Plasma Circulating Tumor DNA Sequencing Reveals the Landscape of Acquired Mutations in Patients with Hepatocellular Carcinoma: a Potential Predictive Value in Liquid Biopsy

Plasma Circulating Tumor DNA Sequencing Reveals the Landscape of Acquired Mutations in Patients with Hepatocellular Carcinoma: a Potential Predictive Value in Liquid Biopsy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Plasma Circulating Tumor DNA Sequencing Reveals the Landscape of Acquired Mutations in Patients with Hepatocellular Carcinoma: a Potential Predictive Value in Liquid Biopsy Xiaolin Wu, Sven Borchmann, Jan-Michel Heger, Philipp Kasper, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4099291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Recent advances in circulating tumor DNA (ctDNA) analysis offer a promising approach for diagnosing and monitoring hepatocellular carcinoma (HCC). This study focused on the potential clinical role of ctDNA analysis in HCC management. Materials and methods: Thirty patients with HCC and 10 with non-malignant liver disease were enrolled in this study. Circulating free nucleic acids, germline DNA, and tumor DNA (tDNA) from both blood samples and paraffin-embedded tumor biopsies were analyzed by a panel targeting 100 common HCC-related genes. Results The ctDNA mutations were identified in 66.6% of HCC patients. New ctDNA mutations were identified, among them NCOR2 having the highest frequency (13%), the same with classical mutation CTNNB1 . Gene sets composed of several mutations in ctDNA have the potential to predict the prognosis of HCC. A higher proportion of concordant mutations was also detected in HCC patients with tumor vascular invasion (p=0.045). Combining the ctDNA mutations and the Alpha-fetoprotein (AFP) level revealed more diagnostic accuracy than either the mutations or AFP alone, with p-values of 0.028 and 0.009, respectively. Conclusion Liquid biopsy-based analysis of ctDNA mutations may offer considerable benefits to diagnostic systems for HCC. circulating tumor DNA cell-free DNA HCC genetic profiling liquid biopsy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Hepatocellular carcinoma (HCC) is currently the sixth most commonly diagnosed cancer and ranks as the third most common cause of cancer-related death, with an estimation of 830,000 annual deaths worldwide [1, 2]. While liver resection and liver transplantation have been the mainstay curative treatments, locoregional and systemic therapies represent further treatment options in HCC management [3] . Early diagnosis of HCC is crucial since patients with advanced HCC have a poor prognosis with a 5-year survival rate of 70% [4]. However, the diagnosis of early-stage HCC remains a challenge in everyday clinical practice, as patients commonly do not present with noticeable symptoms. Moreover, HCC develops not only in patients with cirrhosis, who are frequently included in surveillance programs but also in non-cirrhotic patients, particularly in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), who are commonly not included in such surveillance programs, which further complicates its early identification. Current diagnostic approaches for HCC detection rely on abdominal imaging (e.g. ultrasound) with or without concurrent biomarker testing, such as alpha-fetoprotein (AFP). However, AFP has even in patients with a known viral hepatitis a sensitivity of only 62.4% and a specificity of 89.4%, which seems not sufficiently accurate for early HCC detection [5]. State-of-the-art radiological abdominal imaging techniques (CT and MRI) have a higher overall sensitivity but perform poorly in detecting small HCC lesions (<1cm) [6]. Thus, establishing HCC diagnosis solely based on current non-invasive methods reveal several limitations. This is currently further challenged by the need for molecular information to initiate individualized treatment concepts, that requires biopsies / tissue sampling. In recent years, advancements in liquid biopsy technologies analyzing circulating cancer products in the bloodstream have facilitated profound research in the field of cancer, and liquid biopsy techniques may provide a novel promising approach to address the challenges of early HCC diagnosis [7]. In this context, an assessment of circulating tumor DNA (ctDNA) or cell-free DNA (cfDNA) is one vital method that aims to measure double-stranded DNA fragments, that is released by cancer cells into body fluids. These fragments typically measure 150-200 base pairs (bp) in length, peaking at nearly 166 bp [8, 9] . Cell-free DNA is released from necrotic and apoptotic cells into the circulatory system and the origin of ctDNA is cfDNAs only released from the tumor cells [10]. The cfDNA level is low in healthy individuals (10-15 ng/ml) [11] but elevated after inflammatory diseases or after tumor development. The proportion of ctDNA substantially fluctuates, ranging from 90% of cfDNA [10], thus, the main obstacle of ctDNA research at present is how to precisely distinguish ctDNA from the cfDNA background originating from non-malignant cells. Due to its origin, ctDNA carries specific information about tumor biology and has been proposed as an alternative promising source for molecular profiling of tumor DNA of HCC patients [12]. These constitute the potential advantages of ctDNA in clinical applications, especially for the early detection and monitoring of cancer progression or cancer relapse. Most studies on ctDNA published to date focus on markers of genetic variation, including copy number variations (CNVs), gene integrity, genetic mutations, and methylation [13]. Mutant genes have great potential to be effective clinical biomarkers, which has been demonstrated in several studies [14]. The number of mutation sites in ctDNA is related to carcinoma size or metastasis, showing that genetic mutations might reflect some conditions of HCC disease stage [15]. Several common mutant genes that appear in HCC nodules can also be detected in plasma ctDNA, including ARID1A , CTNNB1 and TP53 [16]. However, the number of mutation targets identified in the ctDNA is still small. Since ctDNA release is influenced by the number of necrotic cells in the tumor, the extensive mutations present in HCC tumor tissue are not found within the ctDNA. Furthermore, there is also a lack of studies assessing the influence of ctDNA mutation measurements in HCC diagnostic assessment. In addition, no studies have specifically explored the potential impact of combining AFP with ctDNA mutations for HCC diagnosis so far. Another issue that needs to be explored in realizing the application of liquid biopsy is the concordance between the plasma samples and tumor tissue from HCC patients. Although free nucleic acid carries genetic information of the tumor cells, some disparities appear between ctDNA and tissue tDNA [17]. The matching degree between the mutation information from ctDNA and the tumor DNA requires further exploration. The association between concordant degree and clinical outcomes has also been scarcely discussed. Therefore, a more in-depth study is needed to evaluate the clinical implications of these mutations. Our study focused on ctDNA mutations, which may provide a basis for exploring the ctDNA profile in patients with HCC. To this end, a multi-mutation next-generation sequencing (NGS) panel including 100 HCC genes was designed to explore (previously unknown in ctDNA) mutation targets and to analyze the relationship between ctDNA mutations and AFP, as the most widely used biomarker for HCC diagnosis in clinical routine. We also evaluated different clinical characteristics that might affect the gene concordance between ctDNA and tDNA. 2. Materials and Methods 2.1. Patients and samples In total, 30 adult patients with a confirmed diagnosis of HCC were recruited from 2016–2019 at the University Hospital of Cologne undergoing liver surgery, while 10 adult patients with non-malignant liver disease (liver cirrhosis, hepatic adenoma, hepatic hemangioma, hepatic echinococcosis, focal nodular hyperplasia and nonalcoholic steatohepatitis) were included as controls. The diagnosis of HCC and non-malignant liver disease was established by histopathologic evidence. The inclusion criteria were as follows: (1) age > 18 years with HCC or non-malignant liver disease; (2) no previous liver resection or previous systemic therapy for HCC; and (3) no previous history of any other malignancies. The study was conducted in accordance with the Declaration of Helsinki (1975) and was approved by the local ethics committee (Biological Material Collection for Optimisation ID: 13-091). All patients provided written informed consent before participating. For molecular analysis, thirty milliliters of perivenous blood were collected in EDTA tubes from the patient during the operation before the surgery and sent to the reception laboratory within 4 hours. Plasma and interphase from the blood cellular component were isolated initially from the whole blood by performing centrifugation at 4000 ×g for 10 minutes at ambient temperature. Plasma and buffy coat samples were aliquoted into Eppendorf microtubes. All samples were stored at -80°C for further use. The following clinical and pathological data of all the patients were recorded at the same time as blood sample collection: age, sex, Child-Turcotte-Pugh (CTP) score [18], pathology tumor/ nodes/ metastasis (pTNM) and Barcelona clinic liver cancer (BCLC) stage of HCC with information on vascular tumor invasion and/or metastatic spread, AFP level, presence of concomitant liver diseases (e.g. viral hepatitis, alcoholic liver disease and non-alcoholic fatty liver disease), presence of liver cirrhosis, portal venous thrombosis. The progression-free survival (PFS) and overall survival (OS) were reported at follow-up visits. The follow-up time was at least 24 months for each case, with clinical follow-up assessment. During follow-up CT scans were performed every six months after surgery. 2.2. DNA extraction from plasma, peripheral blood mononuclear cells (PBMCs), and paraffin tumor tissue CfDNA was extracted from 7 ml plasma by a QIAamp MinElute ccfDNA Midi Kit (50) (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Germline DNA (gDNA) from the buffy coat samples was extracted by a QIAamp DNA Blood Mini Kit (50) (Qiagen). The HCC tumor tissue samples were fixed with formalin and embedded in formalin-fixed paraffin-embedded tissue (FFPE) at the Institute of Pathology of the University Hospital of Cologne. Matched FFPE tumor samples were cut from paraffin blocks, and tumor content was enhanced with tumor macrodissection. Tumor tissue DNA extraction was completed by a QIAamp DNA FFPE Tissue Kit (50) (Qiagen) according to the manufacturer’s working manual. The extracted cfDNA, gDNA and tumor tissue DNA samples were stored at -20°C. 2.3. HCC sequencing panel design, library preparation and NGS Plasma cfDNA, germline DNA and tumor DNA were subjected to targeted gene panel sequencing. The sequencing panel was designed to maximize the mutation detection rate in patients and minimize the panel size. We reviewed the genomic profile of HCC using cBioProtal, including The Cancer Genome Atlas (TCGA) database and other HCC clinical data [19-22]. The final design (HCC_Panel_v1.1) targeted 100 genes frequently mutated in HCC with a total size of 692 kbp of the human genome, containing all exonic domains. We used a minimum of 25 ng DNA for plasma cfDNA and 200 ng DNA for FFPE tDNA for library preparation. DNA quantification was performed using the TapeStation 2200 System (Agilent, California, USA). Additionally, size distribution was assessed for plasma cfDNA, requiring a minimum of 25% of total input DNA to be in the size window of interest for cfDNA (50-700 