Identification of synergistic strategies for m6A methylation-involved immunotherapy enhancement in lower grade gliomas

doi:10.21203/rs.3.rs-1519458/v1

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Identification of synergistic strategies for m⁶A methylation-involved immunotherapy enhancement in lower grade gliomas

https://doi.org/10.21203/rs.3.rs-1519458/v1

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Low grade gliomas (LGGs) are often noted for their unpredictable recurrence and transformation of malignancy to higher grade. Due to the extraordinary immunosuppressive microenvironment, LGGs seem to be refractory to T-cell based immunotherapies. Targeting RNA N6-methyladenosine (m⁶A) modulated tumor microenvironment (TME) offers opportunities to trigger anti-LGG immunity. In our study, m⁶Ascore were used for quantifying the m⁶A modification patterns in LGGs. Potential therapeutic drugs and synergistic immunotherapy approaches which might avoid overtreatment and contribute to the TME remodeling were suggested based on the scoring scheme. We found a link between m⁶Ascore and TME diversity. Those patients with high m⁶Ascore and worse outcomes were more likely to experience immunotherapeutic failures. Inhibition of IL-6/JAK/STAT3 signaling that closely correlated with the drug sensitivities was indicated to facilitate the response of LGGs with high-m⁶Ascore instead of overall LGGs to immune checkpoint blockade (ICB). Mechanically, stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by macrophages. In conclusion, synergistic immunotherapy of anti-IL-6 and ICB may offer insight into the m⁶A related immunotherapy enhancement for LGG patients.

Immunology

Lower grade gliomas

m6A

Immunotherapy

IL-6

macrophages

Lower grade gliomas (LGGs), a sort of primary brain and CNS tumor, are taken to denote both World Health Organization grades II and III gliomas ¹. While some advances have been made in imaging ² and treatment ³ of LGGs, the outcomes of the patients still remain unsatisfactory and a considerable proportion of them would experience the recurrence and transformation of malignancy to higher grade ⁴.

N6-methyladenosine, namely m⁶A, is a modified base which has been known to present in ribosomal RNA, noncoding RNAs, polyadenylated RNA and mammalian mRNA ⁵. To form m⁶A methylation in mRNA accounts is considered to be the most abundant and vital mRNA internal modification which has emerged as an extensively gene expression-modulation mechanism in diverse physiological processes ^6–8. m⁶A can be installed by methyltransferases termed "writers", removed by demethylases termed "erasers", and recognized by specific RNA-binding proteins termed "readers" ⁹. Increasing evidences have demonstrated the crucial impacts of m⁶A methylation modification on the maintenance of tumor cell stemness ¹⁰, DNA damage response ¹¹, metastasis ¹² and drug resistance ¹³, et al. Nevertheless, the roles of m⁶A RNA methylation in LGGs still remain unexplored.

Circumvent immune recognition is a hallmark of tumor ¹⁴. It can occur through various mechanisms including tumor cell alterations that decrease immune recognition ^15–17 and increase resistance to immunity cytotoxic effects ^{18, 19} as well as the establishment of the immunosuppressive microenvironment ^20–22. The significance of non-neoplastic stromal cells in the development of LGGs has been gradually recognized ^{23, 24}. Considering its extraordinary immunosuppressive microenvironment, LGGs seem to be refractory to immunotherapies based on T cells including checkpoint inhibition and chimeric antigen receptor T cell transfer ²⁵. This highlights the urgent need to identify novel combination strategies to trigger anti-LGG immunity.

Present studies have revealed the effects of m⁶A RNA methylation on TME modification ^26–28. Here, we systematically correlated the m⁶A modification phenotypes with genomic and clinical characteristics of LGGs and established a novel scoring scheme to quantify the m⁶A methylation modification patterns for individual LGG patients. Based on the scoring scheme, potential therapeutic drugs were prepared for TME remodeling related pathway analysis. We found that IL-6/JAK/STAT3 signaling inhibition had the potential to sensitize those patients with high m⁶Ascore to immune checkpoint blockade (ICB), and suggested the stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by macrophages (Mϕs). Overall, our research provides possible strategies for overcoming the resistance to chemotherapy and immunotherapy under the specific m⁶A modification pattern for LGG patients.

