Explore the vast range of titles available.

We report a covalent chemistry-based hepatocellular carcinoma (HCC)-specific extracellular vesicle (EV) purification system for early detection of HCC by performing digital scoring on the purified EVs. Earlier detection of HCC creates more opportunities for curative therapeutic interventions. EVs are present in circulation at relatively early stages of disease, providing potential opportunities for HCC early detection. We develop an HCC EV purification system (i.e., EV Click Chips) by synergistically integrating covalent chemistry-mediated EV capture/release, multimarker antibody cocktails, nanostructured substrates, and microfluidic chaotic mixers. We then explore the translational potential of EV Click Chips using 158 plasma samples of HCC patients and control cohorts. The purified HCC EVs are subjected to reverse-transcription droplet digital PCR for quantification of 10 HCC-specific mRNA markers and computation of digital scoring. The HCC EV-derived molecular signatures exhibit great potential for noninvasive early detection of HCC from at-risk cirrhotic patients with an area under receiver operator characteristic curve of 0.93 (95% CI, 0.86 to 1.00; sensitivity = 94.4%, specificity = 88.5%). Extracellular vesicles (EVs) are present in circulation at relatively early stages of disease, providing potential opportunities for early cancer diagnosis. Here, the authors report a covalent chemistry-based hepatocellular carcinoma (HCC)-specific EV purification system for early detection of HCC by performing digital scoring on the purified EVs.

The current noninvasive prognostic evaluation methods for hepatocellular carcinoma (HCC), which are largely reliant on radiographic imaging features and serum biomarkers such as alpha-fetoprotein (AFP), have limited effectiveness in discriminating patient outcomes. Identification of new prognostic biomarkers is a critical unmet need to improve treatment decision-making. Epigenetic changes in cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis. Thus, we aim to evaluate the potential of cfDNA methylation as a noninvasive predictor for prognostication in patients with active, radiographically viable HCC. Using Illumina HumanMethylation450 array data of 377 HCC tumors and 50 adjacent normal tissues obtained from The Cancer Genome Atlas (TCGA), we identified 158 HCC-related DNA methylation markers associated with overall survival (OS). This signature was further validated in 29 HCC tumor tissue samples. Subsequently, we applied the signature to an independent cohort of 52 patients with plasma cfDNA samples by calculating the cfDNA methylation-based risk score (methRisk) via random survival forest models with 10-fold cross-validation for the prognostication of OS. The cfDNA-based methRisk showed strong discriminatory power when evaluated as a single predictor for OS (3-year AUC = 0.81, 95% CI: 0.68-0.94). Integrating the methRisk with existing risk indices like Barcelona clinic liver cancer (BCLC) staging significantly improved the noninvasive prognostic assessments for OS (3-year AUC = 0.91, 95% CI: 0.80-1), and methRisk remained an independent predictor of survival in the multivariate Cox model (P = 0.007). Our study serves as a pilot study demonstrating that cfDNA methylation biomarkers assessed from a peripheral blood draw can stratify HCC patients into clinically meaningful risk groups. These findings indicate that cfDNA methylation is a promising noninvasive prognostic biomarker for HCC, providing a proof-of-concept for its potential clinical utility and laying the groundwork for broader applications.

Hepatocellular carcinoma (HCC) is a major global burden, ranking as the third leading cause of cancer-related mortality. HCC due to chronic hepatitis B virus (HBV) or C virus (HCV) infection has decreased due to universal vaccination for HBV and effective antiviral therapy for both HBV and HCV, but HCC related to metabolic dysfunction-associated steatotic liver disease and alcohol-associated liver disease is increasing. Biannual liver ultrasonography and serum α-fetoprotein are the primary surveillance tools for early HCC detection among high-risk patients (e.g., cirrhosis, chronic HBV). Alternative surveillance tools such as blood-based biomarker panels and abbreviated magnetic resonance imaging (MRI) are being investigated. Multiphasic computed tomography or MRI is the standard for HCC diagnosis, but histological confirmation should be considered, especially when inconclusive findings are seen on cross-sectional imaging. Staging and treatment decisions are complex and should be made in multidisciplinary settings, incorporating multiple factors including tumor burden, degree of liver dysfunction, patient performance status, available expertise, and patient preferences. Early-stage HCC is best treated with curative options such as resection, ablation, or transplantation. For intermediate-stage disease, locoregional therapies are primarily recommended although systemic therapies may be preferred for patients with large intrahepatic tumor burden. In advanced-stage disease, immune checkpoint inhibitor-based therapy is the preferred treatment regimen. In this review article, we discuss the recent global epidemiology, risk factors, and HCC care continuum encompassing surveillance, diagnosis, staging, and treatments.

