morbidity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Currently, the mainstay of treatment for glioma involves surgery, followed by a multidisciplinary approach incorporating radiotherapy, chemotherapy, and targeted therapies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Surgical removal, particularly gross total resection (GTR), is well-established treatment that benefits patient survival. However, achieving GTR remains a significant challenge for neurosurgeons due to the difficulty in distinguishing tumor boundaries from normal brain parenchyma, attributed to the infiltrative nature of gliomas. Additionally, the unique anatomy of the brain and the risk of injuring eloquent structures, which can impair quality of life, often hinder the attainment of GTR. This is especially true for glioblastoma multiforme (GBM), the most common (50% of all gliomas) and lethal type of glioma. GBM is a diffusely infiltrating and widespread malignant neoplasm that typically invades multiple lobes and sometimes both hemispheres of the brain, even at the time of diagnosis, resulting in a median survival of only 20 months\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe surgical component of therapy can range from a minimally invasive biopsy to a craniotomy aimed at achieving maximum safe resection. The extent of resection varies significantly and is closely associated with the strategies of postoperative radiotherapy and chemotherapy and survival\u003csup\u003e8,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, currently, neurosurgeons and clinical oncologists assess the extent of surgical resection qualitatively, relying on intraoperative assessments and pre- and post-operative radiographic comparisons. Quantifying the extent of resection accurately and even digitally could potentially improve postoperative management and prognosis.\u003c/p\u003e \u003cp\u003eThis urgent need has compelled researchers to seek an accurate and sensitive evaluation system. Pioneers have attempted traditional imaging evaluations, imaging-based machine learning, and multi-omics approaches\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, these efforts are indirect measurements, making it challenging to directly quantify the true extent of tumor resection. Given that genomic mutations serve as hallmarks for all tumors, we hypothesize whether we can longitudinally and quantitatively compare mutational changes to determine if tumor cells carrying these mutations are completely removed or persist as residual after surgery. Encouragingly, recent advances in cancer genomic profiling have greatly enhanced our understanding of gliomas' molecular landscape. Techniques such as next-generation sequencing (NGS) allow us to analyze tumor genomes to uncover mutations, copy number changes, and structural variations that potentially drive cancer formation, evolution, and impact the responses to clinical treatments\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e. These findings resulted in the 2016 integrated classification system of gliomas, which improves diagnostic accuracy, provides better prognostic information, and enables personalized treatment strategies\u003csup\u003e17\u003c/sup\u003e. So next, it is crucial to find a practical and effective tool that can represent the mutational burden carried by glioma cells before and after surgery.\u003c/p\u003e \u003cp\u003eRapid advancements in sequencing technologies have paralleled the significant development of liquid biopsy, particularly circulating tumor DNA (ctDNA). Numbers of laboratory and clinical studies comparing tumor tissue profiling with simultaneous ctDNA analysis have demonstrated that ctDNA effectively recapitulates the tumor's genomic profile\u003csup\u003e18\u003c/sup\u003e. As a result, ctDNA is now widely used to diagnose, characterize, and manage metastatic and recurrent cancers across various cancer types\u003csup\u003e19\u0026ndash;22\u003c/sup\u003e. Surpassing traditional biopsies, ctDNA allows for minimally invasive sampling and early detection before clinical or radiographic evidence of metastasis or relapse, facilitating real-time assessment and rapid evaluation in clinical decision-making. In addition to identifying actionable tumor mutations, monitoring ctDNA levels over time can predict early responses or lack thereof to therapies\u003csup\u003e19,21,23\u0026ndash;30\u003c/sup\u003e. Recently, FDA-approved clinical tests like FoundationOne\u0026reg;Liquid CDx and Guardant360\u0026reg;CDx to accurately detect genetic changes in cancers such as non-small cell lung cancer, breast