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Review

Is Cell-Free DNA Testing in Hepatocellular Carcinoma Ready for Prime Time?

by
Sravan Jeepalyam
1,
Ankur Sheel
2,
Aslam Ejaz
3,
Eric Miller
4 and
Ashish Manne
5,*
1
Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS 66103, USA
2
Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH 43210, USA
3
Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center, 320 W. 10th Ave., M-260 Starling-Loving Hall, Columbus, OH 43210, USA
4
Department of Radiation Oncology, The Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
5
Department of Internal Medicine, Division of Medical Oncology, The Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(18), 14231; https://doi.org/10.3390/ijms241814231
Submission received: 28 August 2023 / Revised: 14 September 2023 / Accepted: 17 September 2023 / Published: 18 September 2023
(This article belongs to the Special Issue Epigenetics and Immunotherapy in Cancer)
Browse Figure
Review Reports Versions Notes

Abstract

:
Revamping the current biomarker landscape of hepatocellular carcinoma (HCC) with cell-free DNA (cfDNA) could improve overall outcomes. The use of commercially available cfDNA testing (also known as liquid biopsy) is limited by the low prevalence of targetable mutations and does not have any prognostic or predictive value. Thus, current cfDNA testing cannot be relied upon for perioperative risk stratification (POR), including early detection of recurrence, long-term surveillance, predicting outcomes, and treatment response. Prior evidence on cfDNA mutation profiling (non-specific detection or gene panel testing) suggests that it can be a reliable tool for POR and prognostication, but it still requires significant improvements. cfDNA methylation changes or epigenetic markers have not been explored extensively, but early studies have shown potential for it to be a prognostic biomarker tool. The predictive value of cfDNA (mutations and EM) to assist treatment selection (systemic therapy, immune-checkpoint inhibitor vs. tyrosine kinase inhibitor) and to monitor response to systemic and locoregional therapies should be a future area of focus. We highlighted the unmet needs in the HCC management and the current role of cfDNA testing in HCC in addressing them.

1. Introduction

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide, with a 5-year survival rate of 18% [1,2]. HCC management requires a multidisciplinary approach, and multiple societies around the world have proposed guidelines for risk-stratification and appropriate treatment selection [3,4]. In early/intermediate-stage tumors, curative surgical resection (SR) and locoregional therapies (LRT) ranging from image-guided ablation, transarterial chemoembolization (TACE), stereotactic body radiotherapy (SBRT), and transarterial radioembolization (TARE) are preferred in the first line. Systemic therapy (ST) with immune-checkpoint inhibitors (ICI) and tyrosine kinase inhibitors (TKI), and vascular endothelial growth factor inhibitors (VEGFi) are often reserved for unresectable/advanced tumors. In this review, we focused on the gaps in HCC management and summarized the current status of cfDNA testing in addressing them.

1.1. Unmet Need in HCC Management

SR for early-stage HCC may be curative, but outcomes are less favorable for patients with aggressive features (macrovascular invasion or MVI) found on final pathology [5,6]. The 5-year survival rate is around 75% in early-stage tumors and 35% in intermediate/advanced-stage tumors (median overall survival (OS) of 37 m) [5,7]. Median progression-free survival (PFS) after surgery is around 9 months, and 38% recur in less than 2 years [8]. Post-recurrence 5-year survival rate is around 43% and current evidence does not justify the use of adjuvant (AT) or neoadjuvant (NAT) as standard-of-care (SOC). However, interim results of IMbrave 050 (atezolizumab and bevacizumab vs. surveillance in high-risk HCC) presented at the American Association for Cancer Research (AACR) this year are encouraging, with a 28% improvement in progression-free survival (PFS) (HR= 0.72, 95% CI, 0.56, 0.93; p = 0.0120) with adjuvant therapy. The risk stratification is based on tumor burden, MVI, and differentiation.
LEGACY study, TRACE, and RASER trials have shown that TARE is a favorable option in uncomplicated solitary HCC in patients with Child–Pugh score (CP) A and good performance status (PS), but the failure of other prominent trials (SORAMIC, SARAH, SIRvsNIB) indicates that it may not be an ideal option over ST in the treatment of advanced tumors (CP B, high tumor burden, MVI) [9,10,11,12,13,14]. Milan’s prognostic scale (based on tumor location, MVI, total bilirubin, and tumor burden) has prognostic value based on retrospective review, but is not typically used in clinical practice [15,16]. SBRT is another local therapy for patients not eligible for resection. Recent data from RTOG 1112 showed an improvement in overall survival (OS) and PFS when SBRT was combined with sorafenib over sorafenib alone. Multiple other studies have demonstrated the utility of SBRT for the treatment of HCC [17,18,19,20,21,22,23,24,25].
Treatment selection (SR vs. TACE vs. SBRT vs. TARE vs. ST) is often based on the patient and tumor-related characteristics such as size, number, and location of the lesions, baseline liver functional status (by CP or Albumin-Bilirubin (ALBI) score), PS, and feasibility of the procedure intended. One of the main reasons for poor outcomes is the lack of biomarkers with a reliable prognostic or predictive value that guides HCC management. Alpha-fetoprotein (AFP) has poor sensitivity and specificity to detect recurrence or assess treatment response [26]. Similarly, imaging modalities (CT or computerized tomography and MRI or magnetic resonance imaging) lack the necessary accuracy to detect early recurrences, particularly after LRTs such as TARE or SBRT. Oftentimes, identification of residual disease or progression is delayed for months due to post-treatment changes seen early following treatment [27].
Genomic testing and tumor or cell-free DNA (cfDNA) do not have roles in HCC management in its current form except in identifying rare targetable mutations in advanced cancers. Circulating tumor DNA (ctDNA), genomic material from the tumor cells, is often used synonymously with cfDNA, but there is a considerable difference between them. cfDNA refers to DNA floating in the bloodstream, including ctDNA, circulating tumor cells (CTC), exosomal DNA, and DNA from normal cells [28]. The access to tissues is limited in HCC as it is generally not acquired for diagnosis, relying on distinctive imaging features. Hence, cfDNA testing is the main source of genomic testing for HCC. The use of cfDNA has the potential to address three areas of unmet need biomarker testing in HCC: (i) perioperative risk stratification (POR) to guide surgeons in identifying high-risk populations for recurrence and poor survival; (ii) prognostic biomarkers to estimate poor outcomes in intermediate/unresectable HCC; (iii) predictive biomarkers to select appropriate ST (ICI vs. TKI) or LT (SBRT vs. TACE vs. TARE) and identify the population who benefit from post-procedural ST.

1.2. cfDNA Landscape in HCC

Mutation profiling of cfDNA in HCC patients is commercially available and accessible for daily practice. The most frequently detected mutations are TP53 (32–80%), TERT (42–69%), CTNNB1 (17–42%), ATM (25–39%), and ARID1A (7–13%) [29,30,31,32,33,34,35,36]. Other important mutations with <10% prevalence are PIK3CA, CDKN2A, BRAF, MYC, and HER2. Concordance rates between tumor and cfDNA are reliable (52 to 83%), and especially relevantly, 27–72% of additional mutations were identified in cfDNA that were not identified in the tumor sample [31,34,37,38,39,40,41]. Multiple studies have reported high detection rates (60–100%) of non-specific cfDNA mutations, but their clinical importance is unclear [32,33,42,43,44,45]. Alternatively, epigenetic marker (EM) testing (DNA-methylation, histone modification, chromatin remodeling, and RNA-associated silencing) in HCC is still in its primitive stage, and there is increasingly encouraging evidence in other solid tumors [46,47]. This paper will focus on DNA-methylation changes and will be addressed as EM going forward. Exploring cfDNA EM is well supported by overwhelming evidence from tumor (tissue)-based studies in the literature [48,49,50,51,52,53].
HCC patients tend to have lower methylation levels (median: 59.3% vs. 76.9%) in cfDNA than normal controls [54]. Signatures constructed after analyzing the data from whole genome methylation sequencing (WGMS) were proposed in some studies for diagnosis and prognostication, but most prior studies tested a panel of EM [55]. There is a commercially available multianalyte test that uses a panel of more than 500 genes for methylation [56]. Smaller panels with four promoters, APC, GSTP1, RASSF1A, and SFRP1, also proved to be effective [57]. All HCC patients had at least one methylated marker from this panel. Other studies with individual markers showed good prevalence, such as LINE1 (100%), RASSF1A (87%), p16 (76%), IGFBP7 (65%), XPO4 (64%), APC (62%), CCND2 (55%), TFPI2 (47%), and DAPK (41%) [58,59,60,61,62,63]. LINE-1 is hypomethylated and the rest are hypermethylated. Concordance rates with tissue methylation profiling are reliable (68% to 89%), making it a formidable biomarker for clinical use [64]. Newer technology known as enzymatic methylation sequencing is very sensitive and requires less blood than traditional bisulfite methylation sequencing [65]. Apart from straightforward mutation or methylation testing, many cfDNA-based studies, such as DNA integrity index (DII), combinations of mutations and microRNA or circulating tumor cell and methylation, and cfDNA fragmentation patterns, are being explored to develop new biomarkers for HCC [56,66,67,68,69].
Etiology-specific cfDNA markers were reported in previous studies that can help in certain situations. Hepatitis B (HBV) carriers with HCC tend to have higher circulating ERBB2 and TERT mutations, higher methylation rates in RASSF1, TFPI2, TRG5 (along with AFP), and XPO4, and low methylation rates in CDKN2A than those without HCC [43,59,63,70,71,72]. Higher RASSF1 methylation rates are frequent in hepatitis C (HCV) patients with HCC (compared to HCC-negative) [73]. Some of them can help in detecting the recurrence (e.g., virus-host chimera DNA (vh-DNA), generated from junctions of HBV integration in the HCC chromosome in HBV-HCC patients, or forecasting the outcomes (e.g., higher cfDNA levels in HCV-carriers) [74,75]. The current evidence on cfDNA testing in HCC is summarized below based on the areas of unmet need discussed above.