Extracellular vesicles (EVs) are emerging as promising noninvasive biomarkers, yet their clinical translation faces substantial hurdles, primarily due to the challenge of identifying assay-compatible markers. Here, in this Review, we outline sophisticated computational frameworks, particularly leveraging artificial intelligence, to bridge this gap. We detail the integration of diverse data resources, including disease-specific omics, EV, protein localization, tissue-specific, drug, model system and immune databases. This Review comprehensively describes computational selection strategies, from rule-based sequential filtering to advanced machine learning for data fusion and deep learning for multi-omics integration. Crucially, it discusses the refinement of biomarker candidates using artificial-intelligence-driven predictions of protein structure and physicochemical properties, ensuring compatibility with existing assay systems. By systematically evaluating biomarkers for predictive performance, biological plausibility and clinical utility, this framework aims to accelerate the transition of EV research from discovery to clinical application, thereby enhancing precision medicine. Bridging research and practice in extracellular vesicle biomarkers Extracellular vesicles (EVs) are nanosized particles released by cells, carrying RNA, proteins and lipids. They hold promise as noninvasive markers for diseases such as cancer and neurodegenerative disorders. However, using EVs in clinical settings is challenging. Many candidate markers identified in research do not work well with current testing methods. In 2021 alone, over 1,000 studies on EV markers were published, but only 4 were clinically validated. This Review emphasizes the need for advanced computational tools to identify clinically viable markers. The authors discuss various data resources and computational strategies, including artificial intelligence approaches that predict protein structures, interactions and assay compatibility to prioritize candidates. The study concludes that combining advanced computational approaches with EV assays can speed up the transition from research to clinical practice. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Background Hepatocellular carcinoma (HCC) is a common primary liver cancer with poor overall survival. We hypothesized that there are HCC-associated cell-types that impact patient survival. Methods We combined liver single nucleus (snRNA-seq), single cell (scRNA-seq), and bulk RNA-sequencing (RNA-seq) data to search for cell-type differences in HCC. To first identify cell-types in HCC, adjacent non-tumor tissue, and normal liver, we integrated single-cell level data from a healthy liver cohort ( n  = 9 non-HCC samples) collected in the Strasbourg University Hospital; an HCC cohort ( n  = 1 non-HCC, n  = 14 HCC-tumor, and n  = 14 adjacent non-tumor samples) collected in the Singapore General Hospital and National University; and another HCC cohort ( n  = 3 HCC-tumor and n  = 3 adjacent non-tumor samples) collected in the Dumont-UCLA Liver Cancer Center. We then leveraged these single cell level data to decompose the cell-types in liver bulk RNA-seq data from HCC patients’ tumor ( n  = 361) and adjacent non-tumor tissue ( n  = 49) from the Cancer Genome Atlas (TCGA) multi-center cohort. For replication, we decomposed 221 HCC and 209 adjacent non-tumor liver microarray samples from the Liver Cancer Institute (LCI) cohort collected by the Liver Cancer Institute and Zhongshan Hospital of Fudan University. Results We discovered a tumor-associated proliferative cell-type, Prol (80.4% tumor cells), enriched for cell cycle and mitosis genes. In the liver bulk tissue from the TCGA cohort, the proportion of the Prol cell-type is significantly increased in HCC and associates with a worse overall survival. Independently from our decomposition analysis, we reciprocally show that Prol nuclei/cells significantly over-express both tumor-elevated and survival-decreasing genes obtained from the bulk tissue. Our replication analysis in the LCI cohort confirmed that an increased estimated proportion of the Prol cell-type in HCC is a significant marker for a shorter overall survival. Finally, we show that somatic mutations in the tumor suppressor genes TP53 and RB1 are linked to an increase of the Prol cell-type in HCC. Conclusions By integrating liver single cell, single nucleus, and bulk expression data from multiple cohorts we identified a proliferating cell-type (Prol) enriched in HCC tumors, associated with a decreased overall survival, and linked to TP53 and RB1 somatic mutations.

