Survival prediction and response to immune checkpoint inhibitors: A prognostic immune signature for hepatocellular carcinoma

Prognostic biomarkers associated with immune checkpoint inhibitors in hepatocellular carcinoma

The treatment of hepatocellular carcinoma (HCC), particularly advanced HCC, has been a serious challenge. Immune checkpoint inhibitors (ICIs) are landmark drugs in the field of cancer therapy in recent years, which have changed the landscape of cancer treatment. In the field of HCC treatment, this class of drugs has shown good therapeutic prospects. For example, atezolizumab in combination with bevacizumab has been approved as first-line treatment for advanced HCC due to significant efficacy. However, sensitivity to ICI therapy varies widely among HCC patients. Therefore, there is an urgent need to search for determinants of resistance/sensitivity to ICIs and to screen biomarkers that can predict the efficacy of ICIs. This manuscript reviews the research progress of prognostic biomarkers associated with ICIs in HCC in order to provide a scientific basis for the development of clinically individualised precision medication regimens.

Association of a gene-expression subtype to outcome and treatment response in patients with recurrent/metastatic head and neck squamous cell carcinoma treated with nivolumab

BACKGROUND

Immune checkpoint inhibitors have been approved and currently used in the clinical management of recurrent and metastatic head and neck squamous cell carcinoma (R/M HNSCC) patients. The reported benefit in clinical trials is variable and heterogeneous. Our study aims at exploring and comparing the predictive role of gene-expression signatures with classical biomarkers for immunotherapy-treated R/M HNSCC patients in a multicentric phase IIIb trial.

METHODS

Clinical data were prospectively collected in Nivactor tiral (single-arm, open-label, multicenter, phase IIIb clinical trial in platinum-refractory HNSCC treated with nivolumab). Findings were validated in an external independent cohort of immune-treated HNSCC patients, divided in long-term and short-term survivors (overall survival >18 and <6 months since the start of immunotherapy, respectively). Pretreatment tumor tissue specimen from immunotherapy-treated R/M HNSCC patients was used for PD-L1 (Tumor Proportion Score; Combined Positive Score (CPS)) and Tumor Mutational Burden (Oncopanel TSO500) evaluation and gene expression profiling; classical biomarkers and immune signatures (retrieved from literature) were challenged in the NIVACTOR dataset.

RESULTS

Cluster-6 (Cl6) stratification of NIVACTOR cases in high score (n=16, 20%) and low score (n=64, 80%) demonstrated a statistically significant and clinically meaningful improvement in overall survival in the high-score cases (p=0.00028; HR=4.34, 95% CI 1.84 to 10.22) and discriminative ability reached area under the curve (AUC)=0.785 (95% CI 0.603 to 0.967). The association of high-score Cl6 with better outcome was also confirmed in: (1) NIVACTOR progression-free survival (p=4.93E-05; HR=3.71, 95% CI 1.92 to 7.18) and objective-response-rate (AUC=0.785; 95% CI 0.603 to 0.967); (2) long survivors versus short survivors (p=0.00544). In multivariate Cox regression analysis, Cl6 was independent from Eastern Cooperative Oncology Group performance status, PDL1-CPS, and primary tumor site.

CONCLUSIONS

These data highlight the presence of underlying biological differences able to predict survival and response following treatment with immunotherapy in platinum-refractory R/M HNSCC that could have translational implications improving treatment selection.

TRIAL REGISTRATION NUMBER

EudraCT Number: 2017-000562-30.

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes

BACKGROUND

Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years.

RESULTS

We identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients' prognoses in both TCGA and GEO datasets.

CONCLUSION

This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.

