Monitoring Immune Checkpoint Regulators as Predictive Biomarkers in Hepatocellular Carcinoma

The global burden of hepatocellular carcinoma (HCC), one of the frequent causes of cancer-related deaths worldwide, is rapidly increasing partly due to the limited treatment options available for this disease and recurrence due to therapy resistance. Immune checkpoint inhibitors that are proved to be beneficial in the treatment of advanced melanoma and other cancer types are currently in clinical trials in HCC. These ongoing trials are testing the efficacy and safety of a few select checkpoints in HCC. Similar to observations in other cancers, these immune checkpoint blockade treatments as monotherapy may benefit only a fraction of HCC patients. Studies that assess the prevalence and distribution of other immune checkpoints/modulatory molecules in HCC have been limited. Moreover, robust predictors to identify which HCC patients will respond to immunotherapy are currently lacking. The objective of this study is to perform a comprehensive evaluation on different immune modulators as predictive biomarkers to monitor HCC patients at high risk for poor prognosis. We screened publically available HCC patient databases for the expression of previously well described immune checkpoint regulators and evaluated the usefulness of these immune modulators to predict high risk, patient overall survival and recurrence. We also identified the immune modulators that synergized with known immune evasion molecules programmed death receptor ligand-1 (PD-L1), programmed cell death protein-1 (PD-1), and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and correlated with worse patient outcomes. We evaluated the association between the expression of epithelial-to-mesenchymal transition (EMT) markers and PD-L1 in HCC patient tumors. We also examined the relationship of tumor mutational burden with HCC patient survival. Notably, expression of immune modulators B7-H4, PD-L2, TIM-3, and VISTA were independently associated with worse prognosis, while B7-H4, CD73, and VISTA predicted low recurrence-free survival. Moreover, the prognosis of patients expressing high PD-L1 with high B7-H4, TIM-3, VISTA, CD73, and PD-L2 expression was significantly worse. Interestingly, PD-L1 expression in HCC patients in the high-risk group was closely associated with EMT marker expression and prognosticates poor survival. In HCC patients, high tumor mutational burden (TMB) predicted worse patient outcomes than those with low TMB.