cancer, colorectal cancer, ovarian cancer, and prostate cancer by analyzing plasma for ctDNA. However, pan-cancer studies that evaluated ctDNA tumor fraction from various technologies have revealed that gliomas are the most challenging malignancy for liquid-biopsy applications\u003csup\u003e31,32\u003c/sup\u003e. The blood-brain barrier (BBB) is a possible major anatomical barrier that may limit the performance of ctDNA detection in plasma since it likely prevents tumor ctDNA in the central nervous system (CNS) from reaching the peripheral circulation system. Given the direct contact with the brain and relative ease of access, cerebrospinal fluid (CSF) ctDNA sampling is the preferred method for evaluating ctDNA in patients with primary brain tumors\u003csup\u003e18,33\u0026ndash;35\u003c/sup\u003e. In this study, we investigated whether comparing ctDNA levels before and after glioma surgeries can sensitively and quantitatively evaluate the extent of resection, and whether it can serve as an effective tool for assessing patient prognosis and survival.\u003c/p\u003e \u003cp\u003eWe initially determine the capacity of ctDNA in representing tumor genetic profiling. Our results showed that ctDNA was detected in all cases. Brain tumors and pre-operative CSF ctDNA (pre-ctDNA) consistently presented major somatic mutations in \u003cem\u003eTERT, TP53\u003c/em\u003e, and \u003cem\u003ePTEN\u003c/em\u003e, with \u003cem\u003eIDH1\u003c/em\u003e mutations mirrored in pre-ctDNA, resulting in a high concordance of 83.50% between tumor lesions and pre-ctDNA. Although the mean mutant allele frequency (MAF) in pre-ctDNA was lower than that in brain tumors, a significant correlation was observed between MAF values of each alteration in pre-ctDNA and brain tumors. Additionally, tumor mutational burden (TMB) in lesions significantly correlated with pre-ctDNA. Next, the comparison of mutated genes in pre-ctDNA and post-ctDNA revealed notable decreases. Mean MAF per individual decreased between pre- and post-ctDNA, as well as the TMB. Analysis showed that high-MAF remaining (\u0026lt;\u0026thinsp;10% reduction) and a high detection rate of driver mutations were significant determinants of patient prognosis and survival. Furthermore, the quantitative assessment of ctDNA was not aligned with radiographic-determining evaluations of the extent of resection. CtDNA monitoring showed a strong association with survival and demonstrated superior performance in exactly predicting the prognosis of GBM patients, underscoring its potential for enhancing GBM patient outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eWe retrospectively profiled 96 samples (32 brain lesions and 64 parallelly matched pre- and post-operative CSF samples) from 32 histologically confirmed brain tumor patients. The cohort included 21 high-grade gliomas (HGGs), consisting of 19 glioblastomas (GBMs), 2 diffuse midline gliomas (DMGs), 10 low-grade gliomas (LGGs), including 8 astrocytomas, 2 oligodendrogliomas, and 1 medulloblastoma (similar to the prognosis of LGGs, we classify them as LGGs in certain subsequent analyses\u003csup\u003e36,37\u003c/sup\u003e). The demographic, clinical, and pathological characteristics of these patients are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B and Suppl. Table\u0026nbsp;1. Of the 32 patients, 16 had lesions across multiple lobes (50.0%), while the remaining had tumors predominantly in the frontal (7 cases, 21.9%) and temporal (4 cases,12.5%) lobes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Suppl. Figure\u0026nbsp;1A-B). In addition, 9 out of 32 patients (28.1%) developed multiple lesions. The distribution of single- and multiple-lesion cases is shown in Suppl. Figure\u0026nbsp;1. The mean sequencing depth for the 32 brain tumor samples was 1150.06x. Target sequencing for ctDNA of pre- and post-operative CSF was successfully performed for all patients, with a mean depth of 1116.74x and 1912.23, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular assessment of tumor resection using pre- and post-ctDNA analysis\u003c/h3\u003e\n\u003cp\u003eMaximal resection of the tumor is central to achieving long-term disease control. Currently, the evaluation of surgical removal relies on the surgeons\u0026rsquo; judgment during the surgery and MRI after the procedure. However, these assessments cannot determine whether total removal has been completed or the exact removal rate of the tumor. To precisely assess the accomplishment of surgical resection and link to the prognosis, we introduced the comparison between pre-ctDNA and