- Open Access
Liquid biopsy-based identification of prognostic and immunotherapeutically relevant gene signatures in lower grade glioma
Journal of Big Data volume 10, Article number: 19 (2023)
Recent studies have shown that immunotherapies, including peptide vaccines, remain promising strategies for patients with lower grade glioma (LGG); however new biomarkers need to be developed to identify patients who may benefit from therapy. We aimed to investigate the feasibility of liquid biopsy-based gene signatures in predicting the prognosis of LGG patients, as well as the benefits of immunotherapy.
We evaluated the association between circulating immune cells and treatment response by analyzing peripheral blood mononuclear cell (PBMC) samples from LGG patients receiving peptide vaccine immunotherapy, identified response-related genes (RRGs), and constructed RRG-related Response Score. In addition, RRG-related RiskScore was constructed in LGG tumor samples based on RRGs; association analysis for RiskScore and characteristics of TME as well as patient prognosis were performed in two LGG tumor datasets. The predictive power of RiskScore for immunotherapy benefits was analyzed in an anti-PD-1 treatment cohort.
This study demonstrated the importance of circulating immune cells, including monocytes, in the immunotherapeutic response and prognosis of patients with LGG. Overall, 43 significant RRGs were identified, and three clusters with different characteristics were identified in PBMC samples based on RRGs. The constructed RRG-related Response Score could identify patients who produced a complete response to peptide vaccine immunotherapy and could predict prognosis. Additionally, three subtypes were identified in LGG tumors based on RRGs, with subtype 2 being an immune “hot” phenotype suitable for immune checkpoint therapy. The constructed RRG-related RiskScore was significantly positively correlated with the level of tumor immune cell infiltration. Patients with high RiskScore had a worse prognosis and were more likely to respond to immune checkpoint therapy. The therapeutic advantage and clinical benefits of patients with a high RiskScore were confirmed in an anti-PD-1 treatment cohort.
This study confirmed the potential of liquid biopsy for individualized treatment selection in LGG patients and determined the feasibility of circulating immune cells as biomarkers for LGG. Scoring systems based on RRGs can predict the benefits of immunotherapy and prognosis in patients with LGG. This work would help to increase our understanding of the clinical significance of liquid biopsy and more effectively guide individualized immunotherapy strategies.
Gliomas are the most common central nervous system tumors, accounting for approximately 75% of primary malignant brain tumors . Of these, World Health Organization (WHO) grade II and III gliomas are considered lower grade gliomas (LGGs), which have a slower course with a better prognosis than grade IV glioblastoma (GBM) [2, 3]. The prognosis of patients with LGG depends on a variety of factors, including IDH mutation status and molecular subtypes, and survival time can range from 1 to 15 years . Despite the current multimodal standard of treatment for maximal safe surgical resection followed by chemotherapy and radiotherapy, tumor recurrence and progression are almost inevitable due to its high malignancy and treatment resistance . Therefore, the development of new therapeutic strategies is crucial for the treatment of LGG. Accurate and convenient individualized assessment methods are indispensable for the prognostic stratification of LGG patients and for personalized treatment selection.
Recently, immunotherapy has attracted widespread attention among oncologists due to its great success in a variety of tumors, including melanoma [6,7,8]. Current immunotherapy approaches include immune checkpoint inhibitors (ICIs), tumor vaccines, and cytokine therapy . Although in previous studies, only a small proportion of glioma patients responded objectively to ICI therapy and struggled to achieve a survival benefit [10, 11], in a recent clinical trial, Zhao et al. showed that a proportion of molecularly and individually screened GBM patients may benefit from the therapy . For LGG, nearly half of the patients in a recent glioma-associated antigens (GAAs) peptide vaccine trial experienced an objective response and some survival benefit . These results suggest that immunotherapy, particularly ICI and tumor vaccine therapies, remains a promising therapy for patients with glioma. However, this requires screening and filtering of patients to identify those who may respond to immunotherapy to avoid unnecessary, or even harmful, treatment.
In the search for effective biomarkers to predict which patients will respond to immunotherapy, several studies have shown that molecular analysis based on pre-treatment tumor tissue can effectively predict patient response to treatment [14,15,16,17]. These approaches have mainly addressed the expression levels of immune checkpoint molecules, tumor mutation burden (TMB), and T cell function in tumor tissues. However, biomarkers based on tumor tissue samples may have certain limitations. For example, in patients with advanced LGG, it may be difficult to obtain sufficient tissue volume for molecular testing, and in addition, there is a risk of biopsy in sensitive areas or performing serial biopsies of tumors [18, 19]. Another limitation is that assays relying on individual biopsies may be affected by intra-tumor heterogeneity or low tissue volume, resulting in unreliable results . Therefore, there is a large need for the development of new individual patient prognostic and treatment sensitivity prediction methods.