Acquisition and processing of low grade glioma datasets

Public attainable expression profiles and the corresponding clinical annotation were obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/), the Chinese Glioma Genome Atlas (CGGA) database (http://cgga.org.cn/) and Gene Expression Omnibus database (GSE151213). A total of 1040 LGG patients and mouse bone marrow-derived Mϕs treated with IL-6 were enrolled in the study, including those from TCGA-LGG cohort (n = 457), CCGA_mRNA_seq325 (n = 182), CGGA_mRNA_seq693 (n = 401) and GSE151213. Batch effects of raw data from CGGA were processed by the “sva” package. Then, FPKM values in data from TCGA, CGGA and GEO were transformed into transcripts per kilobase million (TPM) format for further analysis.

Phenotype Classification

Unsupervised clustering analysis was performed to identify m⁶A methylation modification phenotypes based on the expression of 24 m⁶A regulators and m⁶A-modulated phenotypes based on the expression of perturbation genes through “ConsensuClusterPlus” package ²⁹.

Functional annotation

To investigate the contrasts on biological process among m⁶A methylation modification phenotypes, GSVA enrichment analysis ³⁰ was utilized using “GSVA” packages. The hallmark gene sets were obtained from MSigDB database. Gene Ontology (GO) analysis on m⁶A signature genes was performed via “clusterProfiler” package ³¹, which was also used to carry out “GSEA” analysis ³² to determine pathways up- and downregulated between LGG-m⁶Ascore high and low groups. Adjusted p values less than 0.05 were held to be statistically significant.

Exploration of gene expression perturbation among m6A phenotypes

To identify genes correlated with m⁶A modification patterns, LGG patients were grouped into m⁶A clusters. Differently expression genes (DEGs) among three clusters were determined using the “DEseq2” package ³³. The counts data of TCGA-LGG were downloaded from UCSC XENA (https://xenabrowser.net/). The significance criteria for gene filtering were set as log2FC > 1 while adjusted p < 0.05.

Tumor microenvironment estimation and immune response prediction

The infiltration abundance of each cell in the tumor microenvironment (TME) was quantified by ssGSEA algorithm. The gene sets marked immune cell types were achieved from the research of Charoentong³⁴. CIBERSORT, a deconvolution algorithm was performed to infer cell type proportions in bulk tumor samples ³⁵. In addition, we used the ESTIMATE algorithm to predict immune and stromal cells infiltration and further infer tumor purity ³⁶. Finally, TIDE algorithm was utilized to quantify the tumor immune evasion patterns in tumor samples and predict immunotherapy benefits of each patient ³⁷.

Generation of m⁶A methylation modification signature

To quantify the m⁶A modification patterns in individual samples, a scoring system termed m6Ascore was contrasted as following procedures:

The DEGs among different m⁶A clusters were identified for the overlap gene extract. Then, the LGG patients were stratified for deeper analysis by the overlap DEGs using unsupervised clustering method. After that, principal component analysis (PCA) was constructed to generate m⁶A modification signature. Both principal component 1 and component 2 were considered to serve as signature scores. The advantage of this method focuses the score on the largest block of well-correlated/ inverse-correlated genes in the set, while the contributions of genes which little track with other members were weighted down. Finally, we adopted a former formula to define the m6Ascore of each LGG patient ^{38, 39}: m6Ascore= ∑(PC1i + PC2i), where i is the gene expression in the signature.

Acquisition and processing of drug sensitivity data

Expression profile data from human cancer cell lines were obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) project (https://portals.broadinstitute.org/ccle/) for working as the training set. Drug sensitivity data was achieved from the Cancer Therapeutics Response Portal (CTRP) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset. Both two datasets provide AUC (the area under the curve) values as a drug sensitivity measure. Compounds with more than one fifth of missing data were removed, while K nearest neighbor (k-NN) imputation was used for imputing the missing values. We identified the candidate compounds with different AUC values between LGG-m⁶Ascore groups (log2FC > 0.1). Spearman correlation analysis was adopted to estimate the relationship between AUC value and m⁶Ascore (Spearman’s r < − 0.30).

