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Biomarkers and future prospects of immunotherapy for hepatocellular carcinoma
Hepatocellular carcinoma is the sixth most common malignant tumor in the world, causing hundreds of thousands of deaths in 2020.
However, treatment options for patients with advanced hepatocellular carcinoma are still limited. Tumor immunotherapy has developed rapidly in recent years, especially in the field of immune checkpoint inhibitors (ICI).
These drugs are designed to activate and enhance anti-tumor immunity and provide new prospects for the treatment of advanced cancer patients, including liver cancer.
Currently, atezolizumab (anti-PD-L1 blocker) combined with bevacizumab (VEGF antibody) has shown encouraging clinical results in patients with liver cancer.
Nevertheless, only a small percentage of patients with liver cancer currently benefit from ICI treatment, and the incidence of immune-related adverse events (IRAE) is also an important issue, which highlights the need for a better understanding of the interaction between ICI and tumors and the identification of immunotherapy responses Of predictive biomarkers.
PD-L1 is widely expressed on the surface of tumor cells, antigen-presenting cells and other immune cells. According to reports, the expression of PD-L1 in HCC is generally low (approximately 10% of tumor cells), and is associated with relapse and shorter OS.
Some clinical trial results have been published in which PD-L1 is evaluated as a predictive biomarker of ICI response. In the Phase III clinical trial of CheckMate 459, although nivolumab did not cause a significant improvement in OS, patients with PD-L1 positive tumors responded better to nivolumab than sorafenib. However, the predictive value of PD-L1 is unclear. In the CheckMate 040 and NCT02658019 trials, the response rate of all patients was disappointing regardless of the level of PD-L1 expression. Even so, further analysis of patients in the CheckMate 040 trial showed that tumor PD-L1 expression is related to the improvement of OS.
The conflicting results of these clinical trials may be partly due to the limitations of PD-L1 level detection, including the lack of standard methods to assess PD-L1 expression and its temporal and spatial heterogeneity, and the lack of standard thresholds that allow the determination of “overexpression”. There are two methods for defining PD-L1 positive expression, the ratio of PD-L1 positive tumor cells, the so-called Tumor Proportion Score (TPS), and the ratio of PD-L1 stained tumors and immune cells, the so-called combination Positive score (CPS). The KEYNOTE-224 phase II trial used two scoring methods to evaluate PD-L1, and CPS proved to be a more suitable biomarker. In addition, the predictive value of PD-L1 expression may be underestimated because it is usually assessed at a single time point, while PD-L1 expression may be dynamic and inducible.
Tumor mutation burden/microsatellite instability
Tumor mutation burden (TMB) refers to the number of non-synonymous mutations found in a single tumor genome, including DNA damage response genes and gene changes encoding the catalytic subunits of DNA polymerase ε (POLE) and δ (POLD), and has been evaluated Its potential as a biomarker for multiple tumor types.
A study evaluated the frequency of genomic biomarkers for ICI response in 755 HCC patients and found that the median TMB was 4 mutations/Mb, and only 6 tumors (0.8%) found high TMB. In addition, in a small sample of cases (N=17), there was no correlation between TMB and ICI response. However, as in the case of PD-L1 expression, TMB measurement lacks a standardized threshold, and there are differences in quantification methods. Therefore, the clinical value of TMB should be interpreted with caution.
DNA mismatch repair (MMR) is a key mechanism for maintaining the integrity and stability of the genome. The lack of MMR activity leads to a hypermutated phenotype called microsatellite instability (MSI). A study evaluated the efficacy of pembrolizumab as a single agent in the treatment of 86 patients with advanced MMR-deficient cancer. The ORR and complete response rate (CR) were 53% and 21%, respectively. The results of this study support that MSI can be used as a solid tumor to prevent PD-1. Predictors of clinical response. Although MSI is considered to be an indicator for selecting responders to ICI treatment, there is still a lack of data on HCC patients. Ang et al. evaluated the MSI of 542 HCC specimens and found that only one case (0.2%) had high MSI and high TMB. In several other studies, the ratio of MSI in HCC was generally low. These findings emphasize that MSI may not be an ideal biomarker for predicting the response of HCC patients to ICIs.
Tumor infiltrating lymphocytes
Since T cell infiltration in TME is a prerequisite for immune checkpoint blockade, baseline T cell density and phenotype in tumors have been extensively studied. The retrospective biomarker analysis performed in the CheckMate 040 trial showed that although it was not statistically significant, the increase in the number of CD3+ or CD8+ tumor-infiltrating T cells in HCC patients treated with nivolumab was associated with a trend toward improvement in OS. The study further showed that there is a correlation between the increase in CD3+ T cell frequency and the best overall response.
