Dissecting Novel Tumor Antigens and Immune Subtypes Guided mRNA Vaccine Development for HBV Related-Hepatocellular Carcinoma

doi:10.21203/rs.3.rs-1962830/v1

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Dissecting Novel Tumor Antigens and Immune Subtypes Guided mRNA Vaccine Development for HBV Related-Hepatocellular Carcinoma

https://doi.org/10.21203/rs.3.rs-1962830/v1

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The mRNA vaccines are considered to be effective treatment strategies for cancers, but its progress in chronic hepatitis B virus (HBV) related-hepatocellular carcinoma (HCC) was slow. This study aimed to find potential antigens and identify suitable patients in HBV related-HCC for guiding mRNA vaccine development. We integrated the transcriptome RNA expression matrices and somatic mutation data from TCGA and ICGC datasets. A consistency matrix was constructed by using ConsensusClusterPlus to identify the immune subtypes. Graph learning based dimensional reduction was analyzed to establish immune landscape. Four upregulated and mutated antigens (EPS8L3, TCOF1, EZH2, and NOP56) were highly correlated with unfavorable clinical outcomes and antigen presenting cells (APCs). And two distinct immune phenotypes with differential clinical, cellular, and molecular characteristics were identified by in the ICGC and TCGA cohorts. IS1 is immune “hot” and immunosuppressive phenotype, with low tumor mutation burden (TMB) and high immune checkpoints (ICPs). On the contrary, IS2 is immune “cold” phenotype with high TMB and low ICPs. Monocle3 package was used to further study the intra-cluster heterogeneity, which identified cluster IS2A/2B within IS2 subtype was determined to be more suitable for mRNA vaccine. In summary, EPS8L3, TCOF1, EZH2, and NOP56 are potential antigens for mRNA vaccine development against HBV related-HCC, and patients in IS2A/2B are relatively more suitable for vaccination.

HBV related-HCC

mRNA vaccine

immune subtypes

immune landscape

Hepatocellular carcinoma (HCC) is the sixth most prevalent malignancy and the fourth leading cause of cancer-related death worldwide ¹. Chronic hepatitis B virus (HBV) infection is the predominant risk factors of HCC, accounting for about 80% of HCC in China ². HBV related HCC is heterogeneous and has a wide range of clinical outcomes. Additionally, the incidence and mortality rates of HBV-related HCC are higher than the world average ³. However, due to poor liver function or distant metastasis, over 80% of HCC patients fail to meet surgical resection criteria ⁴. Two types of FDA-approved drugs, that is, tyrosine kinase inhibitors (TKIs) (sorafenib, regorafenib, cabozantinib, and lenvatinib) and immune checkpoint inhibitors (ICIs) (nivolumab and pembrolizumab), have been used for advanced HCC ⁵. The overall 5-year survival rate of HCC is still poor ⁶. Thus, it is an urgent need to designing new treatment strategies of HBV related HCC.

mRNA cancer vaccines are receiving increasing attention from scientists and oncologists and has become a hotspot, especially in the context of the coronavirus disease-2019 (COVID-19) pandemic ^7–9. The function of mRNA vaccine is to transfer RNA to cells for expression and produce protein antigens, thereby inducing an immune response to the antigens and expanding the immune ability of the body. The mRNA vaccines offer a number of advantages over known tumor vaccines that use peptides, tumor cells, dendritic cells (DC), and DNA as antigens. Firstly, unlike traditional peptide vaccines that require genetic analysis of cancer, mRNA sequences can be easily modified to encode any protein ^10,11. This greatly shortens therapeutic empty window of the patients and improves the productivity of the vaccine. Besides, the genome could not be integrated by the exogenous mRNA, preventing gene deletion or insertional mutagenesis ¹². Importantly, the half-life of mRNA can be modulated by RNA sequence modifications or delivery systems to improve safety ^9,13. Moreover, the widespread use of in vitro transcription (IVT) has made the production of mRNA vaccines cheaper and easier ¹⁴. Taken together, mRNA vaccines targeting tumor-specific antigens have great potential in immunotherapy. However, no potent antigens for anti-HBV related HCC mRNA vaccine has been identified, and no standard for identification of suitable patients has been developed.

