Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics.

BACKGROUND: Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection. METHODS: One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance. RESULTS: The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved. CONCLUSIONS: Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

DendroX: multi-level multi-cluster selection in dendrograms.

BACKGROUND: Cluster heatmaps are widely used in biology and other fields to uncover clustering patterns in data matrices. Most cluster heatmap packages provide utility functions to divide the dendrograms at a certain level to obtain clusters, but it is often difficult to locate the appropriate cut in the dendrogram to obtain the clusters seen in the heatmap or computed by a statistical method. Multiple cuts are required if the clusters locate at different levels in the dendrogram. RESULTS: We developed DendroX, a web app that provides interactive visualization of a dendrogram where users can divide the dendrogram at any level and in any number of clusters and pass the labels of the identified clusters for functional analysis. Helper functions are provided to extract linkage matrices from cluster heatmap objects in R or Python to serve as input to the app. A graphic user interface was also developed to help prepare input files for DendroX from data matrices stored in delimited text files. The app is scalable and has been tested on dendrograms with tens of thousands of leaf nodes. As a case study, we clustered the gene expression signatures of 297 bioactive chemical compounds in the LINCS L1000 dataset and visualized them in DendroX. Seventeen biologically meaningful clusters were identified based on the structure of the dendrogram and the expression patterns in the heatmap. We found that one of the clusters consisting of mostly naturally occurring compounds is not previously reported and has its members sharing broad anticancer, anti-inflammatory and antioxidant activities. CONCLUSIONS: DendroX solves the problem of matching visually and computationally determined clusters in a cluster heatmap and helps users navigate among different parts of a dendrogram. The identification of a cluster of naturally occurring compounds with shared bioactivities implicates a convergence of biological effects through divergent mechanisms.

Genetic Intratumor Heterogeneity Remodels the Immune Microenvironment and Induces Immune Evasion in Brain Metastasis of Lung Cancer.

INTRODUCTION: Brain metastasis, with the highest incidence in patients with lung cancer, significantly worsens prognosis and poses challenges to clinical management. To date, how brain metastasis evades immune elimination remains unknown. METHODS: Whole-exome sequencing and RNA sequencing were performed on 30 matched brain metastasis, primary lung adenocarcinoma, and normal tissues. Data from The Cancer Genome Atlas primary lung adenocarcinoma cohort, including multiplex immunofluorescence, were used to support the findings of bioinformatics analysis. RESULTS: Our study highlights the key role of intratumor heterogeneity of genomic alterations in the metastasis process, mainly caused by homologous recombination deficiency or other somatic copy number alteration-associated mutation mechanisms, leading to increased genomic instability and genomic complexity. We further proposed a selection model of brain metastatic evolution in which intratumor heterogeneity drives immune remodeling, leading to immune escape through different mechanisms under local immune pressure. CONCLUSIONS: Our findings provide novel insights into the metastatic process and immune escape mechanisms of brain metastasis and pave the way for precise immunotherapeutic strategies for patients with lung cancer with brain metastasis.

Genomic and transcriptomic characteristics of 12 novel primary cell lines derived from three patients with cholangiocarcinoma.

Cholangiocarcinoma (CCA) is an aggressive bile duct malignancy with poor prognosis. To improve our understanding of the biological characteristics of CCA and develop effective therapies, appropriate preclinical models are required. Here, we established and characterized 12 novel patient-derived primary cancer cell (PDPC) models using multi-region sampling. At the genomic level of PDPCs, we observed not only commonly mutated genes, such as TP53, JAK3, and KMT2C, consistent with the reports in CCA, but also specific mutation patterns in each cell line. In addition, specific expression patterns with distinct biological functions and pathways involved were also observed in the PDPCs at the transcriptomic level. Furthermore, the drug-sensitivity results revealed that the PDPCs exhibited different responses to the six commonly used compounds. Our findings indicate that the established PDPCs can serve as novel in vitro reliable models to provide a crucial molecular basis for improving the understanding of tumorigenesis and its treatment.