post-ctDNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Generally, mutations identified in pre-ctDNA are undetectable or dramatically reduced in mutational frequency post-operatively. The comparison of the top 20 mutated genes in pre-ctDNA and post-ctDNA profiling highlighted significant changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), For example, \u003cem\u003eTERT\u003c/em\u003e mutation dropped from 68.8% in pre-ctDNA to 28.1% in post-ctDNA, \u003cem\u003eTP53\u003c/em\u003e from 40.6\u0026ndash;18.8%, \u003cem\u003ePTEN\u003c/em\u003e from 28.1\u0026ndash;9.4%, and \u003cem\u003eIDH1\u003c/em\u003e from 18.8\u0026ndash;6.2%. \u003cem\u003ePIK3CA\u003c/em\u003e, \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eNF1\u003c/em\u003e, and \u003cem\u003eNOTCH1\u003c/em\u003e mutations were undetectable in post-ctDNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We then calculated the MAF of each mutation in both pre- and post-ctDNA. The data showed that the MAF in post-ctDNA were dramatically decreased compared to that in pre-ctDNA (17.03% vs.1.282%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The mean MAF in each individual also significantly dropped between pre- and post-ctDNA (16.44% vs.1.915%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, Suppl. Figure\u0026nbsp;3A-B). Next, we compared the TMB between pre- and post-ctDNA. The results indicated that TMB was dramatically decreased in post-ctDNA (6.733 vs. 1.506, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) with an average decrease rate of 78.47% (median: 100%, 95% CI: 66.67%-100%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F, Suppl. Figure\u0026nbsp;3C-D). The detectable driver mutations (such as \u003cem\u003ePTEN, ATRX, IDH1, ARID1B, PIK3CA, TP53, BRAF\u003c/em\u003e, and \u003cem\u003eEGFR\u003c/em\u003e) also greatly dropped in post-ctDNA (Suppl. Figure\u0026nbsp;4E) (\u003cem\u003eFisher's\u003c/em\u003e exact test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0211). There was no significant difference in the TMB decreasing rate between LGG and HGG patients (70.93% vs 82.42%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4716) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). We analyzed the population that remained mutations in post-ctDNA (red dashed circle in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G). The data indicated that patients with remaining mutations in post-ctDNA predominantly suffered from multiple lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-H, red solid dots) (\u003cem\u003eFisher's\u003c/em\u003e exact test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0055). Consistent with these data, HGG patients with multiple lesions, who had lower mutation clearance rates, had a worse overall survival rate (median survival: 13.50m vs. 19.00m, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0473) (Suppl. Figure\u0026nbsp;3F-G).\u003c/p\u003e\n\u003ch3\u003eImpact of ctDNA dynamics on prognosis in HGGs\u003c/h3\u003e\n\u003cp\u003eNumbers of previous studies have demonstrated that GTR could significantly benefit the OS of HGG patients. We first validated this finding in our HGG cohort. The data showed that GTR of lesions based on T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) was associated with better OS (Log-rank test, T1WI: median survival: 10.00m vs. 18.50m, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0831; T2WI: median survival: 11.50m vs. 21.00m, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0516) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). Although statistical significance was not achieved, likely due to the low sample size in our cohort. Next, we explored whether molecularly tracing the dynamic changes of CSF ctDNA could determine the prognosis of HGG patients. Previous studies suggested that the MAF was tightly associated the prognosis and acted as an independent predictive factor\u003csup\u003e38\u0026ndash;40\u003c/sup\u003e. We found that patients with 90% or greater decrease in mean MAF had significantly better OS compared to those with less reduction (19.00m vs. 10.00m, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0126) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Furthermore, we explored the clearance rate of driver mutations, which strongly contribute to tumorigenesis. The results revealed that a high detection rate of driver mutations in post-ctDNA was linked to significantly better OS (17.50m vs. 8.00m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Additionally, we analyzed tumor size, Ki67 expression, pre-ctDNA MAF and TMB, and found that none of these factors had a definitive relationship with OS (Suppl. Figure\u0026nbsp;4A-D). Summarizing the various factors mentioned above (multiple lesions, non-GTR, high remaining MAF, and high burden of detectable driver mutations) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), we analyzed all HGG patients and identified two patients (14 and 29) who were positive for all risk factors and had short OS of 6 months and 8 months, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G).