The aim of our study was to find novel antigens for the development of mRNA vaccine and map the immune landscape of HBV related-HCC to select suitable patients for mRNA vaccine. Four novel tumor antigens correlated with poor survival prognoses and infiltration of antigen-presenting cells were identified from the pool of overexpressed and mutated genes in HBV related-HCC. Subsequently, we defined two robust immune subtypes of HBV related-HCC in the ICGC cohort. The two immune subtypes were associated with differential clinical, cellular, and molecular features which were consistent with TCGA cohorts. At last, we constructed the immune landscape to reveal heterogeneity within the subtypes and select suitable patients for vaccination. Our study may pave an avenue for the development of HBV related-HCC mRNA vaccine and helpful for oncologists for administering cancer vaccines.

Screening of candidate antigens in HBV related-HCC. The aberrantly expressed genes were first explored, where 5391 overexpressed genes that likely encode tumor-associated antigens were screened to identify potent HBV related-HCC antigens (Fig. 1A). Subsequently, we identified 5663 genes that mutated in HBV related-HCC and the top 20 mutated genes were showed in Fig. 1B. Of note, TP53, CTNNB1, and TTN were the most frequently mutated genes (Fig. 1B). Combining the expression and mutation data of HBV related-HCC, 720 genes that were highly expressed and mutated in HBV related-HCC were identified as potential candidate antigens (Fig. 1C).

To explore the best candidates for mRNA vaccine targets from the aforementioned overexpressed and mutated genes, we further found 96 genes were significantly associated with overall survival (OS), of which 23 genes significantly correlated with the relapse free survival (RFS) (Fig. 1D and 1E). In view of the critical role of antigen-presenting cells (APCs) in the function of mRNA vaccines, we analyzed the correlation between these 23 genes with APCs using TIMER algorithm. We found that the expression levels of EPS8L3, TCOF1, EZH2 and NOP56 were positively associated with macrophages, dendritic cells (DCs), and B cells (Fig. 1F and 1G), and could act as potential tumor antigens that can be processed and presented by the APCs. More importantly, the survival analysis demonstrated that 4 gene candidates were also associated with poor prognosis (Fig. 1H), suggesting the critical role for HBV related-HCC development and progression. Taken together, EPS8L3, TCOF1, EZH2 and NOP56 were identified as potential candidates for HBV related-HCC with potential immune provocative effects and can be processed and presented by APCs to induce anti-tumor immune response.

Identifying specific immune subtypes. Immune related genes might mirror the distinct tumor immune microenvironments (TIME), and thus helpful to identify suitable patients for mRNA vaccination. A total of 1286 immune related gene profiles of HBV related-HCC were extracted from ICGC and 121 prognosis genes were identified to construct consensus clustering. Two immune phenotypes (IS1 and IS2) were obtained according the maximum variance across the groups and minimum variance within the group (Fig. 2A). The principal component analysis (PCA) further validated that these two subtypes could be well distinguished (Fig. 2B). IS2 had the poor prognosis while IS1 was associated with better prognosis (Fig. 2E). Similarly, in the TCGA cohort, IS2 had a worse survival probability than IS1 (Fig. 2C, 2D, and 2F), indicating the stability and reproducibility of the results. Taken together, the immunotyping associated with the clinical outcomes of HBV induced-HCC, and patients with IS2 tumors could have worse prognoses.

