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Tumor-based biomarkers and circulating tumor DNA for precision medicine in advanced hepatocellular carcinoma

Sabrina Sidali1,*, Claudia Campani1,2,*, Jihyun An3, Ju Hyun Shim4, Jean-Charles Nault1,3,5orcid
Clinical and Molecular Hepatology 2026;32(1):69-90.
Published online: August 20, 2025

1Centre de Recherche des Cordeliers, Sorbonne Université, Inserm, Université de Paris, Team «Functional Genomics of Solid Tumors», Equipe labellisée Ligue Nationale Contre le Cancer, Labex OncoImmunology, Paris, France

2Department of Experimental and Clinical Medicine, Internal Medicine and Hepatology Unit, University of Firenze, Florence, Italy

3Department of Gastroenterology and Hepatology, Hanyang University College of Medicine, Guri, Korea

4Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

5Liver Unit, Hôpital Avicenne, Hôpitaux Universitaires Paris-Seine-Saint-Denis, Assistance-Publique Hôpitaux de Paris, Bobigny, France

Corresponding author : Jean-Charles Nault Liver unit, Hôpital Avicenne, Hôpitaux Universitaires Paris-Seine-Saint-Denis, Assistance-Publique Hôpitaux de Paris, 125 rue de Stalingrad, Bobigny 93000, France Tel: +33-6-10-67-94-61, E-mail: naultjc@gmail.com

Co-first authors.


Editor: Ju Dong Yang, Cedars-Sinai Medical Center, USA

• Received: July 8, 2025   • Revised: August 2, 2025   • Accepted: August 18, 2025

