ABSTRACT
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.
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Keywords: Liver cancer; Biomarker; Hepatocellular carcinoma
INTRODUCTION
Hepatocellular carcinoma (HCC) is the most common histological type of primary liver cancer with poor long-term prognosis [
1]. Moreover, its prognosis did not improve significantly despite the development of diagnostic and therapeutic tools compared to other common malignancies such as breast, prostate, and colon cancer. This poor outcome is attributable to absence of symptoms in the early stage, the unsatisfied sensitivity of the current surveillance method, limited therapeutic strategies, and a high recurrence rate after curative treatment [
2]. Accurate and convenient cancer surveillance is the first step toward improving the overall prognosis of HCC.
Currently, several biomarkers are available for early detection of HCC. Alpha-fetoprotein (AFP) is the most widely used biomarker in combination with ultrasound for HCC surveillance among high-risk patients. It can also be used for the prognostication and prediction of treatment response. Despite its wide use in clinical practice, AFP alone is not endorsed by current practice guidelines for detecting HCC due to its limited sensitivity [
3]. Given these unmet needs, developing highly accurate and non-invasive tools for early HCC detection has been the main interest of healthcare professionals and researchers for the past decades [
4]. Alternative protein-based serum tumor markers such as AFP lectin fraction (AFP-L3), des-y-carboxyprothrombin (DCP), and the derived algorithm GALAD score (Gender, Age, AFP-L3, AFP, DCP) are promising biomarkers that can potentially improve HCC detection rate [
3,
4]. Recently, liquid biopsy technology has also emerged as a solution to address this growing need.
Liquid biopsy refers to the detection of byproducts that are shed from or produced during the proliferation of cancer cells [
5]. Common targets of liquid biopsy include circulating tumor cells (CTCs), cell-free DNA (cfDNA), and extracellular vesicles (EVs). As a non-invasive procedure, liquid biopsy allows obtaining molecular information from cancer cells, making it an attractive option as a novel biomarker for early detection, prognostication, and disease monitoring in HCC.
This review article aims to provide an overview of CTCs, cfDNA, and EVs, discuss the technical challenges in applying these liquid biopsies, and address recent advancements in technology (
Fig. 1), current clinical applications, and future directions in this field (
Fig. 2).
LIQUID BIOPSY – CTC, cfDNA, AND EV IN HCC
The term “liquid biopsy” refers to a test that detects circulating cancer cells or DNA, RNA, and other molecules released by tumor cells through all types of body fluids [
5]. Through this process, genetic, epigenetic, transcriptomic, and proteomic information representing cancer could be collected in a non-invasive manner. In this section, we focus on three common types of liquid biopsy in HCC: CTCs, cfDNA, and EVs. We briefly introduce their biology and characteristics, outline the technical challenges of applying these liquid biopsies, provide updates on recent technological advancements, and discuss their advantages and disadvantages (
Table 1).
CIRCULATING TUMOR CELLS (CTCs)
Biology and characteristics
CTCs refer to cells that have detached from the tumor mass and circulated in the bloodstream for distant metastases. As malignant tumors grow, matrix metalloproteinases are secreted, breaking down the basement membrane and allowing the tumor cells to enter blood vessels or lymphatic vessels [
6]. Once entering the blood vessels, tumor cells become CTCs, which undergo epithelial-to-mesenchymal transition (EMT) process. This process equips CTCs with mesenchymal properties to survive immune attacks, shear stress, and anoikis - a form of programmed cell death that occurs in anchorage-dependent cells when they detach from the surrounding extracellular matrix [
7]. Only a few CTCs escape immune system surveillance and extravasate to metastasize to other organs. Given the nature of CTCs, their presence is regarded as a biomarker for both cancer detection and prognostication.
CTCs are extremely rare in circulation, with a prevalence of one CTC per 10
6–10
7 white blood cells (WBCs) [
8,
9]. As such, efficient enrichment of CTCs from blood is crucial for downstream analysis. There are two main categories of technologies to enrich CTCs: immunoaffinity-based and physical property-based CTC enrichment techniques. The immunoaffinity-based techniques use antibodies to target antigens on the cell membrane, which can be employed for either positive selection to detect CTCs or negative selection to filter out WBCs [
10]. For positive selection of CTCs, the most used marker is epithelial cell adhesion molecule (EpCAM), as many cancers originate from epithelium. The CellSearch
® System is the first and only EpCAM-based method approved by Food and Drug Administration (FDA) for enrichment and enumeration of CTCs in metastatic breast, colorectal, and prostate cancers [
11-
13]. This system uses ferromagnetic beads coated with anti-EpCAM antibodies to capture CTCs, followed by magnetic-activated cell sorting for isolation. However, cancer cells often undergo EMT, and HCC CTCs are derived from hepatocytes rather than epithelial cells. Therefore, using anti-EpCAM antibodies alone may not be sufficient for effective enrichment of HCC CTCs [
14]. To address this limitation, cocktails of different antibodies targeting asialoglycoprotein receptor (ASGPR), glypican‐3 (GPC3), and vimentin have been developed, demonstrating increased capture rates of HCC CTCs [
15]. Microfluidic devices, such as CTC-Chip
TM [
16-
19] and NanoVelcro Chip
TM [
20-
23], which can also be used with different antibodies, have been developed to improve the capture efficiency and the purity of HCC CTCs. For example, nanosubstrates that mimic Velcro® and the overlying chaotic mixer of NanoVelcro Chip
TM significantly increase the contact frequency and affinity between CTCs and the chip surfaces, achieving >80% capture efficiency in HCC patients [
15]. Compared to the positive selection strategy mentioned above, negative selection utilizes anti-CD45 antibodies to target and deplete hematopoietic cells from the background [
24], thereby enriching CTCs. However, negative selection is typically used in conjunction with positive selection, physical property-based isolation, or microfluidic devices, as the purity of enriched CTCs from negative selection alone is usually low [
10]. The third generation of CTC-Chip
TM, CTC-iChip [
17,
19,
25], and its updated version combine either positive selection or negative selection with an arrayformat, inertial-enhanced design to fulfill high-throughput (10
7 cells/s) and enrich all CTC populations with high purity. A 10-gene signature in HCC CTCs enriched by CTC-iChip was quantified as a surrogate for the presence of CTCs and demonstrated as a promising biomarker for early detection of HCC [
19].
The second CTC enrichment method, physical property-based isolation, leverages the differences in size, mechanical plasticity and dielectric mobility of CTCs [
26]. Techniques in this category include microfiltration, density-based gradient centrifugation, dielectrophoresis, and microfluidic devices. As these methods are antigen-independent, they can address the issue of EpCAM loss during EMT and the heterogeneous antigen expression of CTCs. They also offer the advantages of relatively low cost and ease of downstream analysis, as there is no need to separate CTCs from coated antibodies. The Parsortix
® PC1 system is an example of microfluidic device recently cleared by FDA to enrich CTCs in metastatic breast cancer patients based on cell size and deformability [
27].
Despite enrichment using the aforementioned technologies, more than 1,000 WBCs per CTC still inevitably remain in the background [
8]. Therefore, immunofluorescence staining is typically applied to distinguish CTCs from the background WBCs. CTCs are usually defined as cells with DAPI+ oval nuclei, CK+ cytoplasma, CD45- cytoplasma, and positive staining of targeted surface markers such as EpCAM, vimentin, or ASGPR, particularly for HCC [
15,
28]. The identified CTCs could either be enumerated or collected for single-cell analysis [
28,
29]. In addition to manual enumeration and identification of CTCs, bioinformatics analysis was proposed to select a panel of genes that are highly expressed in CTCs but lowly expressed in background WBCs [
19,
22,
30]. This approach enables “bulk” genetic analysis of the samples after CTC enrichment, streamlining the entire analysis process for further clinical applications like HCC detection and prognostication.
Compared to other types of liquid biopsy, CTCs preserve the most complete molecular information due to the protection of their cell membranes. With the advancement of CTC isolation technologies and single-cell analysis, CTCs can be released from the captured substrates with high cell viability [
28]. This enables downstream analyses of DNA, RNA and proteins to investigate the mechanisms of tumor progression, EMT, and metastasis, as well as to surrogate clonal evolution of the cancer. In addition,
in vitro proliferation of the CTCs allows for functional tests to assess drug resistance and to understand the interactions between CTCs and epithelial cells, platelets, and immune cells during EMT, providing insights into the blood microenvironment [
28].
