Characterization of somatic mutations and pathway alterations during hepatocellular carcinoma vascular invasion using next-generation sequencing.

Background: Vascular invasion is an independent risk factors for recurrence and poor prognosis in patients with hepatocellular carcinoma (HCC). However, the molecular mechanisms of HCC vascular invasion are largely unknown. Deciphering the molecular changes associated with the vascular invasion process will aid in the identification of therapeutic targets and treatment for patients with HCC.
Methods: DNA was extracted from tumor specimens and blood samples collected from 50 patients with HCC. Next-generation sequencing (NGS) was performed to detect HCC gene variants. Bioinformatics methods were used to comprehensively analyze the three sets of sequencing data grouped by vascular invasion, including differences in tumor mutation burden (TMB), mutation characteristics, and alterations in signaling pathways.
Results: Bioinformatics analysis detected a total of 762 single nucleotide variants (SNVs). The TMB was not significantly different between patients with macrovascular invasion, microvascular invasion (MVI), or avascular invasion. Ten genes related to prognosis or recurrence, and one oncogene related to vascular invasion were screened. Compared with the avascular invasion cluster, the variant genes in the macrovascular and MVI clusters were mainly enriched in the thyroid hormone signaling pathway. In addition, macrovascular invasion variant genes were also enriched in the insulin signaling pathway and the Fanconi anemia pathway. Conclusions: Somatic mutations and pathway changes associated with vascular invasion in HCC were identified. The discovery of the molecular drivers of vascular invasion in HCC provides novel insights that can help guide further patient diagnosis and personalized therapy. 2022 Journal of Gastrointestinal Oncology. All rights reserved.

Introduction

Hepatocellular carcinoma (HCC) is one of the most common malignant solid tumors worldwide, accounting for more than 90% of primary liver cancers, and is the fourth most common cause of cancer-related deaths (1,2). Due to factors such as hepatitis B virus infection, aflatoxin exposure, alcohol abuse, and environmental pollution, China has become the country with the highest incidence of liver cancer (about 55% of the world’s total incidence) and the largest number of deaths (3). Surgical resection and local ablation remain the most commonly used curative treatments for HCC (4,5). However, the tumor recurrence rate is as high as 70% after liver resection and 25% after liver transplantation, and the overall survival (OS) rate remains unsatisfactory, with the 5-year OS rate at 15–20% (6,7).

Vascular invasion is often associated with tumor metastasis, recurrence, and poor prognosis (8). After liver transplantation, patients with microvascular invasion (MVI) experience a more than four-fold increased risk of HCC recurrence (9). Unfortunately, preoperative imaging cannot detect potential microscopic vascular invasion (10). Therefore, identifying the molecular changes associated with vascular invasion to determine the risk of HCC recurrence is a primary task in predicting long-term prognosis. HCC has significant phenotypic and molecular heterogeneity (11). Menyhárt et al. found that the BIRC5, CDC20, and PLK1 genes could adequately predict the prognosis of Asian patients (12). Vascular invasion is often divided into macrovascular invasion and MVI, however, the molecular changes related to the extent of vascular invasion have been rarely investigated (13). Next-generation sequencing (NGS) technology can realize large-scale parallel sequencing of multiple genes, and fundamentally solve the practical problems of difficult diagnosis caused by heterogeneity of single-gene inheritance, multiple genes, and complex phenotypes.

To identify robust genes related to the prognosis of HCC, 50 patients with HCC were enrolled and screened. Patients were grouped according to the degree of vascular invasion, namely, the no vascular invasion group, the MVI group, and the macrovascular invasion group. Using high-throughput targeted gene sequencing and whole exome sequencing (WES) methods to explore the molecular mechanism changes and pathway changes related to HCC vascular invasion, this information will be useful in guiding auxiliary diagnosis and individualized treatment of Chinese HCC patients. We present the following article in accordance with the REMARK reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-22-556/rc).

Methods

Collection of clinical samples

Between 2018 and 2019, a total of 50 HCC patients were enrolled in this study. The median follow-up time was 26.7 months. All tumor samples were estimated for invasive tumor content by two molecular pathologists to ensure that more than 50% of the cells were tumor cells. Avascular invasion was defined as no portal vein, hepatic vein, or inferior vena cava invasion on imaging and no vascular invasion on electron microscopy. MVI refers to the observation of tumor thrombus or cancer cells under the electron microscope without invasion of large blood vessels. Macrovascular invasion refers to the formation of tumor thrombi in the portal vein and its main branches. High-depth targeted gene panel sequencing and WES were performed after passing the nucleic acid quality test. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) (14). The protocol was approved by the Ethics Committee of Nanfang Hospital, Southern Medical University (ID of the approval: 2012-092). Written informed consent was obtained from all patients prior to inclusion in the study.