bp). Library preparation was performed using the Agilent SureSelectXT Low Input protocol, including enzymatic fragmentation (for gDNA and tDNA samples), end-repair, adapter ligation, index polymerase-chain-reaction (PCR), enrichment with the NGS panel we designed, and postenrichment PCR (12 cycles). Different circle numbers were used for different types of DNA fragments for index PCR: 10 cycles for cfDNA, 8 cycles for gDNA samples, and 11 cycles for tDNA samples. Subsequently, libraries were quantified (Qubit, Tape Station), pooled equimolarly and sequenced on a NovaSeq 6000 device with a paired-end, 2x100 bp sequencing protocol. We targeted 5 Gb data output for plasma cfDNA and FFPE tDNA, and 1 Gb data output for gDNA. 2.4. Basic data processing, variant calling and filtering Basic data processing was performed by Bcl2fastq2 (v2.20.0.422), AGeNT Trimmer (Agilent), Burrows Wheeler Aligner (BWA, v0.7.17) and Samtools (v1.14). Somatic single base substitution calling was augmented by comparative error suppression (CES) for improved sensitivity and specificity as previously reported [23]. Genetic aberrations and clinical annotations were visualized using the R-package ‘ComplexHeatmap’ [24]. All the genetic data excluded the interference of germline DNA. 2.6. Gene Ontology (GO) enrichment. All ctDNA mutation targets were subjected to enrichment analysis using the DAVID Bioinformatics Resource (https://david.ncifcrf.gov/), and the p value was set to ≤ 0.05. Then, bubble dot diagrams of the results were drawn, and a graphic display was constructed by the website www.bioinformatics.com.cn. 2.7. Mutation and statistical analyses Statistical analyses were performed by GraphPad Prism 8, SPSS statistics 26.0 and Excel sheets. Clinical variables are depicted as the median (interquartile range [IQR]) or mean ± standard deviation. Overall survival and progression-free survival were described by Kaplan–Meier plots. Correlations among the ctDNA concentrations, genetic mutations and clinical variables were assessed by nonparametric tests, Fisher’s exact test, Wilcoxon rank-sum test or receiver operating characteristic (ROC) curve as appropriate. All our statistical analyses are explained at a significance level of 5%. 3. Results 3.1. Clinical characteristics of enrolled HCC patients The workflow of the study design is shown in Fig.1. A total of 30 HCC patients aged 39-81 years with long-term follow-up were enrolled. The mean age of the study cohort was 69 years. The median follow-up of HCCs was 30 months. The clinical characteristics of HCC patients are shown in Table 1 and the baseline characteristics of the control group are displayed in Table S1. The HCC cohort consisted of 18 males and 12 females. Thirteen patients suffered from viral hepatitis. Of these, one had a chronic hepatitis B, and twelve had a chronic hepatitis C infection. The number of HCC patients suffering from alcoholic liver disease and non-alcoholic fatty liver disease was 6 and 12, respectively. Liver cirrhosis could be found in 56.7% (17/30) of cases. Vascular tumor invasion was observed in 53.3% of HCC patients (16/30). The metastasis rate of HCC cohort was 13.3%, with predominant lung metastasis (4/30). The median diameter of the largest tumor nodule was 41.5mm (IQR 22.8-60.8). Two patients (23.0%) had portal vein thrombosis. The HCC patients were classified to the following BCLC stages: 40% (12/30) were in the early stage (BCLC stage 0/A), 40 % (12/30) were in the intermediate stage (BCLC stage B) and 20 % (6/30) were in the advanced stage (BCLC stage C). Moreover, 46.7% of HCCs were TNM stage 1, with a tumor size less than 2 cm, and 54.2% of HCCs were TNM stage 2-4. In the HCC cohort, the prognosis of HCC in the advanced stage was worse than that in the early and intermediate stages (Fig. S1A). A progressive decrease in both overall survival (OS) and progression-free survival (PFS) was observed between TNM stage 1 and stage 2-4 (Fig. S1B). 3.2. Genetic mutations of ctDNA show value in distinguishing between ctDNA and cfDNA The median cfDNA concentration of the HCC group was 10.4 ng/ml (IQR 3.4-17.8 ng/ml), which was higher than in the control group (6.3 ng/ml IQR 3.0-10.5 ng/ml) but not significantly different (Fig. 2A). After observing the levels of cfDNA, we focused on the genetic mutations. The genetic variants in ctDNA contained several types: exonic variants, intronic variants, intergenic variants, splice site variants, and variants in the 3'-untranslated region and 5'-untranslated region. Exonic variants accounted for the largest proportion of these variants, reaching 77.5 % (Fig. 2B). We filtered the data for non-silent mutations according to the following steps: (1) exclusion of variants not located in exonic regions; (2) exclusion of variants without a clear defined function; (3) exclusion of synonymous and unknown variants of the exon. The subsequent screening indicated that in 20 out of 30 (66.7 %) patient plasma samples in the HCC group, at least one functionally relevant mutation gene was detected, while no mutation was detected in cfDNA for the control group (Table 2). There was a significant difference in the proportion of patients with mutations between the experimental and control groups (Fig. 2C). Mutant genes were clearly distinguished cfDNA and ctDNA, and ctDNA concentration in plasma was 2.2 log10 [haploid genome equivalents/ml]. The mean mutated allele frequency (mAF) in ctDNA was 5.2 %, and the median mAF of ctDNA was 2 % (IQR 1 %-10 %; n = 20). Mutations in HCCs included 49 eligible mutant genes containing 91 exons in ctDNA, and 72 eligible mutant genes with 170 exons in tDNA (Table S2). There were fewer mutations in ctDNA than in tDNA in terms of total patients with mutations, genes involved, mutant exons and mean of exonic mutations per patient (Table 2). Moreover, the median mutant allelic frequency of ctDNA was lower than that of tDNA (Fig. S2A). 3.3. Mutational landscapes of ctDNA Next, we generated a genetic heatmap displaying 32 mutations in ctDNA (Fig. 3A). This included all mutations with a frequency greater than 10%, as well as some mutations with a frequency of 6.7%. The number of patients carrying the mutated exons showed an elevated trend in patients with advanced TNM stage II-IV compared with patients with TNM stage I, but this difference was not statistically significant (p=0.259). Different species of mutations include non-synonymous single nucleotide variant (SNV), stop gain, frameshift deletion, and non-frameshift deletion mutations. As shown in the genetic mutation landscape, the top genes identified include NCOR2 (13.3%), CTNNB1 (13.3%), PDE4DIP (10%), ROBO1 (10%), TP53 (10%), KMT2C (10%), RANBP2 (6.7%), ATM (6.7%), ACVR2A (6.7%), HGF (6.7%), MECOM (6.7%) , MKI67 (6.7%) . In the genetic heatmap, NCOR2, ROBO1, RANBP2, HGF, MECOM, MKI67, PTPN13, and ZFHX3 were all detected for the first time in the ctDNAs of HCC. In the subsequent phase, we focused on 17 mutations that occurred with frequencies greater than 6.7% in the plasma samples (Fig. 3A) and conducted a comparison analysis of the frequencies between ctDNA and tDNA (Fig. 3B). More than half of the genes exhibit high consistency in mutation frequencies between ctDNA and tDNA, including NCOR2 , ROBO1 , RANBP2 , ATM , ACVR2A , RNF213 , HGF , ERBB4 , and BRCA2 . In contrast, there was a more than 2-fold difference between ctDNA and tDNA mutation frequencies for CTNNB1 , PDE4DIP , KMT2C , MTCOM , MKI67 , IGF1R , and KMT2D. We additionally compared the mutation frequencies of the 17 genes between tDNA and a public database (n=630), including cBioportal, which incorporates the TCGA database and other clinical data (Fig. S2B) [19-22]. The mutation frequency of the tDNA samples from our HCC cohort was generally higher than the frequencies reported in existing databases, except for those for CTNNB1 and TP53 . This may be related to the different sequencing depths and the utilization of macrodissection on tumor paraffin sections. In KEGG analysis, mutant genes in ctDNA and tDNA were highly enriched in the same HCC-related signalling pathways, indicating that although while some discrepancies existed in the identified mutations between blood and tissue samples, the distribution and related pathways demonstrated remarkable consistency (Fig. S2C). 3.4. Concordance between mutations identified in plasma ctDNA and matched HCC tissue DNA After analyzing the mutational landscape in plasma, we sought to investigate the accuracy of ctDNA in carrying oncogene information. To accomplish this, we compared ctDNA with matched tDNA from the obtained HCC tissue to identify mutant genes that could be identified simultaneously. Concordant mutations could be identified in both ctDNA and matched tDNA from 50% (10/20) of HCC patients who had at least one functional mutant exon (Fig. 4A). A maximum of 4 concordant mutant exons can be measured in single HCC patient. In total, 19 genes compromised the concordant mutation, containing 23 exon sites, and accounted for 25.2% (23/91) of ctDNA mutant exons and 18.0% (23/128) of tDNA mutant exons (Fig. 4B). CTNNB1, with 4 exons, demonstrated the highest mutation frequency in HCC patients who carried concordant mutations, reaching 40% (4/10). And 20% of patients were identified to harbor mutations in TP53 , containing 2 exons. For the remaining genes, each contained one mutant exon and was present in only 1 HCC patient (Table. 3, Fig. S3A). To evaluate a possible relationship between concordant mutations and clinical outcomes, we combined and analysed the information on concordant mutations with clinical outcome data of HCC patients. A comparable relationship between concordant mutations and clinical characteristics was observed for vascular invasion and BCLC stage (Fig. 4C). HCC patients with tumor vascular invasion had higher concordant mutations, both in macrovascular and microvascular invasion (Fig. 4D). In addition, patients with intermediate and advanced HCC tumor stages showed increased concordant mutations than early stages (Fig. S3B). 3.5. The combination of ctDNA mutations and AFP could be beneficial for HCC diagnosis Next, we evaluated the efficacy of ctDNA mutation assessment and compared these mutations with the established circulating biomarker of HCC, AFP. In the studied cohort, 22 HCC patients and 5 patients with non-malignant liver diseases analyzed AFP levels. The cut-off value of AFP was 20 ng/ml [25]. Comparison of the areas under the curve (AUC) of the ROC curve for ctDNA mutations with the ROC curve of AFP revealed a slightly higher value (0.89 ct-mutations in comparison to 0,71 AFP ), but this difference was not statistically significant (p=0.71) (Fig. 5A). However, the combination of the AUC of ctDNA mutations and of the AFP level resulted even in a value of 0.98. Thus, the diagnostic accuracy of this combination outperformed either ctDNA mutations or AFP alone, with p values of 0.028 and 0.009, respectively. The collaborative analysis displayed a sensitivity of 95.5% and specificity of 100% in our HCC cohort (Fig. 5B). To explore the diagnostic efficacy of the combination of the two methods for HCC patients in the early stage or with small tumor size (<2 cm), we performed ROC analysis for HCCs in BCLC stage 0-A and TNM stage 1 and obtained the same results (Fig. 5C and D). 