2. Perioperative Risk Stratification (POR) with cfDNA Testing in HCC

The prior cfDNA studies for POR can be broadly divided into three main categories (Table 1): (a) the detection and quantification of cfDNA levels (high vs. low); (b) detection and quantification by mutation allelic fraction (MAF) of specific mutations using small panels (hot-spots) or larger panels with next-generation sequencing (NGS) or whole exosome sequencing (WES); (c) EM. Evidence suggests that mutation profiling with larger panels (by NGS or WES) are not valuable over carefully selected hot-spot panels, and epigenetic testing needs a considerable amount of work before we can use it in clinical practice.
cfDNA testing preoperatively (preop) helps select a population of patients who can benefit from NAT, and by serial testing, we can determine the ideal time for resection. Similarly, postoperative (post-op) serial testing can complement current SOC (AFP and imaging). The mutation profiling was shown to be more robust than imaging and other protein markers such as AFP, AFP-L3%, and des-gama-carboxy prothrombin (DCP) [76]. The ctDNA testing detected recurrence on an average of 4.6 months earlier than imaging, which is the current gold standard [77]. Patients with postoperative ctDNA-positivity were identified as significant adverse risk factors for PFS and OS. DCP-positivity combined with ctDNA was shown to increase sensitivity for detection of minimal residual disease.
A tissue agnostic serial ctDNA mutation test proved useful to predict recurrence and microvascular invasion by serial monitoring [40]. The ctDNA detection was identified by PCR on preoperative samples, and whole exosome sequencing (WES) was performed on postoperative cfDNA samples. Serial ctDNA testing was more sensitive for poor recurrence rates than AFP or DCP. Interestingly, the ctDNA detection rate was lower (15%) than reported in other studies and there were non-synonymous mutations both in cfDNA and tumor tissue.
Table 1. cfDNA testing for perioperative risk stratification in hepatocellular carcinoma.
Table 1. cfDNA testing for perioperative risk stratification in hepatocellular carcinoma.
Publication (n) Methods Tested Markers Findings Additional Information
cfDNA levelRen et al., 2006 [78] (79) Preop UV transilluminatorCirculating DNA levelsHigh levels are associated with worse PFS and OS (p = 0.017 and 0.001, respectively)Cut-off ≥ 36.6 ng/mL

Circulating DNA level was associated with tumor size (≤5 vs. >5 cm) (p = 0.008) and TNM (I-II vs. III-IV) stage (p = 0.040).

TNM-6th edition
Tokuhisa et al., 2007 [75] (87) Preop Polymerase chain reaction (PCR)GSTP1 gene Patients with high cfDNA levels had shorter OS (p = 0.017).Cut-off is 117.8 ng·mL−1

High cfDNA level to be an independent prognostic factor for OS (HR, 3.8; 95% CI, 1.7–8.5; p = 0.001) and cancer recurrence in distant organs (HR, 4.5; 95% CI, 1.3–14.9; p = 0.014).

Higher cfDNA levels are associated with early intrahepatic recurrence but were not significant on logistic regression.
MutationsFu et al., 2022 [79] (258) Preop PCRHigh-risk genes APC, ARID1A, CDKN2A, FAT1, LRP1B, MAP3K1, PREX2, TERT, and TP53 Number of mutant genes. Early recurrence (HR = 2.2, p < 0.001) and worse PFS in high-risk group (HR = 13.0, p < 0.001)Low vs. mid vs. high-level based on the number of mutant genes.

Nomogram constructed with TNM staging and risk level has accuracy with C index of 0.76 (95% CI 0.70–0.82).
Liao et al., 2016 (41) Preop [80]PCRHotspot mutations in TERT, CTNNB1, and TP53ctDNA+ tumors have poor PFS (89 vs. 365 days, p< 0.001) and are at high risk of vascular invasion (p = 0.041)100% specificity of recurrence in ctDNA+ patients

No relationship between detectable mutations and concentration of cfDNA (p = 0.818).
Ye et al., 2022 (96) Postop [81]NGSHot exons of 293 genes ctDNA-positive tumors have worse PFS and OS.

ctDNA + and high-AFP have the worst PFS		Detection of even one mutation was considered ctDNA+

AFP cutoff was >400 ng/mL
An et al., 2019 (26) Postop [82]NGS354 genes ctDNA-positive patients have worse PFS (17.5 vs. 6.7 months (m), HR = 7.655, p < 0.0001)ctDNA positivity is the independent risk factor for prognosis (HR = 10.293, p < 0.0001)

The number of mutations, VAF, and ctDNA concentration correlated with tumor size.
Zhou et al., 2020 (97) Postop [83]NGS 1021 gene panelctDNA + associated with recurrence (100%), shorter PFS (5 m vs. NR, p < 0.001) ctDNA+ with high AFP (>400 ng/mL) is the worst prognostic feature for recurrence.
Cai et al., 2019 (34) Serial [76]Targeted sequencing for SNV and WGS for CNVsTissue agnostic Somatic SNVs and CNVsctDNA+ have worse PFS and OS (p =0.001 for both)Can predict recurrence on an average of 4.6 m before imaging could catch it

Better than AFP, AFP-L3%, and Des-Gamma-Carboxy Prothrombin (DCP).
Ono et al., 2015 (46) Serial [40]ctDNA detection by PCR on preop sample.
Whole exome sequencing (WES) of cfDNA in postop sample 		Tissue agnostic.
Whole genome sequencing (WGS) on tumor tissue		High recurrence rates and extra-hepatic metastasis
ctDNA+ group (p = 0.01 and 0.03, respectively)		Detected in 7/46 patients

ctDNA+ patients are at high risk of microscopic vascular invasion (OR 6.10; 95% CI, 1.11–33.33, p = 0.038)

Concordance rate was 83%

Included patients with hepatectomy and liver transplant, and one patient with HCC-CCA variant
Zhu et al., 2022 (41) Serial [42]NGSNGSctDNA-positive (both pre- and post- op) patients tend have intrahepatic recurrence, and have shorter PFS

Preop ctDNA-positive tumors tend be larger (>5 cm), have MVI, and higher differentiation. 		Detection of NRAS, NEF2L2, and MET mutations associated with shorter PFS

Higher MAF preop is an is a strong prognostic factor for PFS
Epigenetic markersTsutsui et al., 2010 (70) Preop [61]PCRCCND2 (methylated)Patients with CCND2 gene methylation have shorter PFS (p = 0.02)Cut off was >70 pg/mL serum)

CCDN2 methylation is an independent risk factor for PFS (HR = 1.866, 95% CI: 1.106–3.149).
Li et al., 2018 (155) Postop [84] PCRIGFBP7 methylation statusIGFBP7 promoter methylation was significantly correlated with OS (p < 0.001), was an independent prognostic predictor for OS (p = 0.000) and early tumor recurrence (p = 0.008), and was associated with vascular invasion (p = 0.014)DNMTs mRNA levels, malondialdehyde (MDA), xanthine oxidase (XOD), glutathione hormone (GSH), and glutathione-S-transferases (GST) levels were also tested

MDA and XOD levels were significantly higher in the IGFBP7 methylated group than the unmethylated group, while GSH level was lower in the methylated group than in unmethylated group (DA p  = 0.001) (XOD p < 0.001).

The DNMT1 and DNMT3a mRNA levels were higher in the IGFBP7 methylated group than unmethylated group (p = 0.002)
Preop—preoperative sample was tested, postop- postoperative sample was tested, UV—ultraviolet. PFS—progression free survival, OS—overall survival, HR—hazard ratio. AFP—alpha fetoprotein, HCC—hepatocellular carcinoma. CCA—cholangiocarcinoma, OR—odds ratio, MAF—mutation allelic fraction.