Revealing Prognostic and Immunotherapy-Sensitive Characteristics of a Novel Cuproptosis-Related LncRNA Model in Hepatocellular Carcinoma Patients by Genomic Analysis

Immunotherapy has shown strong anti-tumor activity in a subset of patients. However, many patients do not benefit from the treatment, and there is no effective method to identify sensitive immunotherapy patients. Cuproptosis as a non-apoptotic programmed cell death caused by excess copper, whether it is related to tumor immunity has attracted our attention. In the study, we constructed the prognostic model of 9 cuproptosis-related LncRNAs (crLncRNAs) and assessed its predictive capability, preliminarily explored the potential mechanism causing treatment sensitivity difference between the high-/low-risk group. Our results revealed that the risk score was more effective than traditional clinical features in predicting the survival of HCC patients (AUC = 0.828). The low-risk group had more infiltration of immune cells (B cells, CD8+ T cells, CD4+ T cells), mainly with anti-tumor immune function (p < 0.05). It showed higher sensitivity to immune checkpoint inhibitors (ICIs) treatment (p < 0.001) which may exert the effect through the AL365361.1/hsa-miR-17-5p/NLRP3 axis. In addition, NLRP3 mutation-sensitive drugs (VNLG/124, sunitinib, linifanib) may have better clinical benefits in the high-risk group. All in all, the crLncRNAs model has excellent specificity and sensitivity, which can be used for classifying the therapy-sensitive population and predicting the prognosis of HCC patients.

Construction of a novel signature and prediction of the immune landscape in gastric cancer based on necroptosis-related genes

Necroptosis, a type of programmed cell death, has become a potential therapeutic target for solid tumors. Nevertheless, the potential roles of necroptosis-related genes (NRGs) in gastric cancer (GC) remain unknown. The objective of the present study was to create a necroptosis-related prognostic signature that can provide more accurate assessment of prognosis in GC. Using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data, we identified differentially expressed NRGs. Univariate analysis and Lasso regression were performed to determine the prognostic signature. Risk scores were calculated and all GC patients were divided into high- and low-risk score group according to the median risk score value. The robustness of this signature was externally validated with data from GSE84437 cohort (n = 431). Survival analysis revealed high-risk score patients had a worse prognosis. Results evidenced that the signature was an independent prognosis factor for survival. Single-sample sequence set enrichment analysis (ssGSEA) exhibited different enrichment of immune cells and immune-related pathways in the two risk groups. Furthermore, a predictive nomogram was generated and showed excellent predictive performance based on discrimination and calibration. In addition, the risk score positively correlated with tumor mutational burden and was associated with sensitivity to multiple anti-cancer drugs. Overall, our work demonstrates a close relationship between necroptosis and the prognosis of GC. The signature we constructed with potential clinical application value, can be used for prognosis prediction and being a potential therapeutic responses indicator in GC patients.

Identification of a Prognostic Model Based on Fatty Acid Metabolism-Related Genes of Head and Neck Squamous Cell Carcinoma

The fatty acid metabolism (FAM) is known to impact tumorigenesis, tumor progression and treatment resistance via enhancing lipid synthesis, storage and catabolism. However, the role of FAM in head and neck squamous cell carcinoma (HNSCC) has remained elusive. In the present study, we obtained a total of 69 differentially expressed FAM-related genes between 502 HNSCC samples and 44 normal samples from The Cancer Genome Atlas (TCGA) database. The HNSCC samples were divided into 2 clusters according to 69 differentially expressed genes (DEGs) via cluster analysis. Then DEGs in the two clusters were found, and 137 prognostic DEGs were identified by univariate analysis. Subsequently, combined with the clinical information of 546 HNSCC patients from TCGA database, a 12-gene prognostic risk model was established (FEPHX3, SPINK7, FCRLA, MASP1, ZNF541, CD5, BEST2 and ZAP70 were down-regulation, ADPRHL1, DYNC1I1, KCNG1 and LINC00460 were up-regulation) using multivariate Cox regression and LASSO regression analysis. The risk scores of 546 HNSCC samples were calculated. According to the median risk score, 546 HNSCC patients were divided into the high- and low-risk (high- and low score) groups. The Kaplan-Meier survival analysis showed that the survival time of HNSCC patients was significantly shorter in the high-risk group than that in the low-risk group (p < 0.001). The same conclusion was obtained in the Gene Expression Omnibus (GEO) dataset. After that, the multivariate Cox regression analysis indicated that the risk score was an independent factor for patients with HNSCC in the TCGA cohort. In addition, single-sample gene set enrichment analysis (ssGSEA) indicated that the level of infiltrating immune cells was relatively low in the high-risk group compared with the low-risk group. In summary, FAM-related gene expression-based risk signature could predict the prognosis of HNSCC independently.