Association between immune phenotypes and mutational status. As tumor mutational burden (TMB) is closely associated with immunotherapeutic efficacy, including mRNA vaccine ¹⁵, we assessed the TMB and mutation number in TCGA between distinct immune phenotypes. As shown in Fig. 2G, IS2 showed significantly higher TMB compared to IS1. Consistently, IS2 also had a significantly higher mutation number than IS1 (Fig. 2H). The waterfall diagrams of these two immune phenotypes were showed in Fig. 2I and 2J. Comparing with IS1 (88.46%), IS2 had higher mutation rates (96.08%). These results demonstrated that immune phenotypes can predict mutational status in HBV related-HCC, with IS2 having higher TMB and mutated genes than IS1, and that patients with IS2 may be more suitable for the mRNA vaccine.

Cellular and molecular characteristics of different immune phenotypes. Since the effectiveness of mRNA vaccine was influenced by tumor immune status, previously reported 28 immune infiltrating cells were determined by ssGSEA across TCGA and ICGC cohorts. The results showed that the immune cell composition displayed significantly different in the two subtypes. As shown in Fig. 3A and 3B, 26 immune cells including central memory CD8 T cells, central memory CD4 T cells, effector memory CD8 T cells, effector memory CD4 T cells, monocytes, neutrophil, CD56^bright natural killer, regulatory T cell, and myeloid-derived suppressor cells (MDSC) were significantly higher in IS1 compared to IS2 in TCGA cohort. Therefore, IS1 is immune “hot” and immunosuppressive phenotypes, while IS2 is an immune “cold” phenotype. Meanwhile, ICGC cohort show similar trends (Fig. 3C and 3D). Immune infiltration in patients with IS2 (immune “cold” phenotype) might be induced by mRNA vaccine. In order to demonstrate the reliability of this immunotyping, we further determined the relationship between these two immune subtypes investigated in our study and previously reported six immune categories (C1: Wound healing; C2: IFN-γ; dominant; C3: Inflammatory; C4: Lymphocyte depleted; C5: Immunologically quiet; C6: TGF-β dominant) ¹⁶. As exhibited in Fig. 3E, the C3 subtype is less frequent in IS2. Conversely, we recorded a wider representation of the C4 subtype in the IS2 group. Interestingly, C3 and C4 were associated with better and inferior prognoses respectively. These findings confirm the prognostic value of our immune subtypes. Additionally, C4 was mainly clustered into IS2, confirming that IS2 is immunologically cold. These findings further confirm the efficacy and prognostic value of our immune subtypes.

Moreover, we compared the 56 previously defined molecular signatures between the immune subtypes and identified 20 molecular signatures were significantly different (Fig. 3F). As shown in Fig. 3F, IS1 had the higher scores for lymphocyte infiltration, leukocyte fraction, macrophage regulation, dendritic cells, Th17 cells, TCR richness, TCR shannon, stromal fraction, and TGF-β response. Additionally, in both TCGA and ICGC cohorts, immunosuppressive checkpoint BTLA, CD200, and PDCD1LG2 were significantly higher in IS1 than IS2 (Supplementary Fig. 1). IS1 is an immune-hot phenotype and immunosuppressive microenvironment, while IS2 is an immune-cold phenotype, consistent with the cellular signature results. Therefore, mRNA vaccine used in IS2 might turn “cold” tumor to “hot” by stimulate the immune response.

Immune landscape of HBV related-HCC. By using the immune gene expression profiles of individual patients, immune landscape of HBV related-HCC was constructed to visualize the immune distribution. The two immune subtypes are inversely distributed in the immune landscape (Fig. 4A and 4B). As shown in Fig. 4C, principal component 2 (PC2, vertical axis) was most negative correlated with central memory CD4 T cells, effector memory CD4 T cells, gamma delta T, immature dendritic cell, and type 2 T helper cell, and most positively correlated with activated CD8 T cell. On the contrary, principal component 1 (PC1, vertical axis) was most negatively correlated with activated CD4 T cell and most positively correlated with eosinophil and natural killer cell (Fig. 4C). Additionally, the same subtype also displayed opposing distribution, suggesting significant intra-cluster heterogeneity within the subtypes (Fig. 4D). IS2 were then divided into three subsets based on the location of immune landscape. The enrichment scores of several immune cells were significantly low in IS2A and IS2B than IS2C (Fig. 4F). Thus, the mRNA vaccine might be relatively more effective in IS2A and IS2B. Survival analysis revealed that the OS was significantly different among the five immune subtypes, with IS1A and IS1B showing better prognosis and IS2A and IS2B showing worse prognosis (Fig. 4E). Taken together, immunophenotype-based immune landscape can accurately identify the immune components and predict the prognosis of each HBV related HCC patient, which is conducive to the selection of personalized mRNA vaccine treatment regimen.