3.6. The set of ctDNA mutations at TNM stage 2-4 may facilitate the prediction of survival We proceeded a focus on patients in TNM stage 2-4 with a significantly poorer survival prognosis (Figure S1B) to check, whether there is a correlation of specific mutations with patient outcomes. According to the TNM stages, mutant genes in ctDNA were categorized into three mutation sets: those appearing only in TNM stages 2-4, those exclusive in TNM stage 1, and those occurring in both stages (Fig. 6A). As TNM stages 2-4 showed a poorer OS and PFS in our HCC cohort, we focused on mutation set A as the specific gene set. This set contained 12 genes: ARID2, ERBB4, ERCC5, KMT2A, MSH6, NCOR2, PIK3CA, PIK3CG, POLQ, PEPRB, TERT, and TSC1 , distributing in 9 patients. (Fig. 6B, S1A). Among the patients in our HCC cohort (n=30), mutation set A showed a strong association with inferior OS (p=0.024), and a tendency to correlate with poor PFS (p=0.080) (Fig. 6C). To verify the accuracy of these findings, we also evaluated the relationship between mutation set A and prognosis in the TCGA database, including 165 HCCs. In this validation cohort, HCCs with mutation set A displayed significantly poorer PFS (p=0.023) and a worse trend in OS (p=0.074) (Fig. 6D). Our prognosis data are generally consistent with the results in the TCGA cohort. Hence, we substantiated that the mutation set A could serve as a promising predictor for HCC prognosis. 4. Discussion With increasing knowledge of molecular diagnostic techniques, ctDNA assessment as a liquid biopsy based approach, has evolved as a promising tool for HCC diagnosis and surveillance [26]. One major issue is how to minimize the interference of normal cfDNA in detecting ctDNA. Genetic mutation has presented promising potential as a biomarker [27]. Here, we performed NGS sequencing of 100 common HCC genes, uncovering numerous hotspot mutations, including some observed exclusively in HCC tumors and never in ctDNA before. In our study, a gene panel containing common mutations in HCC was designed for ctDNA. Importanly multiple genes (49/100) of this panel were detected in ctDNA. The mutant targets were found only in the patient’s plasma from the HCC group (20/30) but were completely absent in the control group, which implied that the mutant genes of ctDNA showed high validity in distinguishing ctDNA from cfDNA. This has important implications for ctDNA mutations as a diagnostic modality. To the best of our knowledge, Cohen et al. were the first to to explore the combined diagnostic effect of ctDNA and cancer protein markers, they developed a new detection tool called “CancerSEEK” for tumors that combines ctDNA mutations and circulating proteins [28]. A liquid biopsy assay (HCCscreen) consisting of AFP, Des-Gamma-Carboxy prothrombin (DCP) and ctDNA mutations was developed to diagnose HCCs, but was only limited to HBV-associated HCC [29]. Our HCC cohort contained patients with HBV infection, HCV infection and patients without hepatitis virus infection, assessing the effect of ctDNA in more dimensions. While the detected ctDNA mutations displayed only a weak trend of superior performance compared to AFP, the combination of the two biomarkers showed a substantial advantage in the diagnosis of HCC, regardless of tumor stage. The collaborative diagnosis yielded better accuracy (AUC=0.98) than ctDNA mutations or the AFP level, with a sensitivity of 95.5% and specificity of 100%. The strengths of our study are taking a unique approach by combining ctDNA mutations with AFP to assess their combined HCC diagnostic efficacy. The detection of tumor-mutated genes in HCC lacks high specificity, even the most prevalent, TP53 , is only about 30%, thus affecting the specificity of ctDNA [30]. The earliest studies for mutant genes in ctDNA of HCC investigated the Ser-249 p53 in Gambian patients [31]. In the following studies, more mutated sites were detected in the ctDNA of HCC: CTNNB1, ARID1A, and AXIN1. CTNNB1 and ARID1A mutations were found in both European and Chinese cohorts[16] [32]. Although classical prevalent genes for HCC mentioned above have been identified, the number of mutated genes found in ctDNA is still much lower than in tumors so far. We have found several mutant genes that has not been observed in the ctDNA of other HCC cases. Among them , six had a mutation rate over 6.7%, including NCOR2, ROBO1, RANBP2, HGF, MECOM, MKI67. This expands the range of ctDNA mutations in HCC and lays the foundation for exploring liquid biopsy features of HCC. Moreover, our mutation landscape of ctDNA presents distinct characteristics. Since our NGS panel only includes the exon portion, the frequency will be low for genes where mutations often occur at the promoter site ( TERT ) [33]. NCOR2 and CTNNB1 were the two genes with the highest mutation frequency (13.3%). Although CTNNB1 was a classical mutation in HCC, its frequency in plasma is lower than in tumor tissue, with a detection rate of 36.6% in our HCC FFPE samples and 27% in the reported literature (27%) [30]. However, as one of the newly discovered genes in ctDNA, NCOR2 not only exhibited a high mutation frequency but was consistent with its frequency in tDNA. The frequency was much higher than that in public databases (2.2%). NCOR2 encodes the nuclear receptor co-repressor protein with the function of repressing basal transcription, which inhibits the progression of HCC cell through the miR‐10a‐5p/ NCOR2 axis [34]. NCOR2 was also one of twelve genes exclusively distributed in TNM stage 2-4, constituting the mutation set that explored cancer prognosis. It is imperative to validate NCOR2 in a larger cohort to validate its impact on the prognosis of HCC in the future. Previous studies have pointed out the predictive value of ctDNA mutations for HCC prognosis, but the majority of these studies focused on a single mutant gene [35] [36]. Our results demonstrate that the clustering of specific mutant genes into mutation sets can help to predict the prognosis of patients with HCC. Due to the low frequency of single mutant genes in HCC, our study highlights the value of a mutation set approach. Finally, by assessing the matching degree between ctDNA and tDNA, we validated the precision of circulating nucleic acid fragments as carriers of tumor genetic information. Here, some discrepancies existed in the identified mutations between blood and tumor samples. We speculate that might be due to tumor heterogeneity [37]. The overall trend of mutated genes of ctDNA still demonstrated remarkable consistency with tDNA, both of which were involved in the classic HCC signaling pathways. The genetic concordance between ctDNA and tDNA was also analyzed in a recent study by Howell et al [16], although no influencing factors were examined here. In addition to an identification of genetic concordance, we found clinical factors affecting this concordance. HCC patients with vascular invasion were more likely to capture ctDNA fragments closely matching the tumor, even in cases of microvascular invasion. This indicates a strong correlation between genetic concordance and the presence of vascular invasion. There are also several limitations of this study that we acknowledge. First, although we included as many common HCC genes as possible in the NGS panel to explore the genetic profile, the sample size of the cohort was small. A large size of samples will help to explore the influence of different HCC etiologies on ctDNA mutations. While previous studies have shown the presence of mutations in patients with non-malignant liver diseases, no mutant genes were detected in our control group [38] [39]. This could be also due to the small sample size of the control group. In total, our study highlights the potential of ctDNA mutations as a novel biomarker for the diagnosis and monitoring of HCC. Notably, we found ctDNA mutations achieve a great ability to distinguish HCC and non-malignant liver diseases, thus combining ctDNA mutations with established biomarkers (e.g. AFP levels) for diagnostic purposes will generate promising rewards. We also identified several new mutation targets in HCC ctDNA, including NCOR2 , expanding the mutant gene library of ctDNA in HCC. Moreover, the specific mutation set in ctDNA can profoundly impact prognosis. Finally, we confirmed that the concordance of ctDNA information of the primary tumor correlates with tumor burden. 5. Conclusion Our results support the implications of ctDNA mutations for precision medicine. It will surely be rewarding to pursue this direction, enlarging the sample size for more features and increasing the clinical value of ctDNA mutations. Declarations Acknowledgments We warmly thank the technical assistance from Susanne Neiss, Michaela Heitmann, Anke Wienand-Dorweiler, and Lisa Raatz. We also appreciated the Cologne Center for Genomics facility for next-generation sequencing and the valuable advice from Jialei Weng. Authors' contributions XW designed, performed most of the experiments and wrote the manuscript; UD performed collection of FFPE samples; JA and KB performed next generation sequence. MB performed clinical data collection. SB, JMH performed data analysis; JL, MP, AG, HA, MO, DS and CJB supported data discussion; YZ and RW guided the project and critically reviewed the manuscript. All authors read and approved the final manuscript. Ethics approval and consent to participate This study was performed in accordance with the Declaration of Helsinki and was conducted after obtaining approval from the Independent Ethics Committee at the University Hospital of Cologne: Ethics-No. 13-091, BioMaSOTA (Biologische Maerial Sammlung zur Optimierung Therapeutischer Ansätze). Informed consent was obtained from all patients. All methods were performed in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Data availability The datasets supporting the conclusions of this article are available from the corresponding author upon reasonable request. Competing interests The authors declared that no competing interest exists. Funding information This work was supported by the Cologne Fortune Projects (NR.462/2020) for the project: Liquid biopsy: genetic profiling of molecular in peripheral blood as prognostic and predictive biomarkers in primary liver cancer. Xiaolin Wu and Jiahui Li were financially supported by the CSC scholarship (The China Scholarship Council). References Villanueva A. Hepatocellular carcinoma. The New England Journal Of Medicine.380(15):1450-62. (2019). Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Tables Table 1: Clinical variables among HCC cohorts (N=30) Clinical variable Number (%) Gender Male 18 (60%) Female 12 (40%) Mean age (years ± SD) 69.0 ± 9.0 Etiology of chronic liver disease Hepatitis B virus (HBV) 1 (3.3%) Hepatitis C virus (HCV) 12 (40%) Cirrhosis 17 (56.7%) Alcoholic liver disease 6 (20%) Non-alcoholic fatty liver disease (NAFLD) 12 (40%) Largest tumor diameter (mm) 216 Median largest tumor diameter (mm) 41.5 (IQR 22.8-60.8) Median AFP pre-operative (ng/ml, N=22) 9.5 (IQR 4.8-47.5) Macrovascular invasion 3 (10%) Microvascular invasion 13 (43.3%) Portal vein thrombosis 2 (6.7%) Presence of metastasis 4 (13.3%) CTP classification A 27 (90%) B 3 (10%) C 0 BCLC classification 0 5 (16.7%) A 7 (26.7%) B 12 (40%) C 6 (20%) D 0 pTNM classification 1 14 (46.7%) 2 6 (20%) 3 5 (16.7%) 4 5 (16.7%) Table 2: Characteristics of mutations in experimental and control groups Characteristic Experimental group Control group Plasma ctDNA Tumor DNA Plasma cfDNA Number of Samples 30 30 10 Patients with mutation 20/30 30/30 0/10 Genes involved 49/100 72/100 0/100 Mutant exons 91 170 0 Exonic mutations per sample - Mean 4.6 5.7 - Exonic mutations per sample - Median (Range) 2 (1-40) 3.5 (1-53) - Table 3: Details of the concordant mutations identified both in ctDNA and matched tDNA from HCC patients ID Gene Cyto Band Position DNA change Alteration with exonic function 0072 CTNNB1 3p22.1 3:41266113 C → T nonsynonymous SNV 0269 CTNNB1 3p22.1 3:41266098 A → G nonsynonymous SNV 0552 CTNNB1 3p22.1 3:41266137 C → T nonsynonymous SNV 1326 CTNNB1 3p22.1 3:41266100 T → C nonsynonymous SNV 0197 TP53 17p13.1 17:7578505 GGGCAGGTCTTGGCCAG → - frameshift deletion 0269 TP53 17p13.1 17:7577580 T → C nonsynonymous SNV 0552 ACVR2A 2q22.3 2:148683651 C → T nonsynonymous SNV 1447 ALB 4q13.3 4:74275076 T → - frameshift deletion 1214 ARID2 12q12 12:46245643 AGG → - nonframeshift deletion 1447 BAP1 3p21.1 3:52437589 G → - frameshift deletion 1447 BRCA2 13q13.1 13:32907114 G → T nonsynonymous SNV 0072 ERBB2 17q12 17:37882817 T → A nonsynonymous SNV 1326 ERBB4 2q34 2:212248585 A → C nonsynonymous SNV 0072 ERCC5 13q33.1 13:103515307 T → C nonsynonymous SNV 1077 HGF 7q21.11 7:81359077 T → G nonsynonymous SNV 0591 KMT2D 12q13.12 12:49443732 C → A nonsynonymous SNV 0022 MSH6 2p16.3 2:48027428 T → G nonsynonymous SNV 0072 NCOR2 12q24.31 12:124841250 C → G nonsynonymous SNV 0552 PIK3CA 3q26.32 3:178952085 A → G nonsynonymous SNV 0552 RANBP2 2q12.3 2:109352117 A → - frameshift deletion 1447 RNF213 17q25.3 17:78265553 C → G stopgain 1077 ROBO1 3p12.3 3:79174643 G → - frameshift deletion 1077 TSC1 9q34.13 9:135801023 T → C nonsynonymous SNV Additional Declarations No competing interests reported. 