Liquid biopsy represents an exciting new frontier in cancer diagnosis and management, as it is convenient and minimally invasive, giving it significant advantages over tissue biopsy. The material for liquid biopsies can be derived from a variety of body fluids such as blood, saliva, and cerebrospinal fluid , and covers a wide range of circulating biological indicators, such as circulating tumor cells, circulating immune cells, and circulating nucleic acids . Further, liquid biopsy is applied for clinical use in a variety of solid tumors, including breast and colorectal cancers . Currently, little progress has been made in circulating biomarkers in glioma, with one study of peptide vaccine therapy in LGG patients in which peripheral blood mononuclear cells (PBMCs)-based liquid biopsy responded to the peripheral immune status of LGG patients and identified several possible biomarkers . However, further in-depth studies on the application of liquid biopsies in LGG patients are still needed, and the potential role of liquid biopsy-based identified biomarkers in tumor tissues remains to be elucidated.
The aim of this study was to explore gene signatures used to predict immunotherapeutic response in liquid biopsy-based PBMC samples from LGG patients and to explore the potential application of gene signatures in LGG tumor samples. We explored the predictive role of circulating immune cells in response to peptide vaccine therapy in LGG patients and identified 43 response-related genes (RRGs). Based on these RRGs, we identified three tumor subtypes with different immune features in LGG tumor samples and constructed an RRGs-related RiskScore that can be used for prognosis and immunotherapeutic response prediction.
Data extraction and preprocessing
Illumina RNA sequencing (RNA-seq) raw data of 54 PBMC samples from 12 LGG patients were retrieved from ArrayExpress repository (accession number: E-MTAB-6270, https://www.ebi.ac.uk/arrayexpress/). RNA-seq data were converted to transcripts per kilobase million (TPM) values, and log2(TPM + 1) transformation was performed. All patients underwent peptide vaccine immunotherapy, and PBMC samples from patients' blood were collected for RNA-seq at various time points (0, 6, 15, 34, 70, and 86 week) before and during immunotherapy. All clinical data, including treatment response, were obtained from previous studies [13, 24]. Normalized RNA-seq data for 510 LGG tumor samples from The Cancer Genome Atlas (TCGA) database and 431 LGG tumor samples from the Chinese Glioma Genome Atlas (CGGA) database, as well as clinical information, were obtained from GlioVis (http://gliovis.bioinfo.cnio.es/) . Normalized RNA-seq data of 14 pre-treatment tumor samples from the GBM anti-PD-1 treatment cohort and clinical information were obtained from a previous study , and all patients were treated with PD-1 inhibitors (nivolumab or pembrolizumab). Only samples with full prognostic information were retained in this study, and all relevant data that could not be extracted from previous publications were obtained from the corresponding authors of the publications. Basic information on all samples in this study is summarized in Additional file 1: Table S1.
Inference of circulating immune cells in PBMC samples and infiltrating cells in the tumor microenvironment (TME) of tumor samples
We used CIBERSORTx to quantify the relative abundance of circulating immune cells in PBMC samples , which was used by Nabet et al. to analyze the circulating immune cell profile of non-small cell lung cancer . For LGG tumor samples, we used single-sample gene set enrichment analysis (ssGSEA) to estimate the relative abundance of 28 previously reported immune cells in LGG tumor microenvironment (TME) .
Identification of immunotherapeutic RRGs in PBMC samples
The empirical Bayesian approach in the “limma” R package was used to calculate the differentially expressed genes (DEGs) between three treatment responses (SD, stable disease; PR, partial response; CR, complete response) at 0, 6, 15, and 34 week separately . Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was carried out using the R package “clusterProfiler” after taking the union of DEGs between different treatment responses at each time point . Subsequently, response-associated DEGs at different time points were intersected to obtain DEGs whose expression was altered at all time points. We considered these DEGs as immunotherapeutic RRGs in LGG PBMC samples. Statistical significance was set at p < 0.05.
Unsupervised clustering for 43 RRGs
Based on the gene expression profiles of the identified 43 RRGs in 54 PBMC samples, robust LGG PBMC clusters were identified using unsupervised clustering based on the partition around medoids (PAM) algorithm. As we described previously, a total of 1000 bootstraps were executed, and each bootstrap resampled 80% of samples [31, 32]. The maximum number of clusters was 10, and K values were evaluated using a consensus cumulative distribution function and consensus heatmap . Using the same approach, we identified RRG-associated tumor subtypes in LGG tumor samples.
Gene set enrichment analysis (GSEA) of different RRG-related clusters
Using the GSEA software (Version 4.1.0) obtained from the GSEA website (http://software.broadinstitute.org/gsea/index.jsp) and downloading the “c2.cp.kegg.v7.4.symbols” gene set and the HALLMARK gene set from the MSigDB database, we evaluated the different pathways and molecular mechanisms in the different LGG PBMC-related clusters . The minimum gene set was set to 5, the maximum gene set was set to 5000, and resamples were 1000. GSEA was performed using the same approach for high and low RRG-related RiskScore groups in LGG tumor samples. In addition, different immune-related biological processes between different clusters or subtypes were explored using previously published gene sets, including immune activation-relevant gene set , immune checkpoint-relevant gene set , and epithelial–mesenchymal transition (EMT)-relevant gene set . P value of < 0.05 was considered statistically significant.
