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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.

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.

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow. Early cancer detection by cell-free DNA (cfDNA) is challenged by the low amount of tumour DNA in cfDNA, tumour heterogeneity and the small patient cohorts. Here, the authors develop a method, cfMethyl-Seq, for cost-effective methylome profiling of cfDNA and for detecting and locating cancer.

Abstract Context Crosstalk through receptor ligand interactions at the maternal-fetal interface is impacted by fetal sex. This affects placentation in the first trimester and differences in outcomes. Sexually dimorphic signaling at early stages of placentation are not defined. Objective Investigate the impact of fetal sex on maternal-fetal crosstalk. Design Receptors/ligands at the maternal-fetal surface were identified from sexually dimorphic genes between fetal sexes in the first trimester placenta and defined in each cell type using single-cell RNA-Sequencing (scRNA-Seq). Setting Academic institution. Samples Late first trimester (~10-13 weeks) placenta (fetal) and decidua (maternal) from uncomplicated ongoing pregnancies. Main outcome measures Transcriptomic profiling at tissue and single-cell level; immunohistochemistry of select proteins. Results We identified 91 sexually dimorphic receptor-ligand pairs across the maternal-fetal interface. We examined fetal sex differences in 5 major cell types (trophoblasts, stromal cells, Hofbauer cells, antigen-presenting cells, and endothelial cells). Ligands from the CC family chemokine ligand (CCL) family were most highly representative in females, with their receptors present on the maternal surface. Sexually dimorphic trophoblast transcripts, Mucin-15 (MUC15) and notum, palmitoleoyl-protein carboxylesterase (NOTUM) were also most highly expressed in syncytiotrophoblasts and extra-villous trophoblasts respectively. Gene Ontology (GO) analysis using sexually dimorphic genes in individual cell types identified cytokine mediated signaling pathways to be most representative in female trophoblasts. Upstream analysis demonstrated TGFB1 and estradiol to affect all cell types, but dihydrotestosterone, produced by the male fetus, was an upstream regulator most significant for the trophoblast population. Conclusions Maternal-fetal crosstalk exhibits sexual dimorphism during placentation early in gestation.

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.

Hepatocellular carcinoma (HCC) is a leading cause of mortality. Checkpoint inhibitors of programmed cell death protein‐1 (PD‐1) and programmed death‐ligand 1 (PD‐L1) have shown great efficacy, but lack biomarkers that predict response. Circulating tumor cells (CTCs) have promise as a liquid‐biopsy biomarker; however, data on HCC CTCs expressing PD‐L1 have not been reported. We sought to detect PD‐L1‐expressing HCC‐CTCs and investigated their role as a prognostic and predictive biomarker. Using an antibody‐based platform, CTCs were enumerated/phenotyped from a prospective cohort of 87 patients with HCC (49 early‐stage, 22 locally advanced, and 16 metastatic), 7 patients with cirrhosis, and 8 healthy controls. Immunocytochemistry identified total HCC CTCs (4′,6‐diamidino‐2‐phenylindole–positive [DAPI+]/cytokeratin‐positive [CK+]/clusters of differentiation 45–negative [CD45−]) and a subpopulation expressing PD‐L1 (DAPI+/CK+/PD‐L1+/CD45−). PD‐L1+ CTCs were identified in 4 of 49 (8.2%) early‐stage patients, but 12 of 22 (54.5%) locally advanced and 15 of 16 (93.8%) metastatic patients, accurately discriminating early from locally advanced/metastatic HCC (sensitivity = 71.1%, specificity = 91.8%, area under the receiver operating characteristic curve = 0.807; P < 0.001). Compared to patients without PD‐L1+ CTCs, patients with PD‐L1+ CTCs had significantly inferior overall survival (OS) (median OS = 14.0 months vs. not reached, hazard ratio [HR] = 4.0, P = 0.001). PD‐L1+ CTCs remained an independent predictor of OS (HR = 3.22, P = 0.010) even after controlling for Model for End‐Stage Liver Disease score (HR = 1.14, P < 0.001), alpha‐fetoprotein (HR = 1.55, P < 0.001), and overall stage/tumor burden (beyond University of California, San Francisco, HR = 7.19, P < 0.001). In the subset of 10 patients with HCC receiving PD‐1 blockade, all 5 responders demonstrated PD‐L1+ CTCs at baseline, compared with only 1 of 5 nonresponders, all of whom progressed within 4 months of starting treatment. Conclusion: We report a CTC assay for the phenotypic profiling of HCC CTCs expressing PD‐L1. PD‐L1+ CTCs are predominantly found in advanced‐stage HCC, and independently prognosticate OS after controlling for Model for End‐Stage Liver Disease, alpha‐fetoprotein, and tumor stage. In patients with HCC receiving anti‐PD‐1 therapy, there was a strong association with the presence of PD‐L1+ CTCs and favorable treatment response. Prospective validation in a larger cohort will better define the utility of PD‐L1+ CTCs as a prognostic and predictive biomarker in HCC.