Statistical analysis

R-4.0.2 was employed for statistical analyses. For quantified data, statistical significance between normally distributed variables was calculated by Student’s t-tests, while that between non-normally distributed variables was checked by the Wilcoxon test. Chi-square test or Fisher exact test was used to exam correlation between two categorical variables. For survival analysis, Kaplan-Meier curves and the Cox proportional hazards models were performed by ‘Survminer’ package. The receiver operating characteristic (ROC) curves was utilized to verify the prognostic prediction performance via ‘timeROC’ package. Statistical significance was corresponded to p values: ns > 0.05, * <0.05, ** <0.01, *** <0.001.

Construction of m6A methylation signatures in LGGs

The structural framework of this study was depicted (Fig. 1).To systematically investigate the m⁶A modification patterns, a total of 24 m⁶A regulators (the landscape of genetic alterations and prognostic analysis were shown in Figure S1) were enrolled for stratifying TCGA-LGG patients, among which IGF2BPs were significantly associated to the patients’ prognosis (Fig. 2A, Figure S2A-G). Those patients with high immune infiltration were observed to have poor prognosis (Fig. 2B-D). Then, we tested known signatures to better describe the function of m⁶A methylation modification in LGG, and confirmed its important regulatory roles in immune, stromal as well as mismatch repair (Fig. 2E). To learn more about the biological behaviors among the distinct m⁶A modification phenotypes, GSVA analysis was performed. The phenotypes with poor prognosis related to pathways enriched in epithelial-mesenchymal transition, mismatch repair and cytokine-related pathway (Fig. 2F). After that, we used the ESTIMATE algorithm to assess the tumor purity levels which was significantly higher in Cluster C (Fig. 2G). CIBERSORT was also used to evaluate the fraction of TME cells among the m⁶A clusters (Fig. 2H). In addition, LGGs with wild type IDH were mainly clustered into Cluster B&C, while IDH mutant samples were closely relevant to Cluster A (Fig. 2I). Finally, we determined 215 overlapping m⁶A phenotype-related DEGs as the m⁶A gene signature in LGGs (Fig. 2J).

To further investigate the clinical traits and potential biological behavior of the m⁶A modification signature, unsupervised clustering analyses was performed to identify three typical subtypes termed ‘Gene cluster’ (Fig. 3A). Overall survival analysis and progression free survival analysis revealed that patients in gene cluster B experienced the outcomes of poor prognosis (Fig. 3B-C). 24 m⁶A regulators involved in this study showed significant expression perturbations among the three gene clusters (Fig. 3D). A significant higher infraction level of TME cells and elevated expression level of immune-checkpoints were indicated in the gene cluster B (Fig. 3E-F). In addition, we also evaluated the enrichment of mismatch repair and stromal relevant signatures which were remarkably difference among the three gene clusters (Fig. 3G). Furthermore, functional annotations on the genes in the m⁶A gene signature were carried out. The results showed that the m6A-related genes were widely involved in the maintenance of tumor stemness, radiotherapy, proliferation and metabolism and immunity (Fig. 3H).