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Wahba","email":"","orcid":"","institution":"University Hospital of Cologne","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Wahba","suffix":""}],"badges":[],"createdAt":"2024-03-14 09:30:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4099291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4099291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54595252,"identity":"9c21cc5e-ee65-46f1-8e4a-4d341a50d5c1","added_by":"auto","created_at":"2024-04-12 18:42:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow chart of data generation and analysis. \u003c/strong\u003eA total of 40 patients with liver diseases from various institutions underwent plasma ctDNA/cfDNA and gDNA extraction, with 30 HCC patients as the experimental group and 10 patients with non-malignant liver lesions as the control group. The tDNA was taken from FFPE samples only in HCC group. After all DNA samples were NGS tested and analyzed, several mutations were found in the HCC group, while no mutations were detected in the control group.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/0d5c62906a50d838e131ae30.png"},{"id":54595032,"identity":"3ce103e9-831a-4f61-88a1-afe890c4f400","added_by":"auto","created_at":"2024-04-12 18:34:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe cfDNA concentration and genetic mutation of ctDNA. A.\u003c/strong\u003e Comparison of cfDNA concentrations between HCC and patients with non-malignant liver disease, p=0.22. \u003cstrong\u003eB.\u003c/strong\u003eSix different kinds of variants in ctDNA.\u003cstrong\u003e C.\u003c/strong\u003e Comparison of mutation proportion in HCC and control group. Fisher’s exact test p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/59c9c95a9345e2c59f7d3b46.png"},{"id":54595034,"identity":"c8548134-8d3a-4e85-82ba-e6a2e6585d19","added_by":"auto","created_at":"2024-04-12 18:34:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutations landscape and signaling pathways. A. \u003c/strong\u003eCtDNA mutation profiling of HCC patients. The top part shows the number of tumor mutations in each patient, while the middle panel details the mutation genes. The bottom panel shows the TNM stage, BCLC stage, microvascular invasion, macrovascular invasion, HBV, HCV, alcohol liver disease, non-alcohol fatty liver disease, cirrhosis, and metastasis for 30 HCC. \u003cstrong\u003eB. \u003c/strong\u003eComparison of gene mutation rates in ctDNA and tDNA.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/8fff8b522a8d7c97abef5ebb.png"},{"id":54595035,"identity":"891c38e2-7985-4d6a-8c2e-47ec64564cc3","added_by":"auto","created_at":"2024-04-12 18:34:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConcordance between mutations identified in ctDNA and matched HCC tDNA. A.\u003c/strong\u003eThe HCC cohort shows a comparison of the number of mutations between ctDNA and tDNA, with concordant mutations found in 10 of the samples. Patient samples were arranged in order for decreasing concordant mutation. \u003cstrong\u003eB. \u003c/strong\u003eThe Venn diagram shows mutant genes’ concordant and mutually exclusive parts in ctDNA and tDNA. \u003cstrong\u003eC. \u003c/strong\u003eA forest plot displays the association between the clinical variable and concordant mutations.\u003cstrong\u003e D. \u003c/strong\u003eThe stacked charts show the comparison of concordant mutations and the clinical variable with p\u0026lt;0.05, including macro and microvascular invasion, Fisher’s exact test, macrovascular invasion, p=0.030, and microvascular invasion, p=0.045.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/671d5140d218965a1c6cd251.png"},{"id":54595254,"identity":"f373a035-7e88-4612-a2a1-c01cd05f996b","added_by":"auto","created_at":"2024-04-12 18:42:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis of the diagnosis efficacy for ctDNA mutation and AFP level.\u003c/strong\u003e \u003cstrong\u003eA. \u003c/strong\u003eFor HCCs in all stages, the change in AUC between ctDNA and AFP levels was not statistically significant (p=0.172). AUC of the combination of ctDNA mutation and AFP level showed a significant increase in ctDNA (p=0.028) or AFP (p=0.009). \u003cstrong\u003eB. \u003c/strong\u003eSensitivity and specificity results of the collaborative diagnosis in the HCC cohort. \u003cstrong\u003eC. \u003c/strong\u003eFor HCCs in BCLC stage 0-A, combination AUC revealed a significant increase in ctDNA mutation AUC (p=0.042),\u003cstrong\u003e \u003c/strong\u003eor AFP AUC (p=0.038).\u003cstrong\u003e D. \u003c/strong\u003eFor HCCs in TNM stage 1, the combination AUC displayed a more significant period than AFP AUC (p=0.025).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/b3be8dcc357c27458269d88d.png"},{"id":54595531,"identity":"90ddd163-1558-4a7b-a7d2-23a59427d0dc","added_by":"auto","created_at":"2024-04-12 18:50:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutation set of ctDNA and survival. A\u003c/strong\u003e. A Venn diagram shows the distribution of mutation sets in different TNM stages. The mutation set A contains 12 mutant genes found only in patients with TNM stages 2-4. \u003cstrong\u003eB.\u003c/strong\u003e In our HCC cohort, 9 out of 30 exhibited genes from mutation set A. \u003cstrong\u003eC. \u003c/strong\u003eKaplan–Meier analysis for overall survival (p=0.024) and progression-free survival (p=0.080) in our HCC cohort. \u003cstrong\u003eD. \u003c/strong\u003eKaplan–Meier analysis for overall survival (p=0.074) and progression-free survival (p=0.023) in the TCGA cohort. P values were calculated from the log-rank test.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/7a18883e75802304c0bc3ccf.png"},{"id":54673478,"identity":"91f16003-adbf-44f2-9ebb-13229d627b87","added_by":"auto","created_at":"2024-04-15 05:50:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":787006,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/942af59b-a3de-4ec2-bac5-b252e66ca8de.pdf"},{"id":54595037,"identity":"a7cc3d0f-800a-4e2c-9fc3-213d57bc5deb","added_by":"auto","created_at":"2024-04-12 18:34:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":172388,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementdata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4099291/v1/2394988f6259badcccaf334d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plasma Circulating Tumor DNA Sequencing Reveals the Landscape of Acquired Mutations in Patients with Hepatocellular Carcinoma: a Potential Predictive Value in Liquid Biopsy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is currently the sixth most commonly diagnosed cancer and ranks as the third\u0026nbsp;most common cause of cancer-related death, with an estimation of 830,000 annual deaths worldwide\u0026nbsp;[1, 2]. While liver resection and liver transplantation have been the mainstay curative treatments, locoregional and systemic therapies represent further treatment options in HCC management\u0026nbsp;[3]\u003cem\u003e.\u003c/em\u003e Early diagnosis of HCC is crucial since patients with advanced HCC have a poor prognosis with a 5-year survival rate of \u0026lt; 16%, while patients with early-stage HCC have a 5-year survival rate of \u0026gt; 70%\u003cem\u003e\u0026nbsp;\u003c/em\u003e[4]. However, the diagnosis of early-stage HCC remains a challenge in everyday clinical practice, as patients commonly do not present with noticeable symptoms. Moreover, HCC develops not only in patients with cirrhosis, who are frequently included in surveillance programs but also in non-cirrhotic patients, particularly in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), who are commonly not included in such surveillance programs, which further complicates its early identification. Current diagnostic approaches for HCC detection rely on abdominal imaging (e.g. ultrasound) with or without concurrent biomarker testing, such as alpha-fetoprotein (AFP). However, AFP has even in patients with a known viral hepatitis a sensitivity of only 62.4% and a specificity of 89.4%, which seems not\u0026nbsp;sufficiently\u0026nbsp;accurate for early HCC detection\u0026nbsp;[5]. State-of-the-art radiological abdominal imaging techniques (CT and MRI) have a higher overall sensitivity but perform poorly in detecting small HCC lesions (\u0026lt;1cm)\u0026nbsp;[6]. Thus, establishing HCC diagnosis solely based on current non-invasive methods reveal several limitations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis is currently further challenged by the need for molecular information to initiate individualized treatment concepts, that requires biopsies / tissue sampling. In recent years, advancements in liquid biopsy technologies analyzing circulating cancer products in the bloodstream have facilitated profound research in the field of cancer, and liquid biopsy techniques may provide a novel promising approach to address the challenges of early HCC diagnosis\u0026nbsp;[7]. In this context, an assessment of circulating tumor DNA (ctDNA) or cell-free DNA (cfDNA) is one vital method that aims to measure double-stranded DNA fragments, that is released by cancer cells into body fluids. These fragments typically measure 150-200 base pairs (bp)\u0026nbsp;in length, peaking at nearly 166 bp\u0026nbsp;[8, 9]\u003cem\u003e.\u003c/em\u003e Cell-free DNA is released from necrotic and apoptotic cells into the circulatory system and the origin of ctDNA is cfDNAs only released from the tumor cells\u0026nbsp;[10]. The cfDNA level is low in healthy individuals (10-15 ng/ml)\u0026nbsp;[11]\u0026nbsp;but elevated after inflammatory diseases or after tumor development. The proportion of ctDNA substantially fluctuates, ranging from \u0026lt;0.1% to \u0026gt;90% of cfDNA\u0026nbsp;[10], thus,\u0026nbsp;the main obstacle of ctDNA research at present is how to precisely distinguish ctDNA from the cfDNA background originating from\u0026nbsp;non-malignant\u0026nbsp;cells. Due to its origin, ctDNA carries specific information about tumor biology and has been proposed as an alternative promising source for molecular profiling of tumor DNA \u0026nbsp;of HCC \u0026nbsp;patients\u0026nbsp;[12]. These constitute the potential advantages of ctDNA in clinical applications, especially for the early detection and monitoring of cancer progression or cancer relapse.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost studies on ctDNA published to date focus on markers of genetic variation, including copy number variations (CNVs), gene integrity, genetic mutations, and methylation\u0026nbsp;[13]. Mutant genes have great potential to be effective clinical\u0026nbsp;biomarkers, which has been demonstrated in several studies\u0026nbsp;[14]. The number of mutation sites in ctDNA\u0026nbsp;is\u0026nbsp;related to carcinoma size or metastasis, showing\u0026nbsp;that\u0026nbsp;genetic\u0026nbsp;mutations might reflect\u0026nbsp;some conditions of HCC disease stage\u0026nbsp;[15]. Several common mutant genes that appear in HCC nodules can also be detected in plasma ctDNA, including \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eCTNNB1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e [16].