Copyright © 2026 by The Korean Association for the Study of the Liver

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Hepatocellular carcinoma (HCC) is the most common primary liver cancer and remains a major cause of cancer-related mortality worldwide. Systemic therapies, including targeted therapies and immune checkpoint inhibitors, have revolutionized the management of advanced HCC. Although the prognosis of patients with advanced HCC remains poor, significant progress has been made with recent advances in drug development, particularly with the introduction of effective treatments such as atezolizumab plus bevacizumab or durvalumab plus tremelimumab. Indeed, treatment response varies significantly among patients, highlighting the need for robust biomarkers. In addition, the development of molecular driver-targeted therapies remains an active research focus as most genetic alterations observed in HCC are currently undruggable. Meeting these goals will require additional efforts to obtain histological material in clinical trials, in order to enable robust translational research. This review explores the current landscape of biomarkers of response to systemic treatments in HCC, including molecular, immune-based markers as well as circulating tumor DNA and highlights potential paths of improvement.
  • Keywords: Hepatocellular carcinoma; Systemic treatment; Biomarkers; Alpha-fetoprotein; Circulating tumor DNA
Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death worldwide [1-3]. Various risk factors, including hepatitis B and C, alcohol consumption, and metabolic syndrome, lead to HCC development [2,4]. Overall, the prognosis of patients with HCC is poor, with 10% survival at 5 years as the disease is often discovered at advanced stages [2]. The current therapeutic strategies for advanced HCC involve the combination of immune checkpoint inhibitors (ICIs) (anti-programmed death 1 [PD-1]/programmed death-ligand 1 [PD-L1]+anti-cytotoxic T-lymphocyte antigen 4 [CTLA-4]) or ICI+antiangiogenics (bevacizumab) in the first line. Indeed, the combination of atezolizumab (PD-L1 inhibitor) and bevacizumab (anti-vascular endothelial growth factor [VEGF] antibody) (A/B) has demonstrated superiority over sorafenib across various oncological outcomes as well as in terms of patients’ quality of life (median overall survival [OS] 19.2 months versus 13.4 months, respectively) [5,6]. Similar results have been observed with the combination of durvalumab (PD-L1 inhibitor) and tremelimumab (CTLA-4 inhibitor) (Durva/Treme) in the phase III HIMALAYA trial, which reported OS of 19.6% at 5 years [7-9]. These findings establish both combinations as the current first-line treatment options for patients with advanced HCC worldwide. Moreover, emerging therapies such as nivolumab plus ipilimumab, camrelizumab plus rivoceranib, and sintilimab plus bevacizumab biosimilar (IBI305) showed a benefit in terms of OS compared to sorafenib or lenvatinib, further bolstering the arsenal of firstline treatment options in advanced HCC [10-12]. In a second-line setting, different other antiangiogenics drugs are currently used, including sorafenib, regorafenib (multi-target tyrosine kinase inhibitor [TKI]), cabozantinib (VEGF receptor 1–3, c-MET and AXL inhibitor), apatinib (VEGF receptor inhibitor) and ramucirumab (IgG1 monoclonal antibody directed against VEGF receptor 2), even if these drugs have never been tested in clinical trials following failure of immunotherapy [13-17]. Apart from serum alpha-fetoprotein (AFP) for ramucirumab, no tumor biomarkers have been linked to treatment response in patients with advanced HCC. Moreover, histological evidence is not required for HCC diagnosis in international guidelines, making non-invasive blood-based biomarkers crucial for treatment stratification and monitoring in advanced cases [18].
This review explores the current landscape of biomarkers of response to systemic treatments in advanced HCC, including molecular, genetic and immune-based tumor and circulating biomarkers.
Deeper knowledge of the mechanisms of cirrhosis has in turn improved our understanding of liver carcinogenesis, including its natural history and risk factors [19]. Next-generation sequencing identified the precise genetic landscape of HCC which is characterized by a median rate of approximately 50–70 protein-altering mutations per tumor (Fig. 1) [20-24]. The vast majority of these mutations are passengers while only a few of them, about 2–6, are considered as functional ‘‘driver” mutations that alter key signaling pathways leading to a selective advantage. The main pathways involved in liver carcinogenesis include: telomere maintenance, with TERT promoter mutations (60%), amplifications (5–10%), translocations (3%), and viral insertions (HBV, AAV2; 5–30%); Wnt/β-catenin activation via CTNNB1 mutations (35%), and less frequently AXIN1 (15%) or APC (2%) inactivation; p53/cell cycle disruption through TP53 mutations (15–48%), RB1 mutations, or CDKN2A deletions (2–9%); phosphatidylinositol 3-kinase (PI3K)/AKT-mTOR and MAPK pathways via FGF19 amplification (6%), TSC1/2 inactivation (8%), VEGF amplification (4%), and RPS6KA3 mutations (5–10%); epigenetic alterations through ARID1A (17%) and ARID2 (18%) mutations; and oxidative stress response via NFE2L2 (6%) or KEAP1 (4%) mutations [20-22,25]. Unfortunately, most of drivers detected in HCC are not clinically actionable and only ~20–25% of HCC harbor alterations that are potentially targetable with existing drug [22]. When combining mutational landscape and transcriptomic analysis (G1–G6 classification), 3 main molecular subgroups emerge (proliferative, mature hepatocytes and B-catenin HCC) subdivided into 7 molecular subtypes that are related to risk factors, clinical and histological features and prognosis (G1a-IRF2/AXIN1 [11%]; G1b-BAP1 [6%]; G2-cyclin [5%]; G3-TP53 [22%]; G4-unclassified-immune hot [9%]; G4-unclassified-immune-intermediate [16%] and G5/G6-CTNNB1 [26%]) highlighting the biological heterogeneity of HCC and reinforcing the need for robust biomarkers capable of identifying and distinguishing these molecular subgroups (Fig. 1) [20,22,26].
A biomarker is defined as a characteristic that can be objectively measured and evaluated as an indicator of one or more physiological processes, disease-related processes, or responses to a treatment [27]. Biomarkers that can be used prior to treatment include prognostic biomarkers, which predict patient outcome regardless of the treatment given, whereas predictive biomarkers provide information on the effects of a specific therapeutic intervention, usually compared with another [27].
Currently, the only validated biomarker in HCC is high serum AFP level (≥400 ng/mL), as it predicts survival benefit to second-line ramucirumab in patients who had previously received sorafenib in the biomarker enriched REACH-2 phase III trial [15]. Candidate tumor biomarkers of response to TKIs in advanced HCC are described Table 1 and candidate tumor biomarkers of response to ICI as are presented in Table 2 and Figure 2.
Biomarkers of response to TKIs
Sorafenib was the first approved multitarget TKI (inhibiting Raf-1, B-Raf, VEGFR, platelet-derived growth factor receptor [PDGFR]-β and KIT) and has been used as the firstline treatment of advanced HCC since 2008 [28,29]. Currently, TKIs, including sorafenib, are mainly used in patients progressing under immunotherapy. Given that sorafenib primarily targets proliferation and neoangiogenesis, numerous studies have explored the predictive value of the MAPK and VEGF signaling pathways. In this context, phosphorylated ERK (p-ERK), a surrogate marker of MAPK pathway activation, has been identified as a potential biomarker for sorafenib efficacy [30]. Mutations in the PI3K–AKT–mTOR pathway have been associated with reduced response to sorafenib, reflected by lower disease control rates (DCRs) and shorter median OS and progression-free survival (PFS) compared to patients without these mutations [31]. Conversely, VEGFA amplification in tumors has been correlated with a favorable response to sorafenib [32]. However, none of these mutations have been externally validated as predictive of response to TKI.
Several blood-based biomarkers have been investigated to predict response to sorafenib in advanced HCC. High levels of angiogenesis-related markers such as VEGF, Angiopoietin-2 (Ang-2), and certain cytokines (including interleukin (IL)-2, IL-6, and IL-8) have been associated with poor response and shorter survival [33,34]. In contrast, a decrease in VEGF levels during treatment has been linked to better outcomes [35]. The SHARP trial confirmed that some of these markers are prognostic, although not clearly predictive of treatment response [36]. Other serum biomarkers include Dickkopf-related protein 1 (DKK1), a secreted protein that can inhibit Wnt signaling, where higher levels were associated with longer survival [37,38], and the neutrophil-to-lymphocyte ratio (NLR), with lower pre-treatment values predicting greater benefit [39].
Lenvatinib (TKI targeting VEGF receptors 1–3, fibroblast growth factor receptors [FGFR] 1–4, PDGFRα, KIT, and RET) was non-inferior to sorafenib in first line in the REFLECT trial [40]. Similar to sorafenib, high baseline levels of VEGF and Ang-2 have been associated with shorter OS in patients treated with lenvatinib, highlighting their potential role as prognostic biomarkers [41,42]. In addition, biomarkers related to fibroblast growth factor (FGF) signaling, including FGF19, FGF21, and FGF23, have been correlated with treatment outcomes [42]. While elevated FGF21 was generally linked to worse survival, patients with high FGF21 levels appeared to derive greater benefit from lenvatinib than from sorafenib, suggesting a possible predictive role [42].
Another TKI, regorafenib, was shown to provide survival benefit in HCC patients progressing on sorafenib treatment in the RESORCE trial [13]. Five proteins (Ang-1, cystatin B, LAP TGF-β1, LOX-1, MIP-1α) were identified as potential predictors for OS with decreased levels associated with treatment benefit with regorafenib in a translational study of the RESORCE trial [43].
Cabozantinib significantly improved OS and PFS versus placebo in patients with previously treated advanced HCC in phase III CELESTIAL trial [14]. Low baseline blood levels of MET, hepatocyte growth factor (HGF), GAS6, IL-8, and Ang-2 and high levels of insulin-like growth factor 1 were associated with longer OS, suggesting their potential role as favorable prognostic biomarkers [44].
Regarding the prognostic and predictive role of the most well-known biomarker, AFP, elevated pre-treatment levels have been associated with poor prognosis in patients treated by sorafenib, although they have not shown predictive value for treatment response [36,39]. In the phase III REACH and REACH-2 trials of ramucirumab, baseline AFP>1,000 ng/mL and NLR were both associated with worse outcomes [45]. Notably, serum level AFP≥400 ng/mL was used as a predictive biomarker for patient selection in REACH-2, making it the first phase III trial in HCC to enroll patients based on a biomarker-defined criterion [15]. Moreover, a reduction of serum AFP levels>20% between 2 and 8 weeks after the start of treatment was associated with a good response to systemic treatments in several retrospective studies [46-48].
In conclusion, several tissue and circulating biomarkers have been investigated in the context of TKI treatment for advanced HCC, reflecting the mechanisms of action of these agents, primarily targeting angiogenesis and growth factor signaling. However, except for serum AFP and ramucirumab, the lack of consistent validation and standardization across studies has so far limited their integration in clinical practice, underscoring the need for prospective biomarker-driven trials.
Biomarkers of response to immunotherapy
Currently available biomarkers of response to ICI span from immunohistochemical markers (e.g., PD-L1, CD3, and CD8) to genomic (e.g., tumor mutational burden [TMB], microsatellite instability [MSI], driver mutations and transcriptomic signatures) as summarized in Figure 2 and Table 2. To note, none of these biomarkers have been externally validated in prospective trials, and therefore none are currently used in clinical practice.
Historically, PD-1/PD-L1 and CD8+ T cell infiltration were among the first proposed biomarkers of ICIs response [49-51]. However, the immune heterogeneity of HCC has limited the reliability of PD-L1 staining as a predictive tool, and it has never been validated in phase 2 or 3 clinical trials evaluating immunotherapy in HCC [52]. Zhu et al. [53], showed that intra-tumoral infiltration of CD8+ T cell and pro-inflammatory CXCL10+ macrophages were associated with better clinical outcomes in A/B treated patients, and an “atezolizumab+ bevacizumab response signature” (ABRS) was derived. Nevertheless, high CD8 immunohistochemistry scores have been associated with response in some studies [53,54], the diverse functional states of CD8+T cells subtypes (provs. anti-tumoral) limit its standalone utility. Recent data suggest that CXCL13+ T helper cells and Granzyme K+ effector-like CD8+ T cells are more reliable indicators of effective immune response while terminally exhausted CD8+ T cells are enriched in non-responders [55]. Moreover, transcriptomic analyses have identified interferon signaling and major histocompatibility complex-related genes as associated with anti-PD-1 response [56].
Increasing attention has also been given to the spatial organization of immune cells within the tumor. Tertiary lymphoid structures (TLSs) are germinal center-like collections of T and B cells that mimic lymph node architecture but arise in chronically inflamed non-lymphoid tissue [57] and provide a critical microenvironment for generating anti-tumor immune responses and are associated with improved response in solid tumors treated with ICIs [58-61]. In melanoma, sarcoma and lung cancer, mature TLSs enriched in follicular dendritic cells, B cells, and T follicular helper (Tfh) cells have been associated with ICI response and improved survival [58,60,61]. In early stage HCC, the presence of TLS was associated with a lower risk of relapse following resection [62], and in a phase 2 trial of neoadjuvant cemiplimab (anti-PD-1), significant pathological responses were observed [63]. Moreover, high intratumoral TLSs density was associated with pathologic tumor response and improved relapse-free survival with neoadjuvant immunotherapy [55]. Similarly, a phase 1b trial of neoadjuvant cabozantinib plus nivolumab showed that responders to systemic treatment had prominent TLS, enriched effector T cells, CD138+ plasma cells, and a distinct spatial organization of B cells [64]. Single-cell and spatial transcriptomic analyses of HCC have confirmed that TLSs in responders contain memory B cells and Tfh cells that may orchestrate CD8+ T cell activation [65]. These findings support the role of TLS as both prognostic and predictive markers of immunotherapy response in HCC but these data are largely derived from early HCC and the predictive value of TLSs in advanced HCC, where ICIs are most frequently used, remains to be fully elucidated. As adjuvant immunotherapy clinical trials in unselected HCC patients have yielded negative results, TLS could be evaluated in future studies to guide the use of adjuvant immunotherapy in patients at high risk of tumor recurrence after surgery.Increasing attention has also been given to the spatial organization of immune cells within the tumor. Tertiary lymphoid structures (TLSs) are germinal center-like collections of T and B cells that mimic lymph node architecture but arise in chronically inflamed non-lymphoid tissue [57] and provide a critical microenvironment for generating anti-tumor immune responses and are associated with improved response in solid tumors treated with ICIs [58-61]. In melanoma, sarcoma and lung cancer, mature TLSs enriched in follicular dendritic cells, B cells, and T follicular helper (Tfh) cells have been associated with ICI response and improved survival [58,60,61]. In early stage HCC, the presence of TLS was associated with a lower risk of relapse following resection [62], and in a phase 2 trial of neoadjuvant cemiplimab (anti-PD-1), significant pathological responses were observed [63]. Moreover, high intratumoral TLSs density was associated with pathologic tumor response and improved relapse-free survival with neoadjuvant immunotherapy [55]. Similarly, a phase 1b trial of neoadjuvant cabozantinib plus nivolumab showed that responders to systemic treatment had prominent TLS, enriched effector T cells, CD138+ plasma cells, and a distinct spatial organization of B cells [64]. Single-cell and spatial transcriptomic analyses of HCC have confirmed that TLSs in responders contain memory B cells and Tfh cells that may orchestrate CD8+ T cell activation [65]. These findings support the role of TLS as both prognostic and predictive markers of immunotherapy response in HCC but these data are largely derived from early HCC and the predictive value of TLSs in advanced HCC, where ICIs are most frequently used, remains to be fully elucidated. As adjuvant immunotherapy clinical trials in unselected HCC patients have yielded negative results, TLS could be evaluated in future studies to guide the use of adjuvant immunotherapy in patients at high risk of tumor recurrence after surgery.
