Skip to main content
NCBI home page
Cancer Immunology, Immunotherapy : CII logoLink to Cancer Immunology, Immunotherapy : CII
. 2022 Jan 19;71(8):1923–1935. doi: 10.1007/s00262-021-03123-y

Prognostic value, DNA variation and immunologic features of a tertiary lymphoid structure-related chemokine signature in clear cell renal cell carcinoma

Wenhao Xu 1, Chunguang Ma 1,#, Wangrui Liu 2,#, Aihetaimujiang Anwaier 1,#, Xi Tian 1, Guohai Shi 1, Yuanyuan Qu 1,✉, Shiyin Wei 2,✉, Hailiang Zhang 1,✉, Dingwei Ye 1,✉
PMCID: PMC10992571  PMID: 35043231

Abstract

Background

The tumor microenvironment (TME) and tertiary lymphoid structures (TLS) affect the occurrence and development of cancers. How the immune contexture interacts with the phenotype of clear cell renal cell carcinoma (ccRCC) remains unclear.

Methods

We identified and evaluated TLS clusters in ccRCC using machine learning algorithms and the 12-chemokine gene signature for TLS. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features and predictive value of CXCL13 in ccRCC were performed.

Results

We found a prominently enrichment of the 12-chemokine gene signature for TLS in patients with ccRCC compared with other types of renal cell carcinoma. We identified a prognostic value of CCL4, CCL5, CCL8, CCL19 and CXCL13 expression in ccRCC. DNA deletion of the TLS gene signature significantly predicted poor outcome in ccRCC compared with amplification and wild-type gene signature. We established TLS clusters (C1–4) and observed distinct differences in survival, stem cell-like characteristics, immune cell distribution, response to immunotherapies and VEGF-targeted therapies among the clusters. We found that elevated CXCL13 expression significantly predicted aggressive progression and poor prognosis in 232 patients with ccRCC in a real-world validation cohort.

Conclusion

This study described a 12-chemokine gene signature for TLS in ccRCC and established TLS clusters that reflected different TME immune status and corresponded to prognosis of ccRCC. We confirmed the dense presence of TILs aggregation and TLS in ccRCC and demonstrated an oncogenic role of CXCL13 expression of ccRCC, which help develop immunotherapies and provide novel insights on the long-term management of ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-021-03123-y.

Keywords: Clear cell renal cell carcinoma, Tertiary lymphoid structures, Tumor microenvironment, Prognosis, CXCL13, Machine learning algorithm

Introduction

Renal cell carcinoma (RCC) is one of the most common malignant tumors in the genitourinary system [1, 2]. In China, approximately 70,000 new cases of kidney cancer are diagnosed each year. Clear cell renal cell carcinoma (ccRCC) is the most common and most malignant type of RCC [3, 4]. VEGF-targeted therapies, such as pazopanib, sunitinib, sorafenib, axitinib and cabozantinib, have improved the outcome of patients with metastatic RCC, and the treatment of advanced RCC has continued to evolve with the development of immunotherapies, as represented by immune checkpoint inhibitors (ICIs) [5]. Despite the development of molecular targeted therapy and immunotherapy, the prognosis of patients with advanced ccRCC remains unsatisfactory [6, 7]. Therefore, further classification of advanced ccRCC patients based on molecular biological characteristics is required for better screening of suitable treatment options and implementing precision treatment strategies, with the aim of improving treatment efficacy and patient prognosis [8].

Tumor-infiltrating lymphocytes (TILs) and their metastases with effect and memory functions in primary tumors have proved the importance of the tumor microenvironment (TME) in cancer development [9, 10]. Research on the TME has revealed the production and regulation of anti-tumor defenses in both secondary lymphoid organs and tumor tissues. These organized cell aggregates are called tertiary lymphoid structures (TLSs), which has been proven to exist and be valuable in each disease progression [11–13]. TLS is found in the interstitium, infiltration margin and core of different types of tumors [14, 15]. Also, TLS plays a role in a variety of pathophysiological conditions, including autoimmune diseases, inflammation, and tumorigenesis, driving a series of related effects [16–20]. Dendritic cells in the TLS present tumor antigens to CD8+ cells, CD4+ T cells and B cells and then further activate, proliferate and differentiate [21].

The TLS has normally been detected in the stroma and the invasive margin of different tumors, thus predicting anti-tumor treatment response and overall prognosis in solid malignant tumors [22–25]. Invasive cancer cells characteristically induce alterations in the adjacent stroma, which promotes cancer-associated fibroblasts, regulates tumor immune microenvironment, and in fact actively leads to carcinoma progression. TLS in tumors elicits systemic tumor-specific immune responses, and the presence of TLS is correlated with a better prognosis and predicts favorable response to ICIs [11, 13]. TLS is common in lung metastases from cancers and has been correlated with elevated tumor grade or increased risk of recurrence in cancers [21, 26–29].