High-fat diet and obesity are associated with differential angiogenic gene expression in epithelial ovarian cancer.

OBJECTIVE: We sought to evaluate the association between diet and angiogenic biomarkers in KpB mice, and the association between these markers, body mass index (BMI), and overall survival (OS) in high-grade serous cancers (HGSC). METHODS: Tumors previously obtained from KpB mice subjected to high-fat diets (HFD, n = 10) or low-fat diets (LFD, n = 10) were evaluated for angiogenesis based on CD-31 microvessel density (MVD). Data from prior microarray analysis (Agilent 244 K arrays) conducted in 10 mice were utilized to assess associations between diet and angiogenetic biomarkers. Agilent (mouse) and Affymetrix Human Genome U133a probes were linked to 162 angiogenic-related genes. The associations between biomarkers, BMI, and OS were evaluated in an HGSC internal database (IDB) (n = 40). Genes with unadjusted p < 0.05 were evaluated for association with OS in the TCGA-OV database (n = 339). RESULTS: There was no association between CD-31 and diet in mice (p = 0.66). Sixteen angiogenic-related genes passed the p < 0.05 threshold for association with HFD vs. LFD. Transforming growth factor-alpha (TGFA) demonstrated 72% higher expression in HFD vs. LFD mice (p = 0.04). Similar to the mouse study, in our HGSC IDB, higher TGFA expression correlated with higher BMI (p = 0.01) and shorter survival (p = 0.001). In the TCGA-OV dataset, BMI data was not available and there was no association between TGFA and OS (p = 0.48). CONCLUSIONS: HFD and obesity may promote tumor progression via differential modulation of TGFA. We were unable to confirm this finding in the TCGA dataset. Further evaluation of TGFA is needed to determine if this is a target unique to obesity-driven HGSC.

Identifying Serum Small Extracellular Vesicle MicroRNA as a Noninvasive Diagnostic and Prognostic Biomarker for Ovarian Cancer.

There remains a lack of effective and noninvasive methods for the diagnosis and prognosis prediction of epithelial ovarian carcinoma (EOC). Here, we investigated the possibility of serum-derived small extracellular vesicle (sEV) microRNAs (miRNAs) as potential biomarkers for distinguishing between benign and malignant adnexal masses and predicting the prognosis of EOC patients. A serum sEV miRNA model for identifying the EOC (sEVmiR-EOC) was successfully established in the training cohort. Furthermore, the sEVmiR-EOC model was confirmed in the testing cohort and validation cohort, demonstrating robust diagnostic accuracy. The sEVmiR-EOC model showed better performance than carbohydrate antigen 125 (CA125) in discriminating patients with stage I EOC from benign patients. Using EOC samples and follow-up data, we identified miR-141-3p and miR-200c-3p as potential prognostic predictors. Finally, we confirmed the change of the sEVmiR-EOC RiskScore between the preoperative and postoperative samples and found that the sEVmiR-EOC model could predict the prognosis of EOC patients.

Comut-viz: efficiently creating and browsing comutation plots online.

BACKGROUND: Comutation plot is a widely used visualization method to deliver a global view of the mutation landscape of large-scale genomic studies. Current tools for creating comutation plot are either offline packages that require coding or online web servers with varied features. When a package is used, it often requires repetitive runs of code to adjust a single feature that might only be a few clicks in a web app. But web apps mostly have limited capacity for customization and cannot handle very large genomic files. RESULTS: To improve on existing tools, we identified features that are most frequently adjusted in creating a plot and incorporate them in Comut-viz that interactively filters and visualizes mutation data as downloadable plots. It includes colored labels for numeric metadata, a preloaded palette for changing colors and two input boxes for adjusting width and height. It accepts standard mutation annotation format (MAF) files as input and can handle large MAF files with more than 200 k rows. As a front-end only app, Comut-viz guarantees privacy of user data and no latency in the analysis. CONCLUSIONS: Comut-viz is a highly responsive and extensible web app to make comutation plots. It provides customization for frequently adjusted features and accepts large genomic files as input. It is suitable for genomic studies with more than a thousand samples.