On the other hand, CTNNB1 mutations have been associated with tumor immune exclusion in HCC [62], and resistance to ICIs [63,64] due to alterations of the Wnt–β-catenin pathway that modulate the tumor microenvironment (TME) [64]. However, Montironi et al. [65] identified a subgroup of HCC patients harboring CTNNB1 mutations presenting with an inflamed TME, challenging previous observations. Furthermore, a recent study involving 83 HCC patients found no significant correlation between CTNNB1 mutations and anti-PD-1 treatment response [56]. These findings indicate that the role of CTNNB1 as a potential predictive biomarker remains controversial and that additional factors modulate immune responsiveness beyond mutation status alone.
Unlike other solid tumor types, TMB (number of DNA mutations per megabase in the coding genome) and MSI have shown limited relevance in HCC [66]. TMB is generally low/intermediate in HCC and does not correlate with ICI response [67], and MSI-high status is extremely rare (less than 1%) and thus not useful for treatment stratification in this setting [68].
Circulating biomarkers have also gained interest. A low NLR before pembrolizumab therapy has been associated with response consistent with the role of systemic inflammation in modulating immunity [69]. In parallel, flow cytometry profiling has revealed that high baseline levels of CD14+/CD16+ monocytes correlate with non-response, while CXCR3+ effector CD8+ T cells and CD11c+ antigen-presenting cells during treatment are associated with response, PFS and immune-related toxicities [70].
As with TKIs, serum AFP levels have relevance in the immunotherapy setting. A decline in AFP≥20% 3 weeks after treatment initiation has been associated with improved outcome in patients receiving (A/B) [71]. Moreover, the C-reactive protein (CRP) and AFP in ImmunoTherapY (CRAFITY score), combining baseline serum AFP≥100 ng/mL and CRP≥1 mg/dL, was developed to stratify patients by prognosis and predict disease control under ICIs [72].
In summary, many tumor-based and circulating biomarker—including immune cell infiltration, TLSs, transcriptomic signatures, genomic alterations, and serum markers like AFP—have been investigated to predict response to ICIs in HCC. Some, like TLSs have shown promise in early-stage disease, ABRS signature in advanced stages while others, such as the CRAFITY score, provide simple non-invasive prognostic tools. Nevertheless, none have been prospectively validated or integrated into routine clinical practice.
FGF19 amplification and FGFR inhibitors
FGF19 ligand and its receptor, FGFR4, play a crucial role in HCC pathogenesis and tumor proliferation [20,22]. FGF19 amplification (5% to 10% of HCC) was independently associated with shorter survival and a higher risk of recurrence in patients with HCC [73].
A phase I clinical trial reported an overall response rate (ORR) of 17% in patients with HCC with overexpression of FGF19 treated with fisogatinib (FGFR4 inhibitor) and 0% in FGF19-negative patients [74]. Although these results confirm FGFR4 as a viable therapeutic target in FGF19-positive advanced HCC, fisogatinib is not currently available in clinical practice due to the discontinuation of the phase 3 clinical trial. Moreover, mutations were identified in the gatekeeper and hinge-1 residues in the kinase domain of FGFR4 upon disease progression in 2 patients treated with fisogatinib, which were confirmed to mediate resistance in vitro and in vivo [75]. Recently, FGF401 (FGFR4 inhibitor) alone or combined with spartalizumab (anti PD-1) showed safety and clinical efficacy in patients with FGFR4/KLB-positive tumors including HCC in a phase 1/2 study [76].
Several early biomarker-driven trials are currently underway investigating FGFR inhibitors in patients with advanced HCC harboring high FGF19 expression. The NCT06978933 trial is evaluating the combination of ABSK-011, a selective FGFR4 inhibitor, with ABSK-043, a potent and selective inhibitor of the PD-1/PD-L1 interaction. Meanwhile, the NCT04828486 trial is assessing the combination of futibatinib, a pan-FGFR1–4 inhibitor, and pembrolizumab.
MET amplification and MET inhibitors
MET is the receptor tyrosine kinase for HGF ligand. Upon binding to its ligand, MET activates RAS-MAPK and PI3KAKT signaling pathways which contributes to tumor development, angiogenesis and metastasis in HCC [1,20,77]. For these reasons, MET amplification and overexpression have been explored as therapeutic targets. The phase 3 METIV-HCC trial showed that tivantinib failed to improve OS compared with placebo in patients with MET-high advanced HCC [78]. This lack of efficacy may be attributed to tivantinib functioning primarily as an antimitotic compound rather than a true MET inhibitor [79]. More selective agents like tepotinib, an orally available and potent MET inhibitor, improved time-to-progression (TTP) versus sorafenib in treatment-naive Asian patients with advanced HCC and MET overexpression [80] though data remain limited to earlyphase trials and case reports. Moreover, a sub-analysis of another phase 2 clinical trial testing tepotinib indicated that MET amplification was associated with a significant radiological response, especially in case of gene copy number >10 [81]. An ongoing phase II trial of capmatinib (INC280, MET inhibitor) will provide further insight into the clinical utility of MET-targeted therapy in HCC (NCT01737827).
Other biomarker-driven phase 2 trials
Refametinib is a potent nonadenosine triphosphate competitive inhibitor targeting MEK 1 and 2, which play a central role in the RAS signal transduction cascade [82]. RAS-MAPK signaling has been implicated in tumor progression and dissemination in HCC even if the frequency of RAS mutations in HCC is reported to be <2% [1,20]. A phase II study of refametinib yielded negative results in Asian patients with RAS-mutated HCC [83], partly due to low specificity of the method of detection of RAS mutation (sequencing of circulating tumor DNA [ctDNA]) and the low efficacy of refametinib to target RAS mutations.
Several other early phase biomarker-driven clinical trials for patient with advanced HCC are underway, including clinical trials that assess the anti-PD-1 agent spartalizumab in patients with elevated PD1-high-expressing tumors (NCT04802876); a DKN-1 inhibitor in patients with advanced HCC and Wnt alterations displaying positive staining of glutamine synthetase (NCT03645980); and the cell cycle inhibitor PD-0332991 in HCC with positive staining of retinoblastoma protein (RB) (NCT01356628).
Precision medicine in HCC: the example of the French Genomic Medicine Initiative
Targeted therapies guided by genomic alterations are the standard of care in several types of cancer. Efforts to implement precision medicine in HCC face significant barriers, as most commonly mutated genes, including pTERT, TP53, and CTNNB1, are not directly targetable. However, rare genetic alterations (<5% to 10% frequency) could be potential candidates for targeted therapy (Fig. 2). In a recent proof-of-concept study from the French Genomic Medicine program, whole-genome/whole-exome sequencing and RNA sequencing guided targeted treatment in patients with advanced HCC and hepatocellular-cholangiocarcinoma (H-CCK) refractory to A/B [84]. Among 20 patients, 9 received matched therapies based on molecular alterations. Notably, one patient with CDK4 amplification was treated with palbociclib and achieved a partial radiological response lasting 16 months, while another patient with biallelic TSC2 inactivation achieved a complete response under everolimus. These findings suggest that molecular-based guided therapy is feasible in patients with HCC/H-CCK but further validation in a larger cohort of patients is needed.
In conclusion, biomarker-driven therapies have shown some potential, particularly in patients with FGF19 or MET alterations. However, most treatments remain experimental and are not yet part of standard care. Failures in later-phase trials underscore the challenges of translating molecular insights into meaningful clinical benefits. While small proof-of-concept studies support the feasibility of precision medicine in this context, larger, well-designed trials are needed to validate these findings and enable broader access to targeted therapies.
ctDNA constitutes a small part of the total cell-free DNA (cfDNA) found in the blood of cancer patients, with levels depending on the type and stage of the tumor [85]. ctDNA is a double-stranded DNA fragment approximately 150 base pairs in length, released into the bloodstream through different passive mechanisms such as apoptosis and necrosis, as well as by active secretion from tumor cells [85-88]. It has a short half-life (from 16 minutes to 2.5 hours), which may be influenced by factors such as whether it is enclosed in vesicles, bound to proteins, or by tumor characteristics and treatment [89-91]. Theoretically, detecting mutations in ctDNA enables a comprehensive characterization of the tumor’s mutational profile by potentially capturing different mutations from various tumor regions within a single blood sample (Fig. 3). However, the capacity of ctDNA to fully reflect this tumoral heterogeneity warrants further investigation [92]. Because ctDNA is short-lived and easy to collect non-invasively, it may allow real-time monitoring of treatment response and detection of resistance mechanisms [85,86,92]. Currently, ctDNA appears to be a candidate non-invasive biomarker for patients with advanced HCC undergoing systemic treatment including immunotherapy (A/B, Durva/Treme) or TKI (sorafenib, lenvatinib, regorafenib, cabozantinib) (Fig. 3, Table 3).
Prognostic relevance of cfDNA and ctDNA at baseline levels
Several studies suggest that higher plasma cfDNA levels at baseline may be associated with worse outcomes. In a cohort of 151 patients treated with sorafenib, high cfDNA levels were associated with poor OS, TTP, and DCR [93]. Similarly, among 85 patients treated with A/B, high cfDNA levels correlated with worse OS, PFS, and ORR [94].
To focus more on tumor-specific biomarkers, many studies have analyzed ctDNA or aberrant gene methylation in plasma (Fig. 3). In an exploratory study involving 46 patients enrolled in arm A of the GO30140 trial, higher baseline ctDNA levels (measured with a 16-gene panel) were observed in patients with progressive disease (PD) [95]. Ultra-low-pass whole genome sequencing (ULP-WGS) in 31 patients treated with various TKIs and ICIs found ctDNA detectability at baseline associated with worse OS. Our study using ultra-deep target sequencing found no link between mutations in five key genes (TERT promoter, CTNNB1, TP53, NFE2L2, and PIK3CA) and survival in 35 patients treated with A/B, possibly due to limited sample size [96]. Hirai et al. [97] found that TERT promoter mutations (C228T and C250T) detected via digital droplet polymerase chain reaction (ddPCR) in over half of 130 patients on systemic therapy were associated with poorer OS. Similar results were seen in an A/B-treated cohort [94], though other studies (including ours [96]), found no survival difference [98]. These discrepancies might be due to smaller sample sizes and differences in tumor burden, as ctDNA detectability depends on the amount of tumor present.
von Felden et al. [99], reported that baseline PI3K/mTOR pathway mutations at baseline were associated with shorter PFS in patients treated with sorafenib. CTNNB1 mutations showed no correlation with outcome in A/B treated cohorts [96,100] contrasting with preclinical data [31,63]. This highlights the challenge of translating preclinical insights to the clinic and suggests CTNNB1 mutation alone may not be a reliable biomarker to predict resistance to ICI. Moreover, none of the 50 genes tested by Chuma et al. [101], in baseline ctDNA from 24 patients treated with A/B predicted response. Conversely, high genomic instability and the loss of 5q and 16p detected by low pass whole genome sequencing (LP-WGS) or ULP-WGS were associated with poorer OS in both TKI and ICI-treated patients [93,102].
ctDNA dynamics during systemic therapy
In advanced-stage tumors such as metastatic non-small cell lung cancer or colorectal cancer, ctDNA is used alongside tissue genotyping to help select therapy in treatment-naive patients [103]. In advanced HCC, Ikeda et al. [104], found actionable mutations in 5 out of 14 patients using ctDNA and tailoring therapy based on these mutations (palbociclib and celecoxib in a CDKN2A and CTNNB1 mutated patient and sirolimus and cabozantinib in a MET, TP53, and PTEN mutated patient) resulted in a favorable response.
In HCC patients receiving systemic treatment, ctDNA levels and detection of specific mutations appear to change over time. Some studies have shown increased TERT promoter mutations during treatment [105,106]. Fujii et al. [107], reported that patients with a decrease in variant allele frequency (VAF) four weeks after starting lenvatinib had better PFS and response rate. In our study, the disappearance of baseline mutations at the time of the first imaging was associated with disease control in patients treated with A/B, while the persistence of baseline mutation was associated with PD [96]. These findings align with data from the GO30140 trial, which showed that ctDNA levels decrease or are undetectable in patients who achieve an RR at the first imaging evaluation, whereas in almost all patients with stable disease (SD) or PD, ctDNA can still be detected or its levels are further increased [95]. However, how these insights should guide clinical decision-making remains an open question.
ctDNA as a tool to track resistance and tumor evolution
The dynamic monitoring of ctDNA during treatment allows for the identification of emergence of resistant subclones under targeted therapy (Fig. 3). Although this has been demonstrated in only a few HCC patients so far [101], our group described the occurrence of mutations in CTNNB1 in the plasma of patients progressing to systemic treatment, which were subclonal in the tumor collected at the time of locoregional treatment in one patient and undetectable in the tumor for the other [96]. These results suggest that ctDNA may detect emerging tumor clones not captured by tissue biopsies and could serve as a valuable alternative, since repeating a biopsy many times during treatment is not feasible given its invasiveness.
These findings highlight the potential of ctDNA as a dynamic and non-invasive biomarker for monitoring treatment response, identifying resistance mechanisms, and guiding personalized therapeutic strategies in patients with advanced HCC. However, these results require validation using biobanked samples from phase 2 and 3 clinical trials.
The treatment landscape for HCC has evolved significantly with the advent of immunotherapy and combination regimens. However, despite these advances, the identification of robust and clinically relevant biomarkers remains a major unmet need for enabling personalized treatment selection. Integrating tumor biology into therapeutic decision-making is becoming increasingly feasible, particularly through baseline molecular profiling and real-time monitoring using ctDNA. Nevertheless, these approaches require further validation in prospective clinical trials. The complementary use of tissue and liquid biopsies provides an opportunity to characterize the molecular profile of HCC at treatment initiation and to monitor its evolution over time, including the emergence of resistance mechanisms. Such strategies can support personalized treatment approaches and enhance clinical trial design by enabling molecular stratification of patients. Biomarker-driven trials may reduce biological heterogeneity within study populations and allow for more precise assessments of therapeutic efficacy and safety. Moreover, the combined analysis of tissue and circulating biomarkers may uncover potentially actionable alterations. However, many of the commonly altered genes in HCC—such as TP53, CTNNB1, and TERT promoter mutations—currently lack effective targeted therapies. This underscores the need for translational research to identify novel drugs targeting these pathways. While basket trials have emerged as a promising strategy to evaluate targeted therapies across cancers with shared molecular alterations, their applicability in HCC remains limited due to the scarcity of targetable mutations in its most frequently altered genes.
In conclusion, the identification and implementation of predictive biomarkers could accelerate the integration of precision medicine into routine clinical practice for HCC, ultimately improving patient outcomes while reducing unnecessary treatment-related risks and costs. However, this progress depends on continued efforts to discover new targeted therapies and to incorporate biomarkers into clinical trials.