Construction and evaluation of predictors for suitable patients. Firstly, differential gene expressions were analyzed between IS1 and IS2 (Supplementary Fig. 2). Then, by using machine learning methods, the most representative genes of IS2 were chosen to identify immune “cold” phenotypes. Through binomial deviance and Boruta analyses, 24 genes and 66 genes were identified respectively (Fig. 5A-C). Thirteen genes shared by the two methods were identified as specific features of IS1 and IS2 tumors (Fig. 5D). The 13 genes predictor genes had an AUC of 1 in the ICGC cohort and 0.828 in the TCGA cohort, respectively. Taken together, the gene-set of 13 predictors (XAF1, TRIM22, LYRM4, SERPING1, SKA3, CDK4, SLC35B2, RPSA, CHKA, DTYMK, FOXM1, NLE1, and POLR1D) has an excellent performance in distinguish of HBV related-HCC suitable for mRNA vaccine.

HBV related-HCC is a lethal malignancy worldwide and is especially common in China, with more than 80% of patients being diagnosed at an advanced stage ⁴. Although recent advances in various treatments including surgery, radiotherapy, chemotherapy, and targeted therapies, the prognosis of HBV related-HCC remains poor ^17,18. New therapy targets and/or combined therapy strategies are urgently needed. The mRNA cancer vaccines are a new type of immunotherapy for the treatment of cancer because tumor antigens are expressed in cancerous cells. Tumor antigens can be used as targets for mRNA vaccines, leading to tumor regression in both preclinical models and patients ^9,19. However, the effect of the mRNA cancer vaccine in HBV related-HCC is still being explored. We hope that our research will open a further breakthrough.

In our study, we constructed the somatic mutational landscape and overexpressed genes of HBV related-HCC and identified a series of potent antigen targets. EPS8L3, TCOF1, EZH2, and NOP56 were associated with poorer OS and RFS, as well as higher APC infiltration by narrowing analysis, and were considered promising candidates of mRNA vaccine. These suggested that the four antigens play critical role for HBV related-HCC development and progression and can be processed and presented by the APCs to induce an immune attack. Although further clinical functional evaluation is still needed, some studies have shown the potential for mRNA development. For example, EPS8L3 has pivotal oncogenic functions in liver cancer, inhibition of EPS8L3 can prevent the progression of liver cancer and improve the efficacy of sorafenib treatment ²⁰. Furthermore, EPS8L3 depletion significantly inhibited cell proliferation and migration of liver cancer, and decreased the levels of phosphorylated PI3K and AKT in the PI3K/AKT signaling pathway ²¹. TCOF1 expression is aberrantly elevated in HCC and correlates with progression and poor outcome ²². EZH2 is involved in tumor cells proliferation, apoptosis, and senescence. EZH2 targeted therapy has achieved certain success in both experimental and clinical studies ²³.