In 2020, Catherine et al. proposed a review on the composition and detection of tertiary lymphoid structures in cancer [11]. They summarized the gene signatures for the detection of tertiary lymphoid structures identified from transcriptomic analyses of human cancers, emphasizing the 12-chemokine signature, including CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11 and CXCL13. Organized lymphocyte aggregates release the major chemokines involved in TLS formation in response to chronic inflammation. For example, CXCL13, a B cell chemoattractant, has recently been shown to promote immunotherapy response and prognostic prediction in many cancers [12]. Beside regulating B cell and Tfh cell chemotaxis, CXCL13 normally involved in germinal center formation, lymph node development, humoral immunity and cell proliferation. However, the application of a 12-chemokine gene signature for TLS in ccRCC remains unclear. However, the prognostic role of TLS and its application of a 12-chemokine gene signature for TLS heterogeneity detection in ccRCC remains unclear.

In this study, we explored the role of a TLS-related chemokine gene signature in ccRCC from large-scale multi-omics to real-world data. We hypothesized that the 12-chemokine gene signature could reveal the spatial distribution and maturity of TLS, reflect ccRCC progression and predict treatment responses for ccRCC patients.

Methods and materials

Samples collection and data preprocessing from online and real-world cohorts

Gene expression, copy number variants, tumor somatic mutations and matched clinical information of kidney ccRCC, kidney renal papillary cell carcinoma and kidney chromophobe (KIRC, KIRP and KICH, respectively) from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov) cohort were obtained. Patients without survival information were removed from further prognostic analysis. A total of 232 ccRCC samples with available clinical and pathologic electronic records were enrolled from our institute (Fudan University Shanghai Cancer Center, FUSCC, Shanghai, China). The clinicopathological characteristics of 530 ccRCC patients from TCGA and 232 ccRCC patients from the FUSCC cohort are shown in Table S1 and Table S2.

Survival analysis and unsupervised clustering of the 12-chemokine gene signature for TLS in ccRCC

Pearson’s correlation analysis was applied to assess the associations among the 12-chemokine gene signatures for TLS. For Kaplan–Meier curves, P values and hazard ratio with 95% confidence interval were generated by log-rank tests and univariate Cox proportional hazards regression. Using expression of the 12-gene chemokine signature, different TLS clusters were identified by unsupervised cluster analysis, and the patients were classified for further analysis. The number of clusters and their stability were determined by a consensus clustering algorithm. We performed the above steps using the “ConsensusClusterPlus” software package with 1000 repeats to ensure the stability of the classification.

Differential gene expression analysis and functional enrichment analysis

To explore the potential biological differences between TLS clusters, the “Limma” package was used to identify differentially expressed genes (DEGs); the threshold value was set as P<0.05, | log2 (fold change) | ≥1.0. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, were performed to explore the potential functions of the DEGs.

TME immune cell infiltration estimation

To evaluate the absolute proportion of 22 types of infiltrating immune cells in ccRCC samples from TCGA, we used the CIBERSORT algorithm. The analysis was performed with the CIBERSORT algorithm based on the deconvolution using the “CIBERSORT” R package (http://cibersort.stanford.edu/). The reliability of the deconvolution method was utilized for transcriptional enrichment of immunologic cell types. The “CIBERSORT” R package provides a P value for each sample using the default feature matrix with perm=100 times for analysis.

Copy number variant analysis, immunotherapy response prediction and IC50 evaluation for VEGF-targeted drug efficacy of TLS clusters

To explore the potential associations between copy number variants and TLS clusters, we used Genomic Identification of Significant Targets in Cancer (GISTIC, version 2.0) to find the significantly amplified or deleted regions using TCGA copy number data obtained from Gene Set Cancer Analysis (GSVA, http://bioinfo.life.hust.edu.cn/GSCA/). Tumor Immune Dysfunction and Exclusion (TIDE) was used to estimate the immunotherapy response on the basis of expression profiles [30]. Genomics of Drug Sensitivity in Cancer (GDSC) is currently the largest public pharmacogenomics database (https://www.cancerrxgene.org/). We evaluated VEGF-targeted drug efficacy based on individual transcriptomes and half inhibitory concentrations (IC50), an important indicator predicting the drug treatment response, of each ccRCC sample using the “pRRophetic” R package.