Authors’ contributions

Concept and design: SS, CC, JCN. Acquisition of data: SS, CC. Drafting and critical revision of manuscript: SS, CC, JHS, JA, JCN.

Acknowledgements

This work was supported by Association Française pour l’Étude du Foie (AFEF) 2022 projet radio-moléculaire, Agence Nationale De La Recherche (ANR) 2022 SYSTHEC, Agence nationale de recherches sur le sida et les hépatites virales (ANRS) 2023 CSS13 HBV-LIRAGE ECTZ232901, Ligue contre le cancer Recherche Clinique 2024, SIRIC CAncer Research in multiple dimensions to accelerate PrEcision Medicine (CARPEM) INCa-DGOS-Inserm-12561.

Conflicts of Interest

Sabrina Sidali received funding from Roche and Astra- Zeneca for invited talks. Claudia Campani has no COI. Jean-Charles Nault received research funding from Bayer and Ipsen. Jihyun An has no COI. Ju Hyun Shim has no COI.

Figure 1.
Genomic diversity of hepatocellular carcinoma. We represented the main genomic subgroups of HCC based on transcriptomic and mutational data and their correlation with histological features, risk factors and prognosis. AFP, alpha-fetoprotein; HBV, hepatitis B virus; HCC, hepatocellular carcinoma.
cmh-2025-0746f1.jpg
Figure 2.
Precision medicine in patients with advanced hepatocellular carcinoma. The upper section represents genetic alterations in HCC potentially targetable using targeted therapies. These data were based either on clinical trials, on case reports available in the literature or on biomarker-based treatments approved for other tumor types. The lower section represents the main candidate biomarkers of response or resistance to immunotherapy, and prognostic biomarkers in patients with advanced HCC. The analysis of tumor tissue samples allows us to derive gene expression signatures and to investigate the TME, including lymphoid and myeloid cell populations. Additionally, tumor-related or immune-related circulating factors, circulating tumor cells, and ctDNA can be measured in blood samples, providing a non-invasive way to gain insights into tumor biology. Most of these biomarkers were associated with response to immunotherapy. The role of CTNNB1 mutation as a predictive biomarker associated with resistance to immunotherapy remains controversial due to conflicting results across studies. ABRS, atezolizumab+bevacizumab response signature; AFP, alpha-fetoprotein, CRAFITY, CRP and AFP in ImmunoTherapY; CRP, C-reactive protein; ctDNA, circulating tumor DNA; HCC, hepatocellular carcinoma; IL-6, interleukin-6; TME, tumor microenvironment; Treg, regulatory T cells.
cmh-2025-0746f2.jpg
Figure 3.
Role of circulating tumor DNA in the management of hepatocellular carcinoma. The upper portion of the figure illustrates key alterations detectable in ctDNA, including cfDNA quantification, SNVs, CNAs, gene fusions, and methylation changes. The lower portion depicts the potential clinical applications of ctDNA in patients with HCC receiving systemic therapy: (1) Prognosis: baseline ctDNA features— such as elevated levels, presence of mutations or CNAs, and methylation profiles—may correlate with tumor burden and survival outcomes and serve as surrogate markers of minimal residual disease. (2) Response: Longitudinal monitoring of ctDNA can reveal reductions in mutation burden in responders, as illustrated by the loss of detectable mutations (e.g., from mutant to wild-type status), corresponding to radiological response. (3) Resistance: Serial ctDNA analysis may uncover the emergence of additional mutations (e.g., acquisition of a second mutation) under selective therapeutic pressure, enabling early identification of resistance mechanisms. cfDNA, cellfree DNA; ctDNA, circulating tumor DNA; CNA, copy number alteration; SNV, single nucleotide variation.
cmh-2025-0746f3.jpg
Table 1.
Circulating and tumor biomarkers of response to TKIs in advanced HCC
Table 1.
Treatment Treatment’s target Biomarker Type of biomarker Outcome Reference
Sorafenib VEGFR1–3, PDGFR, RAF, BRAF, KIT AFP Serum Early AFP decline >20% from baseline after 2 to 4 weeks of sorafenib associated with better OS, PFS, ORR, and disease control rate. Shao et al., 2010 [46]
Early AFP decline >20% from baseline after 6–8 weeks of sorafenib predicts survival. Lee et al., 2015 [48]
Early AFP decline >20% from baseline after 6–8 weeks of sorafenib predicts survival and response. Sánchez et al., 2018 [47]
Cytokines (including Ang-2, VEGF) Serum High serum levels significantly associated with poor response. Miyahara et al., 2011 [33]
Ang-2, VEGF Serum Baseline Ang-2 and VEGF levels predict OS. Llovet et al., 2012 [36]
VEGFA Tumor VEGFA amplification associated with longer OS Horwitz et al., 2014 [32]
VEGF Serum VEGF decrease at week 8 associated with longer OS. Tsuchiya et al., 2014 [35]
p-ERK Tumor Sorafenib treated patients with p-ERK negative staining in IHC had increased RFS. Pinyol et al., 2019 [30]
Mutations of PI3K-mTOR pathway Tumor Mutations in the PI3K-mTOR pathway lower rates of clinical benefit, had shorter PFS and OS. Harding et al., 2019 [31]
NLR Serum Lower pre-treatment NLR predictive of a greater OS benefit with sorafenib. Bruix et al., 2017 [39]
DKK1 Serum High DKK1 level associated with response rate to sorafenib (longer PFS and OS). Qiu et al., 2019 [37]
IL-8, IL-6 Serum High IL-6 and IL-8 associated with shorter OS. Öcal et al., 2022 [34]
Lenvatinib VEGFR1–3, PDGFR, FGFR1–4, RET, KIT FGF19, Ang-2 Serum Significantly increased FGF19 levels and decreased Ang-2 levels associated with response to lenvatinib. Chuma et al., 2020 [41]
VEGF, FGF21, Ang-2 Serum Higher levels of VEGF, FGF21, and ang-2 associated with shorter OS under sorafenib and lenvatinib. Finn et al., 2021 [42]
ST6GAL1 Serum Serum ST6GAL1-high HCC under lenvatinib showed better survival than sorafenib. Myojin et al., 2021 [108]
Regorafenib VEGFR1–3, PDGFR, RAF, FGFR1–3 Ang-1, cystatinB, LAP TGF-β1, LOX-1, MIP-1α Serum Serum ANG-1, cystatin B, LAP TGF-β1, LOX-1, MIP-1α predictive of regorafenib treatment benefit for OS. Teufel et al., 2019 [43]
miRNA Serum Plasma miRNAs (miR-15B, 30A, 107, 122, 125B, 200A, 320b, 374B, 645) predictive of OS Teufel et al., 2019 [43]
Cabozantinib VEGFR1–3, MET, RET, AXL AFP Serum AFP decrease ≥20% from baseline at Week 8 after treatment was associated with longer OS. Kelley et al., 2020 [109]
AFP≥400 ng/mL Serum Cabozantinib prolonged median PFS vs ramucirumab in patients with AFP ≥ 400 ng/mL. Trojan et al., 2021 [110]
MET, HGF, GAS6, IL-8, Ang-2, IGF-1 Serum Low baseline levels of MET, HGF, GAS6, IL-8, Ang-2 and high levels of IGF-1 associated with longer OS. Rimassa et al., 2021 [44]
Ramucirumab VEGFR2 AFP≥400 ng/mL Serum Serum AFP≥400 ng/mL at baseline: improved OS for ramucirumab versus placebo (prospective validation) Zhu et al., 2015 [15]
AFP>1,000 ng/mL and NLR Serum Baseline AFP>1,000 ng/mL and NLR: poor prognosis. Llovet et al., 2022 [45]