Construction of the RRG-related Response Score
To quantify the response characteristics of individual PBMC samples to immunotherapy, the prognostic value of each RRG in pretreatment samples was analyzed based on the 43 RRGs identified using the univariate Cox regression model. The RRGs with prognostic effect were extracted for further analysis, and the expression of each RRG was transformed into a z-score. Principal component analysis (PCA) was then performed, and principal component 1 was selected as the signature score. This approach has the advantage of focusing the scores on the set of gene blocks with the best correlation in the set, while reducing the contribution of genes that do not track with other set members. Finally, the Response Score was defined using the following formula:
where i is the expression of RRG whose Cox coefficient is positive, and j is the expression of RRG whose Cox coefficient is negative.
Construction of the RRG-related RiskScore
To define RRG-related RiskScore for individual LGG tumor samples, we first identified RRGs with a significant prognostic effect in the TCGA cohort using the univariate Cox regression model. Prognosis-related RRGs were subsequently entered into a stepwise regression to determine the final risk model. The RRG-related RiskScore was calculated using the following formula:
where βi is the coefficient of each RRG in the final risk model, and Expi is the gene expression value.
Differences between two groups were compared using unpaired Student’s t-test or Wilcoxon rank sum test. For comparisons between more than two groups, differences were compared using one-way ANOVA and Kruskal–Wallis test. Correlation coefficients between RRGs and between RiskScore and TME infiltrating immune cells were calculated using spearman correlation analysis. The R packages “survival” and “survminer” were used to calculate the correlation between the variables and survival time and to find the best cutoff value. Survival curves were generated using the Kaplan–Meier (KM) method, and significance was determined using the log-rank test. The R package “pROC” was used to plot receiver operating characteristic (ROC) curves to verify the validity of the model and obtain the area under the curve (AUC). Independent prognostic factors were identified using a multivariate Cox regression model and visualized using the R package “forestplot.” The “oncoplot” function of the R package “maftools” was used to present a waterfall plot of patients with mutations in the TCGA-LGG cohort. All statistical calculations were performed using R software (Version 4.0.3) or GraphPad Prism 5 (La Jolla, CA, USA); p < 0.05 was considered statistically significant, and p values were two-sided.
Association of circulating immune cells with immunotherapeutic response and prognosis in LGG patients
Peripheral immunity is closely related to immunotherapeutic response in tumor patients [24, 27]; therefore, we explored the possibility of circulating immune cells as immunotherapeutic biomarkers in LGG patients. Specifically, we applied CIBERSORTx to quantify the relative abundance of circulating immune cells in 54 PBMC samples, and a total of 15 circulating immune cells were detected. We confirmed that the composition of circulating immune cell components was highly correlated, such that the abundance of activated dendritic cells was highly positively correlated with the abundance of M0 macrophages and follicular helper T cells, whereas the abundance of monocytes was negatively correlated with the abundance of resting memory CD4 T cells (Fig. 1A). We subsequently found that CR patients had significantly higher peripheral monocytes compared to SD and PR patients, whereas SD patients had higher peripheral resting NK cells (Fig. 1B). Notably, CR patients also had higher CD8 T cell compared to PR patients (Fig. 1B).
We observed the dynamics of circulating immune cells at different time points, as shown in Fig. 1C, where CR and PR patients had higher monocytes at week 0 (pretreatment), and CR patients maintained high levels of monocytes at different time points after treatment, whereas PR patients experienced a transient decline at week 6 and SD patients consistently had lower levels of monocytes. CR patients also had low levels of naive B cells at week 0 and only a transient rise occurred at week 6 post-treatment, subsequently maintaining lower levels of naive B cells than PR and SD patients (Fig. 1C). In addition, CR patients also had higher levels of CD8 T cells than PR patients at all time points except week 34, whereas PR patients had higher levels of memory B cells at week 0 and week 6 (Fig. 1C). The level of peripheral myeloid-derived suppressor cells (MDSCs) is known to correlate with the malignancy of gliomas , and we were interested in understanding the prognostic value of peripheral circulating immune cells in LGG patients treated with immunotherapy. We estimated the prognostic role of immune cells in pre-treatment samples and found that high levels of memory B cells and monocytes were associated with better progression-free survival (PFS), whereas high levels of naive B cells were associated with poorer PFS (Fig. 1D). It was further found that high levels of memory B cells, follicular helper T cells were associated with better overall survival (OS), while the opposite was true for naive B cells and activated NK cells (Additional file 1: Fig. S1). Overall, these results suggest a strong association between peripheral circulating immune cells and both immunotherapeutic response and prognosis in patients with LGG.