3. Prognostic Value of cfDNA Testing in HCC

Reliable blood-based (cfDNA, proteins, or CTC) markers that can foretell poor clinicopathological features beyond standard imaging modalities (CT or MRI) may be helpful in treatment planning for patients with HCC (summarized in Table 2). Most prior studies correlated detection of mutations in particular genes such as TERT or BCL9 and RPS6KB1 with outcomes (OS and PFS) or advanced pathological features such as tumor size, PVTT, or TNM stage [34,70,85,86]. The quantitative analysis by mutation allelic fraction (MAF) in cfDNA testing also proved to be useful in some studies [31,44].
EM were less explored than mutations in HCC. EM were usually single-gene-based, such as LINE1, TFPI2, IGFB7, or small panels (APC, GSTP1, RASSF1A, and SFRP1) [59,87,88]. Interestingly, one study by Xu at al. constructed two major models based on methylation markers (CpG sites): one for diagnosis (n = 10 genes) and another for prognosis (n = 8). The former was also valuable in predicting tumor burden and poor outcomes [55].
Table 2. cfDNA mutations and epigenetic markers with prognostic value in hepatocellular carcinoma (HCC).
Table 2. cfDNA mutations and epigenetic markers with prognostic value in hepatocellular carcinoma (HCC).
Publication (n) Methods Tested Markers Findings Additional Information
Mutations Oversoe et al., 2020 (95) [34]Digital droplet PCR (ddPCR)TERT (C228T)TERT mutation conferred increased mortality (HR 2.16 (1.20–3.88), p = 0.01) when detected in plasma (adjusted HR 2.16 (1.20–3.88), p = 0.010), but not in tumor (adjusted HR 1.11 (0.35–3.56), p = 0.860). In treatment-naïve HCC, mortality was more pronounced (HR 4.11 (1.73–9.76) p= 0.001)

Detectable plasma TERT mutation was correlated with advanced TNM stage and vascular invasion (p < 0.0005)

Non-concordance was associated with an early TNM stage.
Jiao et al., 2018 (215) [85]ddPCRTERT (C228T and C250T)Mutation-positive tumors with cirrhosis have worse prognosis (p = 0.0042). This association was significant in MVAMutations associated with worse OS (p = 0.0062) for all HCC patients (with and without cirrhosis). This association was not significant on MVA
Yang et al., 2011 (60) [70]Quantitative fluorescent polymerase chain reaction (FQ-PCR)TERTPlasma TERT DNA levels were closely related to tumor size (<5 vs. 5–10 vs. >10 cm), PVTT and TNM (I-II vs. III-IV) stage (p = 0.013, p = 0.010, and p = 0.029, respectively)Cutoff–variable
Survival analysis is not available.
Youssef et al., 2023 (100) [86]PCRBCL9 and RPS6KB1BCL9 gain and BCL9 + RPS6KB1 gain led to higher mortality rates and reduced survival times.Prevalence of CNV gain in BCL9 and RPS6KB1 genes was detected in 14% and 24% of patients, respectively.

Gain in both genes showed a high risk of HCC with elevated liver enzymes, Schistosomiasis, BCLC C, and PS > 1
He et al., 2019 (29) [31]. Next Gen Sequencing (NGS)2800 COSMIC hotspots in 50 high-frequency mutations in tumor genes.MAF in the TP53, CTNNB1, PIK3CA, and CDKN2A increase the risk of larger tumors (>5 cm)

MAF in the TP53, RET, FGFR3 and APC associated with multiple tumors or distant metastasis		35 mutant genes/50 tested were noted in tumors. TP53 was detected in 50% of tumors.

Cut-off for MAF is >1%
Kim et al., 2020 (107) [44]NGS 2924 SNVs in 69 genes MLH1 SNV, in combination with an increased ctDNA level (≥5.77 ng/mL), predicted poor overall survival among OS was worse in HCC with high ctDNA (vs. low, p = 0.0014); High ctDNA with MLH1 mutation vs. high ctDNA + MLH1 witld type (WT) vs. low ctDNA + MLH1 WT
Epigenetic markersTangkijvanich et al., 2007 (85) [88]Combined bisulfite restriction analysis PCRSerum hypomethylation status of LINE-1 repetitive sequencesHigh serum LINE-1 hypomethylation associated with worse OS (10.5 vs. 5.5 m, p = 0.012)High levels are associated with larger (>5 cm) and advanced (based on CLIP scoring).

Cut off- 53.17 ± 7.74%,
Huang et al., 2011 (72) [57]MSRE-qPCRAPC, GSTP1, RASSF1A, and SFRP1High levels of APC or RASSF1 methylation have worse outcomes.

GSTP1 has a trend towards worst outcome (p = 0.062)		Methylated RASSF1A alone was independent risk factor for OS (hazard ratio = 3.262, 95% CI: 1.476–7.209, p = 0.003).
Sun et al., 2013 (43) [59]PCRTFPI2 methylation percentageHigher stage is associated with increased TFPI2 methylation percentage Higher percentage with stage—39, 38, 64, 75, from Stages I–IV, respectively

(percentage = methylated/unmethylated)
Li et al., 2014 (136) [87]PCRIGFBP7Methylation is associated with vascular invasion (84% versus 60%, p = 0.010). Frequency of serum IGFBP7 promoter methylation in HCC patients was 65% (89/136)
Xu et al., 2017 (1098) [55]Deep sequencing of bis-DNA target-captured with molecular-inversionBMPR1A
PSD
ARHGAP25
KLF3
PLAC8
ATXN1
Chr 6:170
Chr 6:3
ATAD2
Chr 8:20		Higher combined diagnostic score (cd-score) was associated with advanced TNM stage, poor response, early recurrence, and tumor burden cd-score also has a diagnostic value.
Xu et al., 2017 (1098) [55]Deep sequencing of bis-DNA target-captured with molecular-inversionSH3PXD2A
C11orf9
Chr 17:78
PPFIA1
SERPINB5
NOTCH3
TMEM8B
GRHL2		High combined prognosis score (cp-score) was associated with poor survival.

Cp-score and TNM staging better than individual alone. 		Cut-off for cp-score was >0.24.

Cp-score was validated by MVA (HR: 2.405; 95%; p < 0.00)
HR—hazard ratio; OS—overall survival; BCLC—Barcelona Clinic Liver Cancer; MVA—macrovascualr invasion; PVTT—portal vein tumor thrombosis; MAF—Mutant allele frequency; PS—performance status; SNV—single nucleotide variants; CNV—gene copy number variants; MSRE—methylation-sensitive restriction enzymes-based quantitative.

4. Predictive Biomarkers

Genomic biomarkers have not been studied to predict the response to common LRTs performed for HCC such as TACE, TARE, SBRT, or systemic therapy (ICI and TKI). Very few studies have explored this area of HCC management and are summarized in Table 3. These studies follow the same trend as prognostic or POR studies. Most of these genomic biomarkers are non-specific (cfDNA levels), while some studies have used NGS or hotspot panels on pre-treatment samples.
Sefriouri et. al studied the change in MAF of TERT mutations and cfDNA levels from baseline, post-TACE (day 2), and one month later [89]. They reported that patients who do not show any drop/change in either (TERT or cfDNA) are at an increased risk of having residual disease or progression. In another study, patients with TERT mutations who received TACE (44/130, rest had TKIs) had poorer OS (and large size) [90]. Higher cfDNA levels post-radiation (SBRT and external beam radiation) and TARE (from cfDNA testing on patients in SORAMIC trial before systemic therapy) were also indicators of poor response (Table 2) [91,92]. The latter study also gives us insight into biomarkers for sorafenib response as well. There were no studies that used cfDNA EM for this purpose.
Nakatsuka et al. reported that higher baseline cfDNA levels were an independent negative risk factor among HCC patients receiving LRTs (TACE and RFA) and systemic therapy (TKIs and ramucirumab) [93]. Interestingly, a greater rise in cfDNA level post-therapy (day 2 of LRT and average of day 3 after systemic therapy) was an indicator of a favorable response. Higher baseline cfDNA levels and AFP (>400 ng/mL), along with TERT positivity, were associated with poor outcomes in patients treated with atezolizumab/bevacizumab combination. Patients with mutations in PIK3CA/mTOR pathway genes (PIK3CA, PTEN, TSC1, TSC2, and RPS6KA3) have poor response rates to TKIs, while mutations in WNT pathways had no effect on ICI response [29]. EM were not explored enough as predictive biomarkers for HCC.
Table 3. cfDNA testing for predicting response to locoregional therapy and systemic therapy.
Table 3. cfDNA testing for predicting response to locoregional therapy and systemic therapy.
PublicationMethodsTested MarkersFindingsAdditional Information-Frequency
Sefrioui et al., 2022 (28) [89]

TACE		ctDNA for TERT ddPCR

cfDNA was quantified by the fluorometric method.		TERT hotspot

cfDNA levels

Samples collected prospectively: baseline (D − 1), day 2 (D + 2), and one month later (DM)		High-risk patients progressed in one month (80.0% vs. 4.3%, p = 0.001) and worse PFS (1.3 vs. 10.3 months, p = 0.002)Score based on changes in cfDNA and ctDNA. High-risk or non-responders = no change in both; low-risk = change in at least one marker.

cfDNA change from D-1 to D M+ 1 of > 31.4% (44.4% vs. 3.8%) and ctDNA change >0% as high risk (50.0% vs. 5.0%)
Park et al., 2018 (55, 34 fractionated XRT and 21 SBRT) [91]Ultraviolet-visible spectrophotometercfDNA levels before and after XRTHigh pre-XRT cfDNA levels are associated with advanced disease (p = 0.049) and larger tumors (p = 0.017).

High post-XRT cfDNA level is associated with poor response rates (p = 0.017), local control (p = 0.006), or intrahepatic recurrence rates (p = 0.035)		Cut-off values for cfDNA levels: 33.65 ng/mL before XRT is and 37.25 ng/mL after CRT)
Hirai et al., 2021 (130, 44 TACE and 86 with lenvatinib or sorafenib) [90]Digital droplet- polymerase chain reaction (dd-PCR)TERTDetection of TERT mutation and high fractional abundances (≥1%) associated with worse OS (p< 0.01).TERT promoter mutations correlated with large intrahepatic tumor size (p = 0.05) and high des-gamma carboxyprothrombin (p = 0.005)
Alunni-Fabbroni et al., 2019 (13, 10, TARE + sorafenib, 2 RFA + sorafenib, 1placebo) [92]Next-generation sequencing (NGS)597 gene panel.