Necroptosis-Related LncRNAs Signature and Subtypes for Predicting Prognosis and Revealing the Immune Microenvironment in Breast Cancer

Purpose

Necroptosis is a mode of programmed cell death that overcomes apoptotic resistance. We aimed to construct a steady necroptosis-related signature and identify subtypes for prognostic and immunotherapy sensitivity prediction.

Methods

Necroptosis-related prognostic lncRNAs were selected by co-expression analysis, and were used to construct a linear stepwise regression model via univariate and multivariate Cox regression, along with least absolute shrinkage and selection operator (LASSO). Quantitative reverse transcription polymerase chain reaction (RT-PCR) was used to measure the gene expression levels of lncRNAs included in the model. Based on the riskScore calculated, we separated patients into high- and low-risk groups. Afterwards, we performed CIBERSORT and the single-sample gene set enrichment analysis (ssGSEA) method to explore immune infiltration status. Furthermore, we investigated the relationships between the signature and immune landscape, genomic integrity, clinical characteristics, drug sensitivity, and immunotherapy efficacy.

Results

We constructed a robust necroptosis-related 22-lncRNA model, serving as an independent prognostic factor for breast cancer (BRCA). The low-risk group seemed to be the immune-activated type. Meanwhile, it showed that the higher the tumor mutation burden (TMB), the higher the riskScore. PD-L1-CTLA4 combined immunotherapy seemed to be a promising treatment strategy. Lastly, patients were assigned to 4 clusters to better discern the heterogeneity among patients.

Conclusions

The necroptosis-related lncRNA signature and molecular clusters indicated superior predictive performance in prognosis and the immune microenvironment, which may also provide guidance to drug regimens for immunotherapy and provide novel insights into precision medicine.

Identification and validation a costimulatory molecule gene signature to predict the prognosis and immunotherapy response for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients.

METHODS

Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan-Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated based on qRT-PCR and Human Protein Atlas (HPA) databases.

RESULTS

All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR and immunohistochemical results indicated six CMGs with differential expression in HCC tissues and normal tissues.

CONCLUSION

In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.

Identification and Validation of Immune-Related Gene for Predicting Prognosis and Therapeutic Response in Ovarian Cancer

Ovarian cancer (OC) is a devastating malignancy with a poor prognosis. The complex tumor immune microenvironment results in only a small number of patients benefiting from immunotherapy. To explore the different factors that lead to immune invasion and determine prognosis and response to immune checkpoint inhibitors (ICIs), we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify key prognostic IRGs. Patients were divided into high-risk and low-risk groups according to their immune and stromal scores. We used a bioinformatics method to identify four key IRGs that had differences in expression between the two groups and affected prognosis. We evaluated the sensitivity of treatment from three aspects, namely chemotherapy, targeted inhibitors (TIs), and immunotherapy, to evaluate the value of prediction models and key prognostic IRGs in the clinical treatment of OC. Univariate and multivariate Cox regression analyses revealed that these four key IRGs were independent prognostic factors of overall survival in OC patients. In the high-risk group comprising four genes, macrophage M0 cells, macrophage M2 cells, and regulatory T cells, observed to be associated with poor overall survival in our study, were higher. The high-risk group had a high immunophenoscore, indicating a better response to ICIs. Taken together, we constructed a PRSM and identified four key prognostic IRGs for predicting survival and response to ICIs. Finally, the expression of these key genes in OC was evaluated using RT-qPCR. Thus, these genes provide a novel predictive biomarker for immunotherapy and immunomodulation.