\u003cem\u003e\u0026nbsp;\u003c/em\u003eHowever, the number of mutation targets identified in the ctDNA is still small. Since ctDNA release is influenced by the number of necrotic cells in the tumor, the extensive mutations present in HCC tumor tissue are not found within the ctDNA. Furthermore, there is also a lack of studies assessing the influence of ctDNA mutation measurements in HCC diagnostic assessment. In addition, no studies have\u0026nbsp;specifically\u0026nbsp;explored the potential impact of combining AFP with ctDNA mutations for HCC diagnosis so far.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother issue that needs to be explored in realizing the application of liquid biopsy is the concordance between the plasma samples and tumor tissue from HCC patients. Although free nucleic acid carries genetic information of the tumor cells, some disparities appear between ctDNA and tissue tDNA\u0026nbsp;[17].\u0026nbsp;The matching degree between the mutation information from ctDNA and the tumor DNA requires further exploration. The association between concordant degree and clinical outcomes\u0026nbsp;has\u0026nbsp;also been scarcely discussed. Therefore,\u0026nbsp;a more in-depth study\u0026nbsp;is needed\u0026nbsp;to evaluate the clinical implications of these mutations.\u003c/p\u003e\n\u003cp\u003eOur study focused on ctDNA mutations, which may provide a basis for exploring the ctDNA profile in patients with HCC. To this end, a multi-mutation next-generation sequencing (NGS) panel including 100 HCC genes was designed to explore (previously unknown in ctDNA) mutation targets and to analyze the relationship between ctDNA mutations and AFP, as the most widely used biomarker for HCC diagnosis in clinical routine. We also evaluated different clinical characteristics that might affect the gene concordance between ctDNA and tDNA.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1. Patients and samples\u003c/p\u003e\n\u003cp\u003eIn total, 30 adult patients with a confirmed diagnosis of HCC were recruited from 2016\u0026ndash;2019 at the University Hospital of Cologne undergoing liver surgery, while 10 adult patients with non-malignant liver disease (liver cirrhosis, hepatic adenoma,\u0026nbsp;hepatic hemangioma, hepatic echinococcosis, focal nodular hyperplasia and nonalcoholic steatohepatitis) were included as controls. The diagnosis of HCC and non-malignant liver disease was established by histopathologic evidence. The inclusion criteria were as follows: (1) age \u0026gt; 18 years with HCC or non-malignant liver disease; (2) no previous liver resection or previous systemic therapy for HCC; and (3) no previous history of any other malignancies. The study was conducted in accordance with the Declaration of Helsinki (1975) and was approved by the local ethics committee (Biological Material Collection for Optimisation ID: 13-091). All patients provided written informed consent before participating.\u003c/p\u003e\n\u003cp\u003eFor molecular analysis, thirty milliliters of perivenous blood were collected in EDTA tubes from the patient during the operation before the surgery and sent to the reception laboratory within 4 hours. Plasma and interphase from the blood cellular component were isolated initially from the whole blood by performing centrifugation at 4000 \u0026times;g for 10 minutes at ambient temperature. Plasma and buffy coat samples were aliquoted into Eppendorf microtubes. All samples were stored at -80\u0026deg;C for further use.\u003c/p\u003e\n\u003cp\u003eThe following clinical and pathological data of all the patients were recorded at the same time as blood sample collection: age, sex, Child-Turcotte-Pugh (CTP) score\u0026nbsp;[18], pathology tumor/ nodes/ metastasis (pTNM) and Barcelona clinic liver cancer (BCLC) stage of HCC with information on vascular tumor invasion and/or metastatic spread, AFP level, presence of concomitant liver diseases (e.g. viral hepatitis, alcoholic liver disease and non-alcoholic fatty liver disease), presence of liver cirrhosis, portal venous thrombosis. The progression-free survival (PFS) and overall survival (OS) were reported at follow-up visits. The follow-up time was at least 24 months for each case, with clinical follow-up assessment. During follow-up CT scans were performed every six months after surgery.\u003c/p\u003e\n\u003cp\u003e2.2. DNA extraction from plasma, peripheral blood mononuclear cells (PBMCs), and paraffin tumor tissue\u003c/p\u003e\n\u003cp\u003eCfDNA was extracted from 7 ml plasma by a QIAamp MinElute ccfDNA Midi Kit (50) (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions. Germline DNA (gDNA) from the buffy coat samples was extracted by a QIAamp DNA Blood Mini Kit (50) (Qiagen). The HCC tumor tissue samples were fixed with formalin and embedded in formalin-fixed paraffin-embedded tissue (FFPE) at the Institute of Pathology of the University Hospital of Cologne. Matched FFPE tumor samples were cut from paraffin blocks, and tumor content was enhanced with tumor macrodissection. Tumor tissue DNA extraction was completed by a QIAamp DNA FFPE Tissue Kit (50) (Qiagen) according to the manufacturer\u0026rsquo;s working manual. The extracted cfDNA, gDNA and tumor tissue DNA samples were stored at -20\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e2.3. HCC sequencing panel design, library preparation and NGS\u003c/p\u003e\n\u003cp\u003ePlasma cfDNA, germline DNA and tumor DNA were subjected to targeted gene panel sequencing. The sequencing panel was designed to maximize the mutation detection rate in patients and minimize the panel size. We reviewed the genomic profile of HCC using cBioProtal, including The Cancer Genome Atlas (TCGA) \u0026nbsp;database and other HCC clinical data\u0026nbsp;[19-22]. The final design (HCC_Panel_v1.1) targeted 100 genes frequently mutated in HCC with a total size of 692 kbp of the human genome, containing all exonic domains. We used a minimum of 25 ng DNA for plasma cfDNA and 200 ng DNA for FFPE tDNA for library preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDNA quantification was performed using the TapeStation 2200 System (Agilent, California, USA). Additionally, size distribution was assessed for plasma cfDNA, requiring a minimum of 25% of total input DNA to be in the size window of interest for cfDNA (50-700 bp). Library preparation was performed using the Agilent SureSelectXT Low Input protocol, including enzymatic fragmentation (for gDNA and tDNA samples), end-repair, adapter ligation, index polymerase-chain-reaction (PCR), enrichment with the NGS panel we designed, and postenrichment PCR (12 cycles). Different circle numbers were used for different types of DNA fragments for index PCR: 10 cycles for cfDNA, 8 cycles for gDNA samples, and 11 cycles for tDNA samples. Subsequently, libraries were quantified (Qubit, Tape Station), pooled equimolarly and sequenced on a NovaSeq 6000 device with a paired-end, 2x100 bp sequencing protocol. We targeted 5 Gb data output for plasma cfDNA and FFPE tDNA, and 1 Gb data output for gDNA.\u003c/p\u003e\n\u003cp\u003e2.4. Basic data processing, variant calling and filtering\u003c/p\u003e\n\u003cp\u003eBasic data processing was performed by Bcl2fastq2 (v2.20.0.422), AGeNT Trimmer (Agilent), Burrows Wheeler Aligner (BWA, v0.7.17) and Samtools (v1.14). Somatic single base substitution calling was augmented by comparative error suppression (CES) for improved sensitivity and specificity as previously reported\u0026nbsp;[23]. Genetic aberrations and clinical annotations were visualized using the R-package \u0026lsquo;ComplexHeatmap\u0026rsquo;\u0026nbsp;[24]. All the genetic data excluded the interference of germline DNA.\u003c/p\u003e\n\u003cp\u003e2.6.\u0026nbsp;Gene Ontology (GO) enrichment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll ctDNA mutation targets were subjected to enrichment analysis using the DAVID Bioinformatics Resource (https://david.ncifcrf.gov/), and the p value was set to \u0026le; 0.05. Then, bubble dot diagrams of the results were drawn, and a graphic display was constructed by the website www.bioinformatics.com.cn.\u003c/p\u003e\n\u003cp\u003e2.7. Mutation and statistical analyses\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed by GraphPad Prism 8, SPSS statistics 26.0 and Excel sheets. Clinical variables are depicted as the median (interquartile range [IQR]) or mean \u0026plusmn; standard deviation. Overall survival and progression-free survival were described by Kaplan\u0026ndash;Meier plots. Correlations among the ctDNA concentrations, genetic mutations and clinical variables were assessed by nonparametric tests, Fisher\u0026rsquo;s exact test, Wilcoxon rank-sum test or receiver operating characteristic (ROC) curve as appropriate. All our statistical analyses are explained at a significance level\u0026thinsp;of\u0026thinsp;5%.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Clinical characteristics of enrolled HCC patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow of the study design is shown in Fig.1.\u0026nbsp;A total of 30 HCC patients\u0026nbsp;aged\u0026nbsp;39-81 years with long-term follow-up\u0026nbsp;were enrolled. The mean age of the study cohort was 69 years. The median follow-up of HCCs was 30 months.\u0026nbsp;The clinical characteristics of HCC patients are shown in Table 1 and the baseline characteristics of the control group are displayed in Table S1. The HCC cohort consisted of 18 males and 12 females. Thirteen patients suffered from viral hepatitis. Of these, one had a chronic hepatitis B, and twelve had a chronic hepatitis C infection. The number of HCC patients suffering from alcoholic liver disease and non-alcoholic fatty liver disease was 6 and 12, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLiver cirrhosis could be found in 56.7% (17/30) of cases. Vascular tumor invasion was observed in 53.3% of HCC patients (16/30). The metastasis rate of HCC cohort was 13.3%, with predominant lung metastasis (4/30). The median diameter of the largest tumor nodule was 41.5mm (IQR 22.8-60.8). Two patients (23.0%) had portal vein thrombosis.\u0026nbsp;The HCC patients were classified to the following BCLC stages: 40%\u0026nbsp;(12/30)\u0026nbsp;were in the early stage (BCLC stage 0/A), 40 %\u0026nbsp;(12/30)\u0026nbsp;were in the\u0026nbsp;intermediate\u0026nbsp;stage (BCLC stage B) and 20 %\u0026nbsp;(6/30)\u0026nbsp;were in the advanced stage (BCLC stage C). Moreover,\u0026nbsp;46.7%\u0026nbsp;of HCCs\u0026nbsp;were\u0026nbsp;TNM stage 1, with\u0026nbsp;a\u0026nbsp;tumor size less than\u0026nbsp;2 cm, and\u0026nbsp;54.2%\u0026nbsp;of HCCs\u0026nbsp;were\u0026nbsp;TNM stage 2-4.