Samples were collected between local therapy and beginning of sorafenib-based systemic therapy (T1), then at 3 time points—8 weeks apart from the date of starting sorafenib (T2, T3, T4)		High cfDNA concentrations at T1 associated with metastasis (p = 0.012)

CYP2B6 variant detection at T1–T2 associated with worse OS (p = 0.013)		High T2 has a trend towards significance for metastasis (p = 0.07)

T1 and T2 concentration do not affect OS; however, high levels at T3 (p = 0.057) and T4 (p = 0.095) have trends.

Early detection of BAX (for MVI, p = 0.014) and HFN1A (for liver cirrhosis, p = 0.032) confer worse clinicopathological features.
Oh et al., 2019 (151) [94]

Sorafenib		Whole genome sequencing

Baseline		cfDNA level,
VEGFA-to-EIF2C1 ratios

I score for genomic instability.		Patients who failed sorafenib had significantly higher cfDNA levels (0.82 vs. 0.63 ng/μL; p = 0.006) and I-scores (3405 vs. 1024; p = 0.0017) than those achieving disease control.

VEGFA: EIF2C1 ratio was not significantly associated with treatment outcomes.		94% had previous therapy

cfDNA-high group had a worse time to progression compared to the low group (2.2 vs. 4.1 months; HR = 1.71; p = 0.002) and OS (4.1 vs. 14.8 months; HR = 3.50; p < 0.0001)

Genome instability-high group progressed earlier (2.2 vs. 4.1 months; HR = 2.09; p < 0.0001) and had worse OS (4.6 vs. 14.8 months; HR = 3.35; p < 0.0001).
Fuji et al., 2021 (24) [30]

Lenvatinib		NGS

cfDNA at the baseline and 4 weeks later		Guardant 360 panelReduction MAFmean 4 weeks after treatment was useful in predicting PR and CR, but not for SD or PDBaseline mutations and MAF change were not indicative of OS or PFS

MAFmean change ≥ 0 vs. <0 HR 8.4, 95% CI = 2.3–31.2, p = 0.002) as an independent factor was associated with poor PFS.

MAFmean reduction was more sensitive than AFP
Fu et al., 2022 [79]
(n = 258, lenvatinib + ICI)		PCRHigh-risk genes APC, ARID1A, CDKN2A, FAT1, LRP1B, MAP3K1, PREX2, TERT, and TP53Preserved FAT1 or LRP1B variants without TP53 associated with poor PFS (HR = 17.1, p < 0.001).Samples were collected post-surgery
Nakatsuka et al., 2021 (100 pre and 87 post treatment) [93]

Treatments received include TACE, RFA (day 2), TKIs (lenvatinib, 27; sorafenib, 6; regorafenib, 1), and 1 ramucirumab *		Targeted ultra-deep sequencing22,0009 coverage in a panel of 275 cancer-related genesGreater increase in the post-treatment cfDNA levels (>66.5 ng/mL) associated with increased response (AUC of 0.807, sensitivity 60.0%, specificity 92.9%)In particular, the detection rate increased significantly from 31 to 54% in the systemic-therapy-treated cases (p = 0.045).

A higher baseline cfDNA (>70.7 ng/mL) was associated with poor survival (5.5 m vs. 13.7 m, p < 0.001) and is an independent factor for OS.

In Lenvatinib responders,
AMER1, MLL3, and NOTCH2 were mutated.
Matsumae et al., 2022 (85) [95]

(Atezolizumab/bevacizumab)		.Custom-made panel for detecting mutations in 25 HCC-related cancer genesHigher cfDNA levels have poor objective response rates (PR and CR, p = 0.03) and PFS (p = 0.021)

TERT-positive patients have poor OS (p = 0.001), but no difference in PFS.		Higher plasma cfDNA (≥2.23 ng/µL), TERT positivity, high AFP (>400 ng/mL) were independent prognostic factors for OS

OS–TERT+ and AFP− high < TERT+ or AFP− high < TERT− and AFP low (p < 0.001)
Von Feleden et al., 2021 (51, TKI = 23; ICI = 38) [29]Ultra-deep sequencing for 25 genes

ddPCR for TERT		PI3K/mTOR pathway genes for patients on TKI

WNT pathway genes for patients on ICI		Mutation-positive patients have lower PFS
(2.1 vs. 3.7 months, p < 0.001)

No effect of mutations in WNT pathway on ICI response		Serial testing–not clear if it helps in predicting response

In some patients’ primary resistance, there is no change in mutations profiles, but there is increase in VA.

In some patients with partial response, mutations have disappeared—not clear if they help in monitoring response
TACE—Transarterial chemo embolization; PFS—progression-free survival; OS—overall survival; XRT—radiation; SBRT—stereotactic radiation therapy; HR—hazards ratio; MVI—macrovascular invasion; MAF—mutation allelic fraction; TKI—tyrosine kinase inhibitor; ICI—immune-checkpoint inhibitor; * post treatment samples were collected on day 2 for TACE and RFA (radiofrequency ablation), and average of day 3 for systemic therapy.

5. Conclusions

HCC is rising in incidence and overall outcomes remain poor. Despite having robust screening procedures in place, patients are often diagnosed in advanced stages with limited effective treatment options. A lack of reliable biomarkers that could improve current POR and predict or monitor the response to LRT and ST presents a clinical challenge for patients and treating physicians alike. One promising area of interest involves the use of cfDNA testing; however, serial improvements are needed before widespread use. Early reports of EM are promising and should be further explored on how to best incorporate it in current practice (summarized in Figure 1 below). Non-specific cfDNA level testing can help in monitoring treatment response or assessing the tumor burden. Its value without specific mutation or EM testing is questionable in POR. Developing models with specific mutations and EM in blood with high detectable rates will provide a non-invasive biomarker tool for risk-stratification perioperatively, estimate the survival, be vigilant of advanced pathological features (tumor size, PVTT), and help with treatment selection resistance monitoring. cfDNA testing to assist treatment selection (ST vs. LRT) in non-metastatic advanced and intermediate stage HCC and monitoring the treatment response could significantly impact outcomes in HCC patients.