AFP, alpha-fetoprotein; Ang-1, Angiopoietin 1; Ang-2, Angiopoietin 2; AXL, AXL receptor tyrosine kinase; BRAF, B-Raf proto-oncogene, serine/threonine kinase; DCR, disease control rate; DKK1, Dickkopf-1; GAS6, growth arrest specific factor 6; FGF, fibroblast growth factor; FGFR1–4, fibroblast growth factor receptors 1–4; HCC, hepatocellular carcinoma; HGF, hepatocyte growth factor; IGF-1, insulin-like growth factor 1; IHC, immunohistochemistry; IL-6, interleukin-6; IL-8, interleukin-8; KIT, KIT proto-oncogene, receptor tyrosine kinase; LAP, latency-associated peptide; LOX-1, lectin-like oxidized low-density lipoprotein receptor-1; MET, mesenchymal-epithelial transition; MIP, macrophage inflammatory protein; miRNA, microRNA; NLR, neutrophil-to-lymphocyte ratio; ORR, overall response rate; OS, overall survival; PDGFR, platelet-derived growth factor receptor; p-ERK, phosphorylated ERK; PFS, progression-free survival; PI3K, phosphatidylinositol 3-kinase; RET, rearranged during transfection; RFS, recurrence-free survival; ST6GAL1, 6-sialyltransferase 1; TGF, transforming growth factor; TKIs, tyrosine kinase inhibitors; VEGF, vascular endothelial growth factor; VEGFR, vascular endothelial growth factor receptor.

Table 2.
Circulating and tumor biomarkers of response to immunotherapy in early and advanced HCC
Table 2.
Treatment Treatment’s target Biomarker Type of biomarker Outcome Reference
Early stage HCC
Neoadjuvant Cabozantinib+Nivolumab VEGFR1–3, MET, RET, AXL Anti-PD-1 Effector T cells, TLS, CD138+ plasma cells Tumor Enrichment of effector T cells, prominent tertiary lymphoid structures (TLS), CD138+ plasma cells, specific spatial organization of B cells in responders. Ho et al., 2021 [111]
Neoadjuvant Cemiliplimab Anti-PD-1 CD8+ T cells Tumor Higher CD8 T-cell infiltration in the tumor was associated with pathological response. Marron et al., 2022 [112]
Neoadjuvant Cemiplimab or Nivolumab Anti-PD-1s CXCL13+ T helper cells, Granzyme K+ effector-like CD8+ T cells Tumor CXCL13+ T helper cells Granzyme K+ effector-like CD8+ T cells associated with pathological response. Magen et al., 2023 [55]
Anti-PD-1 based treatment* Anti-PD-1 TLSs Tumor High intratumoral TLS density at the time of surgery is associated with pathologic response and improved relapse-free survival. Shu et al., 2024 [57]
Advanced HCC
Atezolizumab+Bevacizumab Anti PDL-1+anti-VEGF CD274, intra tumoral infiltration of CD8 T cell and CXC10+ macrophages Tumor High CD274 expression, intra tumoral CD8+ effector T cell and pro-inflammatory CXC10+ macrophages associated with better PFS. Zhu et al., 2022 [53]
Myeloid inflammation signatures Tumor High levels of myeloid inflammation gene expression signature associated with improved PFS.
ABRS signature Tumor High ABRS associated with longer PFS.
CTNNB1 and TERT promoter mutations Tumor Wild-type CTNNB1 or TERT promoter mutation were associated with longer OS.
AFP Serum Decrease AFP≥20% at 3 weeks was associated with RR and OS. Campani et al., 2023 [71]
IL-6 Serum Low serum IL-6 levels were associated with longer OS and PFS. Miura et al., 2025 [113]
Durvalumab+Tremelimumab Anti-PD-L1+anti-CTLA-4 Ki67+CD8+ T cells Serum RR was associated with an expansion of Ki67+CD8+ T cells. Kelley et al., 2021 [114]
Camrelizumab+Apatinib Anti-PD-1+anti-VEGF2 PD-L1 expression Tumor PD-L1 expression was associated to higher ORR and longer PFS. Xu et al., 2021 [115]
Pembrolizumab Anti-PD-1 PD-L1 combined positive score Tumor PD-L1 combined positive score was associated to response with longer PFS. Zhu et al., 2018 [50]
TGF-β Serum High baseline TGF-β levels (≥200 pg/mL) correlated with lower PFS and OS. Feun et al., 2019 [116]
PD-L1 expression Tumor Tumor PD-L1 expression was associated to RR. Hong et al., 2022 [69]
Cytotoxic T cells infiltration, circulating CD8+ T cells, NLR Tumor and Serum Low pre-treatment NLR, infiltration of of cytotoxic T cells and increases active circulating CD8+ T cells under treatment were associated to RR. Hong et al., 2022 [69]
Nivolumab Anti-PD-1 PD-1 and PD-L1 tumor expression Tumor Tumor PD-L1 positive was associated with increased OS. Sangro et al., 2020 [49]
4-Gene signature (PD-L1, CD8A, LAG3, and STAT1) Tumor 4-Gene signature was associated with better response rate and OS.
AFP Serum Baseline lower AFP<400 μg/L was associated with longer OS.
Tremelimumab (plus ablation) Anti-CTLA-4 CD3 and CD8 T cells Tumor CD3 and CD8 immune cell tumor infiltration after treatment was higher in patients with RR. Duffy et al., 2017 [51]
Anti-PDL-(L)-1 based therapy Anti-PD-(L)-1 CRAFITY score Serum Lower CRAFITY score was associated with longer OS and better RR. Scheiner et al., 2022 [72]
Anti-PD-1 therapy† Anti-PD-1 CXCR3+CD8+ effector memory T cells, CD11c+ antigen-presenting cells Serum CXCR3+CD8+ effector memory T cells and CD11c+ antigen-presenting cells associated with RR, PFS, irAEs. Chuah et al., 2022 [70]
Anti-PD-1 therapy‡ Anti-PD-1 11-Gene signature (IFNAP signature) Tumor 11-Gene signature was associated with RR. Haber et al., 2023 [56]

ABRS, atezolizumab+bevacizumab response signature; AFP, alpha-fetoprotein; AXL, AXL receptor tyrosine kinase; CRAFITY, CRP and AFP in ImmunoTherapY; CRP, C-reactive protein; CTLA-4, cytotoxic T-lymphocyte antigen 4; HCC, hepatocellular carcinoma; ICI, immune check-points inhibitors; IFNAP, interferon and antigen presentation; IL-6, interleukin-6; irAEs, immune-related adverse effects; MET, mesenchymal-epithelial transition; NLR, neutrophil-to-lymphocyte ratio; ORR, overall response rate; OS, overall survival; PD-1, programmed death 1; PD-L1, programmed death-ligand 1; PFS, progression-free survival; RET, rearranged during transfection; RR, radiological response; TGF, transforming growth factor; TKI, tyrosine kinase inhibitor; TLS, tertiary lymphoid structure; VEGF, vascular endothelial growth factor; VEGFR, vascular endothelial growth factor receptor.

*Neoadjuvant treatments were: anti-PD-1+TKI (n=14), anti-PD-1 (n=3), anti-PD-1 + anti-CTLA4 (n=1), anti-PD-1 + anti-CTLA4 + TKI (n=1);

†anti-PD-1 drug used: Nivolumab and Pembrolizumab;

‡anti-PD-1 drug used as first-line: Nivolumab (n=67), Pembrolizumab (n=14), Tislelizumab (n=2).