Single-cell landscape analysis unravels molecular programming of the human B cell compartment in chronic GVHD.

Alloreactivity can drive autoimmune syndromes. After allogeneic hematopoietic stem cell transplantation (allo-HCT), chronic graft-versus-host disease (cGVHD), a B cell-associated autoimmune-like syndrome, commonly occurs. Because donor-derived B cells continually develop under selective pressure from host alloantigens, aberrant B cell receptor (BCR) activation and IgG production can emerge and contribute to cGVHD pathobiology. To better understand molecular programing of B cells in allo-HCT, we performed scRNA-Seq analysis on high numbers of purified B cells from patients. An unsupervised analysis revealed 10 clusters, distinguishable by signature genes for maturation, activation, and memory. Within the memory B cell compartment, we found striking transcriptional differences in allo-HCT patients compared with healthy or infected individuals, including potentially pathogenic atypical B cells (ABCs) that were expanded in active cGVHD. To identify intrinsic alterations in potentially pathological B cells, we interrogated all clusters for differentially expressed genes (DEGs) in active cGVHD versus patients who never had signs of immune tolerance loss (no cGVHD). Active cGVHD DEGs occurred in both naive and BCR-activated B cell clusters. Remarkably, some DEGs occurred across most clusters, suggesting common molecular programs that may promote B cell plasticity. Our study of human allo-HCT and cGVHD provides understanding of altered B cell memory during chronic alloantigen stimulation.

Correlation between immunotherapy biomarker PD-L1 expression and genetic alteration in patients with non-small cell lung cancer.

Programmed death-ligand 1 (PD-L1) has been widely used in immunotherapy evaluation of patients with non-small cell lung cancer (NSCLC). However, the effect is not particularly ideal, and the association between PD-L1 and genetic alterations requires more exploration. Here, we performed targeted next-generation sequencing and PD-L1 immunohistochemistry (IHC) testing for PD-L1 expression on both tumor cells (TCs) and tumor-infiltrating immune cells (ICs) in 1549 patients. Our studies showed that surgical method of resection was positively correlated with IC+, and a low tumor mutation burden (TMB) was negatively correlated with TC+. Furthermore, we found that EGFR was mutually exclusive with both ALK and STK11. In addition, the features between PD-L1 expression status and genomic alterations were characterized. These results suggest that clinical characteristics and molecular phenotypes are associated with PD-L1 expression signatures, which may provide novel insights for improving the efficiency of immune checkpoint inhibitors (ICIs) in immunotherapy.

Development and clinical applications of an enclosed automated targeted NGS library preparation system.

The rapid development of next-generation sequencing (NGS) technology has promoted its wide clinical application in precision medicine for oncology. However, laborious and time-consuming manual operations, highly skilled personnel requirements, and cross-contamination are major challenges for the clinical implementation of NGS technology-based tests. The Automated NGS Diagnostic Solutions (ANDiS) 500 system is a fully enclosed cassette-dependent automated NGS library preparation system. This platform could produce qualified targeted amplicon library in three steps with only 15 min of hands-on time. Rigorous cross-contamination test using simulated contaminant plasmids confirmed that the design of disposable cassette guarantees zero sample cross-contamination. The BRCA1 and BRCA2 mutation detection panel and gastrointestinal cancer-related gene analysis panel for the ANDiS 500 platform showed 100% accuracy and precision in detecting germ-line mutations and somatic mutations respectively. Furthermore, those panels showed 100% concordance with verified methods in a prospective cohort study enrolling 363 patients and a cohort of 45 pan-cancer samples. In conclusion, the ANDiS 500 automated platform could overcome major challenges for implementing NGS assays clinically and is eligible for routine clinical tests.