Identification of response-related genes
To explore differences in gene expression in patients with different treatment responses, we identified DEGs in patients at different treatment time points separately using the “limma” R package. As shown in Fig. 2A, a total of 1981 DEGs at week 0 were identified by comparing the differences between SD, PR, and CR patients (Additional file 2: Table S2). KEGG pathway analysis showed that these DEGs were not only associated with key oncogenic-related pathways such as cell cycle and apoptosis, but are also closely related to T and B cell receptor signaling pathways, Th1 and Th2 cell differentiation, and immune checkpoint pathway (Fig. 2B). This further suggested that the response to peptide vaccine immunotherapy was associated with peripheral immunity in patients with LGG prior to treatment. Next, we identified 4543, 3211, and 2761 response-related DEGs at week 6, 15, and 34 following initial treatment, respectively (Additional file 2: Tables S3–S5). These DEGs were mainly enriched in metabolic pathways, NOD-like receptor signaling pathways, and RNA transport (Additional file 1: Fig. S2A–C). Interestingly, the number of DEGs after initial treatment was much higher than that before treatment, which seems to imply that greater differences in gene expression emerged between patients who responded differently after treatment. Furthermore, based on Venn diagrams of DEGs at different time points, an overlap analysis was performed. As shown in Fig. 2C, we identified a total of 43 treatment response-related DEGs that were differentially expressed at all time points (Additional file 2: Table S6). These genes may be closely associated with immunotherapeutic responses in LGG patients, and we therefore identified them as RRGs. PCA analysis showed that based on these 43 RRGs, PBMC samples from the three different treatment responses were divided into three distinct clusters (Fig. 2D).
Response-related clusters in PBMC samples
To explore the relationship between RRGs, we calculated pairwise correlations between the expression of 43 RRGs, and a wide range of positive and negative correlations were found between RRGs (Fig. 3A). Next, we performed unsupervised consensus clustering on PBMC samples based on the expression profiles of 43 RRGs to identify clusters with different RRG expression patterns. Based on the cumulative distribution function and the functional delta area (Additional file 1: Fig. S3), we chose k = 3, and the PBMC samples could be stably classified into three clusters: Cluster 1 (C1), Cluster 2 (C2), and Cluster 3 (C3) (Fig. 3B). The clinical characteristics of the different clusters, as well as the expression patterns of RRGs, are presented in heatmap form in Fig. 3C. Notably, all CR samples were classified as C3, whereas all PR samples were classified as C2 (Fig. 3D), suggesting the potential predictive value of RRG-based clusters for response to peptide vaccine immunotherapy. More importantly, the alluvial diagram showed that PBMC samples from the same patient at different time points both pre- and post-treatment were consistently classified in the same cluster (Fig. 3E), indicating that the stability of RRG-related clusters for classification of samples with different responses was not affected by sampling time. Interestingly, we found that C3 samples had the highest RASAL2 and MAPKAPK2 expression, whereas C2 samples had the lowest HLA-DRB1 expression, and C1 samples had low RASAL2 and MAPKAPK2 expression as well as the highest HLA-DRB1 expression (Fig. 3F). The detection of the expression levels of these three genes appears to allow the initial identification of three distinct clusters, suggesting the feasibility of potential clinical applications. Not surprisingly, C3 was associated with a relatively good OS in pre-treatment samples (Fig. 3G).
Subsequently, we identified different immune-related features among the different clusters. As shown in Fig. 4A, C3 had the highest level of circulating CD8 T cells, with C2 being the next highest and C1 the lowest, while no other cell types were associated with RRG-related clusters. Based on previous studies, we considered that CLDN3, CLDN7, CLDN4, CDH1, VIM, TWIST1, ZEB1, and ZEB2 were EMT-relevant transcripts ; CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, and TIGIT are immune checkpoint-relevant transcripts ; CD8A, CXCL10, CXCL9, GZMA, GZMB, IFNG, PRF1, and TBX21 are immune activation-relevant transcripts . C2 had high expression of ZEB1, CD274, LAG3, and CD8A, indicating that C2 may have high peripheral immunosuppression with some immune activation, whereas C3 had high expression of CD8A, representing better peripheral immune activation, and C1 had the lowest immune activation (Fig. 4B–D). Furthermore, through GSEA, we found that C1 was mainly associated with oncogenic pathways such as the KRAS signaling pathway and angiogenesis; C2 was mainly enriched in the ERBB signaling pathway and mismatch repair; and C3 was enriched in antigen processing and presentation and intestinal immune network (Fig. 4E).