\u003c/p\u003e\n\u003ch3\u003eCSF ctDNA enhances HGG prognosis assessment\u003c/h3\u003e\n\u003cp\u003eTo evaluate whether MAF decreasing in post-ctDNA paralleled the extent of surgical resections, we explored the GTR rate in groups with detectable and undetectable mutations in post-ctDNA. The results indicated no significant association between radiographically indicated extent of resection and mutation clearance rate (Suppl. Figure\u0026nbsp;5A-B). Patient (Pt) 27 underwent non-GTR surgery, with a residual lesion located near the frontal horn of the lateral ventricle (arrowed). However, the CSF ctDNA profile showed no detectable DNA repair gene \u003cem\u003ePMS2\u003c/em\u003e in post-ctDNA, whereas it had a 13.04% MAF in pre-ctDNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). This patient achieved a long OS of 29 month, significantly higher than the average OS (9.8 months\u003csup\u003e41,42\u003c/sup\u003e, similar to ours) in non-GTR HGGs. Pt 28 had the highest mean MAF (37.10%) in pre-ctDNA and underwent T2WI GTR. In post-ctDNA, the MAF dropped to 0, including undetectable driver mutations \u003cem\u003ePTEN\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e. This patient had a favorable prognosis with an OS of 33 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Pt 12 had the highest TMB in pre-ctDNA (22.73) and received T2WI GTR. In post-ctDNA, the MAF decreased to 0, including undetectable driver mutations \u003cem\u003eATRX, ARID1B\u003c/em\u003e and \u003cem\u003ePIK3CA\u003c/em\u003e. The patient also had a favorable prognosis with an OS of 48 months and remained in good condition up to our follow-up endpoint (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). However, in Pt 16, despite underwent T1WI GTR, the post-ctDNA profile, which did not parallel the surgical resection, indicated a MAF reduction of 86.83% with remaining mutations in \u003cem\u003eTERT\u003c/em\u003e and \u003cem\u003eAURKB\u003c/em\u003e. This patient eventually had a much shorter OS (12 months) compared to those with T1WI GTR (15.2 months\u003csup\u003e41\u003c/sup\u003e, ours 18.5 months) (Fig.\u0026nbsp;6D). Together, pre- and post-operative ctDNA comparison serves as a potential digital and precise metric that can independently and strongly correlate with patient prognosis and guide post-operative treatment strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMaximal safe resection is paramount for improving survival outcomes in glioma patients, particularly those with GBM\u003csup\u003e43\u003c/sup\u003e. It directly impacts the intensity of postoperative treatment\u003csup\u003e44,45\u003c/sup\u003e. However, due to the infiltrative growth pattern of gliomas and the delicate nature of brain tissue, achieving complete or extended resection poses significant clinical challenges. Therefore, accurately determining the extent of tumor removal is critical. Currently, resection extent is assessed by comparing pre- and post-operative neuroimaging, which provides anatomical details but does not capture cellular or molecular changes\u003csup\u003e46,47\u003c/sup\u003e. Developing a precise, digitalized system for assessing the extent of surgical resection is thus crucial. Such advancements could significantly enhance comprehensive glioma treatment and potentially improve survival outcomes.\u003c/p\u003e \u003cp\u003eCtDNA comprises small DNA fragments found in body fluids, originating from apoptotic and necrotic cancer cells\u003csup\u003e48\u0026ndash;50\u003c/sup\u003e. With the advancement of sequencing technology, ctDNA analysis as a liquid biopsy has become instrumental for cancer screening, molecular profiling, detection of minimal residual disease (MRD), and dynamic monitoring of genomic alterations over time. This capability enables early tumor diagnosis and real-time assessment of tumor progression and sensitivity to clinical management\u003csup\u003e19\u003c/sup\u003e. The sensitivity and accuracy of ctDNA analysis depend on the appropriate selection of the source biological fluid. For example, plasma samples