Molecular classification and biomarkers of clinical outcome in breast ductal carcinoma in situ: Analysis of TBCRC 038 and RAHBT cohorts.

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer.

Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.

Novel protein kinase inhibitor TT-00420 inhibits gallbladder cancer by inhibiting JNK/JUN-mediated signaling pathway.

PURPOSE: This study aimed to investigate the efficiency of our chemically synthesized TT-00420, a novel spectrum-selective multiple protein kinase inhibitor, in cultured cells and animal models of gallbladder cancer (GBC) and explore its potential mechanism. METHODS: Multiple GBC models were established to assess the anti-tumor efficiency, toxicity, and pharmacokinetics of TT-00420. Integrated transcriptomic, proteomic and phosphoproteomic analysis was conducted to identify potential downstream effectors of TT-00420. Western blotting, qRT-PCR, nuclear-cytoplasm separation, and immunofluorescence were performed to confirm the multi-omic results and explore the molecular mechanism of TT-00420. Immunohistochemistry was used to detect FGFR1 and p-FGFR1 expression levels in GBC samples. Autodock software was utilized to investigate the potential binding mode between the TT-00420 and the human FGFR1. RESULTS: We found that TT-00420 exerted potent growth inhibition of GBC cell lines and multiple xenograft models. Treatment of mice with 15 mg/kg TT-00420 via gavage displayed a half-life of 1.8 h in the blood and rapid distribution to the liver, kidneys, lungs, spleen, and tumors at 0.25 h, but no toxicity to these organs over 2 weeks. Multi-omic analysis revealed c-Jun as a potential downstream effector after TT-00420 treatment. Mechanistically, TT-00420 showed rigorous ability to block FGFR1 and its downstream JNK-JUN (S63/S73) signaling pathway, and induce c-Jun S243-dependent MEK/ERK reactivation, leading to FASLG-dependent tumor cell death. Finally, we found that FGFR1 and p-FGFR1 expression was elevated in GBC patients and these levels correlated with decreased patient survival. CONCLUSIONS: TT-00420 shows potent antitumor efficacy and may serve as a novel agent to improve GBC prognosis.

Comprehensive Evaluation and Application of a Novel Method to Isolate Cell-Free DNA Derived From Bile of Biliary Tract Cancer Patients.

Cell-free DNA (cfDNA) exists in various types of bodily fluids, including plasma, urine, bile, and others. Bile cfDNA could serve as a promising liquid biopsy for biliary tract cancer (BTC) patients, as bile directly contacts tumors in the biliary tract system. However, there is no commercial kit or widely acknowledged method for bile cfDNA extraction. In this study, we established a silica-membrane-based method, namely 3D-BCF, for bile cfDNA isolation, exhibiting effective recovery of DNA fragments in the spike-in assay. We then compared the 3D-BCF method with four other commercial kits: the BIOG cfDNA Easy Kit (BIOG), QIAamp DNA Mini Kit (Qiagen), MagMAXTM Cell-Free DNA Isolation Kit (Thermo Fisher), and NORGEN Urine Cell-Free Circulating DNA Purification Mini Kit (Norgen Biotek). The proposed 3D-BCF method exhibited the highest cfDNA isolation efficiency (p < 0.0001) from patient bile samples, and bile cfDNA of short, medium or long fragments could all be extracted effectively. To test whether the extracted bile cfDNA from patients carries tumor-related genomic information, we performed next-generation sequencing on the cfDNA and verified the gene-mutation results by polymerase chain reaction (PCR)-Sanger chromatograms and copy-number-variation (CNV) detection by fluorescence in situ hybridization (FISH) of tumor tissues. The 3D-BCF method could efficiently extract cfDNA from bile samples, providing technical support for bile cfDNA as a promising liquid biopsy for BTC patient diagnosis and prognosis.