Quantifying the m6A methylation modification in an independent individual

To quantifying the m⁶A methylation modification in an independent individual, we generated a scoring scheme termed “m⁶Ascore” based on the m⁶A signature via the PCA algorithm. The attribute changes of LGG patients were illustrated by an alluvial diagram (Fig. 4A). m⁶Ascore was positively correlated with the expression levels of CD8⁺ T effectors and immune checkpoints (Fig. 4B), which suggested the pre-existence of immune response in the tumors with high m⁶Ascore and then followed by T-cell exhaustion. Moreover, the m⁶Ascore was markedly positively correlated with EMT signatures but negatively correlated with stromal relevant signatures. The results of GSEA revealed the differentially enriched pathways between the m⁶Ascore groups, such as “MAPK cascade”, “ERK1/2 cascades” and “JAK/STAT signaling” (Fig. 4C). After that, we analyzed the somatic mutation differences between high- and low-m⁶Ascore groups in TCGA-LGG cohort (Fig. 4D). The genes with significantly different distributions among the genes with TOP10 mutant frequency in the TCGA-LGG patients were shown in Figure S5. Then, we analyzed the clinical and molecular features between the LGG-m⁶Ascore groups. The significant distinct distributions of primary location, grade, age and molecular subtypes were observed between the m⁶Ascore groups. Patients with high m⁶score were remarkably associated with worse prognosis (Fig. 4F-H). What’s more, an increasing number of studies have revealed the significant roles of TME in the development of LGGs ⁴⁰ and the feasibility of immunotherapy in brain tumors ^{41, 42}. We assessed the benefit of immune checkpoint blockage (ICB) therapy. Those patients with high m⁶Ascore and worse prognosis also predicted to benefit less from ICB therapy (Fig. 4I-J). Thus, it is urgent for LGG patients to identify ICB sensitization strategies.

Identification of the key m⁶A relevant signaling pathway for immunotherapy enhancement

Instead of merely analyzing gene expression perturbation, we expected to obtain the sensitive drug compounds applied in high-m⁶Ascore LGGs for TME remodeling as well as their associated pathways which might contribute to ICB sensitization. We first identified drug candidates with greater sensitivity to high LGG-m⁶Ascore patients ⁴³. The analyses were based on the drug response data of CTRP ^44–46 and PRISM, including 1,639 compounds. Totally 16 compounds were yielded with higher sensitivity by the analysis, while the most widespread chemotherapy for glioma patients, temozolomide, was detected to had lower sensitivity in high m⁶Ascore patients (Fig. 5A-C). Afterwards, correlation analysis was adopted to select TAK-733, clofarabine, dasatinib and birinapant as the optimal therapeutic agents to those LGGs the immune suppressed microenvironment (Fig. 5D-E). It was found that the sensitivities of LGGs to these four candidates were all positively correlated with the enrichment of IL-6/JAK/STAT3 signaling (Fig. 5F). Together, our data revealed that IL-6/JAK/STAT3 might be a potential therapeutic targeting to trigger anti-LGG immunity to avoid ineffective overtreatment.

Inhibition of IL-6/JAK/STAT3 signaling sensitized LGGs with high m⁶Ascore to immune checkpoint blockade

We first evaluated the relationship between IL-6/JAK/STAT3 signaling activation and prognosis. Higher enrichment of IL-6/JAK/STAT3 signaling was associated with the worse prognosis (Fig. 6A-B). Assessing the ICB treatment benefits, however, we did not observe less enrichment of IL-6/JAK/STAT3 signaling in the ICB-benefit group compared with non-benefit group in overall patients (Fig. 6C). Therefore, we stratified TCGA-LGG patients. It was found that IL-6/JAK/STAT3 signaling was lower enriched in the predicted ICB-beneficial patients with the high LGG-m⁶Ascore, while this pathway was significantly enriched in ICB-beneficial patients with median and low m⁶Ascore (Fig. 6D). Similarly, highly enriched IL-6/JAK/STAT3 signaling associated with poor outcomes (Fig. 6E) was observed in ICB non-benefit LGGs with high m⁶Ascore, while there was no significant difference in the overall CGGA-LGG patients (Fig. 6F-G). Tumor-associated Mϕs have a pivotal role in cancer immunosuppression and therapy resistance ^{24, 47, 48}. We first evaluated the infiltration level and polarization of Mϕs in different m⁶Ascore groups. We found that high-m⁶Ascore group had an equal level of M2 macrophages and higher level of M0 and M1 macrophages compared with the other groups, albeit, the expression level of IL-6 which contributed to the macrophage infiltration and polarization was higher in high-m⁶Ascore group (Fig. 6H-I). IL-6 neutralization has been proved to moderately enhance infiltration of CD3⁺ CD8⁺T cells into the tumors but do not activate these T cells, and reduced recruitment of CD45⁺CD11b⁺F4/80⁺ Mϕs ⁴⁹. We therefore speculated that IL-6 neutralization might lead to insufficient activation of T cells by Mϕs, and perhaps there are certain mechanisms in protecting IL-6 downstream from IL-6 inhibition in the patients with high m⁶Ascore.