Generation of RRG-related Response Score
Using the PCA algorithm as well as the RRG signature, we constructed a scoring system to quantify the pattern of treatment response in individual samples, which we termed Response Score. The high Response Score group had higher circulating CD8 T cells, representing better peripheral immune activation (Fig. 5A). At the same time, the high Response Score group also had lower CD274 expression, implying lower immunosuppression (Fig. 5B). It was also found that the C3 samples had the highest Response Scores (Fig. 5C). Consistent with this, the CR samples also had the highest Response Scores (Fig. 5D). By calculating the Response Scores at different time points, it was found that the CR samples consistently had higher Response Scores than the SD and PR samples (Fig. 5E). Furthermore, we explored the predictive power of Response Scores on the prognosis of patients with LGG treated with peptide vaccine immunotherapy. Unsurprisingly, the high Response Score group in the pretreatment samples was found to have significantly better OS and PFS (Fig. 5F).
Identification of RRG-related subtypes in LGG tumor samples
Since 43 RRGs were identified in liquid biopsy-based PBMC samples, we were interested in determining whether RRGs play a key role in tumor samples. Based on the expression profiles of RRGs in the TCGA-LGG cohort, we performed unsupervised consensus clustering to identify potential tumor subtypes. By analyzing the cumulative distribution function, function delta area, and consensus heatmap, we identified three robust RRG-associated tumor subtypes (Additional file 1: Fig. S4): subtype 1 (S1), subtype 2 (S2), and subtype 3 (S3). To understand the immune characteristics of different tumor subtypes, we performed ssGSEA to infer the relative abundance of 28 tumor immune cells in different subtypes. As shown in Fig. 6A, B, S2 tumors had the highest infiltration of immune-activated cells, such as activated CD4 T cells, activated CD8 T cells, and natural killer cells, as well as the highest infiltration of immunosuppressive cells, such as MDSCs and regulatory T cells. This suggested that S2 tumors are immune hot phenotypes with both immune-activating and immunosuppressive features. In contrast, most immune-activated cells including activated CD4 and CD8 T cells in S3 tumors had minimal levels of infiltration, as well as low levels of MDSC and regulatory T cells, indicating that S3 belongs to the immune cold phenotype (Fig. 6A, B), whereas S1 tumors had a complex TME. Furthermore, we characterized the expression of immune-related transcripts in the three tumor subtypes. Unsurprisingly, most of the immune activation-related mRNAs, immune checkpoint-related mRNAs, and EMT-related mRNAs were upregulated in S2 tumors, which further confirmed that S2 tumors possessed immune activation and were highly immunosuppressive (Fig. 6C–E). Notably, EMT-relevant genes ZEB1 and ZEB2 were found to be significantly highly expressed in S1 tumors, whereas CLDN3 and CLDN4 expression was elevated in S3 tumors, suggesting some stromal activation in S1 and S3 tumors (Fig. 6C–E). These results indicate that RRGs have an important TME immunomodulatory role in LGG tumors.
Construction of RRG-related RiskScore
To further assess the clinical relevance of RRGs in LGG patients, we first calculated the prognostic value of RRGs in the TCGA-LGG cohort using a univariate Cox regression model, and a total of 16 RRGs were identified that were significantly associated with LGG prognosis (Additional file 1: Fig. S5). Based on the survival-related RRGs using stepwise regression, we constructed an RRGs signature; this model was termed as the RRG-related RiskScore (Additional file 2: Table S8). Using the median as the threshold, we divided the samples in TCGA-LGG into RiskScore-high and -low groups, and the KM curve suggested that patients with high RiskScore have significantly worse OS (Fig. 7A). In addition, the ROC curve for RRG-related RiskScore showed that the AUCs of 1-, 3- and 5-years OS were 0.89, 0.83, and 0.71, respectively (Fig. 7C). To examine whether RiskScore is an independent prognostic factor in LGG patients, we performed a multifactorial Cox regression analysis with age, histology, grade, gender, and IDH status as covariates in LGG patients. The RRG-related RiskScore was found to be an independent and robust prognostic marker for LGG and can be used for the prognostic assessment of patients with LGG (Fig. 7D). Importantly, we confirmed the reliability of the RRG-related RiskScore using 431 samples from the CGGA-LGG cohort as an independent validation cohort. Consistent with the TCGA-LGG cohort, patients with high RiskScore had significantly shorter OS times (Fig. 7B), and RRG-related RiskScore was an independent prognostic factor (Fig. 7E).