Identification and evaluation of circulating small extracellular vesicle microRNAs as diagnostic biomarkers for patients with indeterminate pulmonary nodules.

BACKGROUND: The identification of indeterminate pulmonary nodules (IPNs) following a low-dose computed tomography (LDCT) is a major challenge for early diagnosis of lung cancer. The inadequate assessment of IPNs' malignancy risk results in a large number of unnecessary surgeries or an increased risk of cancer metastases. However, limited studies on non-invasive diagnosis of IPNs have been reported. METHODS: In this study, we identified and evaluated the diagnostic value of circulating small extracellular vesicle (sEV) microRNAs (miRNAs) in patients with IPNs that had been newly detected using LDCT scanning and were scheduled for surgery. Out of 459 recruited patients, 109 eligible patients with IPNs were enrolled in the training cohort (n = 47) and the test cohort (n = 62). An external cohort (n = 99) was used for validation. MiRNAs were extracted from plasma sEVs, and assessed using Small RNA sequencing. 490 lung adenocarcinoma samples and follow-up data were used to investigate the role of miRNAs in overall survival. RESULTS: A circulating sEV miRNA (CirsEV-miR) model was constructed from five differentially expressed miRNAs (DEMs), showing 0.920 AUC in the training cohort (n = 47), and further identified in the test cohort (n = 62) and in an external validation cohort (n = 99). Among five DEMs of the CirsEV-miR model, miR-101-3p and miR-150-5p were significantly associated with better overall survival (p = 0.0001 and p = 0.0069). The CirsEV-miR scores were calculated, which significantly correlated with IPNs diameters (p < 0.05), and were able to discriminate between benign and malignant PNs (diameter ≤ 1 cm). The expression patterns of sEV miRNAs in the benign, adenocarcinoma in situ/minimally invasive adenocarcinoma, and invasive adenocarcinoma subgroups were found to gradually change with the increase in aggressiveness for the first time. Among all DEMs of the three subgroups, five miRNAs (miR-30c-5p, miR-30e-5p, miR-500a-3p, miR-125a-5p, and miR-99a-5p) were also significantly associated with overall survival of lung adenocarcinoma patients. CONCLUSIONS: Our results indicate that the CirsEV-miR model could help distinguish between benign and malignant PNs, providing insights into the feasibility of circulating sEV miRNAs in diagnostic biomarker development. TRIAL REGISTRATION: Chinese Clinical Trials: ChiCTR1800019877. Registered 05 December 2018, https://www.chictr.org.cn/showproj.aspx?proj=31346 .

ΔNp63α promotes Bortezomib resistance via the CYGB-ROS axis in head and neck squamous cell carcinoma.

Bortezomib, a proteasome inhibitor, proved potent in the treatment of recurrent multiple myeloma or mantle cell lymphoma. However, slow progress was made when it was applied to treat solid tumors. We discovered that different head and neck squamous cell carcinoma (HNSCC) cell lines had significantly different sensitivities to bortezomib, and also demonstrated that individual relatively sensitive HNSCC cell lines had fewer ΔNp63α expressions. Based on these findings, we speculated that ΔNp63α may be a key factor in the resistance of HNSCC cells to bortezomib. ΔNp63α knockdown made HNSCC more sensitive to bortezomib, while ΔNp63α overexpression made it more resistant. RNA sequencing (RNA-seq) analysis of ΔNp63α-knockdown cells revealed clear alterations in the subset of genes that were associated with oxidative stress and antioxidant defense. The gene CYGB was downregulated significantly. CHIP-seq detection showed that CYGB was the transcriptional regulatory site of ΔNp63α. CHIP-PCR showed evidence of ΔNp63α binding. The detection of the dual-luciferase reporter gene demonstrated that ΔNp63α significantly enhanced the CYGB promoter activity. Furthermore, we confirmed that CYGB plays a role in clearing excess ROS induced by bortezomib to inhibit HNSCC apoptosis. Consequently, ΔNp63α regulated the expression of CYGB in HNSCC. CYGB was the target of transcription regulation of ΔNp63α. It reduced apoptosis by clearing excess ROS produced by bortezomib, and thus exerted drug resistance.