However, CTCs also have several disadvantages. First, no standardized pre-analytical protocols exist for CTC studies.9 While the two FDA-cleared devices, CellSearch
® and Parsortix
® PC1 Systems, each have their own protocols and corresponding blood collection tubes (BCTs), they are not approved for use in HCC patients. Additionally, the choice of BCT varies based on the downstream CTC analysis. 9 For example, the CellSave BCT is used for CTC enumeration, 31 while acid citrate dextrose solution A (ACD-A) and ethylenediaminetetraacetic acid (EDTA) BCTs are preferred for CTC mRNA analysis [
32,
33]. With appropriate BCTs and storage at 4°C, CTCs and their molecular contents are generally stable for up to 3 days [
34,
35]. However, for molecular analyses, it is recommended to verify the stability of the target molecular panels over the processing interval to ensure study reliability. Second, despite advancements in automated reading system and artificial intelligence/deep learning that assist in the identification of CTCs [
36,
37], enumeration still heavily relies on manpower and requires significant experience, which is inevitably subjective to some degree. Third, CTCs are heterogenous as their originated tumor cells [
38,
39]. While continuously profiling these heterogeneous CTCs could help unravel the dynamic changes in the tumor and its evolving biology [
24], this poses challenges for the CTC enrichment process. This is particularly true for immunoaffinity-based methods, as different CTC populations may express varying surface antigens. Lastly, due to their nature, CTCs are relatively rare in patients with early-stage cancer, which makes their utility as a biomarker for early detection of HCC challenging.
CELL-FREE DNA (cfDNA)
Biology and characteristics
CfDNA are small fragments of degraded DNA (<200 bp) that originate from disrupted or apoptotic cells and circulate in the bloodstream [
40]. CfDNA derived from malignant cells is also known as circulating tumor DNA (ctDNA). In normal conditions, the concentration of cfDNA is typically low, but it increases in certain conditions such as exercise, inflammation, diabetes, and malignancy [
41,
42]. In HCC patients, cfDNA exhibits both quantitative changes, such as elevated levels of cfDNA, and qualitative changes, including copy number alterations (CNVs), single-nucleotide variations (SNVs), methylation patterns, 5-hydroxymethylcytosine (5hmC), and fragment features [
40]. For CNVs in HCC cfDNA, arm-level amplifications of 1q and 8q, and deletions of 1p, 4q, and 8p, are frequently observed [
43]. SNVs in
TP53, CTNNB1, and the promoter region of
TERT are common in cfDNA from HCC patients, offering potential for cancer detection [
44,
45]. DNA methylation is known to be involved in DNA regulation and carcinogenesis, notably appearing even in the early stage of tumor development. Therefore, cfDNA methylation has been regarded as a promising biomarker for early-stage detection of HCC [
46]. Recent studies suggested that at least 10 to 50 cfDNA methylation markers are required to robustly distinguish HCC from controls [
47,
48], while around 200 cfDNA methylation markers are needed to differentiate between different cancers [
49]. Alternatively, incorporating relevant clinical variables such as age, sex, AFP, AFP-L3%, and DCP along with a few cfDNA methylation markers is a common approach to enhance the sensitivity for HCC detection [
47,
48]. In addition to mutation and methylation patterns, cfDNA fragmentation has attracted the attention of many researchers. Genome-wide fragmentation profiles, fragmentation size, nucleosome footprint, DNA 5’ end motif, jagged ends, and endpoint locations were shown to provide valuable information to differentiate HCC patients [
50].
It Is estimated that ctDNA could be <1% of the total amount of cfDNA, with levels even lower at around 0.01% in early-stage cancer [
51]. Unlike the various methods available for isolating CTCs, there are currently no reliable methods for enriching ctDNA from cfDNA. As the size of cfDNA in HCC patients tends to be shorter, approximately 144 bps [
52], a study suggested applying fragment-size selection of <150 bp to increase the proportion of ctDNA [
53]. However, this approach is not commonly used in most cfDNA-based biomarker studies. Instead, researchers are focusing on developing PCR and sequencing technologies to either increase sensitivity or broaden the genomic coverage to detect subtle changes in ctDNA. In general, PCR techniques are used to detect known SNVs and methylation markers, offering high sensitivity and quick results [
54]. Among the promising commercial cfDNA platforms for HCC detection [
55], Oncoguard
® Liver by Exact Science has developed a PCR-based technology, the Target Enrichment Long probe Quantitative Amplified Signal (TELQAS) assay [
56], to measure selected methylation markers. The TELQAS assay combines three key features: 1) a multiplexed pre-amplification process, 2) longer and high-melting-temperature probes, and 3) a second round of amplification of individual target loci, which enables sensitive and specific detection of low-abundance methylation targets in cfDNA without requiring whole-genome pre-amplification.
The other methods, besides PCR-based technologies, are next-generation sequencing (NGS), including whole-genome sequencing, whole-exome sequencing, and target-panel sequencing. While NGS provides a comprehensive view of the entire genomic map, those methods are more expensive and time-consuming [
54]. For the detection of low-frequency SNVs in HCC ctDNA, Lee et al. [
45] designed an algorithm that condenses hopped indexes for barcode-based targeted sequencing to eliminate PCR and sequencing errors, thereby achieving ultra-high depth and enhanced sensitivity. Similarly, HCCscreen
TM by GENETRON uses barcode-based targeted sequencing with rapid amplification of cDNA ends and multiple primers to detect the SNVs in
TP53, CTNNB1, and
AXIN1, the promoter region of
TERT, and the HBV integration breakpoint, which are included in a panel for HCC detection. The researchers further developed the Mutation Capsule Plus (MCP) technology [
57], which enables 1) parallel profiling of mutation and methylation changes in the same reaction, 2) repeated profiling of a single cfDNA sample without sacrificing sensitivity, and 3) genome-wide screening for unidentified methylation markers. For methylation sequencing of cfDNA, HelioLiver
TM’s ECLIPSE platform employs a gentler enzymatic conversion approach, yielding a greater quantity and quality of converted DNA compared to traditional bisulfite conversion, which is well-known for causing substantial DNA and signal damage [
58]. Additionally, the ECLIPSE platform incorporates a bioinformatics pipeline named Cell Heterogeneity-Adjusted cLonal Methylation (CHALM) [
59], which is able to assess the heterogeneity of sequenced bulk cfDNA, thereby enhancing the accuracy of quantifying differentially methylated markers. More recently, a research team led by Zhou developed a cost-effective assay named cell-free DNA Methylome Sequencing (cfMethyl-Seq) to profile CpG-rich regions in cfDNA [
49]. Since these regions comprise only 3% of the whole genome, the cost of cfMethyl-Seq is significantly reduced while still covering over 90% of CpG islands and achieving more than 12× enrichment compared to whole-genome bisulfite sequencing. Leveraging cfMethyl-Seq, cancer-specific and tissue-specific hypermethylation and hypomethylation cfDNA markers were identified and demonstrated excellent performance in detecting and locating cancers, including HCC.
Unlike the absence of standardized guidelines for the collection, processing, and storage of CTCs, National Cancer Institute’s (NCI) Biorepositories and Biospecimen Research Branch (BBRB) has introduced a cfDNA-specific guideline [
60] to harmonize the pre-analytical processes and minimize potential cfDNA degradation, analytical artifacts, and contamination. This guideline includes evidence-based recommendations on BCTs, temperature and duration of ambient storage or transport, centrifugation speed and duration for blood processing, as well as cfDNA extraction and storage. Notably, if blood is collected in Streck tubes, processing can be delayed for up to 3 days. In addition, the pre-analytical centrifugation requirement involves only two steps within an hour and does not require specialized centrifugation equipment, making it feasible in standard laboratories. These advantages facilitate multicenter clinical trials investigating cfDNA’s potential for HCC detection and prognostication and simplify the establishment of institutional biorepositories for future studies. Indeed, compared to CTCs and EVs, the genomic landscape of HCC ctDNA is the most extensively profiled, and several cfDNA-based assays are commercially available and validated in large-scale clinical studies.
Despite its advantages, cfDNA-based assays are relatively expensive and time-consuming, especially for NGS-based assays. Since cfDNA is fragmented, some chromosomal analyses like karyotyping and fluorescence in situ hybridization cannot be performed. Additionally, as there are no reliable methods to isolate ctDNA from cfDNA, it is challenging to specifically attribute identified genetic or epigenetic characteristics to HCC. Finally, cfDNA heterogeneity may interfere with quantification results. However, several of the aforementioned studies [
49,
56,
58] are underway to improve cost-effectiveness and minimize the impact of cfDNA heterogeneity.
EXTRACELLULAR VESICLES (EVs)
Biology and characteristics
EVs are a group of lipid-bilayer “cargo” vesicles containing proteins, lipids, RNA, DNA and non-coding RNA released by cells into the extracellular environment [
61,
62]. Their major functions are intercellular communication, transfer of “cargo” cell-to-cell, and modulating physiologic conditions. In HCC, EVs from various cells influence the tumor microenvironment [
63] and drive cancer progression by modulating cancer cell proliferation and migration, angiogenesis, extravasation, immune escape, and metastasis [
64,
65]. Recent evidence also shows that EVs derived from HCC can promote tumorigenesis and cancer stemness [
66,
67]. As such, EVs are regarded as promising HCC biomarkers for early detection of cancer, prognostication, and monitoring cancer progression.