Author Contributions

Conceptualization by A.M.; writing—original draft preparation, A.M. and S.J.; writing—review and editing, A.E.; writing—review, E.M.; writing—review, A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would also like to thank biorender.com (accessed on 28 August 2023) for the figures.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vogel, A.; Meyer, T.; Sapisochin, G.; Salem, R.; Saborowski, A. Hepatocellular carcinoma. Lancet 2022, 400, 1345–1362. [Google Scholar] [CrossRef]
  2. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 2022, 72, 7–33. [Google Scholar] [CrossRef]
  3. Singal, A.G.; Llovet, J.M.; Yarchoan, M.; Mehta, N.; Heimbach, J.K.; Dawson, L.A.; Jou, J.H.; Kulik, L.M.; Agopian, V.G.; Marrero, J.A.; et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology 2023. [Google Scholar] [CrossRef] [PubMed]
  4. Reig, M.; Forner, A.; Rimola, J.; Ferrer-Fàbrega, J.; Burrel, M.; Garcia-Criado, Á.; Kelley, R.K.; Galle, P.R.; Mazzaferro, V.; Salem, R.; et al. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. J. Hepatol. 2021, 76, 681–693. [Google Scholar] [CrossRef] [PubMed]
  5. Glantzounis, G.K.; Paliouras, A.; Stylianidi, M.C.; Milionis, H.; Tzimas, P.; Roukos, D.; Pentheroudakis, G.; Felekouras, E. The role of liver resection in the management of intermediate and advanced stage hepatocellular carcinoma. A systematic review. Eur. J. Surg. Oncol. 2018, 44, 195–208. [Google Scholar] [CrossRef] [PubMed]
  6. Yu, J.-J.; Li, Y.-N.; Shu, C.; Yang, H.-Y.; Huang, Z.; Tao, R.; Chen, Y.-Y.; Chen, X.-P.; Xiao, W. Prognostic value of preoperative circulating tumor cells for hepatocellular carcinoma with portal vein tumor thrombosis: A propensity score analysis. J. Cancer Res. Clin. Oncol. 2023, 149, 8981–8991. [Google Scholar] [CrossRef] [PubMed]
  7. Lee, D.H.; Kim, D.; Park, Y.H.; Yoon, J.; Kim, J.S. Long-term surgical outcomes in patients with hepatocellular carcinoma undergoing laparoscopic vs. open liver resection: A retrospective and propensity score-matched study. Asian J. Surg. 2021, 44, 206–212. [Google Scholar] [CrossRef] [PubMed]
  8. Moazzam, Z.; Alaimo, L.; Endo, Y.; Lima, H.A.; Woldesenbet, S.; Rueda, B.O.; Yang, J.; Ratti, F.; Marques, H.P.; Cauchy, F.; et al. A Prognostic Model To Predict Survival After Recurrence Among Patients With Recurrent Hepatocellular Carcinoma. Ann. Surg. 2023. [Google Scholar] [CrossRef] [PubMed]
  9. Salem, R.; Johnson, G.E.; Kim, E.; Riaz, A.; Bishay, V.; Boucher, E.; Fowers, K.; Lewandowski, R.; Padia, S.A. Yttrium-90 Radioembolization for the Treatment of Solitary, Unresectable HCC: The LEGACY Study. Hepatology 2021, 74, 2342–2352. [Google Scholar] [CrossRef]
  10. Ricke, J.; Klumpen, H.J.; Amthauer, H.; Bargellini, I.; Bartenstein, P.; de Toni, E.N.; Gasbarrini, A.; Pech, M.; Peck-Radosavljevic, M.; Popovic, P.; et al. Impact of combined selective internal radiation therapy and sorafenib on survival in advanced hepatocellular carcinoma. J. Hepatol. 2019, 71, 1164–1174. [Google Scholar] [CrossRef]
  11. Chow, P.K.H.; Gandhi, M.; Tan, S.-B.; Khin, M.W.; Khasbazar, A.; Ong, J.; Choo, S.P.; Cheow, P.C.; Chotipanich, C.; Lim, K.; et al. SIRveNIB: Selective Internal Radiation Therapy Versus Sorafenib in Asia-Pacific Patients With Hepatocellular Carcinoma. J. Clin. Oncol. 2018, 36, 1913–1921. [Google Scholar] [CrossRef]
  12. Vilgrain, V.; Pereira, H.; Assenat, E.; Guiu, B.; Ilonca, A.D.; Pageaux, G.P.; Sibert, A.; Bouattour, M.; Lebtahi, R.; Allaham, W.; et al. Efficacy and safety of selective internal radiotherapy with yttrium-90 resin microspheres compared with sorafenib in locally advanced and inoperable hepatocellular carcinoma (SARAH): An open-label randomised controlled phase 3 trial. Lancet Oncol. 2017, 18, 1624–1636. [Google Scholar] [CrossRef] [PubMed]
  13. Kim, E.; Sher, A.; Abboud, G.; Schwartz, M.; Facciuto, M.; Tabrizian, P.; Knešaurek, K.; Fischman, A.; Patel, R.; Nowakowski, S.; et al. Radiation segmentectomy for curative intent of unresectable very early to early stage hepatocellular carcinoma (RASER): A single-centre, single-arm study. Lancet Gastroenterol. Hepatol. 2022, 7, 843–850. [Google Scholar] [CrossRef] [PubMed]
  14. Dhondt, E.; Lambert, B.; Hermie, L.; Huyck, L.; Vanlangenhove, P.; Geerts, A.; Verhelst, X.; Aerts, M.; Vanlander, A.; Berrevoet, F.; et al. 90Y Radioembolization versus Drug-eluting Bead Chemoembolization for Unresectable Hepatocellular Carcinoma: Results from the TRACE Phase II Randomized Controlled Trial. Radiology 2022, 303, 699–710. [Google Scholar] [CrossRef] [PubMed]
  15. Bargellini, I.; Scalise, P.; Boni, G.; Traino, C.A.; Bozzi, E.; Lorenzoni, G.; Crocetti, L.; Cioni, R. Yttrium-90 Radioembolization for Hepatocellular Carcinoma with Portal Vein Invasion: Validation of the Milan Prognostic Score. J. Vasc. Interv. Radiol. 2020, 31, 2028–2032. [Google Scholar] [CrossRef]
  16. Spreafico, C.; Sposito, C.; Vaiani, M.; Cascella, T.; Bhoori, S.; Morosi, C.; Lanocita, R.; Romito, R.; Chiesa, C.; Maccauro, M.; et al. Development of a prognostic score to predict response to Yttrium-90 radioembolization for hepatocellular carcinoma with portal vein invasion. J. Hepatol. 2018, 68, 724–732. [Google Scholar] [CrossRef]
  17. Kimura, T.; Fujiwara, T.; Kameoka, T.; Adachi, Y.; Kariya, S. The Current Role of Stereotactic Body Radiation Therapy (SBRT) in Hepatocellular Carcinoma (HCC). Cancers 2022, 14, 4383. [Google Scholar] [CrossRef]
  18. Durand-Labrunie, J.; Baumann, A.-S.; Ayav, A.; Laurent, V.; Boleslawski, E.; Cattan, S.; Bogart, E.; Le Deley, M.-C.; Steen, V.; Lacornerie, T.; et al. Curative Irradiation Treatment of Hepatocellular Carcinoma: A Multicenter Phase 2 Trial. Int. J. Radiat. Oncol. 2020, 107, 116–125. [Google Scholar] [CrossRef]
  19. Jang, W.I.; Bae, S.H.; Kim, M.S.; Han, C.J.; Park, S.C.; Kim, S.B.; Cho, E.H.; Choi, C.W.; Kim, K.S.; Hwang, S.; et al. A phase 2 multicenter study of stereotactic body radiotherapy for hepatocellular carcinoma: Safety and efficacy. Cancer 2020, 126, 363–372. [Google Scholar] [CrossRef]
  20. Takeda, A.; Sanuki, N.; Tsurugai, Y.; Iwabuchi, S.; Matsunaga, K.; Ebinuma, H.; Imajo, K.; Aoki, Y.; Saito, H.; Kunieda, E. Phase 2 study of stereotactic body radiotherapy and optional transarterial chemoembolization for solitary hepatocellular carcinoma not amenable to resection and radiofrequency ablation. Cancer 2016, 122, 2041–2049. [Google Scholar] [CrossRef]
  21. Andolino, D.L.; Johnson, C.S.; Maluccio, M.; Kwo, P.; Tector, A.J.; Zook, J.; Johnstone, P.A.S.; Cardenes, H.R. Stereotactic Body Radiotherapy for Primary Hepatocellular Carcinoma. Int. J. Radiat. Oncol. 2011, 81, e447–e453. [Google Scholar] [CrossRef]
  22. Kang, J.-K.; Kim, M.-S.; Cho, C.K.; Yang, K.M.; Yoo, H.J.; Kim, J.H.; Bae, S.H.; Jung, D.H.; Kim, K.B.; Lee, D.H.; et al. Stereotactic body radiation therapy for inoperable hepatocellular carcinoma as a local salvage treatment after incomplete transarterial chemoembolization. Cancer 2012, 118, 5424–5431. [Google Scholar] [CrossRef]
  23. Bujold, A.; Massey, C.A.; Kim, J.J.; Brierley, J.; Cho, C.; Wong, R.K.S.; Dinniwell, R.E.; Kassam, Z.; Ringash, J.; Cummings, B.; et al. Sequential Phase I and II Trials of Stereotactic Body Radiotherapy for Locally Advanced Hepatocellular Carcinoma. J. Clin. Oncol. 2013, 31, 1631–1639. [Google Scholar] [CrossRef]