inTrODUcTiOn Hepatocellular carcinoma (HCC), also known as malignant hepa toma, is the most common form of primary liver malignancy and the third most common cause of cancerrelated deaths worldwide (1)(2)(3). It is a multifactorial disease with viral hepatitis and excessive alcohol intake being the major risk factors globally (4). Nonalcoholic fatty liver, diabetes, aflatoxins, and immune related conditions like autoimmune hepatitis and primary biliary cirrhosis are other common risk factors for HCC (5). HCC is predominant in patients with underlying chronic liver diseases and cirrhosis which limits treatment options for these patients (6,7). Although surgical resection is useful in the early stages of HCC without cirrhosis recurrence continues to be a significant problem in the majority of patients (8). Liver transplantation, an alternate therapy for unresectable HCC with underlying cir rhosis, has not been very effective due to lack of compatible livers (9). Moreover, HCC is usually diagnosed at late stages such that surgical resections and liver transplantation cannot be used, lead ing to poor survival rate (10). Sorafenib, the systemic treatment currently approved for the treatment of advanced disease yields a suboptimal improvement in median survival of 6.5-10.7 months in HCC patients with good liver function (11,12). Therefore, new therapies are urgently needed for this disease. Immunotherapy is an emerging therapeutic modality that could become a promising treatment option for HCC as, first, HCC is an inflammationassociated cancer making immuno therapy more likely to be effective (13). Second, the liver is an immune privileged organ, and thus immunotherapeutic drugs are not metabolized in the liver and have predictable pharma cokinetic profiles in cirrhotic patients (13). Third, the liver is tolerogenic to immune response to antigens that is balanced by naïve Tcell activation and further by various immunosuppressive mechanisms, including dysregulation in cytokine secretion, anti gen and immune checkpoint expression, and changes in the local immune microenvironment (10,14,15). The clinical successes of immunotherapy in the form of immune checkpoint inhibitor (ICI) for the treatment of a number of malignancies including advanced melanoma, have opened prospects for ICIs as the potential immunotherapeutic strategy for treating HCC (16,17).
The immune response is coordinated by a harmony between costimulatory and inhibitory signals (18). The activated Tcell is regulated by coinhibitory immune checkpoint molecules, such as cytotoxic T lymphocyteassociated antigen4 (CTLA4), programmed cell death protein1 (PD1), and its ligand pro grammed death receptor ligand1 (PDL1/B7H1/CD274), all of which are responsible for maintenance of selftolerance and prevent immune overstimulation (13,18). The Tcell effector functions regulated by the immune checkpoint interactions are generally dysregulated or overexpressed in the tumor micro environment leading to Tcell inhibition and downregulation of Tcell response. Thus, the blockade of immune checkpoints (coinhibitory signals) or promotion of costimulatory signals can restore or amplify the antigenspecific Tcell responses for cancer therapeutics (18).
A recent phase I/II trial of nivolumab (antiPD1) has shown it to have an effective anticancer activity with an adequate safety profile in HCC patients (19). However, in another HCC clinical trial, the use of antiCTLA4 antibody in HCC resulted in more adverse events compared to anti PD1 antibodies (20). Currently, there are several ongoing clinical trials with a small number of ICIs directed at PD1 (nivolumab and pembrolizumab) and PDL1 (atezolizumab) in HCC (18,19). Given that a few genes, such as PD1, PDL1, and CTLA4 enable tumors to bypass the immune system, this strategy alone may not be effective in achieving sustained clinical response in most cancer patients and further immunotherapeutic strategies are needed (21). The identification of predictive markers is of the utmost importance in this clinical setting to select a subgroup of HCC patients who are most likely to benefit from ICI therapy. Furthermore, the morphogenetic process of epithelialtomesenchymal transition (EMT) characterized by the acquisition of mesenchymal proper ties such as invasion and metastasis of tumor cells is closely linked to immune evasion of cancer cells (22,23). Emerging evidence supports the close association of EMT status with response to multiple immune checkpoint regulators in a large number of patient tumors (24). One such report has revealed that EMT suppresses antitumor immunity through upregulation of PDL1 in pulmonary cancer (25). However, no studies have compared the EMT markers and immune checkpoint molecule expression in HCC tumors.
With the goal of identifying prognostic immunerelated molecules in HCC, we conducted a study of immunerelated molecules and correlated their expression with patient prognosis in publically available HCC patient databases by deploying SurvExpress webbased platform that provides risk assessment and survival analysis in cancer datasets (26). We also assessed the relationship between the expression of immunerelated mol ecules and EMT status of HCC cancers using this webbased tool.

OncoPrint analysis of immune checkpoints Using cBioPortal
We used the cBioPortal's OncoPrint 1 across HCC patient sam ples to obtain a compact graphical summary of gene expression alterations in immune modulatory genes. We applied cBioPortal to study gene alterations in immune modulatory genes in Liver HCC (TCGA Provisional) case set. Genomic alterations, including copy number alterations (CNAs) (amplifications and homozygous dele tions), mutations, and alterations in gene or protein expressions are summarized by glyphs and color coding. All cases are arranged as per alterations (27).

Performing risk analysis in hcc Patients
SurvExpress utilized prognostic index (PI) or risk score, the linear part of the Cox model, to generate highrisk and lowrisk groups. SurvExpress generates risk groups for risk assessment as previously described (26). Briefly, the first method splits ordered PI into two risk groups with equal number of samples equivalent to splitting the PI by the median (26). The second method uses an optimization algorithm from the ordered PI to produce risk groups (26). A logrank test is performed along all values of the arranged PI for two groups and the split point where the pvalue is minimum is selected by the algorithm (26). In case of more than two groups, the procedure optimizes one risk group at a time repeatedly until no changes are seen (26). The gene expres sion box plots of each gene and risk group are generated by SurvExpress (26).