Multiple EV isolation methods are endorsed by The International Society for Extracellular Vesicles (ISEV) and its latest guideline, “Minimal information for studies of extracellular vesicles (MISEV2023): from basic to advanced approaches.” [
62] Among these methods, differential ultracentrifugation, performed at speeds of 10,000–20,000g for 10 to 90 minutes followed by 100,000–200,000g for 45 to 150 minutes, is the most commonly used for EV isolation in research. However, this method is time-consuming, and ultracentrifugation machines are not commonly available, which hinders its application in clinical settings. Precipitation using hydrophilic polymers, such as polyethylene glycol, is also commonly used to isolate EVs due to its low cost, speed, simplicity, and high yield [
62,
68]. But this method typically results in low-purity EVs, often co-precipitating with vesicle-free miRNA and proteins like albumin and lipoproteins [
69]. Filtration leverages membranes with different pore sizes to isolate EVs, allowing not only for the separation of EVs from proteins but also for the harvesting of EVs of specific sizes. Compared to precipitation, EVs enriched through filtration are purer, though the yield is slightly lower. Additional advantages include the lack of requirement for special equipment and simplicity, although filter clogging and the co-retention of proteins of similar size remain inevitable. Size exclusion chromatography separates nanoparticles based on their size as they pass through the column packed with numerous porous polymers and buffers. Because EVs are larger than the pore size, they cannot enter the pores like smaller molecules, allowing them to flow through the column more quickly and be separated effectively. This method can achieve higher purity of isolated EVs compared to filtration. However, it is more time-consuming and may require an additional step to concentrate the diluted EV samples for downstream analysis.
Although the aforementioned methods are effective to isolate EVs and urine and plasma samples of HCC patients, they cannot distinguish between HCC-derived EVs from those of non-HCC origins, making it challenging to detect tumor-specific molecular information within HCC-derived EVs. To overcome this limitation, immunoaffinity-based isolation methods using antibodies to target specific EV surface molecules have been investigated to capture the HCC-associated EVs [
70,
71]. Julich-Haertel et al. [
72] demonstrated that AnnexinV+ EpCAM+ ASPGR1+ EVs are elevated in HCC patient’s sera and decrease after curative treatment, indicating that utilizing these markers might be a potential method to enrich HCC-associated EVs. Assuming that the surface markers of EVs resemble those of the parental tumor cells due to their shared cytomembrane, Sun et al. [
71] targeted EpCAM, ASPGR1, CD147, and GPC3 to purify HCC-associated EVs. The research team developed methyltetrazine-modified microbeads, termed Click Beads, which trigger a click chemistry reaction upon encountering EVs coated with antibodies modified with trans-cyclooctene. Compared with conventional streptavidin-biotin-mediated immunoaffinity-based approaches, the click chemistry reaction enables faster and irreversible purification [
71,
73], thereby improving capture efficiency and reducing nonspecific EV capture. In combination with duplex real-time immuno-PCR, the authors enabled sensitive quantification of EV subpopulations positive for both the aforementioned HCC-associated markers (EpCAM, ASPGR1, CD147, and GPC3) and EV-related markers (CD63 and CD9) [
62], which have shown promise for detecting early-stage HCC. Microfluidic devices leveraging immunoaffinity and physical characteristics are also a novel strategy to purify HCC-associated EVs [
62,
68,
74]. For example, the same research team developed EV Click Chips [
70,
75], which integrates the following features: 1) an antibody cocktail (EpCAM, ASPGR1, and CD147) to enrich HCC-associated EVs, 2) a chaotic mixer and nanostructured substrate to significantly increase the surface area as EVs flow through, thereby enhancing capture efficiency, and 3) click chemistry-mediated EV capture and disulfide cleavage-driven EV release, which further increases the purity of the targeted EVs for downstream analyses. Although immunoaffinity-based isolation methods, including microfluidics technology, can isolate HCC-associated EVs and achieve high EV purity, they have some limitations [
62,
68,
74], including low EV yield, the cost of antibodies, and the need for separation between EVs and antibodies or devices if further functional studies are required.
Currently, there is no universal pre-analytical protocol for blood collection, processing, and storage in blood-based EV studies. Acknowledging that the initial quality of plasma and serum can significantly affect study results, the ISEV Blood Task Force developed the Minimal Information for Blood EV Research (MIBlood-EV) guideline to help researchers transparently report crucial pre-analytical information, thereby improving the reproducibility of results. Most recently, Dhondt et al. [
76] conducted the first large‐ scale benchmarking study to comprehensively compare the impact of BCTs and blood processing intervals on blood sample quality and downstream analyses. In brief, plasma is preferred over serum for analyses, as less soluble platelet activation is observed in plasma, reducing potential confounding of the results. ACD-A, citrate, or EDTA BCTs are recommended for plasma collection, with ACD-A and EDTA approved for a blood processing interval of up to 8 hours after collection. Unlike CTC and cfDNA, which remain stable after 72 hours, significantly elevated levels of platelet-derived EVs and erythrocyte-derived EVs due to platelet activation and hemolysis are observed after 72 hours from blood collection, potentially affecting the study results. The Streck BCT can also be used for plasma collection in EV studies, although it offers no clear advantage over ACD-A, citrate, or EDTA BCTs regardless of the blood processing interval.
Urine is the second most commonly used biofluid in EV studies and offers the advantages of easy and noninvasive collection. The ISEV Urine Task Force has developed guidelines [
77,
78] for collection, processing, and storage to standardize and facilitate urinary EV studies. Similar to blood samples, urine samples should be processed within 8 hours after collection to prevent microbial growth. Further detailed recommendations for each processing step can be found in the quick reference card [
78].
Similar to CTCs, EVs are protected by a lipid bilayer and contain not only DNA but also RNA, proteins, and lipids, providing additional molecular information, while being easier to collect and preserve. Given the roles of EVs in HCC progression, functional studies of EVs can be conducted to reveal the underlying mechanisms when new EV-based biomarkers are discovered. Although blood samples for plasma collection in EV studies are recommended to be processed within 8 hours, which can pose challenges for fresh sample transfer, plasma can be obtained through a simple two-step centrifugation process that is feasible in both clinical and research laboratories. The plasma can then be stored and transported at –80°C for subsequent clinical EV studies. However, due to the lack of a standardized pre-analytical protocol for EV studies, factors such as BCTs, processing and storage times, and the number of freeze-thaw cycles may introduce technical variations. Unlike CTCs and cfDNA, research and biomarker exploration in the field of HCC EVs are still in the early stages of development. For example, given the small amounts of the HCC-derived EVs enriched using the novel technologies [
70,
71], more sensitive methods are required to detect and quantify scarce molecules, such as RNA, within these EV subpopulations. Lastly, larger scale clinical trials are needed to validate the findings of HCC EV biomarker studies.
ROLE OF LIQUID BIOPSY FOR EARLY DETECTION OF HCC
Early detection would be the most effective way to improve the prognosis in HCC patients. This section discusses the clinical utility of early detection according to the target substances of liquid biopsy. The main research findings are summarized in
Tables 2–
4 [
79-
94].
Earlier efforts to detect CTCs used immunoaffinity against EpCAM, one of the commonly used membrane-associated proteins, not expressed by normal blood cells. However, EpCAM positive rate was only about 35% in CTC [
95]. This result is likely because its expression is low in the early-stage HCC and is often lost during the EMT process. To overcome these issues, research has been conducted using HCC-specific markers such as GPC3, a cell membrane-anchored protein, more frequently observed in moderately or poorly differentiated HCC tumor cells and ASGPR, a transmembrane protein exclusively expressed on the surface of hepatocyte [
96-
98]. While they did not show excellent detection performance as an individual marker, combination of EpCAM, GPC3 and ASGPR antibodies using microfluidic synergistic chip achieved a detection rate of up to 97% in patients with HCC at various stages [
99]. On the other hand, analyzing various types of CTCs, such as epithelial, mesenchymal, and mixed type, can help with enhanced accuracy for early HCC detection [
100]. Chen et al. [
100] enumerated all the CTCs, including epithelial, mesenchymal, and mixed type, which showed better performance in discriminating between HCC and non-malignant liver disease compared to AFP.
More research has been conducted by combining CTCs with other biological markers to overcome the low sensitivity of CTCs. Combining CTCs with AFP resulted in better diagnostic yield compared to CTCs alone or AFP alone (0.821 vs. 0.774 vs. 0.669) [
101].
Guanine nucleotide-binding protein subunit beta-4 (GNB4) and
Riplet gene methylation showed high sensitivity and specificity when they were combined with CTCs [
102]. Despite advancements in CTC technology to detect HCC, CTCs for HCC surveillance are not endorsed due to high error rates, low detection rates in early stages, and difficulties in developing standardized testing methods [
103].
In earlier cfDNA research, the primary focus was on the quantitative changes in cfDNA as a biomarker for early-stage HCC detection. Following the results of the Iizuka group, which showed that cfDNA concentration was higher in HCV-associated HCC patients compared to the HCV carrier group [
104], several other studies reported that the concentration of cfDNA in HCC patients was 2 to 4 times higher than in those with chronic hepatitis, and nearly 20 times higher than in healthy individuals [
105,
106]. Additionally, tumor size, stage, and shorter survival time are correlated to cfDNA concentration. However, since cfDNA concentration can be influenced by liver inflammation, it would not be a reliable indicator for HCC, making it challenging to accurately determine the status of HCC [
40].