  24. Lasley, F.D.; Mannina, E.M.; Johnson, C.S.; Perkins, S.M.; Althouse, S.; Maluccio, M.; Kwo, P.; Cárdenes, H. Treatment variables related to liver toxicity in patients with hepatocellular carcinoma, Child-Pugh class A and B enrolled in a phase 1-2 trial of stereotactic body radiation therapy. Pract. Radiat. Oncol. 2015, 5, e443–e449. [Google Scholar] [CrossRef] [PubMed]
  25. Dawson, L.A.; Winter, K.A.; Knox, J.J.; Zhu, A.X.; Krishnan, S.; Guha, C.; Kachnic, L.A.; Gillin, M.; Hong, T.S.; Craig, T.; et al. NRG/RTOG 1112: Randomized phase III study of sorafenib vs. stereotactic body radiation therapy (SBRT) followed by sorafenib in hepatocellular carcinoma (HCC). J. Clin. Oncol. 2023, 41, 489. [Google Scholar] [CrossRef]
  26. Hanif, H.; Ali, M.J.; Susheela, A.T.; Khan, I.W.; Luna-Cuadros, M.A.; Khan, M.M.; Lau, D.T. Update on the applications and limitations of alpha-fetoprotein for hepatocellular carcinoma. World J. Gastroenterol. 2022, 28, 216–229. [Google Scholar] [CrossRef]
  27. Mendiratta-Lala, M.; Masch, W.; Shankar, P.R.; Hartman, H.E.; Davenport, M.S.; Schipper, M.J.; Maurino, C.; Cuneo, K.C.; Lawrence, T.S.; Owen, D. Magnetic Resonance Imaging Evaluation of Hepatocellular Carcinoma Treated With Stereotactic Body Radiation Therapy: Long Term Imaging Follow-Up. Int. J. Radiat. Oncol. 2019, 103, 169–179. [Google Scholar] [CrossRef] [PubMed]
  28. Chen, Q.; Zhang, Z.H.; Wang, S.; Lang, J.H. Circulating Cell-Free DNA or Circulating Tumor DNA in the Management of Ovarian and Endometrial Cancer. OncoTargets Ther. 2019, 12, 11517–11530. [Google Scholar] [CrossRef] [PubMed]
  29. Von Felden, J.; Craig, A.J.; Garcia-Lezana, T.; Labgaa, I.; Haber, P.K.; D’Avola, D.; Asgharpour, A.; Dieterich, D.; Bonaccorso, A.; Torres-Martin, M.; et al. Mutations in circulating tumor DNA predict primary resistance to systemic therapies in advanced hepatocellular carcinoma. Oncogene 2021, 40, 140–151. [Google Scholar] [CrossRef]
  30. Fujii, Y.; Ono, A.; Hayes, C.N.; Aikata, H.; Yamauchi, M.; Uchikawa, S.; Kodama, K.; Teraoka, Y.; Fujino, H.; Nakahara, T.; et al. Identification and monitoring of mutations in circulating cell-free tumor DNA in hepatocellular carcinoma treated with lenvatinib. J. Exp. Clin. Cancer Res. 2021, 40, 215. [Google Scholar] [CrossRef]
  31. He, G.; Chen, Y.; Zhu, C.; Zhou, J.; Xie, X.; Fei, R.; Wei, L.; Zhao, H.; Chen, H.; Zhang, H. Application of plasma circulating cell-free DNA detection to the molecular diagnosis of hepatocellular carcinoma. Am. J. Transl. Res. 2019, 11, 1428–1445. [Google Scholar]
  32. Ikeda, S.; Tsigelny, I.F.; Skjevik, Å.A.; Kono, Y.; Mendler, M.; Kuo, A.; Sicklick, J.K.; Heestand, G.; Banks, K.C.; Talasaz, A.; et al. Next-Generation Sequencing of Circulating Tumor DNA Reveals Frequent Alterations in Advanced Hepatocellular Carcinoma. Oncologist 2018, 23, 586–593. [Google Scholar] [CrossRef]
  33. Ikeda, S.; Lim, J.S.; Kurzrock, R. Analysis of Tissue and Circulating Tumor DNA by Next-Generation Sequencing of Hepatocellular Carcinoma: Implications for Targeted TherapeuticsTissue and ctDNA NGS in HCC. Mol. Cancer Ther. 2018, 17, 1114–1122. [Google Scholar] [CrossRef] [PubMed]
  34. Oversoe, S.K.; Clement, M.S.; Pedersen, M.H.; Weber, B.; Aagaard, N.K.; Villadsen, G.E.; Grønbæk, H.; Hamilton-Dutoit, S.J.; Sorensen, B.S.; Kelsen, J. TERT promoter mutated circulating tumor DNA as a biomarker for prognosis in hepatocellular carcinoma. Scand. J. Gastroenterol. 2020, 55, 1433–1440. [Google Scholar] [CrossRef] [PubMed]
  35. Ako, S.; Nouso, K.; Kinugasa, H.; Matsushita, H.; Terasawa, H.; Adachi, T.; Wada, N.; Takeuchi, Y.; Mandai, M.; Onishi, H.; et al. Human Telomerase Reverse Transcriptase Gene Promoter Mutation in Serum of Patients with Hepatocellular Carcinoma. Oncology 2020, 98, 311–317. [Google Scholar] [CrossRef]
  36. Li, Y.; Wu, J.; Li, E.; Xiao, Z.; Lei, J.; Zhou, F.; Yin, X.; Hu, D.; Mao, Y.; Wu, L.; et al. TP53 mutation detected in circulating exosomal DNA is associated with prognosis of patients with hepatocellular carcinoma. Cancer Biol. Ther. 2022, 23, 439–445. [Google Scholar] [CrossRef] [PubMed]
  37. Howell, J.; Atkinson, S.R.; Pinato, D.J.; Knapp, S.; Ward, C.; Minisini, R.; Burlone, M.E.; Leutner, M.; Pirisi, M.; Büttner, R.; et al. Identification of mutations in circulating cell-free tumour DNA as a biomarker in hepatocellular carcinoma. Eur. J. Cancer 2019, 116, 56–66. [Google Scholar] [CrossRef] [PubMed]
  38. Higuera, M.; Vargas-Accarino, E.; Torrens, M.; Gregori, J.; Salcedo, M.T.; Martínez-Campreciós, J.; Torres, G.; Bermúdez-Ramos, M.; Bilbao, I.; Guerrero-Murillo, M.; et al. Ultra Deep Sequencing of Circulating Cell-Free DNA as a Potential Tool for Hepatocellular Carcinoma Management. Cancers 2022, 14, 3875. [Google Scholar] [CrossRef]
  39. Xiong, Y.; Xie, C.-R.; Zhang, S.; Chen, J.; Yin, Z.-Y. Detection of a novel panel of somatic mutations in plasma cell-free DNA and its diagnostic value in hepatocellular carcinoma. Cancer Manag. Res. 2019, 11, 5745. [Google Scholar] [CrossRef]
  40. Ono, A.; Fujimoto, A.; Yamamoto, Y.; Akamatsu, S.; Hiraga, N.; Imamura, M.; Kawaoka, T.; Tsuge, M.; Abe, H.; Hayes, C.N.; et al. Circulating Tumor DNA Analysis for Liver Cancers and Its Usefulness as a Liquid Biopsy. Cell. Mol. Gastroenterol. Hepatol. 2015, 1, 516–534. [Google Scholar] [CrossRef]
  41. Huang, A.; Zhao, X.; Yang, X.R.; Li, F.Q.; Zhou, X.L.; Wu, K.; Zhang, X.; Sun, Q.M.; Cao, Y.; Zhu, H.M.; et al. Circumventing intratumoral heterogeneity to identify potential therapeutic targets in hepatocellular carcinoma. J. Hepatol. 2017, 67, 293–301. [Google Scholar] [CrossRef]
  42. Zhu, G.Q.; Liu, W.R.; Tang, Z.; Qu, W.F.; Fang, Y.; Jiang, X.F.; Song, S.S.; Wang, H.; Tao, C.Y.; Zhou, P.Y.; et al. Serial circulating tumor DNA to predict early recurrence in patients with hepatocellular carcinoma: A prospective study. Mol. Oncol. 2022, 16, 549–561. [Google Scholar] [CrossRef] [PubMed]
  43. Kaseb, A.O.; Sánchez, N.S.; Sen, S.; Kelley, R.K.; Tan, B.; Bocobo, A.G.; Lim, K.H.; Abdel-Wahab, R.; Uemura, M.; Pestana, R.C. Molecular profiling of hepatocellular carcinoma using circulating cell-free DNA. Clin. Cancer Res. 2019, 25, 6107–6118. [Google Scholar] [CrossRef] [PubMed]
  44. Kim, S.S.; Eun, J.W.; Choi, J.-H.; Woo, H.G.; Cho, H.J.; Ahn, H.R.; Suh, C.W.; Baek, G.O.; Cho, S.W.; Cheong, J.Y. MLH1 single-nucleotide variant in circulating tumor DNA predicts overall survival of patients with hepatocellular carcinoma. Sci. Rep. 2020, 10, 17862. [Google Scholar] [CrossRef] [PubMed]
  45. Ng, C.K.Y.; Di Costanzo, G.G.; Tosti, N.; Paradiso, V.; Coto-Llerena, M.; Roscigno, G.; Perrina, V.; Quintavalle, C.; Boldanova, T.; Wieland, S.; et al. Genetic profiling using plasma-derived cell-free DNA in therapy-naïve hepatocellular carcinoma patients: A pilot study. Ann. Oncol. 2018, 29, 1286–1291. [Google Scholar] [CrossRef]
  46. Bara, A.W.; Braszewska, A.; Kwasniewska, J. DNA Methylation—An Epigenetic Mark in Mutagen-Treated Brachypodium distachyon Cells. Plants 2021, 10, 1408. [Google Scholar] [CrossRef]
  47. Manne, A.; Woods, E.; Tsung, A.; Mittra, A. Biliary Tract Cancers: Treatment Updates and Future Directions in the Era of Precision Medicine and Immuno-Oncology. Front. Oncol. 2021, 11, 768009. [Google Scholar] [CrossRef]
  48. Hao, Y.; Yang, Q.; He, Q.; Hu, H.; Weng, Z.; Su, Z.; Chen, S.; Peng, S.; Kuang, M.; Chen, Z.; et al. Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction. Eur. J. Med. Res. 2022, 27, 276. [Google Scholar] [CrossRef] [PubMed]