RNA-seq data revealed that IL-6 could stimulate the expression of the members of tumor necrosis factor (TNF) superfamilies (cd40, tnfsf4, tnfsf9, tnfsf14, tnfsf15) in mouse bone marrow-derived Mϕs (Fig. 6J-L). We ranked TCGA and CGGA samples according to their IL-6 expression levels from low to high, selected the LGGs with the lowest IL-6 expression, and ensured that there was no significant difference in IL-6 expression levels between the m⁶Ascore groups (Fig. 6M). We were surprised to find that CD40 and TNFSF9 expression were dramatically higher in high-m⁶Ascore group than in other groups (Fig. 6N-R).

In conclusion, our study built a m⁶A scoring scheme, based on which synergistic immunotherapy of IL-6 neutralization and ICB might be considered as an effective therapeutic strategy for LGGs with high m⁶Ascore, for the stimulation of CD40 or TNFSF9 might reverse IL-6 inhibition induced insufficient activation of T cells by Mϕs.

Tumor associated T cell actication by ICB is one of the most successful approaches for cancer immunotherapy and expected to improve the prognosis of LGG patients. However, it is poorly studied in LGGs. Recently, growing efforts have focused on the m⁶A RNA methylation and it’s impacts on cancer immune editing ⁵⁰. Here, we developed the m⁶Ascore to quantify the m⁶A methylation modification patterns in individuals and a dual-targeted strategy including anti-IL-6 and ICB to reverse T cell exhaustion for those patients with high-m⁶Ascore LGGs.

Based on the existing evidence of m⁶A methylation modification network, 24 m⁶A regulators were involved in the study and stratified the the tumor samples. DEGs among the distinct m⁶A phenotypes were identified and considered as a m⁶A relevant signature in LGG, which was a biomarker designed to classify m⁶A modification patterns. The expressions of m⁶A regulators in gene cluster B were significantly higher than in the other two clusters, and the expressions of immune-checkpoints and gatheration of suppressive cells in gene cluster A&C were remarkably decreased. Patients in gene cluster B experienced the worst clinical outcomes.Then, we constructed a scoring scheme termed ‘m⁶Ascore’ to quantify the m⁶A methylation modification patterns which can act as an indipendent prognostic biomarker for LGG patients. In addition, the results of our analyses showed the correlation between m⁶Ascore with the genomic aberrations, especialy with IDH1, EGFR and TP53 mutation status. Then, we determined whether m⁶Ascore can predict immunotherapy benefit in LGG. It is observed that there were less predicted ICB benefits in m⁶Ascore-high group compared with m⁶Ascore-low group. Thus, we expected to explore potential strategies to make those non-benefit patients benefit from immunotherapy.

IL-6, a pleiotropic cytokine, was recognized as a key cytokine to promote inflammation at first ⁵¹. Recent studies has suggested that IL-6/JAK/STAT3 inhibited functional maturation of DC and suppressed effector T cells which blocked anti-tumor immunity ⁵². IL-6 promoted M2 Mϕs polarization ^{53, 54} and tumors with high IL-6 expression with infiltration of PD1⁺CD8⁺ T cells and M2 Mϕs predicted poor prognosis ⁵⁵. In our study, we used cell line expression profiles and their drug sensitivity results to predict the sensitive drugs of TCGA-LGG with high m⁶Ascore by machine learning, and found the key pathway, IL6/JAK/STAT3, related to TME modulation. However, anti-IL-6 therapy showed the modest efficacy to synergize with ICB out of the patients with high ower m⁶Ascore LGGs, suggesting that there are certain mechanisms to protect the downstreams of IL-6 from anti-IL-6 therapy.