Genomic Instability of Mutation-Derived Gene Prognostic Signatures for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the major cancer-related deaths worldwide. Genomic instability is correlated with the prognosis of cancers. A biomarker associated with genomic instability might be effective to predict the prognosis of HCC. In the present study, data of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used. A total of 370 HCC patients from the TCGA database were randomly classified into a training set and a test set. A prognostic signature of the training set based on nine overall survival (OS)–related genomic instability–derived genes (SLCO2A1, RPS6KA2, EPHB6, SLC2A5, PDZD4, CST2, MARVELD1, MAGEA6, and SEMA6A) was constructed, which was validated in the test and TCGA and ICGC sets. This prognostic signature showed more accurate prediction for prognosis of HCC compared with tumor grade, pathological stage, and four published signatures. Cox multivariate analysis revealed that the risk score could be an independent prognostic factor of HCC. A nomogram that combines pathological stage and risk score performed well compared with an ideal model. Ultimately, paired differential expression profiles of genes in the prognostic signature were validated at mRNA and protein level using HCC and paratumor tissues obtained from our institute. Taken together, we constructed and validated a genomic instability–derived gene prognostic signature, which can help to predict the OS of HCC and help us to explore the potential therapeutic targets of HCC.

Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. As a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancers (GCs). Long non-coding RNAs (lncRNAs) have been revealed to participate in this process. In this study, we aimed to develop a stemness-related lncRNA signature (SRLncSig) with guiding significance for immunotherapy. Three cohorts (TCGA, Zhongshan, and IMvigor210) were enrolled for analysis. A list of stemness-related lncRNAs (SRlncRNAs) was collected by co-expression strategy under the threshold of coefficient value >0.35 and p-value < 0.05. Cox and Lasso regression analysis was further applied to find out the SRlncRNAs with prognosis-predictive value to establish the SRLncSig in the TCGA cohort. IPS and TIDE algorithms were further applied to predict the efficacy of SRLncSig in TCGA and Zhongshan cohorts. IMvigor210 was composed of patients with clinical outcomes of immunotherapy. The results indicated that SRLncSig not only was confirmed as an independent risk factor for GCs but also identified as a robust indicator for immunotherapy. The patient with a lower SRLncSig score was more likely to benefit from immunotherapy, and the results were highly consistent in three cohorts. In conclusion, our study not only could clarify the correlations between stemness and immunotherapy in GC patients but also provided a model to guide the applications of immunotherapy in clinical practice.

Biomarkers and Future Perspectives for Hepatocellular Carcinoma Immunotherapy

Hepatocellular cancer is the sixth most frequently diagnosed malignant disease worldwide, and was responsible for tens of millions of deaths in 2020; however, treatment options for patients with advanced hepatocellular carcinoma remain limited. Immunotherapy has undergone rapid development over recent years, especially in the field of immune checkpoint inhibitors (ICIs). These drugs aim to activate and enhance antitumor immunity and represent a new prospect for the treatment of patients with advanced cancer. Nevertheless, only a small proportion of liver cancer patients currently benefit from ICI-based treatment, highlighting the need to better understand how ICIs and tumors interact, as well as identify predictive biomarkers for immunotherapeutic responses. In this review, we highlight clinical trials and basic research in hepatocellular carcinoma, with a particular focus on predictive biomarkers for the therapeutic efficacy of ICIs. Predictive biomarkers for immune-related adverse events are also discussed.