Validation of the Prognostic effect of immune regulatory Molecules in hcc Patients
Using the SurvExpress online tool, we assessed the gene expres sion of 19 different immune modulators and analyzed their association with the survival of HCC patients (Cox regression analyses) in five databases (GSE10143, GSE10186, and the three TCGA datasets) with patient survival information. We also assessed the correlation of immune checkpoint molecules with recurrencefree survival in two databases (GSE10143 and TCGA LiverCancer) with patient recurrencefree survival information.
For HCC patients, Kaplan-Meier curves were used to estimate the survival times for overall survival and recurrencefree sur vival. The settings we selected for this study for duplicated genes was average of all probe sets of a gene to compute an average per sample and we used the original quantilenormalized database.

analysis of Tumor Mutational Burden (TMB) in hcc Patients
Data on the number of mutations per sample were obtained using cBioportal for all HCCs with available survival from the provi sional TCGA data set. Tumors were classified as "high mutation burden" if they had a quantity of mutations one standard devia tion above the average for the dataset. Kaplan-Meier plots were generated and logrank tests were used to determine statistical significance.

genecards analysis for expression of immune checkpoints
GeneCards is a database that provides comprehensive information on all annotated and predicted human genes (31). 3 GeneCards online portal was used to study protein expression of immune modulators in normal hepatocytes.

immunohistochemistry and Pathological evaluation
Immunohistochemistry was performed as previously described (32). Briefly, paraffin embedded tissue slides with human HCC tissue microarray (TMA) (NBP230221, Novus Biologicals) were deparaffinized and rehydrated, endogenous peroxidise activity was blocked with 3% hydrogen peroxide, antigen retrieval was performed in 10 mmol/L citrate buffer, and nonspecific binding was blocked with blocking reagent. HAVCR2 (ab185703, Abcam) and C10ORF54 (CL3975, Invitrogen) antibodies were applied at 1:300 and 1:20 concentrations, respectively. Slides were incubated overnight at 4°C, followed by 30 min incubation with secondary antimouse or rabbit antibody HRP (Dako). The chromogen used was 3amino9ethylcarbazole. Human normal and can cerous lung tissue was used as the positive control for both the antibodies and a negative control, for which the primary antibodies were substituted with the same concentration of mouse or rabbit IgG. Images were captured using a Olympus CX41 microscope and QCapture software. Immunohistochemical reactivity was evaluated by two independent investigators. The expression of HAVCR2 and C10ORF54 were categorized into positive staining or no staining.

statistical analysis
For risk assessment generated by SurvExpress, a pvalue of the difference in expression among risk groups is obtained from a Student's ttest for two risk groups. A logrank test was used to produce the concordance index and the pvalue testing for equal ity of survival curves for survival analysis using SurvExpres, and the correlation coefficient estimated from deviance residuals (33). In addition, an estimation of the hazard ratio (HR) between the groups is generated. This is estimated by another Cox model using the risk group prediction as the covariate.

The alterations in immune Modulatory genes in hcc
To identify immune modulatory molecules involved in immune escape in HCC, we assessed a panel of 19 genes based on previ ous studies on immune modulatory genes linked with overall survival and progressionfree survival in different cancers. These included those associated with immune stimulatory genes, such as CD80, CD28, CD27, GITR (TNFRSF18), Galectin9 (LGALS9), CD137 (TNFRSF9), FASLG, and immune inhibitory genes, such as TIM3 (HAVCR2), B7-H4 (VTCN1), B7-H3 (CD276),  We performed OncoPrint analysis using cBioPortal to interrogate the expression profiles and any possible genetic alterations for these immune modulatory molecules in tumors high expression in high-risk group low expression in high-risk group In Figure 1, amplification, mRNA upregulation and missense mutation were noted in 24 cases (5%) for C10ORF54, 9 cases ( (Figure 1). Amplification, deep deletion, and mRNA upregulation were identified in TNFRSF9 and CD274 in 15 cases (3%) and 11 cases (2.5%), respectively. samples into highrisk and lowrisk groups. Box plot was gener ated in the results of SurvExpress, where the gene expression per gene is plotted along its risk groups. This plot is useful to visualize differences in gene expression values between high and lowrisk groups.