are preferred over serum samples due to higher overall quantity of ctDNA\u003csup\u003e51,52\u003c/sup\u003e. Furthermore, the selection of the body fluid for ctDNA analysis is also influenced by the types and locations of different cancers. In the central nervous system, both primary and metastatic cancers, CSF is the optimum choice for ctDNA analysis compared to plasma\u003csup\u003e32,33,35\u003c/sup\u003e. This is likely because brain tumors are shielded by the BBB, a significant anatomical barrier that may prevent tumor-derived ctDNA from entering the peripheral circulation. Recent research also indicates that increased BBB permeability can elevate plasma ctDNA levels\u003csup\u003e53\u003c/sup\u003e. Given its direct contact with the brain and easier accessibility, CSF offers substantial advantages over plasma for evaluating ctDNA in patients with brain tumors\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we aimed to develop a system for digitally quantifying the extent of brain tumor resection by comparing CSF ctDNA levels before and after surgery. We initially evaluated ctDNA's capacity to reflect the genetic profile of tumors. Our findings indicated that consistent major somatic mutations in \u003cem\u003eTERT\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, and \u003cem\u003eIDH1\u003c/em\u003e were found in both brain tumors and pre-ctDNA, resulting in a high concordance between tumor tissue and pre-ctDNA. Additionally, the MAF and TMB in tumor lesions showed a significant correlation with those in pre-ctDNA. Previous studies have indicated that tumor progression, tumor mass, and the spread of tumors toward the ventricular system or subarachnoid space are associated with the shedding of tumor DNA into the CSF\u003csup\u003e18\u003c/sup\u003e. In our study, we found a positive association between the TMB of CSF ctDNA and tumor Ki67 expression. This correlation may be explained by the fact that high TMB contributes to genetic instability, which can drive tumor growth and proliferation, as evidenced by elevated Ki67 levels\u003csup\u003e33,55\u003c/sup\u003e. After validating the capacity of CSF ctDNA to recapitulate tumor genetic profiling, we assessed changes in CSF ctDNA levels before and after tumor resection to evaluate tumor removal. Mutations in pre-ctDNA were either undetectable or exhibited a dramatic reduction in mutational frequency post-operatively. In individuals, mean MAF decreased from 16.44\u0026ndash;1.915% and the TMB decreased from 6.733 to 1.506, resulting in an average reduction of 78.47%. Patients with a persistent high mutational burden in post-ctDNA predominantly had multiple lesions, correlating with significantly worse overall survival rates, which aligns with the challenge of achieving complete tumor removal with multiple lesions.\u003c/p\u003e \u003cp\u003eMAF is crucial for cancer prognosis as it reflects tumor size, genetic stability, and treatment response, which are linked to poor outcomes across different types of tumors. MAF thus guides clinicians in understanding tumor dynamics and optimizing treatment strategies for improved patient outcomes\u003csup\u003e40,56\u0026ndash;58\u003c/sup\u003e. To determine whether quantifying dynamic changes in CSF ctDNA could independently predict survival in glioma patients. We applied the comparison of MAF pre- and post-tumor resection. The results revealed that patients who experienced greater reductions in MAF (90%) showed better outcomes compared to those with smaller reductions, independent of achieving GTR. We also investigated driver mutations, which directly contribute to cancer development and progression by providing selective growth advantages to cancer cells\u003csup\u003e59\u003c/sup\u003e. The results indicated that a higher clearance rate of driver mutations in post-CSF ctDNA predicts better future survival outcomes.\u003c/p\u003e \u003cp\u003eOur research has demonstrated that CSF ctDNA effectively represents the mutational profile of gliomas. By comparing pre- and post-operative CSF ctDNA, we can digitally and accurately assess the extent of surgical resection. This is pivotal in guiding post-operative treatment decisions for these patients. Beyond post-surgical management, ctDNA potentially serves diverse functions in patients with CNS tumors. For instance, in gliomas, particularly GBMs prone to recurrence, longitudinal monitoring of CSF ctDNA changes can provide valuable insights into tumor recurrence and real-time sensitivity assessment to corresponding treatments\u003csup\u003e19\u003c/sup\u003e. Additionally, distinguishing