Table 3.
Studies describing the role of ctDNA in predicting prognosis and monitoring systemic treatment in HCC
Table 3.
Study Input Target Technique Patient Country Main result
Oh et al. [93] 1.5 mL plasma Total amount cfDNA, genomic instability Low-depth whole genome sequencing 151 Patients treated with first-line sorafenib Asia Higher cfDNA levels and genomic instability associated: shorter TTP, shorter OS and lower DCR.
Matsumae et al. [94] Plasma Total amount cfDNA, 25 genes Hybridization capture and sequencing 85 Patients treated with atezolizumab/bevacizumab Asia High cfDNA levels associated with lower ORR, shorter OS and PFS. TERT promoter mutations and AFP independent predictors of worse OS.
Hsu et al. [95] Plasma ctDNA levels, 16 gene mutations Target sequencing 48 Patients enrolled in Arm A of G030140 (NCT02715531) Asia Increased baseline ctDNA levels in patients with PD and decreased or non-detectable ctDNA in responders.
Sogbe et al. [102] 10 mL blood (plasma) CNA Ultra-low-pass wholegenome sequencing 31 Patients treated with systemic treatments Europe ctDNA mutations are associated with worse OS. Loss of 5q and 16q associated with a significantly worse OS.
Campani et al. [96] 1 mL plasma cfDNA levels, 5 gene mutations Digital-droplet PGR, target sequencing 53 Patients treated with systemic therapy Europe Dynamic of baseline mutations are associated with radiological response and progression. Emergence of CTNNB1 mutations at radiological progression.
Hirai et al. [97] 1 mL plasma TERT promoter mutations (C228T, C250T) Digital-droplet PGR 130 HCC patients treated with TKI (sorafenib or lenvatinib) or TACE Asia TERT promoter mutations associated with shorter OS in patients treated by TKI. TERT promoter mutations associated with poorer OS, higher VAF associated with worse OS.
Oversoe et al. [98] 5 mL plasma C228T TERT promoter mutation Digital-droplet PGR 26 Patients treated with sorafenib Europe No differences in OS in TERT promoter mutated and non-mutated patients.
von Felden et al. [99] Plasma 25 Gene mutations Digital-droplet PGR, target sequencing 61 Patients (23 treated with TKI and 38 with CPI) USA Mutations in the PI3K/MTOR pathway: shorter PFS in patients treated with TKI. Wnt pathway mutations not associated with outcomes in under CPI. New mutations at radiological progression.
Chuma et al. [100] Serum 50 Gene mutations Target sequencing 24 Patients treated with atezolizumab/bevacizumab Asia No correlation between mutations and treatment response.
Ikeda et al. [104] 20 mL blood (plasma) 68 Gene mutations Target sequencing 14 Advanced HCC Asia rEesponses: (1) CDKN2A and CTNNB1 mutated patient under palbociclib+celecoxib; (2) MET, TP53, and PTEN mutated patients under sirolimus+cabozantinib.
Galle et al. [117] Blood EMT-associated ctDNA methylation changes 12 Patients treated with sorafenib Europe Methylation changes in EMT genes predict tumor response and acquired resistance to sorafenib.
Nakatsuka et al. [118] 1 mL plasma Total amount cfDNA, TERT promoter mutations, 275 gene mutations Digital-droplet PGR, target sequencing 35 Patients treated with systemic treatment Asia Increase in cfDNA levels during systemic treatment, higher early increase in cfDNA levels under systemic treatment in patients who achieved response.
Alunni-Fabbroni et al. [119] 5 mL blood (plasma) Total amount cfDNA, 597 gene mutations Target sequencing 13 (SORAMIC trial) Europe cfDNA levels high during treatment associated with worse OS.
Fujii et al. [107] 2 mL plasma 74 Gene mutations Target sequencing 24 Patients receiving lenvatinib Asia VAFmean* stable after 4 weeks in patients with PD or SD, VAFmean reduced in patients with PR or CR. VAFmean reduction associated to longer PFS.
Cowzer et al. [105] Blood 129 Gene mutations Target sequencing 51 Patients treated with systemic therapy USA High concordance with matched tumor: higher VAF associated with AFP, tumor volume, and no previous systemic therapy, no association with OS.
Muraoka et al. [106] 8 mL blood (plasma) C228T TERT promoter mutation Digital-droplet PCR 41 Patients treated with TKI Asia Increase in TERT promoter mutations levels within 1 week of TKI initiation predict PFS.
Oversoe et al. [120] 4–5 mL plasma CTNNB1 p.T41A hotspot mutation Digital-droplet PCR 37 Patients of whom at least two were treated with sorafenib Europe VAF of CTNNB1 p.T41A increase after 4–8 weeks of sorafenib associated with progression
Ikeda et al. [121] 20 mL plasma ctDNA mutations Target sequencing 26 With advanced HCC, some treated with systemic therapy Asia Emergence of BRCA1 and TP53 mutations during capecitabine treatment.

AFP, alpha-fetoprotein; aHR, adjusted hazard ratio; BAX, Bcl-2–associated X; BRCA1, breast cancer 1; CDKN2A, cyclin-dependent kinase inhibitor 2A; cfDNA, circulating free DNA; CNA, copy number alteration; CPI, immune checkpoint inhibitors; CR, complete response; ctDNA, circulating tumor DNA; CTNNB1, Catenin beta-1; DCR, disease control rate; HR, hazard ratio; EMT, epithelial-mesenchymal transition; HCC, hepatocellular carcinoma; MET, mesenchymal-epithelial transition factor; ORR, objective response rate; OS, overall survival; PCR, polymerase chain reaction; PD, progressive disease; PFS, progression-free survival; PI3K, phosphatidylinositol 3-kinase; PR, partial response; PTEN, phosphatase and tensin homolog; SD, stable disease; TACE, transarterial chemoebolization; TERT, telomerase reverse transcriptase; TKI, tyrosine-kinase inhibitor; TP53, tumor protein 53; TTP, time to progression; VAF, variant allele frequency; VEGF: vascular endothelial growth factor.

*VAFmean: mean of VAF of all mutated genes.

AAV2

adeno-associated virus 2

A/B

Atezolizumab/Bevacizumab

AFP

alpha-fetoprotein

ALBI

albumin-bilirubin

Ang-2

Angiopoietin-2

BSC

best supportive care

cfDNA

cell-free DNA

ctDNA

circulating tumor DNA

CNA

copy number alteration

CRAFITY

CRP and AFP in ImmunoTherapY

CRP

C-reactive protein

CTLA-4

cytotoxic T-lymphocyte antigen 4

DCR

disease control rate

ddPCR

digital droplet PCR

DKK1

Dickkopf-1

Durva/Trema

durvalumab/tremelumab

FGF19

fibroblast growth factor 19

FGFR4

fibroblast growth factor receptor 4

HBV

hepatitis B virus

HCC

hepatocellular carcinoma

H-CCK

hepatocellular-cholangiocarcinoma

HGF

hepatocyte growth factor

ICI

immune checkpoint inhibitor

IHC

immunohistochemistry

IGF-1

insulin-like growth factor 1

IL-2

interleukin-2

IL-6

interleukin-6

IL-8

interleukin-8

irAEs

immune-related adverse effects

LP-WGS

low pass whole genome sequencing

MET

mesenchymal-epithelial transition

MSI

microsatellite instability

NLR

neutrophil-to- lymphocyte ratio

NSCLC

non-small cell lung cancer

OS

overall survival

OR

objective response

ORR

objective response rate

p-ERK

phosphorylated ERK

PD

progressive disease

PD-1

programmed death 1

PD-L1

programmed death-ligand 1

PDGFR

platelet-derived growth factor receptor

PFS

progression-free survival

PS

performance status

RR

radiological response

RCCEP

reactive cutaneous capillary endothelial proliferation

SD

stable disease

ST6GAL1

6-sialyltransferase 1

TKI

tyrosine kinase inhibitor

TLS

tertiary lymphoid structure

TMB

tumor mutational burden

TME

tumor microenvironment

TTP

time to progression

ULP-WGS

ultra-low-pass whole genome sequencing

VAF

variant allele frequency

VEGF

vascular endo¬thelial growth factor

VEGFR

vascular endothelial growth factor receptor
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Tumor-based biomarkers and circulating tumor DNA for precision medicine in advanced hepatocellular carcinoma
Clin Mol Hepatol. 2026;32(1):69-90.   Published online August 20, 2025
DOI: https://doi.org/10.3350/cmh.2025.0746
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Tumor-based biomarkers and circulating tumor DNA for precision medicine in advanced hepatocellular carcinoma
Clin Mol Hepatol. 2026;32(1):69-90.   Published online August 20, 2025
DOI: https://doi.org/10.3350/cmh.2025.0746
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Tumor-based biomarkers and circulating tumor DNA for precision medicine in advanced hepatocellular carcinoma
Image Image Image
Figure 1. Genomic diversity of hepatocellular carcinoma. We represented the main genomic subgroups of HCC based on transcriptomic and mutational data and their correlation with histological features, risk factors and prognosis. AFP, alpha-fetoprotein; HBV, hepatitis B virus; HCC, hepatocellular carcinoma.
Figure 2. Precision medicine in patients with advanced hepatocellular carcinoma. The upper section represents genetic alterations in HCC potentially targetable using targeted therapies. These data were based either on clinical trials, on case reports available in the literature or on biomarker-based treatments approved for other tumor types. The lower section represents the main candidate biomarkers of response or resistance to immunotherapy, and prognostic biomarkers in patients with advanced HCC. The analysis of tumor tissue samples allows us to derive gene expression signatures and to investigate the TME, including lymphoid and myeloid cell populations. Additionally, tumor-related or immune-related circulating factors, circulating tumor cells, and ctDNA can be measured in blood samples, providing a non-invasive way to gain insights into tumor biology. Most of these biomarkers were associated with response to immunotherapy. The role of CTNNB1 mutation as a predictive biomarker associated with resistance to immunotherapy remains controversial due to conflicting results across studies. ABRS, atezolizumab+bevacizumab response signature; AFP, alpha-fetoprotein, CRAFITY, CRP and AFP in ImmunoTherapY; CRP, C-reactive protein; ctDNA, circulating tumor DNA; HCC, hepatocellular carcinoma; IL-6, interleukin-6; TME, tumor microenvironment; Treg, regulatory T cells.
Figure 3. Role of circulating tumor DNA in the management of hepatocellular carcinoma. The upper portion of the figure illustrates key alterations detectable in ctDNA, including cfDNA quantification, SNVs, CNAs, gene fusions, and methylation changes. The lower portion depicts the potential clinical applications of ctDNA in patients with HCC receiving systemic therapy: (1) Prognosis: baseline ctDNA features— such as elevated levels, presence of mutations or CNAs, and methylation profiles—may correlate with tumor burden and survival outcomes and serve as surrogate markers of minimal residual disease. (2) Response: Longitudinal monitoring of ctDNA can reveal reductions in mutation burden in responders, as illustrated by the loss of detectable mutations (e.g., from mutant to wild-type status), corresponding to radiological response. (3) Resistance: Serial ctDNA analysis may uncover the emergence of additional mutations (e.g., acquisition of a second mutation) under selective therapeutic pressure, enabling early identification of resistance mechanisms. cfDNA, cellfree DNA; ctDNA, circulating tumor DNA; CNA, copy number alteration; SNV, single nucleotide variation.