immune Biomarkers Prognosticates clinical Outcome in hcc Patients
The lack of robust predictive biomarkers to monitor HCC patients at high risk for poor prognosis has been a major obstacle in the clinics. To investigate whether the immunerelated genes have prognostic and predictive value in HCC, we utilized six different HCC datasets within SurvExpress to examine the overall survival and recurrencefree survival in HCC patients. Kaplan-Meier sur vival risk curves for the different immune genes were generated. Notably, altered expression of VTCN1  (Figures 3A,B). In the TCGA Liver Cancer 422 patient cohort, HAVCR2 (HR: 1.5, 95% CI: 1.07~2.1, LogRank Equal Curves p = 0.01732) expression in highrisk group correlated with low overall survival ( Figure 3C). In TCGA 12 HCC patients, C10ORF54 expression correlated with worse survival (HR: 9.11, CI = 1.04~79.69, p = 0.01694) (Figure 3D).
To investigate the possible roles of immune genes in HCC relapse, we assessed the relationships between their gene expres sion level and recurrencefree survival using SurvExpress. We observed that VTCN1 expression, which correlated with poor survival was also associated with poor recurrencefree survival in the cohort of TCGA 422 patients (HR: 1.49, CI: 1.04~2.14, Log Rank Equal Curves p = 0.03007) ( Figure 4A). C10ORF54 expres sion also correlated with low recurrencefree survival in the same cohort of 422 patients (HR: 1.44, CI: 1.01~2.06, LogRank Equal Curves p = 0.04327) ( Figure 4B). This cohort also showed that The clinical response to antiPDL1, antiPD1, or antiCTLA4 targeted therapies can vary in different tumor types, and much effort has been directed toward finding predictive biomarkers to help identify patients who will derive the most benefit from these therapies. In HCC, the coordinated expression of other immune regulators with PDL1, PD1, and CTLA4 in tumor tissue have been less wellstudied. The overall survival and recurrencefree survival of immune modulators were analyzed in combina tion with PDL1, PD1, and CTLA4 to assess any additional benefit through the combination.  Figures 6A-E).    (Figures 11A,B). The subcellular location was identified as predominantly cytoplasmic and membranous. C10ORF54 expression was detected in 91% of HCC patient tumors (Figures 11C,D). The subcellular location was identified as predominantly cytoplasmic.

expression of PD-l1 in hcc Tumors is correlated With an eMT Phenotype
EMT is an important biological process involved in the progression and immune evasion of cancers. In HCC, EMT contributes to a poor prognosis (34,35). Emerging research has found higher expression of PDL1 in mesenchymal cells in nonsmall cell lung carcinoma (36). Therefore, we examined the relationship between the EMT phenotype and PDL1 expression in HCC. By analyzing risk assessment using the TCGALiverCancer patient dataset (422 HCC patient samples) we confirmed that high expression of PD-L1 and mesenchymal marker VIM and low expression of epithelial marker CDH1 genes significantly associated with a highrisk signature (p < 0.05) (Figure 12A).
Although PD-L1 gene expression alone did not significantly correlate with poor survival in HCC patient datasets, coordinate expression of CDH1 and VIM showed worse overall survival (HR: 1.85, CI: 1.05~2.05, LogRank Equal Curves p = 0.02543) and recurrencefree survival (HR: 1.72, CI: 1.2~2.48, Log Rank Equal Curves p = 0.003402) when combined with PD-L1 (Figures 12B,C). This study shows that high expression of PDL1 in HCC patients is associated with an EMT phenotype.