\u003c/p\u003e\n\u003cp\u003eIn the HCC cohort, the prognosis of HCC in the advanced stage was worse than that in the early and intermediate stages (Fig. S1A). A progressive decrease in both overall survival (OS) and progression-free survival (PFS) was observed between TNM stage 1 and stage 2-4 (Fig. S1B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGenetic mutations of ctDNA\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eshow value in distinguishing between ctDNA and cfDNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median cfDNA concentration of the HCC group was 10.4 ng/ml (IQR 3.4-17.8 ng/ml), which was higher than in the control group (6.3 ng/ml IQR 3.0-10.5 ng/ml) but not significantly different (Fig. 2A). After observing the levels of cfDNA, we focused on the genetic mutations. The genetic variants in ctDNA contained several types: exonic variants, intronic variants, intergenic variants, splice site variants, and variants in the 3\u0026apos;-untranslated region and 5\u0026apos;-untranslated region. Exonic variants accounted for the largest proportion of these variants, reaching 77.5 % (Fig. 2B). We filtered the data for non-silent mutations according to the following steps: (1) exclusion of variants not located in exonic regions; (2) exclusion of variants without a clear defined function; (3) exclusion of synonymous and unknown variants of the exon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe subsequent screening indicated that in 20 out of 30 (66.7 %) patient plasma samples in the HCC group, at least one\u0026nbsp;functionally relevant\u0026nbsp;mutation gene was detected, while no mutation was detected in cfDNA for the control group (Table 2). There was a significant difference in the proportion of patients with mutations between the experimental and control groups (Fig. 2C). Mutant genes were clearly distinguished cfDNA and ctDNA, and ctDNA concentration in plasma was 2.2 log10 [haploid genome equivalents/ml]. The mean mutated allele frequency (mAF) in ctDNA was 5.2 %, and the median mAF of ctDNA was 2 % (IQR 1 %-10 %; n = 20). Mutations in HCCs included 49 eligible mutant genes containing 91 exons in ctDNA, and 72 eligible mutant genes with 170 exons in tDNA (Table S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were fewer mutations in ctDNA than in tDNA in terms of total patients with mutations, genes involved, mutant exons and mean of exonic mutations per patient (Table 2). Moreover, the median mutant allelic frequency of ctDNA was lower than that of tDNA (Fig. S2A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMutational landscapes of ctDNA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we generated a genetic heatmap displaying 32 mutations in ctDNA (Fig. 3A). This included all mutations with a frequency greater than 10%, as well as some mutations with a frequency of 6.7%. The number of patients carrying the mutated exons showed an elevated trend in patients with advanced TNM stage II-IV compared with patients with TNM stage I, but this difference was not statistically significant (p=0.259). Different species of mutations include non-synonymous single nucleotide variant (SNV), stop gain, frameshift deletion, and non-frameshift deletion mutations. As shown in the genetic mutation landscape, the top genes identified include \u003cem\u003eNCOR2\u003c/em\u003e (13.3%), \u003cem\u003eCTNNB1\u003c/em\u003e (13.3%), \u003cem\u003ePDE4DIP\u003c/em\u003e (10%), \u003cem\u003eROBO1\u003c/em\u003e (10%), \u003cem\u003eTP53\u003c/em\u003e (10%), \u003cem\u003eKMT2C\u003c/em\u003e (10%), \u003cem\u003eRANBP2\u003c/em\u003e (6.7%), \u003cem\u003eATM\u003c/em\u003e (6.7%), \u003cem\u003eACVR2A\u003c/em\u003e (6.7%), \u003cem\u003eHGF\u003c/em\u003e (6.7%), \u003cem\u003eMECOM\u0026nbsp;\u003c/em\u003e(6.7%)\u003cem\u003e,\u003c/em\u003e \u003cem\u003eMKI67\u0026nbsp;\u003c/em\u003e(6.7%)\u003cem\u003e.\u003c/em\u003e In the genetic heatmap, \u003cem\u003eNCOR2, ROBO1, RANBP2, HGF, MECOM, MKI67, PTPN13,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eZFHX3\u0026nbsp;\u003c/em\u003ewere all detected for the first time in the ctDNAs of HCC.\u003c/p\u003e\n\u003cp\u003eIn the subsequent phase, we focused on 17 mutations that occurred with frequencies greater than 6.7% in the plasma samples (Fig. 3A) and conducted a comparison analysis of the frequencies between ctDNA and tDNA (Fig. 3B).\u0026nbsp;More than half of the genes exhibit high consistency in mutation frequencies between ctDNA and tDNA, including \u003cem\u003eNCOR2\u003c/em\u003e,\u003cem\u003e\u0026nbsp;ROBO1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;RANBP2\u003c/em\u003e, \u003cem\u003eATM\u003c/em\u003e, \u003cem\u003eACVR2A\u003c/em\u003e,\u003cem\u003e\u0026nbsp;RNF213\u003c/em\u003e,\u003cem\u003e\u0026nbsp;HGF\u003c/em\u003e,\u003cem\u003e\u0026nbsp;ERBB4\u003c/em\u003e, and \u003cem\u003eBRCA2\u003c/em\u003e. In contrast,\u0026nbsp;there was a\u0026nbsp;more than 2-fold difference between ctDNA and tDNA mutation frequencies for \u003cem\u003eCTNNB1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;PDE4DIP\u003c/em\u003e,\u003cem\u003e\u0026nbsp;KMT2C\u003c/em\u003e,\u003cem\u003e\u0026nbsp;MTCOM\u003c/em\u003e,\u003cem\u003e\u0026nbsp;MKI67\u003c/em\u003e, \u003cem\u003eIGF1R\u003c/em\u003e,\u003cem\u003e\u0026nbsp;and KMT2D.\u003c/em\u003e We additionally compared the mutation frequencies of the 17 genes between tDNA and a public database (n=630), including cBioportal, which incorporates the TCGA database and other clinical data (Fig. S2B)\u0026nbsp;[19-22]. \u0026nbsp;The mutation frequency of the tDNA samples from our HCC cohort was generally higher than the frequencies reported in existing databases, except for those for \u003cem\u003eCTNNB1\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e. This may be related to the different sequencing depths and the utilization of macrodissection on tumor paraffin sections. In KEGG analysis, mutant genes in ctDNA and tDNA were highly enriched in the same HCC-related signalling pathways, indicating that although while some discrepancies existed in the identified mutations between blood and tissue samples, the distribution and related pathways demonstrated remarkable consistency (Fig. S2C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Concordance between mutations identified in plasma ctDNA and matched HCC tissue DNA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter analyzing the mutational landscape in plasma, we sought to investigate the accuracy of ctDNA in carrying oncogene information. To accomplish this, we compared ctDNA with matched tDNA from the obtained HCC tissue to identify mutant genes that could be identified simultaneously. Concordant mutations could be identified in both ctDNA and matched tDNA from 50% (10/20) of HCC patients who had at least one functional mutant exon (Fig. 4A). \u0026nbsp;A maximum of 4 concordant mutant exons can be measured in single HCC patient. In total, 19 genes compromised the concordant mutation, containing 23 exon sites, and accounted for 25.2% (23/91) of ctDNA mutant exons and 18.0% (23/128) of tDNA mutant exons (Fig. 4B). CTNNB1, with 4 exons, demonstrated the highest mutation frequency in HCC patients who carried concordant mutations, reaching 40% (4/10).\u0026nbsp;And\u0026nbsp;20%\u0026nbsp;of\u0026nbsp;patients were identified to harbor mutations in \u003cem\u003eTP53\u003c/em\u003e, containing 2 exons.\u0026nbsp;For the remaining genes, each contained one mutant exon and was present in only 1 HCC patient (Table. 3, Fig. S3A). To evaluate a possible relationship between concordant mutations and clinical outcomes, we combined and analysed the information on concordant mutations with clinical outcome data of HCC patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA comparable relationship between concordant mutations and clinical characteristics was observed for vascular invasion and BCLC stage (Fig. 4C). HCC patients with tumor vascular invasion had higher concordant mutations, both in macrovascular and microvascular invasion (Fig. 4D). In addition, patients with intermediate and advanced HCC tumor stages showed increased concordant mutations than early stages\u0026nbsp;(Fig. S3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;The combination\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of ctDNA mutations and AFP could\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;be beneficial\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for HCC diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we evaluated the efficacy of ctDNA mutation assessment and compared these mutations with the established circulating biomarker of HCC, AFP. In the studied cohort, 22 HCC patients and 5 patients with non-malignant liver diseases analyzed AFP \u0026nbsp;levels. The cut-off value of AFP\u0026nbsp;was 20 ng/ml\u0026nbsp;[25]. \u0026nbsp;Comparison of the\u0026nbsp;areas under the curve (AUC) of the ROC curve for ctDNA mutations with the ROC curve of AFP revealed a slightly higher value (0.89\u003csub\u003ect-mutations\u003c/sub\u003e in comparison to 0,71\u003csub\u003eAFP\u003c/sub\u003e ), but this difference was not statistically significant (p=0.71) (Fig. 5A). However, the combination of the AUC of ctDNA mutations and of the AFP level resulted even in a value of \u0026nbsp;0.98. Thus, the diagnostic accuracy of this combination outperformed either ctDNA mutations or AFP alone, with p\u0026nbsp;values\u0026nbsp;of 0.028 and 0.009, respectively. The collaborative analysis displayed a sensitivity of 95.5% and specificity of 100% in our HCC cohort (Fig. 5B). To explore the\u0026nbsp;diagnostic\u0026nbsp;efficacy of the combination of the two methods for HCC patients in the early stage or with small tumor size (\u0026lt;2 cm), we\u0026nbsp;performed\u0026nbsp;ROC analysis for HCCs in BCLC stage 0-A and TNM stage 1 and obtained the same results (Fig. 5C and D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe set of ctDNA mutations at TNM stage 2-4 may facilitate the prediction of survival\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe proceeded a focus on patients in TNM stage 2-4 with a significantly poorer survival prognosis (Figure S1B) to check, whether there is a correlation of specific mutations with patient outcomes. According to the TNM stages, mutant genes in ctDNA were categorized into three mutation sets: those appearing only in TNM stages 2-4, those exclusive in TNM stage 1, and those occurring in both stages (Fig. 6A). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs TNM stages 2-4 showed a poorer OS and PFS in our HCC cohort, we focused on mutation set A as the specific gene set. This set contained 12 genes:\u003cem\u003e\u0026nbsp;ARID2, ERBB4, ERCC5, KMT2A, MSH6, NCOR2, PIK3CA, PIK3CG, POLQ, PEPRB, TERT,\u003c/em\u003e and\u003cem\u003e\u0026nbsp;TSC1\u003c/em\u003e, distributing in 9 patients. (Fig. 6B, S1A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the patients in our HCC cohort (n=30), mutation set A showed a strong association with inferior OS (p=0.024), and a tendency to correlate with poor PFS (p=0.080) (Fig. 6C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo verify the accuracy of these findings, we also evaluated the relationship between mutation set A and prognosis in the TCGA database, including 165 HCCs. In this validation cohort, HCCs with mutation set A displayed significantly poorer PFS (p=0.023) and a worse trend in OS (p=0.074) (Fig. 6D). Our prognosis data are generally consistent with the results in the TCGA cohort. Hence, we substantiated that the mutation set A could serve as a promising predictor for HCC prognosis.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith increasing knowledge of molecular diagnostic techniques, ctDNA assessment as a liquid biopsy based approach, has evolved as a promising tool for HCC diagnosis and surveillance\u0026nbsp;[26]. One major issue is how to minimize the interference of normal cfDNA in detecting ctDNA. Genetic mutation has presented promising potential as a biomarker\u0026nbsp;[27]. Here, we performed NGS sequencing of 100 common HCC genes, uncovering numerous hotspot mutations, including some observed exclusively in HCC tumors and never in ctDNA before.