Despite the clear evidence that analysis of alteration in cfDNA is highly beneficial for HCC detection, individual cfDNA alterations alone may lack sufficient diagnostic value for HCC [
107]. On the other hand, the development of NGS technology has improved the understanding of the genetic alteration landscape in HCC. Recent studies using NGS have been able to detect most of the mutations present within tumors, and in some cases, the mutation detection rate was reported to be higher in liquid biopsy than in tissue biopsy. This indicates that intratumoral heterogeneity can be effectively identified, making it a powerful tool for improving early-stage diagnosis [
108,
109]. Guo et al. analyzed the cfDNA mutation profile using a targeted gene panel. High frequency of HCC-related gene mutations was observed in patients with chronic hepatitis B or liver cirrhosis. The area under a receiver operating characteristic curve (AUROC) of the gene mutation burden calculated from a panel of genes for HCC detection was 0.92 [
110]. These results indicate that somatic mutation detected in cfDNA may be an excellent biomarker for the early detection of HCC. Also, genetic alteration of ctDNA provides valuable diagnostic value in combination with conventional serum protein tumor markers. Cohen et al applied CancerSEEK test, which is composed of protein biomarkers (CA-125, CEA, CA 19-9, hepatocyte growth factor, prolactin etc.) and 16 gene mutations of ctDNA (
NRAS, CTNNB1, PIK3CA, FBSW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRA, AKT1, TP42, PPP2R1A, GNAS) to detect 8 different solid tumors, including liver cancer. Overall sensitivity of liver cancer was about 98% and specificity was over 99% [
111]. These remarkable results suggest that cfDNA might play a crucial role in the early detection of HCC.
CfDNA methylation pattern also can provide valuable information to detect HCC [
112]. Analyzing the methylation pattern of the SEPT9 promoter, an important tumor suppressor, in cfDNA demonstrated a potential role for HCC detection among cirrhotic patients [
113]. HCC-specific circulating DNA methylation profiles identified 283 CpGs with methylation differences, enabling differentiation between HCC and non-HCC samples with AUROC of 0.957 (sensitivity=90%, specificity=97%) [
114]. Blinded multicenter validation study using HelioLiver
TM test, which is generated using methylated cfDNA markers, serum protein markers and patient demographics, demonstrated better overall detection ability of HelioLiver
TM test (AUROC=0.944) compared to AFP (AUROC=0.851) and GALAD (gender, age, AFP-L3, AFP, and des-carboxy-prothrombin) score (AUROC=0.899). HelioLiver
TM test also showed better sensitivity (75.7%) for detecting early-stage HCC (I/II) compared to AFP (≥20 ng/mL, 57%) or GALAD score (≥−0.63, 65%) [
58]. Another multicenter study used mt-HBT (multitarget HCC blood test), which combined 3 methylation markers (
HOXA1, TSPYL5 and
B3GALT6), AFP and patient sex, demonstrated superior sensitivity for early-stage HCC (I/II, 82%) detection compared to AFP (≥20 ng/mL, 40%) or GALAD (≥–0.63, 71%) [
115]. Another study also demonstrated that combining cfDNA methylation markers with traditional serum protein biomarkers provides excellent accuracy for HCC detection, with an AUROC of 0.87 [
116].
CfDNA fragment pattern also provides valuable information for detecting HCC. Non-tumor cfDNA fragments are, on average, 167 bp in size, while ctDNA fragments are typically shorter, around 145 bp [
117]. This result is potentially due to the highly active transcriptional state of specific regions. A multi-national study that used whole genome cfDNA fragmentome profile was conducted to detect HCC patients using a machine learning model. The sensitivity for detecting cancer was 88% in an average-risk population at 98% specificity and 85% among high-risk individuals at 80% specificity.118 Another study analyzed cancer-related mutations and fragment length profile classification model using three fragmentomic features of ctDNA through deep sequencing of 13 genes associated with HCC. They demonstrated an AUROC of 0.88, a sensitivity of 89%, and a specificity of 82% for HCC detection in the discovery cohort [
119]. Chen et al. [
120] applied a combined profile of 5-Hydroxymethylcytosine/Motif/Fragmentation/nucleosome footprint using NGS technology and showed 95% sensitivity and 97% specificity for detecting HCC from cirrhotic patients. A recent meta-analysis, which analyzed 33 papers, reported an overall sensitivity of 72.2% and a specificity of 82.3% for 7 quantitative studies and an overall sensitivity of 56.8% and a specificity of 88.2% for 26 qualitative studies [
121].
EARLY DETECTION OF HCC USING EVs
It is well known that the concentration of EVs is higher in HCC patients compared to those with liver cirrhosis [
122]. Biomolecular cargo in the EVs is a useful biomarker for HCC, with higher accuracy than EV concentration. EV hnRNPH1 mRNA levels in HCC patients were higher than in the control groups and showed good diagnostic performance to detect HCC [
123]. Wang et al. [
124] demonstrated that EV
miR-122, miR-148a, and
miR-1246 had superior performance to detect HCC compared to AFP. EV long noncoding RNAs (IncRNAs) have shown promising results in early-stage HCC detection too. A Korean study utilizing six upregulated EV IncRNAs reported excellent accuracy, with an AUC of 0.96 [
125]. Another study showed that combining EV lncRNA and AFP further enhances the accuracy of EV-based biomarkers for HCC detection [
126]. EV-derived LDHC mRNA demonstrated better AUC compared with serum-only LDHC mRNA for early HCC detection (EV LDHC 0.945 vs. serum LDHC 0.838) [
127].
Our team developed an HCC EV purification system (i.e., EV Click Chips) by synergistically integrating covalent chemistry-mediated EV capture/release, multi-marker antibody cocktails, nanostructured substrates, and microfluidic chaotic mixers. The purified HCC EVs were subjected to reverse-transcription droplet digital PCR for quantification of 10 HCC-specific genes, i.e.,
alpha-fetoprotein (AFP), glypican 3 (GPC3), albumin (ALB), apolipoprotein H (APOH), fatty acid binding protein 1 (FABP1), fibrinogen beta chain (FGB), fibrinogen gamma chain (FGG), alpha 2-HS glycoprotein (AHSG), retinol binding protein 4 (RBP4), and
transferrin (TF). The HCC EV-derived molecular signatures could detect early-stage HCC (BCLC stage 0-A) with high sensitivity (94.4%), specificity (88.5%), and AUROC of 0.93, which was superior to serum AFP (AUROC of 0.69) [
70]. More recently, our team developed an HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, quantifying subpopulations of HCC derived EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations can detect early HCC with AUROC of 0.94, which was higher compared to AFP (AUROC of 0.79). ECG score also demonstrated excellent early-stage HCC (BCLC 0-A) detectability (sensitivity=91%, specificity=81%) [
71].
ROLE OF LIQUID BIOPSY FOR PROGNOSTICATION
In the following section, we discuss examples of clinical applications related to prognostication for each target substance of liquid biopsy. Main research findings are summarized in
Tables 5–
7 [
128-
142].
Several studies have shown that the number of CTCs is correlated with tumor size, portal vein tumor thrombosis, tumor stage, differentiation, and shorter survival [
15,
143]. Our team developed the HCC-CTC mRNA scoring system for the prognosis of HCC. This system is composed of 2 steps: enrichment of CTCs from blood collected from HCC patients using the NanoVelcro CTC assay and lysis of the enriched CTCs to extract mRNA; second, reverse transcription to obtain HCC-CTC-derived cDNA. The extracted cDNA is reacted with the HCC-CTC RS panel, a final set of 10 genes with prognostic value, using the Nanostring nCounter technique. Based on the results, patients were categorized into low-risk and high-risk groups to compare their prognosis. In the validation cohort, the overall survival (OS) for the high-risk group showed significantly worse results (
P=0.02), HCC-CTC panel was an independent predictor of survival (HR 5.7; 95% CI 1.5–21.3;
P=0.009), along with BCLC stage [
30]. A long-term study tracking patients with locally advanced or metastatic HCC reported that a significant increase in CTC count is associated with worsening treatment response [
144]. The mesenchymal phenotype of CTCs (M-CTCs) is also associated with poor prognosis, including not only tumor aggressiveness and portal vein tumor thrombosis but also reduced OS [
145,
146].
CfDNA holds strong prognostic value for HCC due to its short half-life and reflection of tumor burden and biology. One study demonstrated that SNV and CNV of cfDNA correlate with tumor burden and predict prognostic outcomes better for recurrence-free survival (RFS) and OS than traditional protein biomarkers such as AFP, AFP-L3% and DCP [
147]. A similar study showed that poor disease-free survival is observed in HCC patients with cfDNA mutations in their post-operative plasma [
148]. Oh et al. analyzed the prognosis of HCC patients using cfDNA concentration and the I-score, which indicates genomic instability. cfDNA is an independent prognostic factor for OS (HR 2.51; 95% CI 1.62–3.89;
P<0.001), while the I-score is an independent prognostic factor for both time to progression (TTP) (HR 1.71; 95% CI 1.13–2.58;
P=0.011) and OS (HR 1.85; 95% CI 1.16–2.96,
P=0.010) [
149].