BackgroundOccult metastatic tumors, below imaging thresholds, are a limitation of staging systems that rely on cross-sectional imaging alone and are a cause of the routine understaging of pancreatic ductal adenocarcinomas (PDACs). We investigated circulating tumor cells (CTCs) as a preoperative predictor of occult metastatic disease and as a prognostic biomarker for PDAC patients.Experimental DesignA total of 126 patients (100 with cancer, 26 with benign disease) were enrolled in our study and CTCs were identified and enumerated from 4 mL of venous blood using the microfluidic NanoVelcro assay. CTC enumeration was correlated with clinicopathologic variables and outcomes following both surgical and systemic therapies.ResultsCTCs were identified in 78% of PDAC patients and CTC counts correlated with increasing stage (ρ = 0.42, p < 0.001). Of the 53 patients taken for potentially curative surgery, 13 (24.5%) had occult metastatic disease intraoperatively. Patients with occult disease had significantly more CTCs than patients with local disease only (median 7 vs. 1 CTC, p < 0.0001). At a cut-off of three or more CTCs/4 mL, CTCs correctly identified patients with occult metastatic disease preoperatively (area under the receiver operating characteristic curve 0.82, 95% confidence interval (CI) 0.76–0.98, p < 0.0001). CTCs were a univariate predictor of recurrence-free survival following surgery [hazard ratio (HR) 2.36, 95% CI 1.17–4.78, p = 0.017], as well as an independent predictor of overall survival on multivariate analysis (HR 1.38, 95% CI 1.01–1.88, p = 0.040).ConclusionsCTCs show promise as a prognostic biomarker for PDAC patients at all stages of disease being treated both medically and surgically. Furthermore, CTCs demonstrate potential as a preoperative biomarker for identifying patients at high risk of occult metastatic disease.

Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (<5%) remains challenging for existing SNV callers. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this challenge. Furthermore, by leveraging cfDNA’s comprehensive coverage of tumor clonal landscape, cfSNV can profile mutations in subclones. In both simulated and real patient data, cfSNV outperforms existing tools in sensitivity while maintaining high precision. cfSNV enhances the clinical utilities of cfDNA by improving mutation detection performance in medium-depth sequencing data, therefore making Whole-Exome Sequencing a viable option. As an example, we demonstrate that the tumor mutation profile from cfDNA WES data can provide an effective biomarker to predict immunotherapy outcomes. It is possible to call single-nucleotide variant (SNV) in cell-free DNA (cfDNA), but the accuracy of detection is often affected by low tumour cfDNA content. Here, the authors develop a method, cfSNV, and show that it can be used even for medium-coverage whole exome sequencing of cfDNA.

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and one of the leading causes of cancer-related death worldwide. Despite the improvements in surveillance and treatment, the prognosis of HCC remains poor. Extracellular vesicles (EVs) are a heterogeneous group of phospholipid bilayer-enclosed particles circulating in the bloodstream and mediating intercellular communication. Emerging studies have shown that EVs play a crucial role in regulating the proliferation, immune escape, and metastasis of HCC. In addition, because EVs are present in the circulation at relatively early stages of disease, they are getting attention as an attractive biomarker for HCC detection. Over the past decade, dedicated efforts have been made to isolate EVs more efficiently and make them useful tools in different clinical settings. In this review article, we provide an overview of the EVs isolation methods and highlight the role of EVs as mediators in the pathogenesis and progression of HCC. Lastly, we summarize the potential applications of EVs in early-stage HCC detection.