DNA extraction and NGS

DNA was extracted from serial thick sections cut from tumor samples and matched peripheral blood leukocytes as germline DNA control. The invasive tumor content was estimated by pathologists to ensure that more than 50% of cells were tumor cells. DNA was isolated from the formalin-fixed paraffin-embedded (FFPE) tissue blocks and blood samples using the DNeasy Blood and Tissue Kit (69504, QIAGEN, Venlo, Netherlands). Qubit 3.0 (Thermofisher) was used to detect the quality of the DNA. The Generic Library Construction Kit (Cat. No. M0348L, New England Biolabs, USA) and the Hybrid Capture Kit (Cat. No. 1001111E, Naonda (Nanjing) Biotechnology Co., Ltd., Nanjing) were used to capture targeted region sequences from genomic DNA and construct sequencing computer libraries. The captured libraries of targeted gene sequencing and WES were performed as paired end-readings on the Illumina NoveSeq 6000 sequencing platform.

Variant annotation analysis and visual mapping

Germline variations were identified via SpeedSeq and somatic mutations were identified using Mutect (15). Somatic indels were determined using SomaticIndelDetector (16). The high-quality somatic mutations and indels were selected. The variant data were annotated using ANNOVAR (17) and Oncotator (18), and converted to MAF files using maftools (19). The cancer driver genes were analyzed using Intogen (20), including OncodriveFM and OncodriveCLUST. Based on the fact that tumor development is an evolutionary process, detection of positive selection signals in the pattern of somatic mutation of genes has been used to identify drivers in tumor cohorts.

Statistical analysis

Progress-free survival (PFS) and overall survival (OS) were defined as the time from randomization to disease progression or last follow-up and the time from surgery to death or last follow-up, respectively. All the correlated clinical and biological variables were analyzed using the SPSS Statistics 22.0 package and the ggpubr package (21) in R software (22) by means of Fisher’s test or a non-parametric test when necessary. The Kruskal-Wallis test was used to analyze the differences in tumor mutational burden (TMB) between different groups. A P value <0.05 was considered statistically significant.

Results

Patient characteristics

Tumor tissue and corresponding blood samples were collected from 50 HCC patients at the time of diagnosis, including 46 males (92%) and 4 females (8%), with an average age of 51.7 years (range, 25–80 years). Of these, 42 patients had a history of hepatitis and 19 patients had a history of alcohol consumption. According to the Barcelona Clinic Liver Cancer (BCLC) clinical staging system, 19 cases were stage A, 8 cases were stage B, and 23 cases were stage C. There were 35 patients with vascular infiltration, including 16 cases of microvascular infiltration and 19 cases of macrovascular infiltration. The imaging or surgical samples of the other 15 patients showed no vascular infiltration under electron microscopy. In addition, 6 patients presented with extrahepatic metastases and 8 had lymph node metastases. Among the enrolled patients, 31 were treated with radical surgery and the other 19 patients were treated with medical therapy. The detailed clinical characteristics of the patients are shown in and Table S1.

Table 1

Patient characteristics

Characteristic	No. of cases	Proportion
Total number	n=50
Age, years (mean)	51.7 [25–80]
Sex
Male	46	92%
Female	4	8%
History of hepatitis	42	84%
The history of drinking
Yes	19	38%
No	31	62%
BCLC staging
A	19	38%
B	8	16%
C	23	46%
Vascular invasion
Microvascular	16	32%
Macrovascular	19	38%
Avascular	15	30%
Extrahepatic metastasis
Yes	6	12%
No	44	88%
Lymphatic metastasis
Yes	8	16%
No	42	84%
Therapy method
Excision	31	62%
Medication	19	38%

BCLC, Barcelona Clinic Liver Cancer.

The occurrence of vascular invasion affects the prognosis of hepatocellular patients

According to the degree of vascular invasion of HCC, the 50 patients were divided into the macrovascular invasion group, the MVI group, or the avascular invasion group. Combined with clinical prognostic information, Mantel-Cox tests found that there were significant differences in PFS and OS among the three groups, (P=0.0026 and P=0.0009, respectively) (). The prognosis of patients with macrovascular invasion was significantly worse than that of patients with MVI and those with no vascular invasion.