Background The strong invasive and metastatic nature of non-small cell lung cancer (NSCLC) leads to poor prognosis. Collagen triple helix repeat containing 1 (CTHRC1) is involved in cell migration, motility and invasion. The object of this study is to investigate the involvement of CTHRC1 in NSCLC invasion and metastasis. Methods A proteomic analysis was performed to identify the different expression proteins between NSCLC and normal tissues. Cell lines stably express CTHRC1, MMP7, MMP9 were established. Invasion and migration were determined by scratch and transwell assays respectively. Clinical correlations of CTHRC1 in a cohort of 230 NSCLC patients were analysed. Results CTHRC1 is overexpressed in NSCLC as measured by proteomic analysis. Additionally, CTHRC1 increases tumour cell migration and invasion in vitro. Furthermore, CTHRC1 expression is significantly correlated with matrix metalloproteinase (MMP)7 and MMP9 expression in sera and tumour tissues from NSCLC. The invasion ability mediated by CTHRC1 were mainly MMP7- and MMP9-dependent. MMP7 or MMP9 depletion significantly eradicated the pro-invasive effects mediated by CTHRC1 on NSCLC cells. Clinically, patients with high CTHRC1 expression had poor survival. Conclusions CTHRC1 serves as a pro-metastatic gene that contributes to NSCLC invasion and metastasis, which are mediated by upregulated MMP7 and MMP9 expression. Targeting CTHRC1 may be beneficial for inhibiting NSCLC metastasis.

Placenta accreta spectrum (PAS) is a high-risk obstetrical condition associated with significant morbidity and mortality. Current clinical screening modalities for PAS are not always conclusive. Here, we report a nanostructure-embedded microchip that efficiently enriches both single and clustered circulating trophoblasts (cTBs) from maternal blood for detecting PAS. We discover a uniquely high prevalence of cTB-clusters in PAS and subsequently optimize the device to preserve the intactness of these clusters. Our feasibility study on the enumeration of cTBs and cTB-clusters from 168 pregnant women demonstrates excellent diagnostic performance for distinguishing PAS from non-PAS. A logistic regression model is constructed using a training cohort and then cross-validated and tested using an independent cohort. The combined cTB assay achieves an Area Under ROC Curve of 0.942 (throughout gestation) and 0.924 (early gestation) for distinguishing PAS from non-PAS. Our assay holds the potential to improve current diagnostic modalities for the early detection of PAS. Placenta accreta spectrum (PAS) is a high-risk obstetrical complication associated with significant morbidity and mortality. Here the authors discover a uniquely high prevalence of circulating trophoblasts clusters in PAS and explore their diagnostic potential to augment current diagnostic modalities for the early detection of PAS.

Well‐preserved molecular cargo in circulating extracellular vesicles (EVs) offers an ideal material for detecting oncogenic gene alterations in cancer patients, providing a noninvasive diagnostic solution for detection of disease status and monitoring treatment response. Therefore, technologies that conveniently isolate EVs with sufficient efficiency are desperately needed. Here, a lipid labeling and click chemistry‐based EV capture platform (“Click Beads”), which is ideal for EV message ribonucleic acid (mRNA) assays due to its efficient, convenient, and rapid purification of EVs, enabling downstream molecular quantification using reverse transcription digital polymerase chain reaction (RT‐dPCR) is described and demonstrated. Ewing sarcoma protein (EWS) gene rearrangements and kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutation status are detected and quantified using EVs isolated by Click Beads and matched with those identified in biopsy specimens from Ewing sarcoma or pancreatic cancer patients. Moreover, the quantification of gene alterations can be used for monitoring treatment responses and disease progression. This work demonstrates an efficient and rapid extracellular vesicle (EV) capture platform (“Click Beads”), which enables downstream quantification of gene alterations in both Ewing sarcoma and pancreatic cancer using reverse transcription digital polymerase chain reaction (RT‐dPCR). The streamlined workflow that combines Click Bead‐based EV capture and RT‐dPCR exhibits potential clinical utility in disease detection and treatment response monitoring.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional’s skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.