An immune-related prognostic signature for thyroid carcinoma to predict survival and response to immune checkpoint inhibitors

Thyroid carcinoma (THCA) is the most common endocrine malignancy, and its incidence is increasing worldwide. Several studies have explored whether the tumor immune microenvironment and immune-related genes (IRGs) influence the prognosis of patients with THCA and can be used to predict the response to immune checkpoint inhibitors (ICIs). We developed an IRG prognostic/risk signature using a bioinformatics method, and its predictive capacity was validated in patients in the test set and the total set. Subsequently, we analyzed the correlation between this IRG prognostic signature and tumor-infiltrating immune cells, tumor mutation burden (TMB), and immune checkpoint protein expression in patients with THCA. With a multivariate analysis, the IRG prognostic signature, which comprised eight IRGs, was identified as an independent prognostic factor. High-risk patients had poor overall survival compared with low-risk patients. Plasma cells, monocytes, and dendritic cells infiltrated differently according to the IRG prognostic signature. The low-risk group had a higher TMB and immunophenoscore (IPS), which indicated a better response to ICIs. The qRT-PCR validated eight IRGs with differential expression in thyroid cancer and normal tissues. We conclude that the IRG prognostic signature may be a useful tool to predict survival and response to ICIs. However, further testing is required to assess the predictive capacity of this IRG prognostic signature.

The Role of Immunotherapy in a Tolerogenic Environment: Current and Future Perspectives for Hepatocellular Carcinoma

In contrast to several tumors whose prognoses are radically affected by novel immunotherapeutic approaches and/or targeted therapies, the outcomes of advanced hepatocellular carcinoma (HCC) remain poor. The underlying cirrhosis that is frequently associated with it complicates medical treatment and often determines survival. The landscape of HCC treatment had included sorafenib as the only drug available for ten years, until 2018, when lenvatinib was approved for treatment. The second-line systemic treatments available for hepatocellular carcinoma include regorafenib, cabozantinib, ramucirumab, and, more recently, immune checkpoint inhibitors. However, the median survival remains below 15 months. The results obtained in clinics should be interpreted whilst considering the peculiar role of the liver as an immune organ. A healthy liver microenvironment ordinarily experiences stimulation by gut-derived antigens. This setup elucidates the response to chronic inflammation and the altered balance between tolerance and immune response in HCC development. This paper provides an overview of the mechanisms involved in HCC pathogenesis, with a special focus on the immune implications, along with current and future clinical perspectives.

Combination of tumor mutation burden and immune infiltrates for the prognosis of lung adenocarcinoma

BACKGROUND

Tumor mutation burden (TMB) levels are associated with immune infiltrates in the tumor microenvironment and can modulate the responses to immune checkpoint inhibitors (ICIs) in lung adenocarcinoma (LUAD) patients. This study aimed at exploring the potential role of a signature of genes associated with TMB and immune infiltrates and the relevant nomogram in the prognosis of LUAD.

MATERIALS AND METHODS

The TMB levels in LUAD patients in the Cancer Genome Atlas (TCGA) were analyzed. The differentially expressed genes (DEGs) between the higher- and lower-TMB subgroups were functionally analyzed. The immune-related DEGs and their relationship with immune infiltrates in the tumor environment between two subgroups were analyzed. Nine immune-related DEGs were used to generate a TMB-related immune signature. The sensitivity to immunotherapy in TCGA-LUAD patients was analyzed by immunophenotypic scores (IPS). Subsequently, a nomogram was generated using tumor-related parameters and the signature score. The signature or nomogram values in predicting overall survival (OS) were evaluated and validated in LUAD patients in the GSE30219 and GSE72094.

RESULT

There were 468 DEGs between the higher and lower-TMB subgroups of LUAD patients. The TMB levels were associated positively with the number of immune infiltrates in LUAD patients. Nine DEGs were related to immune infiltrates in the tumor environment. The higher signature scores (high-risk) were associated with poor prognosis of LUAD in the TCGA, which was validated in LUAD patients of the GSE30219 and GSE72094 datasets. Interestingly, the patients in the high-risk group had higher PD-L1 expression in their tumors and the risk scores in LUAD patients. The IPS of LUAD patients in the high-risk group were predicted to benefit from immunotherapy. Finally, the nomogram had high AUC values in predicting the OS of LUAD patients.

CONCLUSION

The TMB-related immune signature or nomogram is valuable for the prognosis of LUAD patients and evaluating their responses to ICIs. These relevant genes may participate into the pathogenesis, ICIs, and drug resistance of LUAD.