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Background and aimsThe sensitivity of current surveillance methods for detecting early-stage hepatocellular carcinoma (HCC) is suboptimal. Extracellular vesicles (EVs) are promising circulating biomarkers for early cancer detection. In this study, we aim to develop an HCC EV-based surface protein assay for early detection of HCC.Approach and resultsTissue microarray was used to evaluate four potential HCC-associated protein markers. An HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, was developed and optimized for quantifying subpopulations of EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations ( E pCAM + CD63 + , C D147 + CD63 + , and G PC3 + CD63 + HCC EVs), was established for detecting early-stage HCC. A phase 2 biomarker study was conducted to evaluate the performance of ECG score in a training cohort ( n  = 106) and an independent validation cohort ( n  = 72).Overall, 99.7% of tissue microarray stained positive for at least one of the four HCC-associated protein markers (EpCAM, CD147, GPC3, and ASGPR1) that were subsequently validated in HCC EVs. In the training cohort, HCC EV ECG score demonstrated an area under the receiver operating curve (AUROC) of 0.95 (95% confidence interval [CI], 0.90-0.99) for distinguishing early-stage HCC from cirrhosis with a sensitivity of 91% and a specificity of 90%. The AUROCs of the HCC EV ECG score remained excellent in the validation cohort (0.93; 95% CI, 0.87-0.99) and in the subgroups by etiology (viral: 0.95; 95% CI, 0.90-1.00; nonviral: 0.94; 95% CI, 0.88-0.99).ConclusionHCC EV ECG score demonstrated great potential for detecting early-stage HCC. It could augment current surveillance methods and improve patients' outcomes.

Transcriptomic profiling of tumor tissues introduces a large database, which has led to improvements in the ability of cancer diagnosis, treatment, and prevention. However, performing tumor transcriptomic profiling in the clinical setting is very challenging since the procurement of tumor tissues is inherently limited by invasive sampling procedures. Here, we demonstrated the feasibility of purifying hepatocellular carcinoma (HCC) circulating tumor cells (CTCs) from clinical patient samples with improved molecular integrity using Click Chips in conjunction with a multimarker antibody cocktail. The purified CTCs were then subjected to mRNA profiling by NanoString nCounter platform, targeting 64 HCC-specific genes, which were generated from an integrated data analysis framework with 8 tissue-based prognostic gene signatures from 7 publicly available HCC transcriptomic studies. After bioinformatics analysis and comparison, the HCC CTC-derived gene signatures showed high concordance with HCC tissue-derived gene signatures from TCGA database, suggesting that HCC CTCs purified by Click Chips could enable the translation of HCC tissue molecular profiling into a noninvasive setting.

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%).