Clinical and mutational characteristics of RRG-related RiskScore
LGG mainly includes three different histological subtypes: astrocytoma, oligoastrocytoma, and oligodendroglioma. We compared the RRG-related RiskScore of different LGG histological subtypes and found that astrocytomas had a significantly higher RiskScore than oligoastrocytomas and oligodendrogliomas (Fig. 8A). WHO grade III LGG has a worse prognosis than grade II LGG, and it was found to have a higher RiskScore in grade III tumors than grade II tumors (Fig. 8B). In addition, we found that S2 tumors had higher RiskScores than S1 and S3 tumors (Fig. 8C). It is well known that TMB is closely related to the effects of immunotherapy [39, 40]. Therefore, TMB in RiskScore-high and RiskScore-low groups were analyzed based on mutation data from the varscan-processed mutation dataset of the TCGA cohort. As shown in Fig. 8D, the RiskScore-high group had a significantly higher TMB. In addition, the RiskScore-high group also had significantly higher neoantigen counts (Fig. 8E). This suggests that LGG patients with a high RiskScore are more likely to respond to immunotherapy. Further, Fig. 8F demonstrates the 30 most frequently mutated genes in the high and low RiskScore groups, with IDH1 having the highest mutation frequency. As shown in Fig. 8G, patients with IDH1 mutations have a lower RiskScore compared to IDH1 wild-type patients, indicating a better prognosis.
Cellular and molecular characterization of RRG-related RiskScore
To explore the role of RiskScore in the regulation of TME in LGG patients, we first compared the relative abundance of 28 immune cells in patients with high and low RiskScore by ssGSEA in the TCGA-LGG cohort. As shown in Fig. 9A, patients with high RiskScore had higher infiltration levels of immune cells, including multiple immunostimulatory cells (e.g., activated CD8T cells) and immunosuppressive cells (e.g., MDSCs), which indicated that high RiskScore patients had a “hotter” TME. More importantly, we found significant positive correlations between RiskScore and infiltration levels of the vast majority of immune cells in LGG patients (Fig. 9B), suggesting that the TME pattern of LGG patients can be quantified by RiskScore, with patients with higher RiskScore having a “hotter” TME and more likely to benefit from ICI treatment. In addition, the results were validated in the CGGA-LGG cohort (Additional file 1: Fig. S6A, B). Furthermore, analysis of the immune-related transcripts in LGG patients showed that patients with high RiskScore had not only higher expression of immune activation-relevant genes, but also higher expression of immune checkpoint-relevant genes and EMT-relevant genes (Fig. 9C–E). This suggests that patients with a high RiskScore have higher immune activation and strong immunosuppression. GSEA based on the HALLMARK gene set showed that the high-RiskScore group was enriched not only for immune activation-related pathways (inflammatory response and IFNγ response), but also for stromal activation-related pathway (EMT) and oncogenic-related pathways (angiogenesis and KRAS signaling pathway) (Fig. 9F). GSEA based on the KEGG gene set likewise showed that the high-RiskScore group was associated with immune activation and stromal activation pathways, including the T-cell receptor signaling pathway and cell adhesion, among others (Fig. 9F).
The RRG-related RiskScore predicts immunotherapeutic benefits
Based on previous results, LGG patients with higher RiskScore are more likely to benefit from ICI immunotherapy, and we hope to find tangible evidence in glioma for this inference to drive clinical application. We extracted the only currently known multi-sample GBM anti-PD-1 treatment cohort with complete RNA-seq and clinical data and calculated the RRG-related RiskScore for pre-treatment samples. Patients with high RiskScore who received anti-PD-1 therapy achieved significant survival benefits, including improved OS and PFS (Fig. 10A, B). Patients with a high RiskScore showed better clinical response to anti-PD-1 therapy compared to RiskScore-low patients (Fig. 10C). Similar to previous results, further studies showed that patients with high RiskScore had higher levels of immune activation cell infiltration, including activated CD4 T cells, CD56 bright natural killer cells, central memory CD8 T cells, and type 1 T helper cells (Fig. 10D, E). In addition, patients with high RiskScore also had higher levels of regulatory T cells and type 2 helper T cells infiltration (Fig. 10D, E), indicating higher immunosuppression. In conclusion, our results strongly suggest that RRG-related RiskScore is significantly associated with response to anti-PD-1 immunotherapy in glioma patients and helps predict response to anti-PD-1 therapy.