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Figure 3.

Tumor-based biomarkers and circulating tumor DNA for precision medicine in advanced hepatocellular carcinoma
Treatment Treatment’s target Biomarker Type of biomarker Outcome Reference
Sorafenib VEGFR1–3, PDGFR, RAF, BRAF, KIT AFP Serum Early AFP decline >20% from baseline after 2 to 4 weeks of sorafenib associated with better OS, PFS, ORR, and disease control rate. Shao et al., 2010 [46]
Early AFP decline >20% from baseline after 6–8 weeks of sorafenib predicts survival. Lee et al., 2015 [48]
Early AFP decline >20% from baseline after 6–8 weeks of sorafenib predicts survival and response. Sánchez et al., 2018 [47]
Cytokines (including Ang-2, VEGF) Serum High serum levels significantly associated with poor response. Miyahara et al., 2011 [33]
Ang-2, VEGF Serum Baseline Ang-2 and VEGF levels predict OS. Llovet et al., 2012 [36]
VEGFA Tumor VEGFA amplification associated with longer OS Horwitz et al., 2014 [32]
VEGF Serum VEGF decrease at week 8 associated with longer OS. Tsuchiya et al., 2014 [35]
p-ERK Tumor Sorafenib treated patients with p-ERK negative staining in IHC had increased RFS. Pinyol et al., 2019 [30]
Mutations of PI3K-mTOR pathway Tumor Mutations in the PI3K-mTOR pathway lower rates of clinical benefit, had shorter PFS and OS. Harding et al., 2019 [31]
NLR Serum Lower pre-treatment NLR predictive of a greater OS benefit with sorafenib. Bruix et al., 2017 [39]
DKK1 Serum High DKK1 level associated with response rate to sorafenib (longer PFS and OS). Qiu et al., 2019 [37]
IL-8, IL-6 Serum High IL-6 and IL-8 associated with shorter OS. Öcal et al., 2022 [34]
Lenvatinib VEGFR1–3, PDGFR, FGFR1–4, RET, KIT FGF19, Ang-2 Serum Significantly increased FGF19 levels and decreased Ang-2 levels associated with response to lenvatinib. Chuma et al., 2020 [41]
VEGF, FGF21, Ang-2 Serum Higher levels of VEGF, FGF21, and ang-2 associated with shorter OS under sorafenib and lenvatinib. Finn et al., 2021 [42]
ST6GAL1 Serum Serum ST6GAL1-high HCC under lenvatinib showed better survival than sorafenib. Myojin et al., 2021 [108]
Regorafenib VEGFR1–3, PDGFR, RAF, FGFR1–3 Ang-1, cystatinB, LAP TGF-β1, LOX-1, MIP-1α Serum Serum ANG-1, cystatin B, LAP TGF-β1, LOX-1, MIP-1α predictive of regorafenib treatment benefit for OS. Teufel et al., 2019 [43]
miRNA Serum Plasma miRNAs (miR-15B, 30A, 107, 122, 125B, 200A, 320b, 374B, 645) predictive of OS Teufel et al., 2019 [43]
Cabozantinib VEGFR1–3, MET, RET, AXL AFP Serum AFP decrease ≥20% from baseline at Week 8 after treatment was associated with longer OS. Kelley et al., 2020 [109]
AFP≥400 ng/mL Serum Cabozantinib prolonged median PFS vs ramucirumab in patients with AFP ≥ 400 ng/mL. Trojan et al., 2021 [110]
MET, HGF, GAS6, IL-8, Ang-2, IGF-1 Serum Low baseline levels of MET, HGF, GAS6, IL-8, Ang-2 and high levels of IGF-1 associated with longer OS. Rimassa et al., 2021 [44]
Ramucirumab VEGFR2 AFP≥400 ng/mL Serum Serum AFP≥400 ng/mL at baseline: improved OS for ramucirumab versus placebo (prospective validation) Zhu et al., 2015 [15]
AFP>1,000 ng/mL and NLR Serum Baseline AFP>1,000 ng/mL and NLR: poor prognosis. Llovet et al., 2022 [45]
Treatment Treatment’s target Biomarker Type of biomarker Outcome Reference
Early stage HCC
Neoadjuvant Cabozantinib+Nivolumab VEGFR1–3, MET, RET, AXL Anti-PD-1 Effector T cells, TLS, CD138+ plasma cells Tumor Enrichment of effector T cells, prominent tertiary lymphoid structures (TLS), CD138+ plasma cells, specific spatial organization of B cells in responders. Ho et al., 2021 [111]
Neoadjuvant Cemiliplimab Anti-PD-1 CD8+ T cells Tumor Higher CD8 T-cell infiltration in the tumor was associated with pathological response. Marron et al., 2022 [112]
Neoadjuvant Cemiplimab or Nivolumab Anti-PD-1s CXCL13+ T helper cells, Granzyme K+ effector-like CD8+ T cells Tumor CXCL13+ T helper cells Granzyme K+ effector-like CD8+ T cells associated with pathological response. Magen et al., 2023 [55]
Anti-PD-1 based treatment* Anti-PD-1 TLSs Tumor High intratumoral TLS density at the time of surgery is associated with pathologic response and improved relapse-free survival. Shu et al., 2024 [57]
Advanced HCC
Atezolizumab+Bevacizumab Anti PDL-1+anti-VEGF CD274, intra tumoral infiltration of CD8 T cell and CXC10+ macrophages Tumor High CD274 expression, intra tumoral CD8+ effector T cell and pro-inflammatory CXC10+ macrophages associated with better PFS. Zhu et al., 2022 [53]
Myeloid inflammation signatures Tumor High levels of myeloid inflammation gene expression signature associated with improved PFS.
ABRS signature Tumor High ABRS associated with longer PFS.
CTNNB1 and TERT promoter mutations Tumor Wild-type CTNNB1 or TERT promoter mutation were associated with longer OS.
AFP Serum Decrease AFP≥20% at 3 weeks was associated with RR and OS. Campani et al., 2023 [71]
IL-6 Serum Low serum IL-6 levels were associated with longer OS and PFS. Miura et al., 2025 [113]
Durvalumab+Tremelimumab Anti-PD-L1+anti-CTLA-4 Ki67+CD8+ T cells Serum RR was associated with an expansion of Ki67+CD8+ T cells. Kelley et al., 2021 [114]
Camrelizumab+Apatinib Anti-PD-1+anti-VEGF2 PD-L1 expression Tumor PD-L1 expression was associated to higher ORR and longer PFS. Xu et al., 2021 [115]
Pembrolizumab Anti-PD-1 PD-L1 combined positive score Tumor PD-L1 combined positive score was associated to response with longer PFS. Zhu et al., 2018 [50]
TGF-β Serum High baseline TGF-β levels (≥200 pg/mL) correlated with lower PFS and OS. Feun et al., 2019 [116]
PD-L1 expression Tumor Tumor PD-L1 expression was associated to RR. Hong et al., 2022 [69]
Cytotoxic T cells infiltration, circulating CD8+ T cells, NLR Tumor and Serum Low pre-treatment NLR, infiltration of of cytotoxic T cells and increases active circulating CD8+ T cells under treatment were associated to RR. Hong et al., 2022 [69]
Nivolumab Anti-PD-1 PD-1 and PD-L1 tumor expression Tumor Tumor PD-L1 positive was associated with increased OS. Sangro et al., 2020 [49]
4-Gene signature (PD-L1, CD8A, LAG3, and STAT1) Tumor 4-Gene signature was associated with better response rate and OS.
AFP Serum Baseline lower AFP<400 μg/L was associated with longer OS.
Tremelimumab (plus ablation) Anti-CTLA-4 CD3 and CD8 T cells Tumor CD3 and CD8 immune cell tumor infiltration after treatment was higher in patients with RR. Duffy et al., 2017 [51]
Anti-PDL-(L)-1 based therapy Anti-PD-(L)-1 CRAFITY score Serum Lower CRAFITY score was associated with longer OS and better RR. Scheiner et al., 2022 [72]
Anti-PD-1 therapy† Anti-PD-1 CXCR3+CD8+ effector memory T cells, CD11c+ antigen-presenting cells Serum CXCR3+CD8+ effector memory T cells and CD11c+ antigen-presenting cells associated with RR, PFS, irAEs. Chuah et al., 2022 [70]
Anti-PD-1 therapy‡ Anti-PD-1 11-Gene signature (IFNAP signature) Tumor 11-Gene signature was associated with RR. Haber et al., 2023 [56]
Study Input Target Technique Patient Country Main result
Oh et al. [93] 1.5 mL plasma Total amount cfDNA, genomic instability Low-depth whole genome sequencing 151 Patients treated with first-line sorafenib Asia Higher cfDNA levels and genomic instability associated: shorter TTP, shorter OS and lower DCR.
Matsumae et al. [94] Plasma Total amount cfDNA, 25 genes Hybridization capture and sequencing 85 Patients treated with atezolizumab/bevacizumab Asia High cfDNA levels associated with lower ORR, shorter OS and PFS. TERT promoter mutations and AFP independent predictors of worse OS.
Hsu et al. [95] Plasma ctDNA levels, 16 gene mutations Target sequencing 48 Patients enrolled in Arm A of G030140 (NCT02715531) Asia Increased baseline ctDNA levels in patients with PD and decreased or non-detectable ctDNA in responders.
Sogbe et al. [102] 10 mL blood (plasma) CNA Ultra-low-pass wholegenome sequencing 31 Patients treated with systemic treatments Europe ctDNA mutations are associated with worse OS. Loss of 5q and 16q associated with a significantly worse OS.
Campani et al. [96] 1 mL plasma cfDNA levels, 5 gene mutations Digital-droplet PGR, target sequencing 53 Patients treated with systemic therapy Europe Dynamic of baseline mutations are associated with radiological response and progression. Emergence of CTNNB1 mutations at radiological progression.
Hirai et al. [97] 1 mL plasma TERT promoter mutations (C228T, C250T) Digital-droplet PGR 130 HCC patients treated with TKI (sorafenib or lenvatinib) or TACE Asia TERT promoter mutations associated with shorter OS in patients treated by TKI. TERT promoter mutations associated with poorer OS, higher VAF associated with worse OS.
Oversoe et al. [98] 5 mL plasma C228T TERT promoter mutation Digital-droplet PGR 26 Patients treated with sorafenib Europe No differences in OS in TERT promoter mutated and non-mutated patients.
von Felden et al. [99] Plasma 25 Gene mutations Digital-droplet PGR, target sequencing 61 Patients (23 treated with TKI and 38 with CPI) USA Mutations in the PI3K/MTOR pathway: shorter PFS in patients treated with TKI. Wnt pathway mutations not associated with outcomes in under CPI. New mutations at radiological progression.
Chuma et al. [100] Serum 50 Gene mutations Target sequencing 24 Patients treated with atezolizumab/bevacizumab Asia No correlation between mutations and treatment response.
Ikeda et al. [104] 20 mL blood (plasma) 68 Gene mutations Target sequencing 14 Advanced HCC Asia rEesponses: (1) CDKN2A and CTNNB1 mutated patient under palbociclib+celecoxib; (2) MET, TP53, and PTEN mutated patients under sirolimus+cabozantinib.
Galle et al. [117] Blood EMT-associated ctDNA methylation changes 12 Patients treated with sorafenib Europe Methylation changes in EMT genes predict tumor response and acquired resistance to sorafenib.
Nakatsuka et al. [118] 1 mL plasma Total amount cfDNA, TERT promoter mutations, 275 gene mutations Digital-droplet PGR, target sequencing 35 Patients treated with systemic treatment Asia Increase in cfDNA levels during systemic treatment, higher early increase in cfDNA levels under systemic treatment in patients who achieved response.
Alunni-Fabbroni et al. [119] 5 mL blood (plasma) Total amount cfDNA, 597 gene mutations Target sequencing 13 (SORAMIC trial) Europe cfDNA levels high during treatment associated with worse OS.
Fujii et al. [107] 2 mL plasma 74 Gene mutations Target sequencing 24 Patients receiving lenvatinib Asia VAFmean* stable after 4 weeks in patients with PD or SD, VAFmean reduced in patients with PR or CR. VAFmean reduction associated to longer PFS.
Cowzer et al. [105] Blood 129 Gene mutations Target sequencing 51 Patients treated with systemic therapy USA High concordance with matched tumor: higher VAF associated with AFP, tumor volume, and no previous systemic therapy, no association with OS.
Muraoka et al. [106] 8 mL blood (plasma) C228T TERT promoter mutation Digital-droplet PCR 41 Patients treated with TKI Asia Increase in TERT promoter mutations levels within 1 week of TKI initiation predict PFS.
Oversoe et al. [120] 4–5 mL plasma CTNNB1 p.T41A hotspot mutation Digital-droplet PCR 37 Patients of whom at least two were treated with sorafenib Europe VAF of CTNNB1 p.T41A increase after 4–8 weeks of sorafenib associated with progression
Ikeda et al. [121] 20 mL plasma ctDNA mutations Target sequencing 26 With advanced HCC, some treated with systemic therapy Asia Emergence of BRCA1 and TP53 mutations during capecitabine treatment.
Table 1. Circulating and tumor biomarkers of response to TKIs in advanced HCC