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, a gene panel containing common mutations in HCC was designed for ctDNA. Importanly multiple genes (49/100) of this panel were detected in ctDNA. The mutant targets were found only in the patient\u0026rsquo;s plasma from the HCC group (20/30) but were completely absent in the control group, which implied that the mutant genes of ctDNA showed high validity in distinguishing ctDNA from cfDNA. This has important implications for ctDNA mutations as a diagnostic modality. To the best of our knowledge, Cohen et al. were the first to to explore the combined diagnostic effect of ctDNA and cancer protein markers, they developed a new detection tool called \u0026ldquo;CancerSEEK\u0026rdquo; for tumors that combines ctDNA mutations and circulating proteins\u0026nbsp;[28]. A liquid biopsy assay (HCCscreen) consisting of AFP, Des-Gamma-Carboxy prothrombin (DCP) and ctDNA mutations was developed to diagnose HCCs, \u0026nbsp;but was only limited to HBV-associated HCC\u0026nbsp;[29].\u0026nbsp;Our HCC cohort contained patients with HBV infection, HCV infection and patients without hepatitis virus infection, assessing the effect of ctDNA in more dimensions. While the detected ctDNA mutations displayed only a weak trend of superior performance compared to AFP, the combination of the two biomarkers showed a substantial advantage in the diagnosis of HCC, regardless of tumor stage.\u0026nbsp;The collaborative diagnosis yielded better accuracy (AUC=0.98) than ctDNA mutations or the AFP level, with a sensitivity of 95.5% and specificity of 100%. The strengths of our study are taking a unique approach by combining ctDNA mutations with AFP to assess their combined HCC diagnostic efficacy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe detection of tumor-mutated genes in HCC lacks high specificity, even the most prevalent,\u003cem\u003e\u0026nbsp;TP53\u003c/em\u003e, is only about 30%, thus affecting the specificity of ctDNA\u0026nbsp;[30].\u0026nbsp;The earliest studies for mutant genes in ctDNA of HCC investigated the \u003cem\u003eSer-249 p53\u003c/em\u003e in Gambian patients\u0026nbsp;[31]. In the following studies, more mutated sites were detected in the ctDNA of HCC: \u003cem\u003eCTNNB1, ARID1A,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;AXIN1. CTNNB1\u003c/em\u003e and \u003cem\u003eARID1A\u003c/em\u003e mutations were found in both European and Chinese cohorts[16]\u0026nbsp;[32]. Although classical prevalent genes for HCC mentioned above have been identified,\u0026nbsp;the number of mutated genes found in ctDNA is still much lower than in tumors so far. We have found several mutant genes that has not been observed in the ctDNA of other HCC cases. Among them\u003cem\u003e,\u0026nbsp;\u003c/em\u003esix had a mutation rate over 6.7%, including \u003cem\u003eNCOR2, ROBO1, RANBP2, HGF, MECOM, MKI67.\u003c/em\u003e This expands the range of ctDNA mutations in HCC and lays the foundation for exploring liquid biopsy features of HCC.\u0026nbsp;Moreover, our mutation landscape of ctDNA presents distinct characteristics. Since our NGS panel only includes the exon portion, the frequency will be low for genes where mutations often occur at the promoter site (\u003cem\u003eTERT\u003c/em\u003e)\u0026nbsp;[33]. \u003cem\u003eNCOR2\u003c/em\u003e and \u003cem\u003eCTNNB1\u0026nbsp;\u003c/em\u003ewere the two genes with the highest mutation frequency (13.3%). Although \u003cem\u003eCTNNB1\u003c/em\u003e was a classical mutation in HCC, its frequency in plasma is lower than in tumor tissue, with a detection rate of 36.6% in our HCC FFPE samples and 27% in the reported literature\u0026nbsp;(27%)\u0026nbsp;[30]. However,\u0026nbsp;as one of the newly discovered genes in ctDNA, \u003cem\u003eNCOR2\u003c/em\u003e not only exhibited a high mutation frequency but was consistent with its frequency in tDNA.\u0026nbsp;The frequency was much higher than that in public databases (2.2%). \u003cem\u003eNCOR2\u003c/em\u003e encodes the nuclear receptor co-repressor protein with the function of repressing basal transcription, which inhibits the progression of HCC cell through the miR‐10a‐5p/\u003cem\u003eNCOR2\u003c/em\u003e axis\u0026nbsp;[34]. \u003cem\u003eNCOR2\u003c/em\u003e was also one of twelve genes exclusively distributed in TNM stage 2-4, constituting the mutation set that explored cancer prognosis. It is imperative to validate \u003cem\u003eNCOR2\u003c/em\u003e in a larger cohort to validate its impact on the prognosis of HCC in the future. Previous studies have pointed out the predictive value of ctDNA mutations for HCC prognosis, but the majority of these studies focused on a single mutant gene\u0026nbsp;[35]\u0026nbsp;[36]. Our results demonstrate that the clustering of specific mutant genes into mutation sets can help to predict the prognosis of patients with HCC. Due to the low frequency of single mutant genes in HCC, our study highlights the value of a mutation set approach.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, by assessing the matching degree between ctDNA and tDNA, we validated the precision of circulating nucleic acid fragments as carriers of tumor genetic information. Here, some discrepancies existed in the identified mutations between blood and tumor samples. We speculate that might be due to tumor heterogeneity\u0026nbsp;[37]. The overall trend of mutated genes of ctDNA still demonstrated remarkable consistency with tDNA, both of which were involved in the classic HCC signaling pathways. The genetic concordance between ctDNA and tDNA was also analyzed in a recent study by Howell et al\u0026nbsp;[16], although no influencing factors were examined here. In addition to an identification of genetic concordance, we found clinical factors affecting this concordance. HCC patients with vascular invasion were more likely to capture ctDNA fragments closely matching the tumor, even in cases of microvascular invasion. This indicates a strong correlation between genetic concordance and the presence of vascular invasion.\u003c/p\u003e\n\u003cp\u003eThere are also several limitations of this study that we acknowledge. First, although we included as many common HCC genes as possible in the NGS panel to explore the genetic profile, the sample size of the cohort was small. A large size of samples will help to explore the influence of different HCC etiologies on ctDNA mutations. While previous studies have shown the presence of mutations in patients with non-malignant liver diseases, no mutant genes were detected in our control group\u0026nbsp;[38]\u0026nbsp;[39]. This could be also due to the small sample size of the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn total, our study highlights the potential of ctDNA mutations as a novel biomarker for the diagnosis and monitoring of HCC. Notably, we found ctDNA mutations achieve a great ability to distinguish HCC and non-malignant liver diseases, thus combining ctDNA mutations with established biomarkers (e.g. AFP levels) for diagnostic purposes will generate promising rewards. We also identified several new mutation targets in HCC ctDNA, including \u003cem\u003eNCOR2\u003c/em\u003e, expanding the mutant gene library of ctDNA in HCC. Moreover, the specific mutation set in ctDNA can profoundly impact prognosis. Finally, we confirmed that the concordance of ctDNA information of the primary tumor correlates with tumor burden.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur results support the implications of ctDNA mutations for precision medicine. It will surely be rewarding to pursue this direction, enlarging the sample size for more features and increasing the clinical value of ctDNA mutations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe warmly thank the technical assistance from Susanne Neiss, Michaela Heitmann, Anke Wienand-Dorweiler, and Lisa Raatz. We also appreciated the Cologne Center for Genomics facility for next-generation sequencing and the valuable advice from Jialei Weng.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXW designed, performed most of the experiments and wrote the manuscript; UD performed collection of FFPE samples; JA and KB performed next generation sequence. MB performed clinical data collection. SB, JMH performed data analysis; JL, MP, AG, HA, MO, DS and CJB supported data discussion; YZ and RW guided the project and critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in accordance with the Declaration of Helsinki and was conducted after obtaining approval from the Independent Ethics Committee at the University Hospital of Cologne: Ethics-No. 13-091, BioMaSOTA (Biologische Maerial Sammlung zur Optimierung Therapeutischer Ans\u0026auml;tze). Informed consent was obtained from all patients. All methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that no competing interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Cologne Fortune Projects (NR.462/2020) for the project: Liquid biopsy: genetic profiling of molecular in peripheral blood as prognostic and predictive biomarkers in primary liver cancer. Xiaolin Wu and Jiahui Li were financially supported by the CSC scholarship (The China Scholarship Council).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVillanueva A. Hepatocellular carcinoma. The New England Journal Of Medicine.380(15):1450-62. (2019).\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinician.71(3):209-49. (2021).\u003c/li\u003e\n\u003cli\u003eCouri T, Pillai A. Goals and targets for personalized therapy for HCC. Hepatology International 13(125\u0026ndash;137). (2019).\u003c/li\u003e\n\u003cli\u003eOcker M. Biomarkers for hepatocellular carcinoma what\u0026apos;s new on the horizon. World J Gastroenterol.24(35):3974-9. 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(2020).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Clinical variables among HCC cohorts (N=30)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"547\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e18 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMean age (years \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e69.0\u0026nbsp;\u0026plusmn;\u0026nbsp;9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eEtiology of chronic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eHepatitis B virus (HBV) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e1 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eHepatitis C\u0026nbsp;virus (HCV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eCirrhosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e17 (56.