Immunotherapeutic approaches, including peptide vaccines, offer a safe and effective treatment option for patients with LGG, whose slower disease progression relative to patients with GBM allows for multiple immunizations to gain anti-tumor immunity and survival benefits. Many studies have supported the potential of peptide vaccine immunotherapy in patients with LGG [41,42,43,44]. Thus, new biomarkers are needed to identify patients who may respond therapeutically. Liquid biopsy offers an easy way to perform minimally invasive biopsies for the diagnosis and treatment of cancer patients and is a rapidly expanding area of translational cancer research . Here, we used PBMC samples from patients with LGG to reveal the potential of circulating immune cells as biomarkers in immunotherapy. Recent studies suggest that circulating monocytes play a key role in immunotherapy and may be a prerequisite for a successful response to anti-PD-1 immunotherapy [46, 47]. In addition, a study by Romano et al. suggested that monocytes may maintain an effective antitumor immune response during anti-CTLA-4 immunotherapy . Consistent with these studies, our results suggest that monocytes are important for the antitumor response of peptide vaccines, and that CR patients maintained high levels of circulating monocytes throughout the treatment period. Interestingly, the study by Krieg et al. found that lung cancer patients with lower levels of circulating CD8 T cells prior to treatment were more likely to experience long-lasting benefits from ICI therapy , and similar results were found in patients with melanoma . In our results, higher baseline CD8 T-cell levels were found in patients with CR. Mechanistically, patients with high levels of tumor-infiltrating lymphocytes are more likely to benefit from ICI, indicating that peripheral circulating CD8 T cells in this group of patients are raised to the tumor [47, 49]. In contrast, tumor vaccines are better suited to activate the immune system in patients with lower levels of tumor-infiltrating lymphocytes [50, 51]. Therefore, patients with higher baseline circulating CD8 T cell levels may be more suitable for tumor vaccines, including peptide vaccines; however, a combination of consideration of the patient’s level of immunosuppression as well as monocyte levels must be taken into account. Overall, our results suggest that circulating tumor cells, especially monocytes and CD8T cells, can be potential biomarkers for immunotherapy in patients with LGG.
Our study identified 43 significant RRGs, based on which we identified three distinct clusters, and PBMC samples from the same patient were always classified as the same cluster, suggesting that this classification can be performed at any time point of treatment without influence. In addition, all CR samples were classified as C3 and all PR samples were classified as C2, implying that patients classified as C3 in clinical applications would be most likely to have the best response to peptide vaccine immunotherapy. For easier classification, RASAL2, MAPKAPK2, and HLA-DRB1 were identified as signature genes for classification, based on which three clusters can be initially distinguished, but this needs to be validated by further clinical trials. We subsequently found a gradual decrease in the overall level of circulating CD8 T cells from C3 to C1, which is consistent with our expectations, and the better treatment response may have stimulated the proliferation of more CD8 T cells. Similarly, the proliferation of peripheral blood CD8 T cells occurred in responders with lung cancer receiving anti-PD-1 therapy . GSEA further confirmed that C3 was indeed enriched in immune-related pathways, such as antigen processing and presentation, indicating immune activation in C3. To better assess treatment response in individual samples, we developed a scoring system known as the RRG-related Response Score. The high Response Score group had higher levels of CD8 T cells, indicating higher immune activation. At the same time, CR patients had significantly higher Response Score than SD and PR patients at any time point of treatment, confirming the feasibility of identifying CR patients by Response Score. Importantly, Response Score can also assess the prognosis of patients treated with peptide vaccines, and since it is established in liquid biopsy-based PBMC samples, it highlights the promise of its application in clinical work to serve a predictive function in a minimally invasive manner.
It is unclear how important the gene signatures identified based on PBMC samples are in tumor samples. To reveal the role of 43 RRGs in LGG tumor samples and to explore whether the gene signatures identified based on liquid biopsies are intrinsically linked to tumor samples, we performed unsupervised clustering of tumor samples using RRGs and identified three robust tumor subtypes. S2 tumors were characterized as “hot” tumors, characterized by immune activation and stromal activation, making this subtype more suitable for ICI therapy, which exerts anti-tumor immunity by blocking immunosuppression. In contrast, relatively cold phenotypes of S1 and S3 tumors can be attempted to convert them from “cold” to “hot” by new strategies such as tumor vaccines , which can then be further combined with ICI therapy. This suggests an important role for RRGs in regulating TME in tumors. Furthermore, we identified an important prognostic role of RRGs in LGG and developed a scoring model called the RRG-related RiskScore. RiskScore predicts the prognosis of LGG patients with good efficiency; it is an independent prognostic factor for LGG; and its predictive power was validated in an independent cohort. The prognostic stratification function of RiskScore is important for long-term management and personalized treatment selection in patients with LGG.
Our study also revealed the relationship between RRG-related RiskScore and TMB, with patients with high RiskScore having significantly higher TMB and neoantigen counts than those with low RiskScore, suggesting that patients with high RS are more likely to benefit from immunotherapy [39, 40]. Importantly, our study also confirmed that RiskScore is significantly positively correlated with tumor immune cell infiltration and that patients with high RiskScore have a “hotter” TME, which means better immune activation and stronger immune suppression. Thus, this not only suggests that RRG-related RiskScore can quantify TME infiltration patterns in individual patients, but also further demonstrates the important modulatory role of RRGs in TME. Based on the properties of RiskScore, patients with higher RiskScore are theoretically more likely to benefit from ICI; however, there is a lack of available LGG ICI treatment cohorts, so we used a GBM anti-PD-1 treatment cohort for validation. Consistent with our expectations, GBM patients with high RiskScore were more likely to respond to anti-PD-1 therapy, and GBM patients with high RiskScore had longer OS and PFS after anti-PD-1 therapy. This makes the predictive power of RiskScore for immunotherapy benefits directly validated by clinical trials in glioma, further confirming the important clinical relevance of RiskScore.