Protein expression in normal hepatocytes
GeneCards online portal was utilized to select tumorassociated immune regulatory genes with minimal or no expression in nor mal tissue and overexpression in HCC tumor cells. GeneCards online portal was used to study protein expression of immune modulators in normal hepatocytes ( Table 2). The majority of This data indicate that these biomarkers may be specifically expressed in HCC tumors and not in normal healthy cells but may be targeted safely.

TMB in hcc Patients
TMB or mutation load is the total number of mutations present in a tumor specimen. TMB is emerging as a reliable biomarker for predicting sensitivity to ICIs as immune checkpoint marker testing alone has proven insufficient in many cancers for patient selection (37). In nonsmall cell lung cancer and melanoma, high TMB has been associated with a higher likelihood of tumor responsiveness to treatment with PD1 or PDL1 immunotherapy strategies (38,39). However, the value of TMB as an objective biomarker in the setting of HCC has not been explored. We sought to determine whether TMB could be associated with overall survival and progressionfree survival in HCC patients. Patients with a high TMB had significantly poor overall survival and progression free survival than those with a lower TMB (Figures 13A,B). As TMBhigh cancers are likely to harbor neoantigens, making them targets of immune cells, utilizing TMB as a biomarker may help select HCC patients for ICI blockade therapy.