The link between TIL and ICI treatment response has also been studied in other immune cell types. Ng et al. analyzed 49 HCC samples from patients treated with ICIs and reported that patients with high CD38+CD68+ macrophages in the tumor had a better median OS than patients with low CD38+CD68+ macrophages (34.43 vs. 9.66). Months), probably because CD38hi macrophages produce more IFN-γ and related cytokines.
Deep single-cell RNA sequencing was performed on 5063 T cells isolated from peripheral blood, tumor tissues and adjacent normal tissues of 6 HCC patients. The results indicate that specific subpopulations, such as depleted CD8+ T cells and regulatory T cells (Treg), are preferentially enriched in HCC.
Although it is important to pay attention to lymphocytes in TME, its clinical application as a biomarker is still far away. How to select lymphocytes with specific characteristics and how to determine the positive standard is one of many unanswered questions. The joint assessment of the number of multiple immune cells and the expression of PD-L1 to develop the TME score may be one of the answers.
Specific genetic changes
In recent years, molecular maps related to the emergence of NGS have provided information about operable targets and identified specific genetic changes associated with ICIs responses.
The CTNNB1 gene mutation that causes the activation of the WNT/β-catenin signaling pathway is a characteristic of immune rejection HCC (cold tumor) and is associated with a significant reduction in the enrichment score of several immune characteristics. Compared with WNT wild-type HCC, in 31 subgroups of patients who received different ICI treatments, the activation of WNT/β-catenin pathway was associated with a lower disease control rate (0% vs. 53%) and a shorter medium. PFS (2.0 vs. 7.4 months) is associated with a shorter median OS (9.1 vs. 15.2 months).
In addition, TP53 gene alteration is the most common mutation in HCC patients and is mutually exclusive with CTNNB1 mutation. Studies have shown that the change of TP53 is closely related to the immune microenvironment of liver cancer, with less CD8+T cell infiltration and more FOXP3+Treg infiltration, leading to down-regulation of immune response.
Recent studies have shown that immune-related long non-coding RNAs (lncRNAs) can predict the response to ICI treatment of liver cancer. Peng et al. reported that the host gene of lncRNA MIR155 is strongly positively correlated with the expression of CTLA-4 and PD-L1 in liver cancer tissues, and has predictive value for the efficacy of ICI therapy. In addition, Zhang et al. discovered an immune-related lncRNA marker associated with poor survival, which is an independent prognostic biomarker for liver cancer patients.
Some studies have shown that the inherent epigenetic changes of cancer cells are related to carcinogenesis and tumor progression and changes in TME, indicating its potential as a predictor of immunotherapy. In addition, given that DNA methylation can be measured in liquid biopsy, epigenetic biomarkers may provide additional advantages.
These observations indicate that there are specific gene mutations, especially those related to CTNNB1, TP53, non-coding RNA and methylation, which may affect the response to ICI treatment through interaction with the immune microenvironment. Although these studies are still in their preliminary stages, these changes may represent new biomarkers of choice for patients with liver cancer.
Immune-related gene markers
Recently, a comprehensive analysis of tumor transcriptome analysis data was performed to characterize the responsiveness of the immune microenvironment to ICI treatment. Ayers et al. determined the T cell inflammatory expression profile of 18 genes and the IFN-γ marker of 6 genes, which can predict the response of a variety of solid tumors to pembrolizumab treatment.
The retrospective analysis of CheckMate 040 test results also includes an evaluation of the predictive value of four-gene inflammatory signals (PD-L1, CD8A, LAG3, and STAT1). During the dose escalation and dose expansion phases, this feature was found to be associated with an improved response and OS associated with nivolumab treatment. Recently, a 5-gene immune-related marker (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) was identified and used to establish a prognostic model of liver cancer treatment response, which can stratify patients who are sensitive to immunotherapy. Although these immune-related gene markers show the potential to predict ICI response, these features still need further clinical application testing.
Peripheral blood biomarkers
The continued availability of tumor samples from patients treated with ICI is essential for biomarker research, however, due to the invasive nature of biopsy, this is difficult to achieve. In contrast, circulating biomarkers can be easily collected and repeatedly measured after treatment, making them more convenient for clinical use.
Studies have shown that TGF-β plays a central role in immune suppression and tumor immune evasion in TME. In HCC, there is a strong correlation between TGF-β signaling and depleted immune signaling. In a phase II trial, several representative circulating biomarkers of 29 patients with unresectable liver cancer treated with pembrolizumab were analyzed, and the results showed that baseline plasma TGF-β levels <200 pg/mL are better Effective predictors of OS and PFS.