Multiple studies revealed the association between epigenetic alteration of cfDNA and HCC prognosis. One study developed a combined prognostic score (cp-score) from 8 methylation markers and revealed that the high-risk group based on cp-score is an independent predictor for survival (HR 1.548; 95% CI 1.246–1.924;
P<0.001) [
48]. A recent multicenter study validated 20 differentially methylated regions (DMRs), which predict a high recurrence risk (HR 3.33; 95% CI 1.87–5.92;
P<0.001) [
150]. A systematic review analyzing eight studies (5 studies with pre-treatment samples, 2 studies with pre & post-treatment samples, and 1 study with post-treatment samples) reported that the mutation of ctDNA was independently associated with reduced disease-free survival (HR 3.01; 95% CI 1.23–11.30) [
151]. A study using data from participants in the SORAMIC trial showed that cfDNA levels significantly correlated with metastases and survival. In addition, cfDNA kinetics over time revealed a trend with the clinical history of the patients, supporting its use as a biomarker to monitor therapeutic response [
152].
Several studies demonstrated the prognostic value of EV in HCC. High EV miRNA-21 levels positively correlated with the stage of HCC and linked to the PTEN/Akt signaling pathway, which enhances EMT and tumor proliferation. That suggests that EV
miRNA-21 could be a potential predictive prognostic marker of HCC [
153]. The EV
miRNA-665, which is linked to the Mark/ERK anti-apoptotic pathway, also showed a correlation with HCC progression and shorter survival time [
154]. Some studies revealed that the prognostic impact of EV is mediated by angiogenesis. EV
miRNA-210 suppresses the expression of SMAD4/STAT6, therefore strengthening tumor angiogenesis [
155]. Another study has reported that EV
miR-103 inhibits VE-cadherin, p120, and zonula occludens, weakening endothelial cell junctions and ultimately leading to tumor metastasis [
156]. On the other hand, the downregulation of tumor suppressor EV miRNAs can also indicate poor prognosis. Studies have reported that decreased EV
miRNA-125b and
miRNA-638 levels are associated with shorter time to recurrence (TTR) and OS [
157,
158]. EV LncRNA-ATB, which induces tumor invasion, EMT, and metastasis, is positively correlated to advanced HCC stage, portal vein thrombosis, leading to reduced OS and progression-free survival (PFS) [
159]. EV circular RNAs also showed prognostic value for HCC. Upregulation of
circPTGR1, which acts in decreasing interaction between
miRNA-449 and MET, is associated with advanced cancer stage including metastasis and poor prognosis [
160]. EV may predict the treatment response to immunotherapy. The high expression of
circUHRF1 is associated with a decreased NK cell proportion in blood (
P<0.001), which inhibits NK cell-derived IFN-γ and TNF-α secretion in HCC, ultimately leading to immunosuppression. Also, high expression of EV
circUHRF1 is an independent prognostic factor for recurrence (HR 1.762; 95% CI 1.172–2.428;
P=0.019) [
161], suggesting that EV
cirUHRF1 may induce poor response to anti-PD1 immunotherapy.
ROLE OF LIQUID BIOPSY FOR MINIMAL RESIDUAL DISEASE (MRD) AND DISEASE MONITORING
MRD in solid tumors refers to a small number of cancer cells remaining after treatment [
162]. For surgical resection, a wider resection margin can improve survival by decreasing the chance of MRD [
163]. Microvascular invasion is also known to be an important factor in predicting early recurrence from MRD [
163]. However, these surrogates need surgical procedures, making continuous monitoring after surgery not feasible in routine clinical practice. With the recent approval of adjuvant treatment for HCC in high-risk patients, the detection of MRD became even more important. Therefore, there is a clinical unmet need in HCC to develop non-invasive, validated methods to detect MRD.
One notable study reported that a CTC count greater than 15 and mesenchymal CTCs greater than 2% serve as predictors for early recurrence and metastasis. Importantly, in the post-operative monitoring of 10 patients, an increase in CTC count was recognized before clinically detectable nodules were found [
146]. These results support that CTC could be a surrogate marker for MRD.
Regarding cfDNA, one study reported that patients with detectable cfDNA at 1 and 4 months after hepatic resection demonstrated a significantly shorter RFS [
164]. Similarly, overexpressed
miR-92b at 1 month after liver transplantation was associated with early recurrence [
165]. Xu et al. [
162] reported that a positive MRD monitoring gene (
TP53, TERT, CTNNB1, APC, RMB10, NTRK3, NOTCH1, NOTCH2, NF1, CREBBP, GLI3, CDKN2A, EZH2) is an independent predictor of shorter RFS (HR 13.00; 95% CI 2.6–69.0). A recent pilot study compared protein surface markers of EVs in patients with recurrent HCC and non-recurrent HCC. When comparing 1 year after hepatic surgery, they revealed significantly lower EV CD31 levels in the recurrent HCC group than in the non-recurrent HCC group (
P<0.01) [
166], suggesting that EVs may play a role in the early detection of recurrent HCC. Yi et al. measured EV lncRNA (
LUCAT-1, EGFR-AS-1) in HCC patients who underwent hepatic resection to assess its prognostic role. Interestingly, a significant number of patients who achieved complete radiologic response experienced recurrence or progression in the context of positive EV dual lncRNA results [
167]. This suggests that EV lncRNA may have the potential to be a biomarker for MRD. Sefrioui et al. analyzed ctDNA with
TERT mutation and cfDNA in patients who underwent transarterial chemoembolization (TACE). When comparing preprocedural and 1 month post procedural cfDNA and ctDNA levels, the progression rate was significantly higher in the non-responder group compared to the responder group in any type of DNA (responder vs. non-responder=4.3% vs. 80%,
P<0.001) [
168].
Monitoring for disease recurrence or progression after cancer treatment is a critical component of HCC care continuum. Protein biomarkers such as serum AFP are well known to play a role in monitoring after surgical resection, locoregional therapy, and immune therapies [
3,
169]. However, due to low sensitivity and applicability, there is an unmet need for more accurate and sensitive biomarkers. Liquid biopsy could be particularly helpful for recurrence and progression monitoring among HCC patients with normal AFP levels.
Winograd et al. observed that in a small set of patients, those with PD-L1 positive CTCs seemed to have a better response to immune checkpoint inhibitors (ICI) compared to those with PD-L1 negative CTCs. This suggests that PD-L1 expression on CTCs could serve as a predictive biomarker, helping to identify which patients are more likely to benefit from immunotherapy [
21]. Nosaka et al. [
170] assessed serial treatment response before and after Atezolizumab plus Bevacizumab by investigating PD1 RNA expression in CTCs. PD-L1 expression decreased significantly in cases with partial response or stable disease [
170]. Rau et al. sequentially measured CTC counts in patients who received various treatments, and in most cases, the CTC counts were consistent with the patient’s radiologic treatment response. Notably, in patients whose AFP levels were normal but did not show a complete response in CT or MRI scan, the CTC count accurately reflected the patient’s actual status [
113]. This highlights the potential value of CTC count as a biomarker for monitoring treatment response and recurrence in the future. Regarding ctDNA, Ikeda et al. [
171] suggested that cfDNA could be a biomarker for treatment response as most HCC patients had detectable cfDNA alteration, which can reflect the burden of diseases. However, more studies are required to establish the use of CTC counts, cfDNA, or EV for monitoring HCC recurrence, given the lack of robust evidence.
CLINICALLY AVAILABLE TESTS AND ONGOING CLINICAL TRIALS
The following section will show currently applicable technologies. HelioLiver
TM test serves as a notable example. As previously described, it demonstrates superior HCC detection rate compared to serum AFP and the GALAD score [
58]. Furthermore, in the very recently completed CLiMB trial, the HelioLiver
TM test showed outperformed sensitivity and non-inferior specificity compared with ultrasound as well as ultrasound with AFP (≥20 ng/mL) (sensitivity=47.8% vs. 28.3% vs. 39.1%). HelioLiver
TM test detected HCC lesion of ≤2 cm with sensitivity of 28.6% (95% CI 11.3–52.2). In contrast, ultrasound failed to detect any HCC lesion of ≤2 cm [
172]. The Oncoguard
® Liver platform, which combined 3 methylation markers (
HOXA1, TSPYL5 and
B3GALT6), AFP and patient sex, demonstrated superior sensitivity for early-stage HCC (I/II, 82%) detection compared to AFP (≥20 ng/mL, 40%) or GALAD (≥–0.63, 71%) [
115]. A new clinical trial, known as ALTUS is on the way. This observation trial aims to evaluate the sensitivity and specificity of Oncoguard
® in adults at increased risk for HCC [
173].
HCCscreen
TM by GENETRON uses barcode-based targeted sequencing with rapid amplification of cDNA ends and multiple primers to detect the SNVs in
TP53, CTNNB1, and
AXIN1, the promoter region of
TERT, and the HBV integration breakpoint, which are incorporated into a panel for HCC detection. This test is commercially available in China based on Qu et al.’s research findings demonstrating that the HCCscreen
TM achieved a sensitivity of 85% and specificity of 93% (AUROC=0.928) in distinguishing 65 HCC patients from 70 non-HCC patients among individuals with hepatic nodules and/or elevated AFP [
174]. After that, Wang et al. applied the Mutation Capsule Plus (MCP) technology to the HCCscreen
TM that follows for parallel profiling of mutation and methylation changes on a single cfDNA sample. Their validated retrospective cohort with 58 HCC and 198 non-HCC showed 90% sensitivity with 94% specificity [
175]. The HCC blood test by Epigenomics, which is PCR assay for the assessment of methylated
Septin 9 in bisulfite-converted DNA, is also available based on the case-control study, which showed 76.7% sensitivity and 64.1% specificity from 60 early stage HCC and 103 non-HCC patients, and also showed superior to serum AFP regarding detectability.176 Although neither of these tests has yet received FDA approval, they nonetheless offer considerable value in patient care.