recurrence from pseudo-progression following radiotherapy in gliomas poses significant challenges that could profoundly impact treatment strategies. CSF ctDNA dynamic tracking shows potential in effectively tackling these concerns\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations in this study. Firstly, the sample size was relatively small, and larger-scale clinical trials would be appreciated. Additionally, there may be a sensitivity threshold for ctDNA's effectiveness in indicating the extent of tumor resection, guiding post-operative treatment, and predicting prognosis. This sensitivity range was not identified in our study and warrants further investigation with a larger sample size.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patients\u003c/h2\u003e \u003cp\u003ePatients newly diagnosed with brain tumors were included in this study. A total of 32 cases were analyzed, comprising 19 glioblastomas (GBMs), 2 diffuse midline gliomas (DMGs), 8 astrocytomas, 2 oligodendrogliomas, and 1 medulloblastoma. MRI scans were conducted for all patients to assess the tumors. Tumor DNA was collected from surgical resections, and tissue DNA extraction was performed from five spatially distinct regions of the tumor. CSF samples (5 ml via lumbar puncture) were collected before and after brain lesion resection for ctDNA analysis. Before CSF extraction, all patients underwent evaluations of their general health condition and the risk of brain herniation. The study protocol was approved by the ethical committee at Xiangya Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHistopathology and Ki67 immunohistochemistry\u003c/h2\u003e \u003cp\u003eHistopathological diagnosis: digital images of diagnostic tumor sections from all cases were reviewed by at least two pathologists. Ki67 analysis: immunohistochemistry was performed using anti-Ki67 (Cell Signaling Technology, #9449). The percentage of Ki67\u003csup\u003e+\u003c/sup\u003e cells were averaged across three tumor sections for each case.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and quantification\u003c/h2\u003e \u003cp\u003eDNA of tumor FFPE and Peripheral blood lymphocytes (PBLs) were extracted using the DNeasy Tissue and Blood Kit (Qiagen). CfDNA was isolated from plasma and cerebrospinal fluid supernatant using QIAamp Circulating Nucleic Acid Kits (Qiagen). The quality control of extracted cfDNA was performed using LabChip nucleic acid analysis (total amount\u0026thinsp;\u0026ge;\u0026thinsp;1.8ng). The end repair was then carried out by adding adapters at both ends of the DNA fragment and introducing an index tag to build the ctDNA library. Somatic mutations were obtained by using PBLs as the control sample to filter out germline mutations. DNA concentration was assessed using a Qubit fluorometer (Invitrogen) and the Qubit dsDNA HS Assay Kit (high sensitivity). The size distribution of circulating tumor DNA (ctDNA) was assessed using Agilent 2100 Bioanalyzer and DNA HS kit (Agilent Technologies).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLibrary preparation and sequencing\u003c/h2\u003e \u003cp\u003eSequencing library construction for ctDNA was performed using the KAPA DNA Library Preparation Kit (Kapa Biosystems). Genomic DNA libraries were constructed according to the instruction manual of the Illumina TruSeq DNA Library Preparation Kit (Illumina). The libraries were hybridized to custom-designed biotinylated oligonucleotide probes (Roche NimbleGen) covering\u0026thinsp;~\u0026thinsp;1.1 Mbp of 1021 genes as previously described\u003csup\u003e61,62\u003c/sup\u003e. DNA sequencing was carried out using GeneSeq2000 with 100-bp paired-end reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData analysis and somatic mutations calling\u003c/h2\u003e \u003cp\u003eFollowing sequencing data collection, clean reads were obtained by removing the terminal adaptor sequences, filtering out low-quality and short sequences. These clean reads were aligned to the reference human genome (hg19) using bwa mem. Somatic single nucleotide variants (SNVs) and small insertions and deletions (InDel) were identified using MuTect and GATK software, respectively, with strict filtering to exclude germline mutations. Candidate variants were selected following criteria: (i) Germline mutations with \u0026lt;\u0026thinsp;30% AFs in ctDNA, (ii) non-synonymous variants with \u0026ge;\u0026thinsp;1 reads and VAF\u0026thinsp;\u0026ge;\u0026thinsp;1% in tissue. Non-synonymous variants with \u0026ge;\u0026thinsp;2 reads in cerebrospinal fluid\u003csup\u003e28\u003c/sup\u003e, (iii) Variants\u0026thinsp;\u0026lt;\u0026thinsp;1% samples