Characterization of a castrate-resistant prostate cancer xenograft derived from a patient of West African ancestry.

BACKGROUND: Prostate cancer is a clinically and molecularly heterogeneous disease, with highest incidence and mortality among men of African ancestry. To date, prostate cancer patient-derived xenograft (PCPDX) models to study this disease have been difficult to establish because of limited specimen availability and poor uptake rates in immunodeficient mice. Ancestrally diverse PCPDXs are even more rare, and only six PCPDXs from self-identified African American patients from one institution were recently made available. METHODS: In the present study, we established a PCPDX from prostate cancer tissue from a patient of estimated 90% West African ancestry with metastatic castration resistant disease, and characterized this model's pathology, karyotype, hotspot mutations, copy number, gene fusions, gene expression, growth rate in normal and castrated mice, therapeutic response, and experimental metastasis. RESULTS: This PCPDX has a mutation in TP53 and loss of PTEN and RB1. We have documented a 100% take rate in mice after thawing the PCPDX tumor from frozen stock. The PCPDX is castrate- and docetaxel-resistant and cisplatin-sensitive, and has gene expression patterns associated with such drug responses. After tail vein injection, the PCPDX tumor cells can colonize the lungs of mice. CONCLUSION: This PCPDX, along with others that are established and characterized, will be useful pre-clinically for studying the heterogeneity of prostate cancer biology and testing new therapeutics in models expected to be reflective of the clinical setting.

Exploration of Potential Diagnostic Value of Protein Content in Serum Small Extracellular Vesicles for Early-Stage Epithelial Ovarian Carcinoma.

Epithelial ovarian carcinoma (EOC) is one of the most common gynecologic malignancies with a high mortality rate. Serum biomarkers and imaging approaches are insufficient in identifying EOC patients at an early stage. This study is to set up a combination of proteins from serum small extracellular vesicles (sEVs) for the diagnosis of early-stage EOC and to determine its performance. A biomarker for early-stage ovarian cancer (BESOC) cohort was used as a Chinese multi-center population-based biomarker study and registered as a Chinese Clinical Trial ChiCTR2000040136. The sEV protein levels of CA125, HE4, and C5a were measured in 299 subjects. Logistic regression was exploited to calculate the odds ratio and to create the sEV protein model for the predicted probability and subsequently receiver-operating characteristic (ROC) analysis. The combined sEV marker panel of CA125, HE4, and C5a as a sEV model obtained an area under curve (AUC) of 0.912, which was greater than the serum model (0.809), by ROC analysis to identify EOC patients from the whole cohort. With the cutoff of 0.370, the sensitivity and specificity of the sEV model were 0.80 and 0.89, which were much better performance than the serum markers (sensitivity: 0.55~0.66; specificity: 0.59~0.68) and the risk of ovarian malignancy algorithm (ROMA) index approved by the U.S. Food and Drug Administration (sensitivity: 0.65; specificity: 0.61), to identify EOC patients from patients with benign ovarian diseases or other controls. The sEV levels of CA125 significantly differed among early-stage and late-stage EOC (p < 0.001). Moreover, the AUC of ROC to identify early-stage EOC patients was 0.888. Further investigation revealed that the sEV levels of these 3 proteins significantly decreased after cytoreductive surgery (CA125, p = 0.008; HE4, p = 0.025; C5a, p = 0.044). In summary, our study showed that CA125, HE4, and C5a levels in serum sEVs can identify EOC patients at the early stage, elucidating the possibility of using a sEV model for the diagnosis of early-stage EOC.