  49. Villanueva, A.; Portela, A.; Sayols, S.; Battiston, C.; Hoshida, Y.; Méndez-González, J.; Imbeaud, S.; Letouzé, E.; Hernandez-Gea, V.; Cornella, H.; et al. DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology 2015, 61, 1945–1956. [Google Scholar] [CrossRef]
  50. Cheng, Y.; Zhang, C.; Zhao, J.; Wang, C.; Xu, Y.; Han, Z.; Jiang, G.; Guo, X.; Li, R.; Bu, X.; et al. Correlation of CpG island methylator phenotype with poor prognosis in hepatocellular carcinoma. Exp. Mol. Pathol. 2010, 88, 112–117. [Google Scholar] [CrossRef]
  51. Li, B.; Liu, W.; Wang, L.; Li, M.; Wang, J.; Huang, L.; Huang, P.; Yuan, Y. CpG Island Methylator Phenotype Associated with Tumor Recurrence in Tumor–Node–Metastasis Stage I Hepatocellular Carcinoma. Ann. Surg. Oncol. 2010, 17, 1917–1926. [Google Scholar] [CrossRef]
  52. Wu, L.-M.; Zhang, F.; Zhou, L.; Yang, Z.; Xie, H.-Y.; Zheng, S.-S. Predictive value of CpG island methylator phenotype for tumor recurrence in hepatitis B virus-associated hepatocellular carcinoma following liver transplantation. BMC Cancer 2010, 10, 399. [Google Scholar] [CrossRef]
  53. Zhang, H.; Dong, P.; Fan, H.; Liang, H.; Zhang, K.; Zhao, Y.; Guo, S.; Schrodi, S.J.; Fan, Y.; Zhang, D. Gene body hypomethylation of pyroptosis-related genes NLRP7, NLRP2, and NLRP3 facilitate non-invasive surveillance of hepatocellular carcinoma. Funct. Integr. Genom. 2023, 23, 198. [Google Scholar] [CrossRef] [PubMed]
  54. Choy, L.Y.L.; Peng, W.; Jiang, P.; Cheng, S.H.; Yu, S.C.Y.; Shang, H.; Olivia Tse, O.Y.; Wong, J.; Wong, V.W.S.; Wong, G.L.H.; et al. Single-Molecule Sequencing Enables Long Cell-Free DNA Detection and Direct Methylation Analysis for Cancer Patients. Clin. Chem. 2022, 68, 1151–1163. [Google Scholar] [CrossRef] [PubMed]
  55. Xu, R.-H.; Wei, W.; Krawczyk, M.; Wang, W.; Luo, H.; Flagg, K.; Yi, S.; Shi, W.; Quan, Q.; Li, K.; et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat. Mater. 2017, 16, 1155–1161. [Google Scholar] [CrossRef]
  56. Lin, N.; Lin, Y.; Xu, J.; Liu, D.; Li, D.; Meng, H.; Gallant, M.A.; Kubota, N.; Roy, D.; Li, J.S.; et al. A multi-analyte cell-free DNA-based blood test for early detection of hepatocellular carcinoma. Hepatol. Commun. 2022, 6, 1753–1763. [Google Scholar] [CrossRef]
  57. Huang, Z.H.; Hu, Y.; Hua, D.; Wu, Y.Y.; Song, M.X.; Cheng, Z.H. Quantitative analysis of multiple methylated genes in plasma for the diagnosis and prognosis of hepatocellular carcinoma. Exp. Mol. Pathol. 2011, 91, 702–707. [Google Scholar] [CrossRef]
  58. Lin, Q.; Chen, L.-b.; Tang, Y.-m.; Wang, J. Promoter hypermethylation of p16 gene and DAPK gene in sera from hepatocellular carcinoma (HCC) patients. Chin. J. Cancer Res. 2005, 17, 250–254. [Google Scholar] [CrossRef]
  59. Sun, F.-K.; Fan, Y.-C.; Zhao, J.; Zhang, F.; Gao, S.; Zhao, Z.-H.; Sun, Q.; Wang, K. Detection of TFPI2 methylation in the serum of hepatocellular carcinoma patients. Dig. Dis. Sci. 2013, 58, 1010–1015. [Google Scholar] [CrossRef] [PubMed]
  60. Hu, L.; Chen, G.; Yu, H.; Qiu, X. Clinicopathological significance of RASSF1A reduced expression and hypermethylation in hepatocellular carcinoma. Hepatol. Int. 2010, 4, 423–432. [Google Scholar] [CrossRef] [PubMed]
  61. Tsutsui, M.; Iizuka, N.; Moribe, T.; Miura, T.; Kimura, N.; Tamatsukuri, S.; Ishitsuka, H.; Fujita, Y.; Hamamoto, Y.; Tsunedomi, R.; et al. Methylated cyclin D2 gene circulating in the blood as a prognosis predictor of hepatocellular carcinoma. Clin. Chim. Acta 2010, 411, 516–520. [Google Scholar] [CrossRef]
  62. Chang, H.; Yi, B.; Li, L.; Zhang, H.-Y.; Sun, F.; Dong, S.-Q.; Cao, Y. Methylation of tumor associated genes in tissue and plasma samples from liver disease patients. Exp. Mol. Pathol. 2008, 85, 96–100. [Google Scholar] [CrossRef] [PubMed]
  63. Zhang, F.; Fan, Y.C.; Mu, N.N.; Zhao, J.; Sun, F.K.; Zhao, Z.H.; Gao, S.; Wang, K. Exportin 4 gene expression and DNA promoter methylation status in chronic hepatitis B virus infection. J. Viral Hepat. 2014, 21, 241–250. [Google Scholar] [CrossRef]
  64. Iyer, P.; Zekri, A.-R.; Hung, C.-W.; Schiefelbein, E.; Ismail, K.; Hablas, A.; Seifeldin, I.A.; Soliman, A.S. Concordance of DNA methylation pattern in plasma and tumor DNA of Egyptian hepatocellular carcinoma patients. Exp. Mol. Pathol. 2010, 88, 107–111. [Google Scholar] [CrossRef] [PubMed]
  65. Guo, P.; Zheng, H.; Li, Y.; Li, Y.; Xiao, Y.; Zheng, J.; Zhu, X.; Xu, H.; He, Z.; Zhang, Q.; et al. Hepatocellular carcinoma detection via targeted enzymatic methyl sequencing of plasma cell-free DNA. Clin. Epigenetics 2023, 15, 2. [Google Scholar] [CrossRef]
  66. Kumar, S.; Nadda, N.; Paul, S.; Gamanagatti, S.; Dash, N.R.; Vanamail, P.; Saraya, A.; Shalimar; Nayak, B. Evaluation of the cell-free DNA integrity index as a liquid biopsy marker to differentiate hepatocellular carcinoma from chronic liver disease. Front. Mol. Biosci. 2022, 9, 1024193. [Google Scholar] [CrossRef]
  67. Trung, N.T.; Hoan, N.X.; Trung, P.Q.; Binh, M.T.; Van Tong, H.; Toan, N.L.; Bang, M.H.; Song, L.H. Clinical significance of combined circulating TERT promoter mutations and miR-122 expression for screening HBV-related hepatocellular carcinoma. Sci. Rep. 2020, 10, 8181. [Google Scholar] [CrossRef] [PubMed]
  68. Liang, W.; Xu, Z.; Kong, F.; Huang, X.; Xiao, Y.; Zhou, W.; Ye, S.; Ye, Q. Circulating tumour cell combined with DNA methylation for early detection of hepatocellular carcinoma. Front. Genet. 2022, 13, 1065693. [Google Scholar] [CrossRef]
  69. Zhou, X.; Zheng, H.; Fu, H.; Dillehay Mckillip, K.L.; Pinney, S.M.; Liu, Y. CRAG: De novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing. Genome Med. 2022, 14, 138. [Google Scholar] [CrossRef]
  70. Yang, Y.-J.; Chen, H.; Huang, P.; Li, C.-H.; Dong, Z.-H.; Hou, Y.-L. Quantification of plasma hTERT DNA in hepatocellular carcinoma patients by quantitative fluorescent polymerase chain reaction. Clin. Investig. Med. 2011, 34, 238. [Google Scholar] [CrossRef]
  71. Telli, P.; Ozturk, N.B.; Hakan, M.T.; Cavus, B.; Ormeci, A.C.; Yakut, A.; Senkal, V.; Imanov, Z.; Poyanli, A.; Isik, E.G.; et al. Cell-free methylation of RASSF1 and CDKN2AIP genes in the diagnosis of hepatocellular carcinoma associated with hepatitis B virus cirrhosis. Hepatol. Forum 2022, 3, 77–81. [Google Scholar] [CrossRef] [PubMed]
  72. Han, L.-Y.; Fan, Y.-C.; Mu, N.-N.; Gao, S.; Li, F.; Ji, X.-F.; Dou, C.-Y.; Wang, K. Aberrant DNA Methylation of G-protein-coupled Bile Acid Receptor Gpbar1 (TGR5) is a Potential Biomarker for Hepatitis B Virus Associated Hepatocellular Carcinoma. Int. J. Med. Sci. 2014, 11, 164–171. [Google Scholar] [CrossRef] [PubMed]
  73. Mohamed, N.A.; Swify, E.M.; Amin, N.F.; Soliman, M.M.; Tag-Eldin, L.M.; Elsherbiny, N.M. Is serum level of methylated RASSF1A valuable in diagnosing hepatocellular carcinoma in patients with chronic viral hepatitis C? Arab. J. Gastroenterol. 2012, 13, 111–115. [Google Scholar] [CrossRef]
  74. Li, C.L.; Ho, M.C.; Lin, Y.Y.; Tzeng, S.T.; Chen, Y.J.; Pai, H.Y.; Wang, Y.C.; Chen, C.L.; Lee, Y.H.; Chen, D.S.; et al. Cell-Free Virus-Host Chimera DNA From Hepatitis B Virus Integration Sites as a Circulating Biomarker of Hepatocellular Cancer. Hepatology 2020, 72, 2063–2076. [Google Scholar] [CrossRef]