Figure 1

The occurrence of vascular invasion affects the prognosis of patients with hepatocellular carcinoma. (A) PFS in patients with macrovascular invasion, microvascular invasion, and avascular invasion. (B) The OS in patients with macrovascular invasion, the microvascular invasion, and avascular invasion. PFS, progression-free survival; OS, overall survival.

Characterization of somatic mutations associated with vascular invasion and tumor mutation burden (TMB)

A total of 50 tumor tissue sections and matched blood DNA samples were collected, of which, 28 underwent whole-exome sequencing and 22 underwent targeted gene sequencing. We successfully analyzed all sections and matched blood second-generation sequencing data using an analysis pipeline of whole exome and targeted sequencing, with an average depth of 203.0X for whole exomes and 572.3X for targeted sequencing samples. A total of 762 somatic mutations were detected by comparing significant changes in non-reference alleles in tumor and control groups (Table S2). Missense mutations were the most common type of mutations, along with frame shift deletion, in frame insertion, frame deletion, and so on. Patient P50 had the most SNVs, followed by patient P23. The top 30 somatic gene mutations are listed according to mutation frequency. The three genes with the highest mutation frequency were TP53 (28%), CTNNB1 (14%), and KMT2B (10%). Mutations in CTNNB1, CMT2B, NF1, ZNF595, and KMT2D were found in the avascular invasion group and the MVI group, but not in the macrovascular invasion group. Conversely, mutations in KIAA2026, ALMS1, FGFR3, LRP1B, and PRDM1 were detected in the macrovascular invasion group and the MVI group, but not in the avascular invasion group ().

Figure 2

Somatic mutation profiles of hepatocellular carcinoma (HCC) samples and TMB values. (A) The distribution of the top 30 gene mutations in 50 HCC samples. (B) A comparison of the TMB values in the three groups of vascular invasion samples from whole exome sequencing. (C) A comparison of the TMB values among the three groups of vascular invasion in targeted sequencing samples. SNV, single nucleotide variants; HCC, hepatocellular carcinoma; TMB, tumor mutation burden.

Additionally, the TMB in the whole-exome and target gene sequencing data was calculated using the number of nonsynonymous mutations occurring in somatic cells. Overall, there were large differences in the TMB values across samples within each group. However, there were no significant differences in the TMB among the avascular invasion group, the MVI group, and the macrovascular invasion group (WES data, P=0.67; targeted gene panel, P=0.4; ).

Candidate biomarkers associated with prognosis in HCC

Combining the recurrence and survival status of patients, 10 genes were found to be significantly associated with OS or PFS (P<0.05). Among these, 5 genes, including ZNF763, FAM71B, ZNF616, TPR, and MYH13, were significantly associated with both OS and PFS (). As shown in significant genes were selected for survival curve analysis.

Table 2

List of candidate biomarkers that were significantly associated with both OS and PFS

Symbol	Gene name	OS, P (HR)	PFS, P (HR)
ZNF616	Zinc finger protein 616	5.51E-12 (101)	7.21E-5 (17.5)
MYH13	Myosin heavy chain 13	7.21E-7 (20.5)	0.0137 (5.8)
FAM71B	Golgi associated RAB2B interactor family member 3	5.84E-4 (9.04)	7.21E-5 (17.5)
ZNF763	Zinc finger protein 763	0.00231 (7.3)	7.21E-5 (17.5)
TPR	Translocated promoter region, nuclear basket protein	0.0085 (6.03)	0.00898 (6.63)
BLM	BLM RecQ like helicase	0.00246 (7.81)	0.304 (2.01)
UNC13B	Unc-13 homolog B	0.0173 (5.13)	0.424 (1.73)
CPLANE1	Ciliogenesis and planar polarity effector complex subunit 1	0.0179 (5.05)	0.401 (1.89)
EXOC2	Exocyst complex component 2	0.0362 (4.45)	0.0506 (4.62)
PRRC2B	Proline rich coiled-coil 2B	0.05 (4.09)	0.285 (2.32)

PFS, progression-free survival; OS, overall survival.

Figure 3

Representative genes associated with PFS and OS. The X-axis represents follow-up time. (A,B) Genes significantly correlated with PFS. (C,D) Genes significantly associated with OS. PFS, progression-free survival; OS, overall survival.