The efficacy of mRNA vaccine is limited to a small subset of patients. For identifying suitable patients for mRNA vaccination, the HBV related-HCC patients were then classified into two immune subtypes. IS2 had a worse survival probability than IS1 in both ICGC and TCGA cohorts, suggesting that immunotyping can predict the survival of HBV related-HCC patients. In addition to prognostic prediction, immunotyping can also reveal therapeutic response to mRNA vaccines. For instance, patients with IS2 tumors with higher TMB and mutation number may have greater responsiveness to mRNA vaccine. Moreover, since the effectiveness of mRNA vaccine was influenced by tumor immune status, previously reported 28 immune infiltrating cells were determined by ssGSEA. The 26 immune cells including central memory CD8 T cells, central memory CD4 T cells, effector memory CD8 T cells, effector memory CD4 T cells, monocytes, neutrophil, CD56bright natural killer, regulatory T cell, and myeloid-derived suppressor cells (MDSC) were significantly higher in IS1 compared to IS2, suggesting IS1 is immune “hot” and immunosuppressive phenotypes while IS2 is an immune “cold” phenotype. Moreover, we compared the 56 previously defined molecular signatures between the immune subtypes, the findings consistent with the cellular signature results. The mRNA vaccine can stimulate the immune system through triggering immune cell infiltration and might be more suitable for patients with low immune cell infiltration. Therefore, mRNA vaccine is more suitable for IS2, which might turn “cold” tumor to “hot” by stimulate the immune response. In order to demonstrate the reliability of this immunotyping, we further determined the relationship between these two immune subtypes investigated in our study and previously reported six immune categories. C4 (lymphocyte depleted) was mainly clustered into IS2, confirming that IS2 is immunologically cold. Furthermore, we conducted the differential gene analysis of IS1 and IS2, then Lasso and Boruta analysis was performed to identify the predictors of patients with IS2 tumors. The gene-set of 13 predictors (XAF1, TRIM22, LYRM4, SERPING1, SKA3, CDK4, SLC35B2, RPSA, CHKA, DTYMK, FOXM1, NLE1, and POLR1D) has an excellent performance in distinguish of HBV related-HCC suitable for mRNA vaccine. All these suggested that immune subtypes can not only predict survival, but also help select appropriate patients for mRNA vaccination. Specifically, the immunologically cold phenotypes (IS2) are the suitable candidates for mRNA vaccine.

The further graph learning-based dimensionality reduction revealed the intra-cluster heterogeneity in IS1 and IS2. Therefore, IS1and IS2 was further classified into IS1A and IS1B, IS2A, IS2B and IS2C respectively. Survival analysis revealed that the OS was significantly different among the five immune subtypes, with IS2A and IS2B showing worse prognosis. IS2C showed higher immune cell infiltration than IS2A and IS2B, indicating that IS2C was immune-hot while IS2A and IS2B were immune-cold.

Thus, the mRNA vaccine might be relatively more effective in IS2A and IS2B. Notably, integrating results of both immune subtypes and the immune landscape are important for more precise classification of the HBV related-HCC patients.

In summary, our study identified EPS8L3, TCOF1, EZH2 and NOP56 as potential effective neoantigens for HBV related-HCC mRNA vaccine development. IS2 with an immunologically cold phenotype might suitable candidates for mRNA vaccine. In addition, through integrating immune subtypes and the immune landscape, IS2A and IS2B were proved to be more suitable than IS2C. Our study laid a foundation for the development of mRNA vaccines in HBV related-HCC and identified the appropriate vaccination population.

Data extraction and preprocessing. The transcriptome RNA expression matrices of 104 and 63 HBV positive HCC samples were collected from TCGA (https://portal.gdc.cancer.gov/) and the ICGC (https://dcc.icgc.org/) database, respectively. For further analysis, the clinical data of the patients were also downloaded from the TCGA and ICGC datasets. To identify the potential tumor antigens, somatic mutation data annotated by Varscan of TCGA datasets were downloaded from GDC repository. In addition, 1793 immune-related genes were identified via the Immunology Database and Analysis Portal (ImmPort) (https://www.immport.org/). The 1286 immune-related genes, expressed simultaneously in TCGA and ICGC datasets, were extracted for further analysis. The fragments per kilobase of exon model per million mapped fragments (FPKM), then further normalized to log2 (FPKM + 1) and scaled. Our datasets used in the study were consistent with the publication guidelines provided by the online database. The informed consent and ethics approval were not required.