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eAlcoholic liver disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e6 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eNon-alcoholic fatty liver disease (NAFLD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLargest tumor diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedian largest tumor diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e41.5 (IQR 22.8-60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedian AFP pre-operative (ng/ml, N=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e9.5 (IQR 4.8-47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMacrovascular invasion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMicrovascular invasion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e13 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePortal vein thrombosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e2 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePresence of metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e4 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCTP classification \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e27 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBCLC classification\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e5 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e7 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e6 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.0073126142596%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003epTNM classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.992687385740403%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e14 (46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e6 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e5 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.587155963302752%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"63.30275229357798%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.11009174311926%\" valign=\"top\"\u003e\n \u003cp\u003e5 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Characteristics of mutations in experimental and control groups\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.27160493827161%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.0352733686067%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.693121693121693%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.378223495702%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma ctDNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.378223495702%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor DNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24355300859599%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma cfDNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.33922261484099%\" valign=\"bottom\"\u003e\n \u003cp\u003eNumber of Samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.731448763250885%\" valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.33922261484099%\" valign=\"bottom\"\u003e\n \u003cp\u003ePatients with mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e20/30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e30/30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.731448763250885%\" valign=\"bottom\"\u003e\n \u003cp\u003e0/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.33922261484099%\" valign=\"bottom\"\u003e\n \u003cp\u003eGenes involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e49/100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e72/100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.731448763250885%\" valign=\"bottom\"\u003e\n \u003cp\u003e0/100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.33922261484099%\" valign=\"bottom\"\u003e\n \u003cp\u003eMutant exons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.731448763250885%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.33922261484099%\" valign=\"bottom\"\u003e\n \u003cp\u003eExonic mutations per sample \u0026nbsp;- Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.731448763250885%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.33922261484099%\" valign=\"bottom\"\u003e\n \u003cp\u003eExonic mutations per sample \u0026nbsp;- Median\u003c/p\u003e\n \u003cp\u003e(Range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(1-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.964664310954063%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003cp\u003e(1-53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.731448763250885%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Details of the concordant mutations identified both in ctDNA and matched tDNA from HCC patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCyto Band\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDNA change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlteration with exonic function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCTNNB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e3p22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e3:41266113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eC \u0026rarr; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCTNNB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e3p22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e3:41266098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eA \u0026rarr; G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCTNNB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e3p22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e3:41266137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eC \u0026rarr; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCTNNB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e3p22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e3:41266100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eT \u0026rarr; C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e17p13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e17:7578505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eGGGCAGGTCTTGGCCAG \u0026rarr; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003eframeshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e17p13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e17:7577580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eT \u0026rarr; C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eACVR2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e2q22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e2:148683651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eC \u0026rarr; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eALB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e4q13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e4:74275076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eT \u0026rarr; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003eframeshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eARID2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e12q12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e12:46245643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eAGG \u0026rarr; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonframeshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eBAP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e3p21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e3:52437589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eG \u0026rarr; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003eframeshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e13q13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e13:32907114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eG \u0026rarr; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e17q12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e17:37882817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eT \u0026rarr; A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eERBB4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e2q34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e2:212248585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eA \u0026rarr; C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eERCC5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e13q33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e13:103515307\u003c/p\u003e\n \u003c/td\u003e\n 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\u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eKMT2D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e12q13.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e12:49443732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eC \u0026rarr; A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e2p16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e2:48027428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eT \u0026rarr; G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eNCOR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e12q24.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e12:124841250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eC \u0026rarr; G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e3q26.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e3:178952085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eA \u0026rarr; G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e0552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eRANBP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e2q12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e2:109352117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eA \u0026rarr; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003eframeshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eRNF213\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e17q25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e17:78265553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eC \u0026rarr; G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003estopgain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eROBO1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e3p12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e3:79174643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eG \u0026rarr; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003eframeshift deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.912457912457913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTSC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.595959595959595%\" valign=\"bottom\"\u003e\n \u003cp\u003e9q34.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.824915824915825%\" valign=\"bottom\"\u003e\n \u003cp\u003e9:135801023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eT \u0026rarr; C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003enonsynonymous SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"circulating tumor DNA, cell-free DNA, HCC, genetic profiling, liquid biopsy","lastPublishedDoi":"10.21203/rs.3.rs-4099291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4099291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent advances in circulating tumor DNA (ctDNA) analysis offer a promising approach for diagnosing and monitoring hepatocellular carcinoma (HCC). This study focused on the potential clinical role of ctDNA analysis in HCC management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThirty patients with HCC and 10 with non-malignant liver disease were enrolled in this study. Circulating free nucleic acids, germline DNA, and tumor DNA (tDNA) from both blood samples and paraffin-embedded tumor biopsies were analyzed by a panel targeting 100 common HCC-related genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ctDNA mutations were identified in 66.6% of HCC patients. New ctDNA mutations were identified, among them \u003cem\u003eNCOR2\u003c/em\u003e having the highest frequency (13%), the same with classical mutation\u003cem\u003e CTNNB1\u003c/em\u003e. Gene sets composed of several mutations in ctDNA have the potential to predict the prognosis of HCC. A higher proportion of concordant mutations was also detected in HCC patients with tumor vascular invasion (p=0.045). Combining the ctDNA mutations and the Alpha-fetoprotein (AFP) level revealed more diagnostic accuracy than either the mutations or AFP alone, with p-values of 0.028 and 0.009, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiquid biopsy-based analysis of ctDNA mutations may offer considerable benefits to diagnostic systems for HCC.\u003c/p\u003e","manuscriptTitle":"Plasma Circulating Tumor DNA Sequencing Reveals the Landscape of Acquired Mutations in Patients with Hepatocellular Carcinoma: a Potential Predictive Value in Liquid Biopsy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 18:34:17","doi":"10.21203/rs.3.rs-4099291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bee06ceb-99e8-4c6d-9f13-7325c84d755f","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-03T06:38:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 18:34:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4099291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4099291","identity":"rs-4099291","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}