found in single nucleotide polymorphism (SNP) databases (dbSNP, 1000G, ExAC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClonal analysis and driver mutation identification\u003c/h2\u003e \u003cp\u003ePyClone software was employed to calculate cancer cell fraction (CCF) and cluster all non-synonymous somatic mutations into putative clonal clusters base on Bayesian clustering method. Somatic mutations located within the cluster with maximum CCF were defined as clonal and the rest were subclonal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eVolumetric analyses\u003c/h2\u003e \u003cp\u003eVolumetric analyses of brain lesions, peritumoral edema and T2 abnormal signals were determined based on enhanced T1-weighted and T2-weighted MRI scans.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses and graphic visualization were performed using R (version 4.3.0.) and GraphPad Prism 10. Statistical significance was determined using Wilcoxon matched-pairs signed-rank test, Mann-Whitney \u003cem\u003eU\u003c/em\u003e test (two-tailed), Log-rank test, Spearman test (two-tailed) and Fisher\u0026rsquo;s exact test, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for significance for all comparisons. Data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e \u003c/div\u003e \u003ch2\u003eConflict of interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e \u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eL.Z and J.W. conceived and designed experiments. Surgeries were performed by J.W., Z.L., T.H., Y.W., T.S. and L.H.; data acquiring and analyzing were performed by L.Z., J.Y., S.L. and C.L.; L.Z. wrote the manuscript; all authors contribute to reviewing and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Clinical Research Fund of Geriatric Disorders, Science Fund for Distinguished Young Scholars of Hunan Province 2024JJ2091 (L.Z.).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung, H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 209-249 (2021). https://doi.org:10.3322/caac.21660\u003c/li\u003e\n \u003cli\u003eSchaff, L. R. \u0026amp; Mellinghoff, I. K. 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Accurate assessment of surgical resection extent is critical for patient prognosis and survival. This study investigates the utility of cerebrospinal fluid (CSF) circulating tumor DNA (ctDNA) as a quantitative biomarker for evaluating glioma resection extent and patient prognosis. We employed next-generation sequencing (NGS) to profile genomic alterations in both tumor DNA and CSF ctDNA collected pre- and post-operatively. A high concordance rate (83.50%) between CSF ctDNA and tumor tissue was found, particularly for key somatic mutations such as \u003cem\u003eTERT, TP53, PTEN\u003c/em\u003e and \u003cem\u003eIDH1.\u003c/em\u003e Post-operative ctDNA analysis revealed a significant reduction in mean mutant allele frequency (MAF) and tumor mutational burden (TMB). Apart from non-GTR and multiple lesions, patients who exhibited a\u0026thinsp;\u0026ge;\u0026thinsp;90% reduction in mean MAF or in the MAF of driver mutations post-surgery demonstrated significantly improved overall survival (OS). These findings suggest that CSF ctDNA effectively represents the genetic profile of gliomas and serves as a sensitive measure for surgical resection efficacy and patient prognosis, highlighting its potential as a non-invasive biomarker for enhancing post-operative management in glioma patients.\u003c/p\u003e","manuscriptTitle":"Cerebrospinal Fluid ctDNA-Based Molecular Assessment of Resection Extent and Prognosis in Glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 16:49:51","doi":"10.21203/rs.3.rs-5061430/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abc1a4b4-fc28-459f-8beb-139f63d85c96","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37754641,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":37754642,"name":"Health sciences/Oncology/Surgical oncology"}],"tags":[],"updatedAt":"2026-04-10T07:11:16+00:00","versionOfRecord":{"articleIdentity":"rs-5061430","link":"https://doi.org/10.1038/s43856-026-01386-z","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2026-02-02 05:00:00","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2024-12-18 16:49:51","video":"","vorDoi":"10.1038/s43856-026-01386-z","vorDoiUrl":"https://doi.org/10.1038/s43856-026-01386-z","workflowStages":[]},"version":"v1","identity":"rs-5061430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5061430","identity":"rs-5061430","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}