  75. Tokuhisa, Y.; Iizuka, N.; Sakaida, I.; Moribe, T.; Fujita, N.; Miura, T.; Tamatsukuri, S.; Ishitsuka, H.; Uchida, K.; Terai, S.; et al. Circulating cell-free DNA as a predictive marker for distant metastasis of hepatitis C virus-related hepatocellular carcinoma. Br. J. Cancer 2007, 97, 1399–1403. [Google Scholar] [CrossRef]
  76. Cai, Z.; Chen, G.; Zeng, Y.; Dong, X.; Li, Z.; Huang, Y.; Xin, F.; Qiu, L.; Xu, H.; Zhang, W.; et al. Comprehensive Liquid Profiling of Circulating Tumor DNA and Protein Biomarkers in Long-Term Follow-Up Patients with Hepatocellular Carcinoma. Clin. Cancer Res. 2019, 25, 5284–5294. [Google Scholar] [CrossRef]
  77. Lyu, X.; Tsui, Y.M.; Ho, D.W.; Ng, I.O. Liquid Biopsy Using Cell-Free or Circulating Tumor DNA in the Management of Hepatocellular Carcinoma. Cell. Mol. Gastroenterol. Hepatol. 2022, 13, 1611–1624. [Google Scholar] [CrossRef] [PubMed]
  78. Ren, N. Circulating DNA level is negatively associated with the long-term survival of hepatocellular carcinoma patients. World J. Gastroenterol. 2006, 12, 3911. [Google Scholar] [CrossRef]
  79. Fu, Y.; Yang, Z.; Hu, Z.; Yang, Z.; Pan, Y.; Chen, J.; Wang, J.; Hu, D.; Zhou, Z.; Xu, L.; et al. Preoperative serum ctDNA predicts early hepatocellular carcinoma recurrence and response to systemic therapies. Hepatol. Int. 2022, 16, 868–878. [Google Scholar] [CrossRef]
  80. Liao, W.; Yang, H.; Xu, H.; Wang, Y.; Ge, P.; Ren, J.; Xu, W.; Lu, X.; Sang, X.; Zhong, S.; et al. Noninvasive detection of tumor-associated mutations from circulating cell-free DNA in hepatocellular carcinoma patients by targeted deep sequencing. Oncotarget 2016, 7, 40481–40490. [Google Scholar] [CrossRef] [PubMed]
  81. Ye, K.; Fan, Q.; Yuan, M.; Wang, D.; Xiao, L.; Long, G.; Chen, R.; Fang, T.; Li, Z.; Zhou, L. Prognostic Value of Postoperative Circulating Tumor DNA in Patients With Early- and Intermediate-Stage Hepatocellular Carcinoma. Front. Oncol. 2022, 12, 834992. [Google Scholar] [CrossRef] [PubMed]
  82. An, Y.; Guan, Y.; Xu, Y.; Han, Y.; Wu, C.; Bao, C.; Zhou, B.; Wang, H.; Zhang, M.; Liu, W. The diagnostic and prognostic usage of circulating tumor DNA in operable hepatocellular carcinoma. Am. J. Transl. Res. 2019, 11, 6462. [Google Scholar]
  83. Zhou, L.; Xu, Y.; Wang, D.; Ye, K.; Xiao, L.; Long, G.; Chen, R.; Xia, X. Perioperative circulating tumor DNA analysis to predict patient prognosis in liver cancer. J. Clin. Oncol. 2020, 38, 4593. [Google Scholar] [CrossRef]
  84. Li, F.; Qiao, C.-Y.; Gao, S.; Fan, Y.-C.; Chen, L.-Y.; Wang, K. Circulating cell-free DNA of methylated insulin-like growth factor-binding protein 7 predicts a poor prognosis in hepatitis B virus-associated hepatocellular carcinoma after hepatectomy. Free Radic. Res. 2018, 52, 455–464. [Google Scholar] [CrossRef]
  85. Jiao, J.; Watt, G.P.; Stevenson, H.L.; Calderone, T.L.; Fisher-Hoch, S.P.; Ye, Y.; Wu, X.; Vierling, J.M.; Beretta, L. Telomerase reverse transcriptase mutations in plasma DNA in patients with hepatocellular carcinoma or cirrhosis: Prevalence and risk factors. Hepatol. Commun. 2018, 2, 718–731. [Google Scholar] [CrossRef] [PubMed]
  86. Youssef, S.S.; El-Araby, R.E.; Abbas, E.A.E.; Hassany, M.; Elbaz, T. Prognostic and survival impact of BCL9 and RPS6KB1 copy number variation detected from circulating free DNA in hepatocellular carcinoma. Expert Rev. Mol. Diagn. 2023, 23, 267–278. [Google Scholar] [CrossRef]
  87. Li, F.; Fan, Y.C.; Gao, S.; Sun, F.K.; Yang, Y.; Wang, K. Methylation of serum insulin-like growth factor-binding protein 7 promoter in hepatitis B virus-associated hepatocellular carcinoma. Genes Chromosomes. Cancer 2014, 53, 90–97. [Google Scholar] [CrossRef]
  88. Tangkijvanich, P.; Hourpai, N.; Rattanatanyong, P.; Wisedopas, N.; Mahachai, V.; Mutirangura, A. Serum LINE-1 hypomethylation as a potential prognostic marker for hepatocellular carcinoma. Clin. Chim. Acta 2007, 379, 127–133. [Google Scholar] [CrossRef]
  89. Sefrioui, D.; Verdier, V.; Savoye-Collet, C.; Beaussire, L.; Ghomadi, S.; Gangloff, A.; Goria, O.; Riachi, G.; Montialoux, H.; Schwarz, L.; et al. CirculatingDNA changes are predictive of disease progression after transarterial chemoembolization. Int. J. Cancer 2022, 150, 532–541. [Google Scholar] [CrossRef] [PubMed]
  90. Hirai, M.; Kinugasa, H.; Nouso, K.; Yamamoto, S.; Terasawa, H.; Onishi, Y.; Oyama, A.; Adachi, T.; Wada, N.; Sakata, M.; et al. Prediction of the prognosis of advanced hepatocellular carcinoma by TERT promoter mutations in circulating tumor DNA. J. Gastroenterol. Hepatol. 2021, 36, 1118–1125. [Google Scholar] [CrossRef]
  91. Park, S.; Lee, E.J.; Rim, C.H.; Seong, J. Plasma cell-free DNA as a predictive marker after radiotherapy for hepatocellular carcinoma. Yonsei Med. J. 2018, 59, 470–479. [Google Scholar] [CrossRef]
  92. Alunni-Fabbroni, M.; Rönsch, K.; Huber, T.; Cyran, C.C.; Seidensticker, M.; Mayerle, J.; Pech, M.; Basu, B.; Verslype, C.; Benckert, J.; et al. Circulating DNA as prognostic biomarker in patients with advanced hepatocellular carcinoma: A translational exploratory study from the SORAMIC trial. J. Transl. Med. 2019, 17, 328. [Google Scholar] [CrossRef] [PubMed]
  93. Nakatsuka, T.; Nakagawa, H.; Hayata, Y.; Wake, T.; Yamada, T.; Nishibatake Kinoshita, M.; Nakagomi, R.; Sato, M.; Minami, T.; Uchino, K.; et al. Post-treatment cell-free DNA as a predictive biomarker in molecular-targeted therapy of hepatocellular carcinoma. J. Gastroenterol. 2021, 56, 456–469. [Google Scholar] [CrossRef] [PubMed]
  94. Oh, C.R.; Kong, S.-Y.; Im, H.-S.; Kim, H.J.; Kim, M.K.; Yoon, K.-A.; Cho, E.-H.; Jang, J.-H.; Lee, J.; Kang, J.; et al. Genome-wide copy number alteration and VEGFA amplification of circulating cell-free DNA as a biomarker in advanced hepatocellular carcinoma patients treated with Sorafenib. BMC Cancer 2019, 19, 292. [Google Scholar] [CrossRef] [PubMed]
  95. Matsumae, T.; Kodama, T.; Myojin, Y.; Maesaka, K.; Sakamori, R.; Takuwa, A.; Oku, K.; Motooka, D.; Sawai, Y.; Oshita, M.; et al. Circulating Cell-Free DNA Profiling Predicts the Therapeutic Outcome in Advanced Hepatocellular Carcinoma Patients Treated with Combination Immunotherapy. Cancers 2022, 14, 3367. [Google Scholar] [CrossRef]
Ijms 24 14231 g001
Figure 1. Current status of cell-free DNA testing in hepatocellular carcinoma.
Figure 1. Current status of cell-free DNA testing in hepatocellular carcinoma.
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Jeepalyam, S.; Sheel, A.; Ejaz, A.; Miller, E.; Manne, A. Is Cell-Free DNA Testing in Hepatocellular Carcinoma Ready for Prime Time? Int. J. Mol. Sci. 2023, 24, 14231. https://doi.org/10.3390/ijms241814231

AMA Style

Jeepalyam S, Sheel A, Ejaz A, Miller E, Manne A. Is Cell-Free DNA Testing in Hepatocellular Carcinoma Ready for Prime Time? International Journal of Molecular Sciences. 2023; 24(18):14231. https://doi.org/10.3390/ijms241814231

Chicago/Turabian Style

Jeepalyam, Sravan, Ankur Sheel, Aslam Ejaz, Eric Miller, and Ashish Manne. 2023. "Is Cell-Free DNA Testing in Hepatocellular Carcinoma Ready for Prime Time?" International Journal of Molecular Sciences 24, no. 18: 14231. https://doi.org/10.3390/ijms241814231

APA Style

Jeepalyam, S., Sheel, A., Ejaz, A., Miller, E., & Manne, A. (2023). Is Cell-Free DNA Testing in Hepatocellular Carcinoma Ready for Prime Time? International Journal of Molecular Sciences, 24(18), 14231. https://doi.org/10.3390/ijms241814231

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