Identification of candidate antigens in HBV related-HCC. Overexpressed and mutated in tumor samples were identified as potential candidate antigens. The candidate tumor antigens were screened from the pool of overexpressed and mutated genes, which considered correlated with poor survival prognoses and infiltration of antigen-presenting cells (APC). Genes with false discovery rate (FDR) < 0.05 and log₂ fold-change > 1.5 were considered significantly overexpressed. The Kaplan–Meier curve was drawn by the “survival” package and “survminer” package in R.

TIMER analysis. Tumor Immune Estimation Resourcem (TIMER) algorithm was used to estimate the relative abundance of APC infiltration. Spearman correlation analysis was used to analyze the correlation between the expression of selected antigens and abundance of APCs.

Identification and validation of immune phenotype. The 121 prognostic immune-related genes were used to construct consensus clustering and annotate their different immune statuses. Using the partition around medoids algorithm with the “1-Pearson correlation” distance metric and 500 bootstraps, patients were successfully clustered into two immune subtypes (IS) in both ICGC cohort and TCGA validation cohort. Specifically, each comprised 80% of patients in the discovery cohort. Clustering number varied from 2 to 9, and the optimal partition was defined according the maximum variance across the groups and minimum variance within the group. The 28 immune cells calculated by ssGSEA were then compared between two immune subtypes.

Establishment of immune landscape. By using the reduceDimension function of the Monocle package, the graph learning-based dimensionality reduction was analyzed. The maximum number of components was set to 4 and Discriminative Dimension Reduction Tree (DDRTree) algorithm was used. Finally, we use the function plot_cell_trajectory (package Monocle) to visualize the immune landscape.

Construction and evaluation of predictors for suitable patients by machine learning methods. The intersecting genes in the least absolute shrinkage and selection operator (LASSO) and Boruta analyses were considered to be the most critical relevant variables in further analyses and were visualized with Venn diagrams. By ROC analysis, the sensitivity and specificity for discriminative performance of the predictors were examined.

Statistical analysis. The Wilcoxon rank sum test was used to compare the difference between the two groups. The Kruskal–Wallis test was used to examine the statistical differences among more than two groups. Spearman correlation analysis was used to analyze the correlation between the expression of selected antigens and abundance of APCs. Univariate Cox regression analysis was used to screen prognostic gene. Kaplan-Meier survival curves and the log-rank method were used to validate the prognostic significance. Statistical significance was set as P-value < 0.05. All statistical analyses were calculated by the R programming language.

Data availability

The datasets analysed during the current study are available in the TCGA ((https://portal.gdc.cancer.gov/) and ICGC (https://dcc.icgc.org/) repository with accession number TCGA-LIHC and LIRI-JP respectively.

Acknowledgements

This research was funded by the National Natural Science Foundation of China (nos. 30972610 and 81273240), National Key Research and Development Program (nos. 2017YFC0910000 and 2017YFD0501300), Jilin Province Science and Technology Agency (nos. JJKH20211210KJ, JJKH20211164KJ, 20200403084SF, JLSWSRCZX2020-009, 20200901025SF，20190101022JH, and 2019J026). All authors contributed to the drafting of this manuscript, and have read and approved the final version of the manuscript.

Author contributions

Y.F.J. and A.L. designed this study. A.L., B.C.Y. and X.Y.M. contributed to the data collection. B.C.Y., F.N.L. and P.W.Z. analyzed the data. Y.F.J. supervised the study. A.L., B.C.Y. and Y.L.W wrote the manuscript. All authors read and revised the manuscript. All authors reviewed the manuscript.

Competing interests

The authors declare no potential conflicts of interest.

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Dissecting Novel Tumor Antigens and Immune Subtypes Guided mRNA Vaccine Development for HBV Related-Hepatocellular Carcinoma

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