Some soluble immune checkpoint-related proteins have recently been shown to have good predictive value in various cancer types. Such as PD-L1 exosomes, in a study, the increase in circulating exosomes PD-L1 levels can distinguish clinical responders from non-responders. In addition, soluble PD-L1 (sPD-L1) is related to the immunotherapy response of NSCLC patients, and there is a lack of relevant data on HCC patients, and further research is needed.
Circulating immune cells in peripheral blood have been widely evaluated as predictive biomarkers. In the combined trial of tremelimumab for HCC patients and local area therapy, it was found that the frequency of CD4+PD-1+ cells in peripheral blood mononuclear cells was higher in patients who responded to treatment than in patients who did not respond at baseline. In addition, in 16 patients with liver cancer treated with nivolumab, it was observed that peripheral blood PD-1+B cell positivity and PD-L1+ monocyte positivity with low baseline levels were associated with disease control.
Circulating tumor DNA (ctDNA) can predict the response of several tumors to ICI treatment. However, a study using ctDNA to analyze mutations in advanced HCC reported that WNT pathway-related mutations have nothing to do with the clinical results after ICI treatment, indicating that further research is needed to determine whether ctDNA can indeed be used as a predictive biomarker for HCC.
High levels of alpha-fetoprotein (AFP) are considered to be a prognostic marker of poor clinical prognosis in patients with liver cancer. Recently, it has been reported that the decrease in serum AFP level after treatment is related to the better ICI treatment effect of advanced HCC. However, the results of the checkmate040 clinical trial showed that although baseline AFP<400µg/L is associated with longer OS than AFP≥400µg/L, ORR and DCR are similar regardless of the baseline AFP level. Given that the AFP level is closely related to the patient’s baseline characteristics, its predictive effect should be interpreted with caution.
Biomarkers in peripheral blood have great advantages of low invasiveness, and a large number of studies have also shown their potential as predictors of immunotherapy effects. However, there is still a lack of supporting data from large-sample clinical trials. In addition, there is currently no evidence that, for any given biomarker, the predictive accuracy of blood samples is better than that of tissue samples.
More and more evidences show that the gut microbiota plays a vital role in the development and regulation of innate and adaptive immunity. Studies have found that there seems to be a certain relationship between the gut microbiota and ICI treatment response. .
Zheng et al. recently reported the dynamic changes in the composition of the gut microbiome during anti-PD-1 immunotherapy for liver cancer through metagenomic sequencing. They observed that compared with non-responders, stool samples of immunotherapy responders showed higher classification richness and more gene counts, and the microbial composition of the response group remained relatively stable, while the number of proteobacteria in the non-responder group It increased significantly from the 3rd week and became dominant in the 12th week. In addition, it has been reported that the use of antibiotics at the beginning of ICI treatment is associated with worse results, indicating the impact of the gut microbiota on HCC treatment. A clinical trial (NCT03785210) is currently underway to evaluate the efficacy of combined antibiotics (vancomycin) and ICI therapy.
Potential biomarkers of IRAE
Most research focuses on identifying biomarkers that predict the efficacy of immunotherapy, and there are relatively few studies on biomarkers related to IRAE. In addition, most of the research results come from patients with melanoma. However, these studies can be used as a reference for identifying biomarkers that predict IRAE in HCC.
In patients treated with ipilimumab, baseline serum IL-6 and IL-17 levels were significantly associated with an increased risk of severe toxicity, among which IL-17 was associated with severe diarrhea/colitis. A retrospective study involving 167 adult patients with solid tumors showed that in patients treated with nivolumab or pembrolizumab, an increase in the baseline lymphocyte count was associated with an increased risk of IRAE.
Future research should explore the potential biological mechanisms related to the efficacy of ICI, and how to balance the incidence of IRAE and immunotherapy response is also worthy of serious consideration.
In HCC, although several studies have been conducted to determine predictive biomarkers, HCC-related immunotherapy is still in its infancy, and basic research and clinical trials exploring the predictive efficacy of immunotherapy biomarkers are still limited, and it is not currently possible Determine which biomarkers can effectively predict the efficacy of immunotherapy.
It is particularly noteworthy that biomarkers undergo dynamic changes in the patient population. Therefore, it is very important to obtain samples from patients before and during treatment to assess these dynamic changes and correctly determine the predictive value of assessing biomarkers.
At the same time, full consideration should be given to its application in clinical practice. The development of research technologies such as NGS, single-cell RNA sequencing, and artificial intelligence will enable us to have a more comprehensive understanding of the various components of TME and their interactions, and to screen potential biomarkers on a genomic scale to determine the therapeutic effect. Predictor.
1.Biomarkers and Future Perspectives for Hepatocellular Carcinoma Immunotherapy. Front Oncol. 2021; 11: 716844.
Biomarkers and future prospects of immunotherapy for hepatocellular carcinoma.
(source:internet, reference only)