DiscUssiOn
Implementation of immune regulatory drugs such as ICIs has elicited a remarkable clinical response and is becoming the new foundation for treatment of various malignancies. Currently, immunotherapy in the form of ICI may represent an effective treatment modality for HCC, mainly for advanced and recurrent HCC where no other effective options are available. This study identified many immune regulatory genes that were aberrantly expressed in HCC patient tumors. Immune regulatory genes VTCN1, PDCD1LG2, HAVCR2, and C10ORF54 were associated with overall poor survival and VTCN1, C10ORF54, and NT5E predicted recurrencefree survival in HCC patients. VTCN1, C10ORF54, HAVCR2, NT5E, and PDCDLG2 in combination with PDL1 functioned as robust markers that could prognosticate poor prognosis in these patients. Identifying robust predictive immune biomark ers as useful tools to monitor patients at high risk for poor prognosis and to predict response to the ICI in patients is becoming popular by study of tumor immune Immune Checkpoints in HCC Frontiers in Oncology | www.frontiersin.org July 2018 | Volume 8 | Article 269 microenvironment. For instance, PDL1 expression in tumors has been shown to be a predictive biomarker for poor prognosis and is also utilized as an important biomarker to predict the response to antiPD1 antibodies (40,41). These findings support the relevance of immune regulatory molecules as biomarkers in the clinics. Given that only a subset of patients express PDL1, and the majority of patients fail to demonstrate durable response and expression level of PDL1 can fluctuate throughout the course of treatment; identifying other immune biomarkers could play an important role to further improve patient outcome. Based on immune biomarker expression, therapies will need to be employed on an individualized basis to ensure the best possible responses.
We found the negative regulator of Tcell response, Vset domaincontaining Tcell activation inhibitor 1, VTCN1, (also named as B7H4, B7S1, or B7x) was aberrantly expressed in HCC patients in the highrisk group and B7H4 positivity was a statistically significant predictor of poor overall survival and recurrencefree survival. Studies have confirmed the high expression of B7H4 in a variety of human tumors, including HCC (42,43). In another study, soluble B7H4 detected in blood samples from HCC patients was closely associated with advanced tumor stage, poor overall survival, and higher recur rence rate (44,45). However, the function of B7H4 in HCC tumors remains unknown. B7H4 has been previously proposed to function as a ligand for BTLA (also known as CD272), an Ig superfamily member. The B7H4-Ig fusion protein inhibits Tcell activation (46).
The inhibitory immune checkpoint molecule, Vdomain immunoglobulin suppressor of T cell activation (VISTA or C10ORF54) is a type 1 transmembrane protein that blocks Tcell activation (47). We found that the overall survival and recurrence free survival was significantly lower in the highrisk group HCC patients with high VISTA expression. Another study showed that VISTA was overexpressed in oral squamous cell carcinoma and correlated with other immune checkpoint markers PDL1 and CTLA4. In addition, the study also showed a poor prognosis in patients with high VISTA and low CD8 + Tcells (48).
The glycophosphatidylinositolanchored receptor enzyme, ecto5′nucleotidase (CD73 or NT5E) inhibits Tcell receptor activation when adenosine binds to its receptor (49). Our study showed NT5E positivity was a statistically significant predictor of poor overall survival and recurrencefree survival in HCC. Our study is consistent with previous studies in triple negative breast cancer, head and neck squamous cell carcinoma, ovarian cancer, and various other gastric carcinoma where NT5E expression in tumor tissues was correlated with poor prognosis (50)(51)(52)(53)(54).
Tcell immunoglobulin and mucin domaincontaining3 (TIM3 or HAVCR2) is an immune checkpoint receptor that binds to its ligand Galectin9 and limits the Tcell responses (55). Our study showed that TIM3 is overexpressed in the high risk group of HCC patients and had significantly worse overall survival. Another study has also confirmed the high expression of TIM3 in HCC patient tumors than in healthy controls (56). Furthermore, the overall survival time for patients with higher TIM3 expression is lower than that of patients with lower TIM3 expression (57). Taken together, these findings indicate that costimulatory and checkpoint genes can be beneficial for the clinical evaluation of HCC patients, especially to identify patients who are at increased risk of worse survival and relapse. A limita tion of our study is the lack of HCC patients treated with immune checkpoint therapies. Further studies to validate the expression of these immune predictors in HCC patient cohorts treated with immune checkpoint therapies will be important. The role of these genes in HCC has not been fully elucidated. However, it is con ceivable that these immune regulatory molecules may play pivotal roles in modulating the immune response in HCC. Expression, distribution, and function of these immune regulatory molecules in HCC tissues warrant further investigation.
While the clinical relevance of immuneregulators expressed on immune cells is well established, this study focused on the altered expression of immune regulatory genes in HCC tumors. In addition to serving as useful prognostic biomarkers for HCC, targeting B7H4, PDL2, TIM3, VISTA, CD73, and PDL1 axis with antagonistic antibodies may prove to be beneficial in a subset of HCC patients with elevated levels of these genes. VTCN1, HAVCR2, NT5E, LGALS9, CD80, and PD1 axis may also represent useful prognostic biomarkers for HCC. Additionally, elevated VTCN1, HAVCR2, LGALS9, TNFRSF14, and CTLA4 axis can also be beneficial as prognostic biomarkers for HCC. Given that ICI depend on the receptor-ligand interactions between Tcells and tumor cells, and the combined elevated expression of immune regulatory molecules on tumorinfiltrating Tcells and tumor cells is more predictive of ICI response, further comprehensive studies are needed to address the relationship of these immune regulatory molecules on both tumor and tumor infiltrating Tcells. A recent study showed improved survival in patients with high chronic inflammatory response in the stroma (58). In support of these findings, clarifying the immune regula tors involved in the effector functions of tumorassociated Tcells has important implications for our understanding of how the immune microenvironment is modulated to promote antitumor immune responses.