Although our study has some limitations, such as its retrospective nature, further validation of the identified gene signatures and scoring systems in a prospective cohort is needed. Second, the sample size of PBMC is limited, and the results need to be confirmed in a larger cohort of samples. Nevertheless, our results confirmed that RNA-seq of liquid biopsy-based PBMC samples is a powerful and promising approach for individualized treatment selection and that peripheral circulating immune cells may be promising biomarkers. In addition, we identified 43 important RRGs, which not only play an important role in peripheral immunity in LGG patients, but also have a critical regulatory role in the TME of LGG tumors, indicating an intrinsic association between gene signatures identified based on liquid biopsies and those identified by tumor biopsy. In clinical practice, RRG-related Response Score can identify patients who are more likely to develop CR to peptide vaccine immunotherapy in a minimally invasive manner and predict the prognosis of treated patients. The RRG-related RiskScore not only identifies patients more likely to respond to ICI therapy by tumor samples but is also a powerful prognostic prediction tool. Our study thus provides new ideas for individualized immunotherapy in patients with LGG.
This study confirmed the potential of liquid biopsy-based RNA-seq for individualized treatment selection in LGG patients, determined the feasibility of circulating immune cells as biomarkers for LGG, and identified 43 RRGs. Scoring systems based on RRGs can predict the benefits of immunotherapy and prognosis in patients with LGG. This work highlights the critical clinical implications of liquid biopsy and contributes to the development of personalized immunotherapeutic strategies for LGG patients.
Availability of data and materials
All data generated and described in this article are available from the corresponding databases and publications, and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. Further information is available from the corresponding author on reasonable request.
Lower grade glioma
World Health Organization
Immune checkpoint inhibitor
Tumor mutation burden
Peripheral blood mononuclear cell
Transcripts per kilobase million
The Cancer Genome Atlas
Chinese Glioma Genome Atlas
Single-sample gene set enrichment analysis
Differentially expressed gene
Kyoto Encyclopedia of Genes and Genomes
Gene set enrichment analysis
Principal component analysis
Receiver operating characteristic
Area under the curve
Myeloid-derived suppressor cells
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The authors would like to thank Prof. Raul Rabadan for data support. The authors also would like to thank GlioVis (http://gliovis.bioinfo.cnio.es/) for data collection.
This work was supported by the National Natural Science Foundation of China (Grant number 81802974) and grants from the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant Number 2014BAI04B01). Sponsoring foundations exert no effect on the writing of this manuscript.
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Additional file 1: Table S1.
Summary of clinical characteristics for patients included in this study. Figure S1. Prognostic role of circulating immune cells in pre-treatment samples. a–d Kaplan–Meier curves for OS showing the association between relative abundance of B cell memory (a), B cell naive (b), NK cell activated (c), T cells follicular helper (d) and OS (log-rank test, p < 0.05). Figure S2. Identification of DEGs among samples with different responses at different post-treatment time points and KEGG pathways analysis. a–c DEGs at week 6 (a), 15 (b) and 34 (c) were identified by comparing the differences between SD, PR, and CR patients and KEGG pathway analysis was performed based on DEGs. The size of the circle represents the number of genes enriched. Figure S3. Consensus clustering analysis of PBMC samples based on the 43 RRGs expression profile. a, b Cumulative distribution function curve (a) and delta area curve (b) of consensus clustering analysis. Figure S4. Consensus clustering analysis of LGG tumor samples based on the 43 RRGs expression profile. a–c Cumulative distribution function curve (a), delta area curve (b) and consensus heatmap (c) of consensus clustering analysis in the TCGA cohort. Figure S5. Forest plot showing the prognostic value of RRGs in LGG tumors as determined by univariate cox regression analysis. Figure S6. Cellular characterization of RRG-related RiskScore in the CGGA cohort. a Heatmap of the relationship between RRG-related RiskScore and 28 immune cell subpopulations identified by a previous study. b Correlation between RiskScore and abundance of 28 immune cells. Correlations are calculated by Pearson correlation analysis. Blue represents weak positive correlation, red represents strong positive correlation.
Additional file 2. Table S2.
DEGs at week 0. Table S3. DEGs at week 6. Table S4. DEGs at week 15. Table S5. DEGs at week 34. Table S6. Intersection of DEGs at week 0, 6, 15 ,34. Table S7. PCA of 17 prognosis-related RRGs. Table S8. Coefficients of RRG-related RiskScore.
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Wu, C., Long, W., Qin, C. et al. Liquid biopsy-based identification of prognostic and immunotherapeutically relevant gene signatures in lower grade glioma. J Big Data 10, 19 (2023). https://doi.org/10.1186/s40537-023-00686-8
- Lower grade glioma
- Liquid biopsy
- Tumor microenvironment