AFP, alpha-fetoprotein; Ang-1, Angiopoietin 1; Ang-2, Angiopoietin 2; AXL, AXL receptor tyrosine kinase; BRAF, B-Raf proto-oncogene, serine/threonine kinase; DCR, disease control rate; DKK1, Dickkopf-1; GAS6, growth arrest specific factor 6; FGF, fibroblast growth factor; FGFR1–4, fibroblast growth factor receptors 1–4; HCC, hepatocellular carcinoma; HGF, hepatocyte growth factor; IGF-1, insulin-like growth factor 1; IHC, immunohistochemistry; IL-6, interleukin-6; IL-8, interleukin-8; KIT, KIT proto-oncogene, receptor tyrosine kinase; LAP, latency-associated peptide; LOX-1, lectin-like oxidized low-density lipoprotein receptor-1; MET, mesenchymal-epithelial transition; MIP, macrophage inflammatory protein; miRNA, microRNA; NLR, neutrophil-to-lymphocyte ratio; ORR, overall response rate; OS, overall survival; PDGFR, platelet-derived growth factor receptor; p-ERK, phosphorylated ERK; PFS, progression-free survival; PI3K, phosphatidylinositol 3-kinase; RET, rearranged during transfection; RFS, recurrence-free survival; ST6GAL1, 6-sialyltransferase 1; TGF, transforming growth factor; TKIs, tyrosine kinase inhibitors; VEGF, vascular endothelial growth factor; VEGFR, vascular endothelial growth factor receptor.

Table 2. Circulating and tumor biomarkers of response to immunotherapy in early and advanced HCC

ABRS, atezolizumab+bevacizumab response signature; AFP, alpha-fetoprotein; AXL, AXL receptor tyrosine kinase; CRAFITY, CRP and AFP in ImmunoTherapY; CRP, C-reactive protein; CTLA-4, cytotoxic T-lymphocyte antigen 4; HCC, hepatocellular carcinoma; ICI, immune check-points inhibitors; IFNAP, interferon and antigen presentation; IL-6, interleukin-6; irAEs, immune-related adverse effects; MET, mesenchymal-epithelial transition; NLR, neutrophil-to-lymphocyte ratio; ORR, overall response rate; OS, overall survival; PD-1, programmed death 1; PD-L1, programmed death-ligand 1; PFS, progression-free survival; RET, rearranged during transfection; RR, radiological response; TGF, transforming growth factor; TKI, tyrosine kinase inhibitor; TLS, tertiary lymphoid structure; VEGF, vascular endothelial growth factor; VEGFR, vascular endothelial growth factor receptor.

Neoadjuvant treatments were: anti-PD-1+TKI (n=14), anti-PD-1 (n=3), anti-PD-1 + anti-CTLA4 (n=1), anti-PD-1 + anti-CTLA4 + TKI (n=1);

anti-PD-1 drug used: Nivolumab and Pembrolizumab;

anti-PD-1 drug used as first-line: Nivolumab (n=67), Pembrolizumab (n=14), Tislelizumab (n=2).

Table 3. Studies describing the role of ctDNA in predicting prognosis and monitoring systemic treatment in HCC

AFP, alpha-fetoprotein; aHR, adjusted hazard ratio; BAX, Bcl-2–associated X; BRCA1, breast cancer 1; CDKN2A, cyclin-dependent kinase inhibitor 2A; cfDNA, circulating free DNA; CNA, copy number alteration; CPI, immune checkpoint inhibitors; CR, complete response; ctDNA, circulating tumor DNA; CTNNB1, Catenin beta-1; DCR, disease control rate; HR, hazard ratio; EMT, epithelial-mesenchymal transition; HCC, hepatocellular carcinoma; MET, mesenchymal-epithelial transition factor; ORR, objective response rate; OS, overall survival; PCR, polymerase chain reaction; PD, progressive disease; PFS, progression-free survival; PI3K, phosphatidylinositol 3-kinase; PR, partial response; PTEN, phosphatase and tensin homolog; SD, stable disease; TACE, transarterial chemoebolization; TERT, telomerase reverse transcriptase; TKI, tyrosine-kinase inhibitor; TP53, tumor protein 53; TTP, time to progression; VAF, variant allele frequency; VEGF: vascular endothelial growth factor.

VAFmean: mean of VAF of all mutated genes.

Table 1.

Table 2.

Table 3.

CMH : Clinical and Molecular Hepatology
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