Although there is interest in the use of ICIs in HCC, the coor dinated upregulation of immune checkpoint and other immune regulated genes in our study suggests that a combinatorial treatment strategy is likely to be more beneficial. Early trial results on the combination of PDL1 and CTLA4 targeting were first found to be valuable in malignant melanoma (59). Subsequently, combination of these ICIs also resulted in remarkable tumor regression and improved overall survival in many cancers (60). These clinical trials showed a significant advantage of combina tion therapy over ICI monotherapies. Recent studies have shown that upregulation of immunerelated molecules such as TIM3 occurs in mice and humans following PD1 inhibition (61) and in the case of antiCTLA4 treatment, VISTA, and PDL1 were FigUre 12 | Gene expression of EMT markers in HCC based on risked group and their relationship in combination with programmed death receptor ligand-1 (CD274) and overall survival and recurrence-free survival in HCC patients. (a) Box plot of gene expression of EMT markers that statistically correlates with high-risk prognostic score in 422 HCC patients from the TCGA-Liver Cancer dataset. Risk assessed is risk of reduced survival. Red box represents high-risk group and green box represents low-risk group. Each gene is shown on the x-axis. X-axis also shows a p-value of the expression difference between the two risk groups. The expression levels are shown on the y-axis. Kaplan-Meier curves produced using the SurvExpress for the analysis of (B) overall survival and gene expression of CD274/CDH1/VIM and (c) recurrence-free survival and gene expression of CD274/CDH1/VIM in HCC patient samples. Green curve represents low-risk group, while red curve represents high-risk group. The study time (months) is presented in the x-axis. The insert shows the hazard ratio, confidence interval, and Log-Rank Equal Curves p value. Markers (+) represent censoring samples.  upregulated (62). The elevation in these additional immune regulatory molecules has been proposed to lead to development of resistance to ICI therapies resulting in a significant fraction of cancer patients who do not benefit from the existing checkpoint inhibitor therapies. These findings provide a clinical incentive to combine different ICI therapies to potentially sensitize HCC tumors. In our study, the coordinated expression of immune regulatory molecules, such as B7-H4, TIM-3, and VISTA with PD-L1 correlated with poor prognosis, while the cooccurrence of B7-H4, TIM3, VISTA, CD73, and PD-L2 with PD-L1 correlated with poor recurrencefree survival. The identification of these additional immune biomarkers can help to select patients who might benefit from combination immunotherapy.
Our study is the first to provide direct evidence that EMT phe notype is associated with PDL1 expression in HCC patient tis sues. This observation is in line with another study in pulmonary adenocarcinoma where an association between the messenger RNA EMT signature and high PDL1 expression was found (24). Another study demonstrated a molecular link between EMT and PDL1 regulation, in both in vitro and in vivo models (63). It has been suggested that EMT and PDL1 may bidirectionally influence each other to promote tumor aggressiveness (64). It is conceivable that HCC patients with EMT phenotype would likely benefit from PD1/PDL1 targeted immunotherapy. Further studies of the precise molecular mechanisms underlying the association between EMT and PDL1 expression in HCC tumor microenvironment are warranted.
Recently, high TMB has been associated with better outcome parameters, such as higher response rates to immunotherapy, longer progressionfree survival, and overall survival in melanoma and nonsmall cell lung cancer (65,66). A study reported that TMB was more reliable in predicting response rate than the expression of PDL1 by immunohistochemistry (67). A recent study demon strated that TMB was a reliable biomarker for predicting response to single checkpoint inhibitor, whereas, outcome after antiPD1/ PDL1/antiCTLA4 combinations appeared to be independent of TMB. Our data suggest that TMB can be used to stratify HCC patients for ICI therapy (66). A limitation of our study is the lack of patients treated with ICIs. Further studies are needed to confirm the relationship between TMB and outcome in immunotherapytreated HCC patients. Moreover, further understanding of the molecular mechanisms which lead to high TMB in HCC is important. In addition to immune markers and TMB, data are emerging on future development of new predictive biomarker strategies for ICI based immunotherapy, including tumorinfiltrating lymphocytes, immune gene signatures, and multiplex immunohistochemistry (37).
The immune biomarker research represents a promising strat egy to guide patient selection and predicts response to immune checkpoint blockade therapies in terms of durable responses or survival benefit. Blockade of immune regulatory molecules iden tified in this study, including B7H4, VISTA, CD73, PDL2, and TIM3 can potentially offer a treatment strategy to reinstate host immune response against HCC and ultimately tumor regression. Furthermore, the potential to reverse resistance to ICI depends on proper combination therapy that targets the antitumor immune response. Although a combinatorial approach is likely to be more beneficial, their use may be limited by a risk of developing more side effects with combination therapy. The translation of combina tion therapy approaches for better clinical success in HCC patients can be improved through further mechanistic insights on immu notherapy combination strategies along with immune biomarkers.