Figure 4

Analysis of the signaling pathways among the macrovascular invasion, microvascular invasion, and avascular invasion clusters. (A) A Venn diagram of the SNVs in clusters of avascular invasion, microvascular invasion, and macrovascular invasion. (B) KEGG signaling pathways enriched by macrovascular invasion cluster variant genes. (C) KEGG signaling pathways enriched by microvascular invasion cluster variant genes. (D) KEGG signaling pathways enriched by avascular invasion cluster variant genes. SNV, single nucleotide variant; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Alternations in signaling pathways in vascular invasion

To further characterize the signaling pathway changes in HCC during vascular invasion, vascular invasion patients with SNV binding were divided into three gene clusters by whole exons and targeted sequencing, including an avascular invasion cluster [150], a MVI cluster [190], and a macrovascular invasion cluster [310]. Venn diagrams revealed mutations in TP53, KMT2C, AXIN1, and ZFHX3 in all three groups. In addition to the crossover gene clusters between groups, there were many genes unique to each cluster, such as 266 mutations specific to the macrovascular invasion group ().

The three sets of gene clusters were collectively enriched in epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway, and Rap1 signaling pathway. The thyroid hormone signaling pathway was enriched in both the macrovascular invasion and MVI clusters, but not in the avascular invasion clusters. In addition, MVI clusters were independently enriched in the insulin signaling pathway and the Fanconi anemia pathway compared to the MVI cluster and the avascular invasion cluster ().

Evaluation of the prognostic value of vascular invasion-related genes

In this study, a total of 35 patients had evidence of vascular invasion, of which, 19 patients presented with macrovascular invasion. Combined with the patient’s gene mutation information, a total of 15 genes were found to be associated with vascular invasion. Among these genes, PTPRS showed statistical significance (P=0.023; ), with no mutation found in the vascular invasion group, but high mutation frequency in the non-vascular invasion group.

Table 3

Hepatocellular carcinoma vascular invasion-related genes

Symbol	Gene name	No. of vascular invasion	No. of avascular invasion	P value
PTPRS	Protein tyrosine phosphatase receptor type S	0	3	0.023214286
TP53	Tumor protein p53	12	2	0.178697863
KIAA2026	KIAA2026	4	0	0.302431611
CTNNB1	Catenin beta 1	4	3	0.414986124
ALMS1	ALMS1 centrosome and basal body associated protein	3	0	0.544642857
DNAH5	Dynein axonemal heavy chain 5	3	0	0.544642857
FGFR3	Fibroblast growth factor receptor 3	3	0	0.544642857
LONP1	Lon peptidase 1, mitochondrial	3	0	0.544642857
LRP1B	LDL receptor related protein 1B	3	0	0.544642857
PRDM1	PR/SET domain 1	3	0	0.544642857
KMT2B	Lysine methyltransferase 2B	3	2	0.629311484
ZNF595	Zinc finger protein 595	3	2	0.629311484
KMT2C	Lysine methyltransferase 2C	4	1	1
KMT2D	Lysine methyltransferase 2D	3	1	1
NF1	Neurofibromin 1	4	1	1

Discussion

Vascular invasion in patients with HCC during diagnosis and treatment often predicts poor prognosis or an increased risk of recurrence later in treatment (23). Vascular invasion can be classified as MVI or macrovascular invasion (13). Macrovascular invasion is the result of the evolution of MVI (24). Previous studies have predominantly focused on the impact of the incidence of vascular invasion on tumor recurrence or prognosis (25-27). Guo and Jiang found that overexpression of matrix metalloproteinase 12 (MMP12) is associated with poor prognosis and vascular invasion in HCC, these data suggest that MMP12 may have potential as a therapeutic target and biomarker in HCC (28). However, few studies have examined gene mutations or signaling pathways during vascular invasion. The ability to recognize molecular changes associated with vascular invasion in advance and identify patients at higher risk of recurrence will aid clinical decision-making in the field of HCC treatment.