Building on the currently commercially available test discussed above, we now turn to an exploration of ongoing clinical trials. Among the ongoing clinical trials listed in
Table 8 [
177-
183], those conducted by Exact Sciences and Helio Genomics are particularly noteworthy. Additionally, emerging research findings suggest these trials may assist in predicting responses to immune checkpoint inhibitors, highlighting the growing interest in focus on this area.
BARRIERS TO CLINICAL TRANSLATION & CONCLUSION
Liquid biopsy allows non-invasive serial monitoring of HCC and detection of intratumoral heterogeneity through genomic analysis.
Figure 2 provides an overview of potential future application of liquid biopsy for early detection, prognostication, and detection of minimal residual disease in HCC. We anticipate that liquid biopsy will enhance the detectability of early-stage HCC, positioning itself as a complementary or alternative test to the current standard imaging study with or without AFP [
2]. Considering the low sensitivity of AFP and the operator-dependent nature of ultrasound, liquid biopsy could become a standardized tool for HCC surveillance. Liquid biopsy can enable the early detection of minimal residual disease post-treatment, leading to additional interventions that ultimately improve patient outcomes. Furthermore, it can monitor treatment efficacy following locoregional or systemic treatments, providing accurate assessments. However, several areas still need improvement for successful clinical translation. Despite advancements in various diagnostic technologies, the lack of standardized protocols for CTCs and EVs, background noise, and relatively high cost of NGS remain a critical issue. Furthermore, while technological advancement progresses, improving accuracy for early HCC detection remains a pressing need. The development of artificial intelligence (AI) would be one of the solutions to address these issues [
184]. Due to the nature of blood tests, liquid biopsy cannot provide spatial information about HCC. Further refinement of liquid biopsy using multi-omics data, which encompass gene mutations, DNA methylation, fragmentome, proteome, and nucleosomes from CTCs, ctDNA and EVs in a patient’s blood sample all at once, may further enhance the accuracy of these novel biomarkers [
185]. Also, research utilizing single-molecule sequencing techniques is now underway. This approach is expected to address limitations associated with conventional NGS, such as the inability to efficiently detect molecules longer than 600 bp. Additionally, it offers a solution to the issue of DNA degradation caused by disulfide treatment during DNA methylation analysis [
186,
187]. Ultimately, large-scale clinical trials are essential to validate its effectiveness and facilitate its adoption in clinical practice for HCC. Through these efforts, liquid biopsy could significantly aid in the entire spectrum of HCC care continuum.
FOOTNOTES
-
Authors’ contribution
Ju Dong Yang, Jaeho Park and Yi-Te Lee made contributions to the following: the conception and design of the study, or acquisition of data, or analysis and interpretation of data.
All authors have made contributions to all of the following: (1) drafting the article or revising it critically for important intellectual content, (2) final approval of the version to be submitted. The manuscript, including related data, figures and tables has not been previously published and that the manuscript is not under consideration elsewhere.
All authors approve the final version of the manuscript, including the authorship list and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
-
Acknowledgements
Figure 1 and Figure 2 are created in BioRender.
This work is supported by National Institutes of Health (R01CA277530, R01CA255727, R01CA253651, R01CA253651-04S1, R01 CA273925, R21CA280444, R01CA246304, U01EB026421, K08CA259534, R44CA288163, and U01CA271887). The funders had no role in the collection of data; the design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
-
Conflicts of Interest
Dr. Ju Dong Yang provides a consulting service for Fuji-Film Medical Sciences, Exact Sciences, AstraZeneca, Eisai, Exelixis, and Merck. Dr. Yazhen Zhu is a co-founder and shareholder in Eximius Diagnostics Corp.
Following the management plan provide by UCLA Conflict of Interest Review Committee, Dr. Hsian-Rong Tseng would like to disclose that (1) he has a financial interest in CytoLumina Technologies Corp. and Pulsar Therapeutics Corp., and (2) the UC Regents have licensed intellectual properties invented by Dr. Tseng to CytoLumina and Pulsar. Dr. Vatche Agopian provides a consulting service for Merck, Eximius Diagnostics Corp, and Early Diagnostics Corp.
Figure 1.Concept and workflow of liquid biopsy in HCC, including CTCs, EVs, and cfDNA. This schematic figure highlights the key components of the concept and workflow of liquid biopsy in HCC: the pre-analytical process, technical challenges associated with each type of liquid biopsy, technology development to address these challenges and clinical validation of liquid biopsy assays. CfDNA, cellfree DNA; CTC, circulating tumor cell; ctDNA, circulating tumor DNA; EV, extracellular vesicle; HCC, hepatocellular carcinoma; NGS, next-generation sequencing; PCR, polymerase chain reaction; WBC, white blood cell.
Figure 2.Future application of liquid biopsy for early detection, prognostication, and detection of minimal residual disease in HCC. (A) Early detection of HCC. Signals from CTCs, cfDNA, and EVs are measured every 6 months in patients with cirrhosis or chronic hepatitis B who require HCC surveillance. Once these signals exceed predefined cut-off values, diagnostic methods such as CT or MRI are used to confirm an HCC diagnosis. (B) Prognostication of HCC. Blood samples are collected from HCC patients before surgery or locoregional therapies. Based on signals from CTCs, cfDNA, and EVs, HCC patients are classified into high-risk and low-risk groups for recurrence or progression. For high-risk patients, adjuvant therapy and close monitoring of early recurrence or progression may be warranted. (C) Detection of MRD in HCC. After curative surgery or locoregional therapies, blood samples are collected from HCC patients. High signals from CTCs, cfDNA, and EVs indicate an increased likelihood of MRD, warranting adjuvant therapy and close monitoring for early recurrence. CfDNA, cell-free DNA; CTC, circulating tumor cell; EV, extracellular vesicle; HCC, hepatocellular carcinoma; MRD, minimal residual disease.
Table 1.Comparison of CTCs, cfDNA, and EVs as liquid biopsies for HCC
Table 1.
|
Comparative features |
CTCs |
CfDNA |
EVs |
|
Molecular contents |
DNA, RNA, proteins, lipids |
DNA |
DNA, RNA, proteins, lipids |
|
Sources |
Blood (peripheral blood mononuclear cells) |
Blood, urine |
Blood, urine |
|
Omics analysis capability |
Genomic, epigenomic, transcriptomic, proteomic, metabolomic analyses |
Genomic and epigenomic analyses |
Genomic, epigenomic, transcriptomic, proteomic, metabolomic analyses |
|
Functional analysis capability |
Yes |
No |
Yes |
|
Single cell analysis |
Yes |
No |
No |
|
Standardized pre-analytical protocols |
No |
Yes (NCI Biospecimen Evidence-Based Practices) |
No |
|
Blood collection tube |
CellSave BCT is used for CTC enumeration; |
EDTA BCT, Streck BCT or other cfDNA stabilizing BCT |
ACD-A, citrate, EDTA, Streck BCTs |
|
ACD-A and EDTA BCTs for CTC mRNA analysis |
|
Blood processing interval |
Up to 3 days at 4°C; stability of molecular panels over processing intervals needs to be verified |
EDTA BCT: ideally within 2 hours; 4 hours at 4°C/RT or 24 hours at 4°C is acceptable |
Within 8 hours |
|
Streck BCT: Up to 3 days at RT |
|
Technical challenges in detection |
Rarity in blood (1 CTC per 106-107 WBCs) |
Requirement of high sensitivity and broad genomic coverage to detect subtle changes in ctDNA |
Purification of HCC-associated EVs from bulk Evs |
|
Other limitations |
Subjectivity in CTC enumeration; Not an ideal candidate for early HCC detection due to its inherent nature |
High cost of NGS |
Challenges in molecular profiling in the small amounts of EV subpopulations |
|
Clinical validation status |
No ongoing clinical trials for early detection and prognostication of HCC |
Several ongoing clinicals trials to validate the performance of cfDNA-based assays for early detection of HCC |
No ongoing clinical trials for early detection and prognostication of HCC |
Table 2.CTCs for early detection of HCC
Table 2.