Multiple studies have revealed the significant impacts of TNF superfamily on macrophage activation and their co-stimulation of T cells to promote anti-tumor immunity ^{56, 57}. Thus, we analyzed the expression profile of Mϕs treated with IL-6, and found that IL-6 significantly up-regulated the expression of cd40, tnfsf4, tnfsf9, tnfsf14 and tnfsf15. Then, we found that the expression level of CD40 and TNFSF9 was significantly higher in m6Ascore-high group than the other groups when the expression of IL-6 remained same low level at different m⁶Ascore groups. These results illustrate a phynomenon that anti-IL-6 monotherapy might not fully reverse macrophage-mediated immunosuppression. Dual-targeting of CD40/TNFSF9 and IL-6 might cooperate immunotherapy of LGGs.

In summary, our study established the m⁶Ascore to quantifying the m⁶A methylation modification in individuals. For LGG patients with high m⁶Ascores, we found some therapeutic drugs to avoid overtreatment and chemotherapy resistance. In the meanwhile, our findings suggested that dual-targeted stratergy including anti-IL-6 and ICB might trigger anti-tumor immunity for the patients with high-m⁶Ascore LGGs which offered opportunities for facilitating T-cell based immunotherapy against LGGs.

Ethical Approval and Consent to participate

The patient data involved in this study were acquired from publicly available datasets with the patients’ informed consent.

Consent for publication

All authors have read and approved the final submitted manuscript.

Availability of supporting data

Public attainable expression profiles and the corresponding clinical annotation can be obtained from the Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) and the Chinese Glioma Genome Atlas (CGGA) database (http://cgga.org.cn/). Methods used for the analyses and all generated results in this study are described in the supplementary data.

Competing interests

No potential conflicts of interest were disclosed.

Authors' contributions

Q. H. designed the study. Q. H. and HM. H. collated the data, carried out data analyses and produced the initial draft of the manuscript. Q. H. contributed to drafting the manuscript.

Acknowledgments

We would like to appreciate Dr. Zewei Tu for the crucial advice on this study.

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Identification of synergistic strategies for m⁶A methylation-involved immunotherapy enhancement in lower grade gliomas

Status:

Version 1

Abstract

Figures

Introduction

Material And Methods

Acquisition and processing of low grade glioma datasets

Phenotype Classification

Functional annotation

Exploration of gene expression perturbation among m6A phenotypes

Tumor microenvironment estimation and immune response prediction

Generation of m⁶A methylation modification signature

Acquisition and processing of drug sensitivity data

Statistical analysis

Results

Construction of m6A methylation signatures in LGGs

Quantifying the m6A methylation modification in an independent individual

Identification of the key m⁶A relevant signaling pathway for immunotherapy enhancement

Inhibition of IL-6/JAK/STAT3 signaling sensitized LGGs with high m⁶Ascore to immune checkpoint blockade

Discussion

Declarations

References

Supplementary Files

Status:

Version 1

Identification of synergistic strategies for m6A methylation-involved immunotherapy enhancement in lower grade gliomas

Status:

Version 1

Abstract

Figures

Introduction

Material And Methods

Acquisition and processing of low grade glioma datasets

Phenotype Classification

Functional annotation

Exploration of gene expression perturbation among m6A phenotypes

Tumor microenvironment estimation and immune response prediction

Generation of m6A methylation modification signature

Acquisition and processing of drug sensitivity data

Statistical analysis

Results

Construction of m6A methylation signatures in LGGs

Quantifying the m6A methylation modification in an independent individual

Identification of the key m6A relevant signaling pathway for immunotherapy enhancement

Inhibition of IL-6/JAK/STAT3 signaling sensitized LGGs with high m6Ascore to immune checkpoint blockade

Discussion

Declarations

References

Supplementary Files

Status:

Version 1

Identification of synergistic strategies for m⁶A methylation-involved immunotherapy enhancement in lower grade gliomas

Generation of m⁶A methylation modification signature

Identification of the key m⁶A relevant signaling pathway for immunotherapy enhancement

Inhibition of IL-6/JAK/STAT3 signaling sensitized LGGs with high m⁶Ascore to immune checkpoint blockade