A total of 50 HCC patients were included in this study, including 19 with macrovascular invasion, 16 with MVI, and 15 with avascular invasion ( and Table S1). Similar to a previous study (29), our patients were grouped according to the above vascular invasion status. Analysis of the prognosis and recurrence information of patients revealed significant differences in OS and PFS among the different vascular invasion groups (). Subsequently, the mutation information of all patients was analyzed through NGS and bioinformatics analysis technology. A total of 762 SNVs were identified (Table S2). Among them, tumor suppressor gene TP53 and proto-oncogene CTNNB1 showed the highest mutation frequency (). The TMB can indirectly reflect the ability and extent of tumor production of neoantigens, and has been shown to predict the efficacy of immunotherapy for a variety of tumors (30). The TMB values can also reflect the potential of tumor neoantigens in tumors, which are closely related to DNA repair defects (31). Mismatch repair deficient (dMMR) and high microsatellite instability (MSI-H) patients have higher TMB in a variety of tumors (32,33). A study has shown that the TMB obtained from gene panels is overestimated compared to WES data (34), hence, in this study, we calculated the TMB value of the WES and gene panel according to the data source (). The macrovascular invasion subgroup analysis of the IMbrave150 study in 2021 showed that atezolizumab combined with bevacizumab was still beneficial in PFS and OS, with a median OS of 24.0 months in the Chinese subgroup (27,35). However, we found no significant difference in the TMB values between the vascular invasion groups, suggesting that the degree of vascular invasion cannot be used as a basis for immunotherapy in HCC, although more data are needed to support this. In addition, with the development of drug therapy, HCC patients who are initially suitable for surgical resection may also benefit from neoadjuvant therapy, and further definitive evidence is required.

A total of 10 genes were found to be associated with prognostic or recurrence factors for HCC, including EXOC2, PRRC2B, PTPRS, and other genes (). Mutations in EXOC2 cause defects in human brain development (36). The protein encoded by EXOC2 is part of the extracellular vesicle complex and is critical for the polarized targeting of extracellular vesicles to specific docking sites on the plasma membrane. This protein has been shown to interact with the Ral subfamily of GTPase to mediate exocytosis by anchoring vesicles to the plasma membrane (37). PRRC2B, which plays an important role in the formation of head and neck tumors, activates RNA binding activity and is involved in cell differentiation (38-40). In addition, the PTPRS gene is significantly related to vascular invasion (). The protein encoded by this gene is a member of the protein tyrosine phosphatase (PTP) family, which contains an extracellular domain, a transmembrane segment, and two tandem catalytic domains (41). PTP is thought to be a signaling molecule regulating a variety of cellular processes, including cell growth, differentiation, mitotic cycle, and oncogenic transformation (42). Although the above genes have been reported to be involved in tumor formation, transformation, and cancer cell differentiation, whether the mutation of these genes in hepatocytes is the dominant factor affecting hepatocellular carcinogenesis or driving the occurrence of vascular invasion has not been proved.

Mutations or differences in the expression of single or multiple genes often lead to perturbation of molecular signals in signaling pathways, including hormones, growth factors, cytokines, neurotransmitters, and other small-molecule compounds. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that when hepatocytes became cancerous, the three signaling pathways including EGFR tyrosine kinase inhibitor resistance, PI3K-Akt signaling pathway, and Rap1 signaling pathway, were significantly altered compared to normal cells (43,44). However, when MVI occurs in HCC, thyroid hormone receives interference from exogenous molecules or dysregulation of the thyroid hormone signaling pathway, which further affects angiogenesis and cancer cell proliferation (45). In the macrovascular invasion group, mutant genes were additionally enriched in the Fanconi anemia pathway and the insulin signaling pathway. Aberrations in the Fanconi anemia pathway are associated with genomic instability and tumorigenesis in a variety of human cancers, especially breast cancer (46). In addition, Fanconi anemia patients are susceptible to human papillomavirus (HPV)-related malignancies, suggesting that loss of Fanconi anemia pathway activity promotes tumorigenesis (47). In physiological functions, insulin mainly regulates the metabolism of glucose and lipids through the insulin signaling cascade (48). However, activation of the insulin receptor (INSR) in cancer cells triggers a signaling cascade of the PI3K-Akt pathway, which promotes cell proliferation and glycolysis, while inhibiting apoptosis (49). This is also one of the reasons for the high incidence of cancer in diabetic patients (50). In addition, due to the limitation of the small sample size, further studies are warranted to further verify the above results.

Conclusions

This investigation demonstrated that patients with macrovascular invasion, MVI, and avascular invasion had significantly different somatic mutations and significant differences in prognosis and risk of recurrence. However, no significant differences in TMB were found between the vascular invasion groups. A total of 10 prognostic-related candidate biomarkers were identified, as well as one oncogene that was significantly associated with vascular invasion. Differences were found in both somatic gene mutations and signaling pathways, indicating that the molecular mechanism of tumor tissue changes at different stages of vascular invasion. Understand the molecular changes associated with the vascular invasion process will aid clinical decision-making in the field of HCC treatment.

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