|
Author |
Year |
Cohort |
Yield (sensitivity, specificity) |
Isolation technique/biomarker |
|
Bahnassy et al. [79] |
2014 |
Prospective |
CK19+ CTCs (sen: 87.1%, spe: 82.5%) |
Flow cytometry, RT-PCR |
|
120 HCC/30 HCV/33 healthy control |
CD90+ CTCs (sen: 82.5%, spe: 89.6%) |
CK19, CD 133 and CD90 |
|
CD133+ CTCs (sen: 40%, spe: 6.3%) |
|
|
Armakolas et al. [80] |
2022 |
Prospective |
Sen: 46% |
EpCAM, vimentin, AFP, sMVP and qRT-PCR |
|
89 HCC/28 cirrhosis |
|
Cheng et al. [101] |
2018 |
113 HCC/57 CLD |
Total CTCs >2 (sen: 62%, spe: 90%) |
CanPatrol: assay to isolate each type of CTCs |
|
Epithelial CTCs >0 (sen: 45%, spe: 79%) |
|
Mesenchymal CTCs >0: (sen: 49.6%, spe: 87.7%) |
|
Liang et al. [102] |
2022 |
17 HCC/11 HBV cirrhosis |
sen: 70.6%, spe: 90.9% |
CTCBIOPSY device |
|
Guo et al. [81] |
2018 |
395 HCC/201 HBV/100 benign liver disease/210 healthy control |
sen: 72.5%, spe: 95% |
Multimarker qRT-RNA |
|
Detection flatform |
|
Bahn et al. [82] |
2018 |
54 HCC/39 CLD |
CTC> 4/10 mL: sensitivity: 81% |
CTC-iChip digital PCR with glypican-3 |
|
10 healthy control |
Table 3.CfDNA for early detection of HCC
Table 3.
|
Author |
Year |
Cohort |
Yield (sensitivity, specificity) |
Isolation technique /biomarker |
|
Chen et al. [83] |
2024 |
Cross-sectional study |
PreCar Score (sen: 51.3%, spe: 95.5%) |
PreCar score using cfDNA genomic features. (Nucleosome footprint, 5-hydroxymethylcytosine, fragment size, 5’ end motif, copy number variation) |
|
510 HCC (BCLC 0/A) |
US (sen: 23.7%, spe: 99.37%) |
|
4367 LC |
PreCar+US (sen: 60.5%, spe: 95.08%) |
|
Lin et al. [58] |
2022 |
HCC 35 (I/II) |
HelioLiverTM test (sen: 76%, spe: 91%) |
NGS with ECLIPSE platform |
|
125 CLD |
AFP (≥12.1 ng/mL,sen: 57%) |
28 target genes (77CpG sites) |
|
GALAD (≥−0.63, sen: 65%) |
|
|
Chalasani et al. [115] (follow-up study of Kisiel JB et al) |
2022 |
78 HCC (BCLC 0-A) |
mt-HBT (Oncoguard® by Exact Science) (sen: 82%, spe: 87%) |
Target Enrichment Long probe Quantitative Amplified Signal (TELQAS) assay |
|
245 CLD |
AFP (≥20 ng/mL, sen: 40%) |
|
GALAD (≥0.63, sen: 71%) |
HOXA1, TSPYL5, B3GALT6
|
|
Kisiel et al. [56] |
2019 |
95 HCC (BCLC 0-D) |
AUROC (6 markers vs. AFP) =0.93 vs. 0.74 |
TELQAS assay/ |
|
51 LC/98 HC |
6 methylation markers |
|
Cai et al. [84] |
2019 |
1204 HCC (BCLC 0-C) |
HCC vs. CLD (sen: 82.7%, spe: 76.4%) |
5hmC-Seal/ |
|
392 LC/958 HC |
BCLC 0 vs. non-HCC: AUC=0.846 |
32 gene markers |
|
Oussalah et al. [113] |
2018 |
98 HCC (BCLC 0-D) |
Pooled AUROC: 0.940 |
MSP/ |
|
191 non-HCC |
HCC (BCLC A): AUROC=0.863 |
SEPT9 methylation |
|
Cohen et al. [111] |
2018 |
44 HCC (BCLC 0-D) |
HCC (sen: 100%, spe>99%) |
NGS/ |
|
8 proteins+1933 ctDNA mutations |
|
Jiang et al. [85] |
2015 |
90 HCC (BCLC A-B) |
HCC vs. HC (sen: 80%, spe: 94%) |
NGS/ |
|
67 CHB/36 LC/ 32 HC |
WGS (plasma DNA size measurement) |
|
Han et al. [112] |
2014 |
160 HCC (TNM I-IV) |
TGR5+AFP (sen: 65%, spe: 85.2%) |
MSP/ |
|
TGR5 promoter hypermethylation |
|
Huang et al. [86] |
2014 |
66 HCC (TNM I-IV) |
sen: 65.3%, spe: 87.2% |
Pyrosequencing/ |
|
34 CLD |
INK4A
|
|
Piciocchi et al. [87] |
2013 |
66 HCC |
sen: 91%, spe: 43% |
qRT-PCR/ |
|
35 LC/41 CH (HCV) |
hTERT
|
|
Huang et al. [105] |
2012 |
72 HCC (TNM I-IV) |
cfDNA (sen: 90.2%, spe: 90.3%) |
qRT-PCR/ |
|
37 LC |
cfDNA+AFP (sen: 95.1%, spe: 94.4%) |
NA |
Table 4.EVs for early detection of HCC
Table 4.
|
Author |
Year |
Cohort |
Sensitivity, specificity |
Marker type |
Expression level |
Isolation technique/biomarker |
|
Sun et al. [71] |
2023 |
Validation cohort |
91%, 81% |
Amounts of EV subpopulations |
|
EV surface protein assay (Click Bead+immune-PCR)/ |
|
HCC 35 (BCLC 0-A) |
ECG score from 3 EV subpopulations |
|
LC 37 |
|
|
Son et al. [88] |
2023 |
External cohort |
AUROCs |
N/A |
↑ |
qRT-PCR (quantitative reverse)/ |
|
28 HCC (TMN I) |
SF3B4=0.940 |
EV-SF3B4
|
|
49 CH/LC |
AFP=0.785 |
|
|
Kim et al. [89] |
2021 |
HCC 72 (BCLC 0-D) |
Positive rate: 86–96% (stage I) |
lncRNA |
↑ |
qRT-PCR/ |
|
LC 25, CH 21, |
MALAT1, DLEU2, HOTTIP, SNHG1
|
|
von Felden et al. [90] |
2021 |
HCC 105 (BCLC 0-A) |
85%, 94% |
smRC |
↑ |
qRT-PCR/ |
|
CLD 85 |
smRC_135709, smRC_119591, smRC_48615
|
|
Ghosh et al. [91] |
2020 |
38 HCC (TMN I-III) |
59%, 95% |
miRNA |
↑ |
ExoEnrichTM instant exosome isolation Kit & anti-ASGR2/ |
|
35 CH+25 LC |
miR-10b-5p, miR-221-3p, miR-223-3p & miR-21-5p
|
|
Kim et al. [125] |
2020 |
32 HCC (BCLC 0) |
94%, 85% |
lncRNA |
↑ |
ExoQuickTM Exosome precipitation solution/ |
|
28 CH+35 LC |
LINC00853
|
|
Huang et al. [92] |
2020 |
122 HCC |
80%, 76.5% |
lncRNA |
↑ |
RiboTM Exosome isolation reagent/ |
|
43 LC |
Lnc85
|
|
Sun et al. [93] |
2020 |
71 HCC (TMN I-IV) |
91%, 78% |
circRNA |
↑ |
Ultracentrifugation/ |
|
40 HD |
Circ_0004001, Circ_0004123, Circ_0075792
|
|
Cui et al. [127] |
2020 |
50 HCC (TMN I, II) |
88%, 93% |
mRNA |
↑ |
exoRNeays Midi Kit/ |
|
100 HD |
LDHC
|
|
Sun et al. [70] |
2020 |
36 HCC (BCLC 0-A) |
84%, 88% |
mRNA |
↑ |
HCC EV digital scoring assay (EV Click Chip+RT-ddPCR)/mRNA Panel (AFP, GPC3, ALB, APOH, FABP1, FGB, FGG, AHSG, RBP4, TF) |
|
26 LC |
|
Wang et al. [124] |
2018 |
50 HCC (TMN I-III) |
86%, 88% |
miRNA+AFP |
↑ |
Ultracentrifugation, filtration & precipitation/ |
|
40 LC |
miR-122, miR-148a & AFP
|
|
Arbelaiz et al. [94] |
2017 |
29 HCC |
83%, 90% |
Protein |
↑ |
Ultracentrifugation/ |
|
12 iCCA |
FGG |
|
Wang et al. [122] |
2013 |
28 HCC (TMN I) |
63%, 89% |
Amounts of EV |
↑ |
Ultracentrifugation/ |
|
40 LC |
Amount of total EVs |
Table 5.CTCs for prognostication of HCC
Table 5.
