| Literature DB >> 36230569 |
Lucia Cerrito1,2, Maria Elena Ainora1,2, Carolina Mosoni1,2, Raffaele Borriello1,2, Antonio Gasbarrini1,2, Maria Assunta Zocco1,2.
Abstract
Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the fourth cause of tumor-related death. Imaging biomarkers are based on computed tomography, magnetic resonance, and contrast-enhanced ultrasound, and are widely applied in HCC diagnosis and treatment monitoring. Unfortunately, in the field of molecular biomarkers, alpha-fetoprotein (AFP) is still the only recognized tool for HCC surveillance in both diagnostic and follow-up purposes. Other molecular biomarkers have little roles in clinical practice regarding HCC, mainly for the detection of early-stage HCC, monitoring the response to treatments and analyzing tumor prognosis. In the last decades no important improvements have been achieved in this field and imaging biomarkers maintain the primacy in HCC diagnosis and follow-up. Despite the still inconsistent role of molecular biomarkers in surveillance and early HCC detection, they could play an outstanding role in prognosis estimation and treatment monitoring with a potential reduction in health costs faced by standard radiology. An important challenge resides in identifying sufficiently sensitive and specific biomarkers for advanced HCC for prognostic evaluation and detection of tumor progression, overcoming imaging biomarker sensitivity. The aim of this review is to analyze the current molecular and imaging biomarkers in advanced HCC.Entities:
Keywords: alpha-fetoprotein; biomarkers; hepatocellular carcinoma; prognosis; systemic treatment
Year: 2022 PMID: 36230569 PMCID: PMC9564154 DOI: 10.3390/cancers14194647
Source DB: PubMed Journal: Cancers (Basel) ISSN: 2072-6694 Impact factor: 6.575
Figure 1Many heterogeneous categories of biomarkers are being investigated as possible prognostic parameters in predicting the therapeutic efficacy and survival in advanced HCC. AFP: alpha-fetoprotein, PD-L1: programmed cell death-1 ligand, DCP: des-γ-carboxy prothrombin, sBTLA: soluble B and T lymphocyte attenuator, ADAM9: a-disintegrin-and-a-metalloprotease-9, HGF: hepatocyte growth factor, VEGF: vascular endothelial growth factor, PDGF: platelet-derived growth factor, FGF-19: fibroblast growth factor 19, TGF-β1: transforming growth factor beta 1, miR: microRNA, hTERT: human telomerase reverse transcriptase.
The prognostic role of clinical biomarkers in patients with advanced hepatocellular carcinoma.
| Article | Patients | Therapy | Biomarker | Prognostic Data |
|---|---|---|---|---|
| Giannelli G [ | 149 | Galunisertib: phase 2 study (NCT01246986) | AFP | OS: |
| Group A: baseline AFP > 1.5 ULN | AFP responders (21% patients in group A; >20% AFP reduction): | |||
| Gyöngyösi B [ | 20 | Sorafenib | Tissue miR-224 | OS (HR = 0.0.24, 95%CI: 0.07–0.79, |
| Kelley RK [ | 707 | Cabozantinib vs. placebo | AFP | Median OS cabozantinib versus placebo: |
| Week 8 AFP response rate: 50% vs. 13% (cabozantinib vs. placebo) | ||||
| Median OS (cabozantinib arm): 16.1 versus 9.1 months (HR, 0.61; 95% CI, 0.45–0.84) with and without AFP response. | ||||
| Kim HY [ | 124 | Sorafenib | PIVKA II, HGF, FGF | OS ( |
| Lee PC [ | 95 | Nivolumab or pembrolizumab | AFP | AFP reduction |
| Li J [ | 46 | NA | miR-221 | OS: 27.6% versus 62.3% (high miR-221 versus low miR-221 expression; |
| Llovet JM [ | 602 | Sorafenib vs. placebo | VEGF-A | Median survival (low versus high baseline VEGF-A): 10 versus 6.2 months |
| Miyahara K [ | 122 | Sorafenib | Ang-2 | PFS (Ang-2: HR 1.84; 95%CI 1.21–2.81) |
| Muraoka M [ | 67 | TACE (32 patients) | Cell-Free Human hTERT mutant DNA | Median survival times: |
| Shao YY [ | 72 | Sorafenib or bevacizumab or thalidomide in combination with metronomic 5-fluoropyrimidine | AFP | ORR 33% vs. 8% ( |
| Vaira V [ | 26 | Sorafenib | miR-425-3p | PFS (HR = 0.5, 95%CI: 0.3–0.9, |
| Zhu AX [ | 292 | Ramucirumab versus placebo | AFP | OS (8.5 vs. 7.3 months; HR 0.71, 95% CI 0.53, 0.95; |
AFP: alpha-fetoprotein, Ang2: angiopoietin-2, CI: confidence interval, DCR: disease control rate, FGF: fibroblast growth factor, HGF: hepatocyte growth factor, HR: hazard ratio, miR: microRNA, ORR: objective response rate, OS: overall survival, PFS: progression free survival, PIVKA II: protein induced by vitamin K absence-II, TACE: transarterial chemoembolization, hTERT: human telomerase reverse transcriptase, TGF-β1: transforming growth factor beta1, TTP: time to progression, ULN: upper limit of normal, VEGF-A: vascular endothelial growth factor-A.
The advantages and disadvantages of the principal biomarkers applied in the management of hepatocellular carcinoma.
| Advantages | Disadvantages | |
|---|---|---|
|
| ||
|
| ||
|
| Further studies are needed | |
|
| Further studies are needed | |
|
| Simple, cheap and obtainable from routinary analysis [ | Further studies are needed |
|
| ||
|
| Data mainly from murine models | |
|
| Potentially useful to identify molecular biomarkers of response to therapy or prognosis [ | Further studies are needed |
|
| Potentially useful to test drug sensibility or to identify other biomarkers [ | Impossibility to reproduce tumoral stroma |
|
| ||
|
| Primary criteria for evaluating therapeutic efficacy in solid tumors [ | Poor definition of vascular changes and therapeutic effects in course of anti-angiogenic therapy |
|
| Critical role in the evaluation of response to antiangiogenetic therapies [ | High costs |
|
| Cheaper than CT or MRI | Not always available |
|
| Prediction of response to systemic therapy or selective internal radiation therapy [ | Poor data regarding therapeutic response to anti-angiogenic or immune therapies |
|
| Prediction of OS and therapeutic response to systemic therapy [ | High cost |
|
| Potentially a new, independent biomarker of prognosis, OS and therapeutic response [ | Recent technique, still not available outside of highly-specialized centres |
|
| Detection of HCC biologic aggressiveness (microvascular invasion, histological characteristics) directly influencing clinical outcome [ | Still lacking a precise association between imaging biomarkers and prognostic factors |