|
Author |
Year |
Cohort |
Associated findings |
Isolation technique/biomarker |
|
Chen et al. [128] |
2024 |
124 HCC |
PD-L1>1 or CTC-NLR>0 |
circulating tumor cell sorter, CytoNanoChip |
|
: independent predictor for OS↓ |
PD-L1+ CTC, CTC-NLR |
|
Zhao et al. [129] |
2023 |
270 HCC |
Independent risk for recurrence |
IF staining, RNA ISH, WGA |
|
: PCP (HR 25.7; 95% CI 5.2–106.6) |
CAPI, CK, Ki67 |
|
: CTC cluster (HR 9.9; 95% CI 2.5–38.6) |
|
|
Yang et al. [145] |
2023 |
105 HCC |
M-CTC >0 per 5 mL |
CanPatrol |
|
: AFP ≥400 ng/mL, tumor size≥ 5 cm |
|
: poorly differentiation, multiplicity |
|
: advanced stage, microvascular invasion |
|
: portal vein tumor thrombosis |
|
Lee et al. [30] |
2022 |
Validation cohort 40 |
Independent predictor of survival |
NanoVelcro CTC assay |
|
HCC |
: HR 5.7; 95% CI 1.5–21.3; P=0.009 |
mRNA Risk Score panel |
|
Chen et al. [130] |
2022 |
Retrospective analysis |
CTC-WBC >0 per 5 mL |
CanPatrol, filtration & multiple mRNA ISH |
|
136 HCC |
: distant metastasis↑, recurrence↑, RFS↓ |
|
Kelley et al. [131] |
2015 |
20 HCC |
CTC >0 per 7.5 mL |
CellSearch |
|
10 NMLD |
: AFP ≥400 ng/mL, vascular invasion |
CTC counts |
|
Liu et al. [132] |
2013 |
60 HCC |
CD45-ICAM-1+
|
qRT-PCR |
|
: DFS↓, OS↓ |
ICAM-1 |
|
Schulze et al. [133] |
2013 |
59 HCC |
Presence of CTC |
CellSearch |
|
: OS↓, advanced BCLC stage |
CTC counts |
|
: microscopic vascular invasion |
|
|
: AFP ≥400 ng/mL |
|
Table 6.CfDNA for prognostication of HCC
Table 6.
|
Author |
Year |
Cohort |
Associated finding |
Isolation technique/biomarker |
|
Guo et al. [150] |
2024 |
293 HCC |
ctDNA methylation+ |
qMSP assay |
|
96 CHB/LC, 23 BHL, 147 HC |
: post-operative cumulative recurrence (HR 3.33; 95% CI 1.87–5.92) |
HCC specific DMR |
|
Sogbe et al. [134] |
2024 |
73 HCC |
Detectable ctDNA |
ULP-WGS |
|
: mPFS↓ (4.2 vs. 8.7 months) |
|
Zhao et al. [135] |
2022 |
80 HCC |
CTCs & ctDNA from PPWES |
NGS/PPWES, UPTS |
|
: RFS↓ (HR 11.88, 95% CI 4.06–34.75) |
|
Kim et al. [136] |
2020 |
107 HCC |
No SNV is associated with OS. |
ddPCR/ |
|
High-ctDNA group has lower OS. |
MLH1, PTEN, STK11, CTNNB1
|
|
Oversoe et al. [137] |
2020 |
95 HCC |
No TERT in non-HCC |
ddPCR/ |
|
45 LC |
mortality↑ (HR 2.16; 95% CI 1.20–3.88) |
TERT
|
|
Li et al. [138] |
2018 |
155 HCC |
Independent prognostic predictor of |
MSP |
|
: OS (P<0.01) |
IGFBP7 promoter methylation |
|
: early tumor recurrence (P=0.008) |
|
Xu et al. [48] |
2017 |
369 HCC (validation set) |
Panel score: independent risk for survival↓ (HR 1.54; 95% CI 1.25–1.92) |
Targeted bisulfite sequencing |
|
8 methylation markers |
|
Ji et al. [139] |
2014 |
72 HCC (TNM I-IV) |
MT1M & MT1G promoter methylation |
MSP/ |
|
37 LC |
: vascular invasion↑ or metastasis↑ |
MT1M, MT1G
|
|
Piciocchi et al. [87] |
2013 |
66 HCC |
cfDNA level>2 ng/uL |
qRT-PCR/hTERT
|
|
35 LC/ 41 CH (HCV) |
: shorter survival (37 vs. 24 months) |
Table 7.EVs for prognostication of HCC
Table 7.
|
Author |
Year |
Cohort |
Associated finding |
Cargo |
Cargo level |
Isolation technique target gene or RNA |
|
Sun et al. [140] |
2021 |
HCC 168 |
High S100A4: independent risk factor for Worse OS, RFS |
protein |
↑ |
RT-PCR |
|
S100A4
|
|
Lee et al. [159] |
2019 |
79 HCC |
Independent risk factor for shorter OS and faster progress |
lncRNA-ATB |
↑ |
RT-PCR |
|
miR-21 |
|
Wang et al. [160] |
2019 |
HCC 71 |
High circPTGR1 |
circPTGR1 |
↑ |
qRT-PCR |
|
NC 41 |
: lower survival rate |
miR449a/MET
|
|
Xue et al. [141] |
2018 |
85 HCC |
Cumulative survival: lower in high miR-93 (P=0.046) |
miR-93 |
↑ |
qRT-PCR |
|
TP53INP1, TIMP2, CDKN1A
|
|
Shi et al. [157] |
2018 |
126 HCC |
3 yr survival: HR 3.52 (95% CI 1.37–6.02) |
miR-638 |
↓ |
qRT-PCR |
|
21 HC |
5 yr survival: HR 2.80 (95% CI 1.24–4.31) |
miR-638
|
|
Xu et al. [142] |
2018 |
60 HCC |
High level of 2 lncRNA |
lncRNA |
↑ |
RT-PCR |
|
85 LC/ CHB 96 |
: associated with worse OS (log-rank test) |
ENSG00000258332.1
|
|
60 HC |
LINC000635
|
|
Xu et al. [123] |
2018 |
88 HCC |
High mRNA-hnRNPH1 |
mRNA |
↑ |
RT-PCR |
|
67 LC, CHB 68 |
: worse OS |
mRNA-hnRNPH1
|
|
68 HC |
|
|
Qu et al. [154] |
2017 |
30 HCC |
High miR-665
|
miRNA-665 |
↑ |
qRT-PCR/ |
|
10 NC |
: OS↓ |
MARK/ERK
|
|
Liu et al. [158] |
2017 |
HCC 128 |
Low miR-125 on multivariate analysis |
miR-125 |
↓ |
qRT-PCR |
|
TTR: HR 0.14 (95% CI 0.07–0.29) |
miR-125
|
|
OS: HR 0.36 (95% CI 0.18–0.74) |
|
Table 8.Ongoing clinical trials for HCC
Table 8.
|
Test category |
Trial identifier |
Estimated enrollment |
Subject |
Study type |
Primary outcome measures |
Status |
Ref. |
|
cfDNA |
NCT05064553 |
2,500 |
Increased risk for HCC |
Observational |
Show non-inferior sensitivity compared to ultrasound |
Recruiting |
[173] |
|
cfDNA |
NCT03694600 |
1,900 |
LC |
Observational |
Compare sensitivity and specificity to ultrasound |
Active, not recruiting |
[172] |
|
cfDNA |
NCT04111029 |
30 |
Unresectable HCC |
Observational |
Comparison of exome sequencing of tumor tissue and cfDNA after non-curative treatment |
Recruiting |
[177] |
|
cfDNA |
NCT06134973 |
1,000 |
Hepatic nodules in CLD patients |
Observational |
Establishment of risk assessment model |
Recruiting |
[178] |
|
ctDNA |
NCT04484636 |
400 |
Histologic confirmed HCC |
Interventional |
Distribution of mutations in solid tumor including HCC |
Completed |
[179] |
|
exo-miRNA |
NCT06342414 |
400 |
Confirmed HCC or ICC |
Observational |
Ability to differentiate HCC and ICC |
Recruiting |
[180] |
|
CTCs |
NCT04800497 |
70 |
HCC (BCLC 0-B) |
Observational |
Evaluate the association between variation in CTCs and prognosis |
Recruiting |
[181] |
|
cfDNA mutation profile |
NCT04965454 |
80 |
Unresectable HCC |
Interventional |
Predict a lack of objective response after 16 weeks of ICI |
Recruiting |
[182] |
|
Mutation in ctDNA |
NCT06028724 |
782 |
Histologically proven solid tumor including HCC |
Observational |
Real world prevalence of clinical useful mutations in solid tumors |
Recruiting |
[183] |
Abbreviations
acid citrate dextrose solution A
alpha 2 - HS glycoprotein
asialoglycoprotein receptor
area under the receiver operating characteristic
biospecimen research branch
barcelona clinic liver cancer
cell-free DNA methylome sequencing
cell heterogeneity-adjusted cLonal methylation
circular ubiquitin-like with PHD and ring finger domain 1 RNA
combined prognostic score
differentially methylated regions
ethylenediaminetetraacetic acid
epithelial to mesenchymal transition
epithelial cell adhesion molecule
fatty acid binding protein 1
food and drug administration
guanine nucleotide-binding protein subunit beta-4
immune checkpoint inhibitors
international society for extracellular vesicles
mesenchymal phenotype of CTCs
minimal information for blood extracellular vesicle research
magnetic resonance imaging
multitarget HCC blood test
national cancer institute
next-generation sequencing
non-small cell lung cancer
polymerase chain reaction
quantitative real-time PCR
retinol binding protein 4
single nucleotide variations
transarterial chemoembolization
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