Assessment of hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining

Histopathological records of unspecific lymphocytic infiltration on HE slides were examined according to previous reports [31] and assessed by two experienced pathologists independently. IHC was performed to evaluate the expression level of CXCL13 (1:1000 dilution; ab246518; Abcam, USA) and PD-L1 (1:300 dilution; 13684, CST, China) in ccRCC samples from FUSCC according to standard procedures. Staining was independently evaluated by two experienced pathologists. The overall IHC score, ranging from 0 to 12, was calculated as the product of the staining intensity score (ranging from 0 to 3) and density score (ranging from 0 to 4), as previously described [32]. Cases with scores from 0 to 3 were classified in the low CXCL13 expression group, and cases with scores from 4 to 12 were classified in the high CXCL13 expression group. The definition of PD-L1 protein expression is the percentage of cell membrane positivity exhibited by tumor cells of any intensity. Cytoplasmic staining (if present) does not participate in scoring. The staining intensity is divided into 0 (absent), 1+ (weak intensity), 2+ (moderate intensity) and 3+ (strong intensity) according to the staining results of the positive control.

Statistical analysis

Kruskal–Wilcoxon test was used to conduct comparisons among TLS clusters. The Kaplan–Meier curve survival method was used to conduct survival analysis, and the cutoff value was defined via “survminer” R package. Log-rank test was used to detect significance. A P value less than 0.05 was considered as statistically significant.

Results

Expression and prognostic implication of the 12-chemokine gene signature for TLS in ccRCC

To elucidate the general role of the 12-chemokine gene TLS-related signature in clear cell, papillary cell and chromophobe RCC (KIRC, KIRP and KICH), we performed GSEA using TCGA datasets. We found a significant enrichment of the TLS-related signature in patients with ccRCC (KIRC) (NSE=1.84, P=1.63e-04) compared with that in patients with KIRP and KICH (Figure 1A). Pearson’s correlation was applied to analyze the correlation among the TLS signatures. The results revealed positive correlations among TLS signatures (Figure 1B).

Fig. 1.

Fig. 1

Open in a new tab

Expression and prognostic implication of the 12-chemokine gene signature for TLS in ccRCC. A GSEAs were performed to elucidate the general role of the chemokine gene signature, including CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11 and CXCL13, in clear cell, papillary cell and chromophobe renal cell carcinoma (KIRC, KIRP and KICH, respectively) in TCGA. B Pearson’s correlation was applied to analyze the correlation among TLS-signatures. C Differential expression patterns of genes in the TLS signature in 533 ccRCC and 72 normal kidney tissues. D Cox regression analysis was performed to demonstrate the association of genes in the TLS signature with survival in 530 ccRCC patients. E Kaplan–Meier survival curve analysis revealed dramatic overall survival benefits in 530 patients with ccRCC on the basis of expression of genes in the TLS-related signature

We next compared the expression patterns of genes in the signature in 533 ccRCC cases and 72 normal kidney tissues. As shown in Figure 1C, CCL3, CCL4, CCL5, CCL18, CXCL9, CXCL10, CXCL11 and CXCL13 showed significant differences in expression between tumor and normal tissues (FDR<0.05, P<0.05). In addition, Cox regression analysis demonstrated significant associations with survival on the basis of CXCL13, CCL8, CCL5, CCL4, CCL3 and CCL19 expression in the 530 ccRCC samples from TCGA cohort (Figure 1D). Kaplan–Meier survival curve analysis revealed dramatic OS benefits depending on CCL4 (HR=1.35, P=0.046), CCL5 (HR=1.56, P=0.005), CCL8 (HR=1.85, P<0.001), CCL19 (HR=1.52, P=0.009) and CXCL13 (HR=1.68, P<0.001) expression in the 530 patients with ccRCC (Figure 1E).

Copy number variation (CNV) of chemokine gene signature for TLS in ccRCC

We then dissected the genomic landscape by whole exome sequencing and analyzed both heterozygous and hemozygous CNVs and prognosis. To investigate CNVs, we parsed the amplification and deletion profiles of the 12-chemokine gene signature for TLS, as shown in Figure 2A. The profiles of heterozygous and hemozygous CNVs of the 12-chemokine gene signature in ccRCC are shown in Figure 2B–C. The results suggested more prevalent CNV alterations in heterozygous amplification and deletion. Only CXCL13 showed hemozygous deletion among genes in the TLS signature. Notably, we found that DNA alteration of the TLS signature significantly predicted progression-free survival (PFS) (P=5e-08) and OS (P=2.3e-05). In addition, ccRCC patients with the deletion of the TLS gene set (n=153) showed a poorer prognosis compared with patients with amplification (n=28) and patients with the wild-type gene set (n=319) (Figure 2D–E).

Fig. 2.

Fig. 2

Open in a new tab

Copy number variation (CNV) of the 12-gene signature for TLS in ccRCC. A We parsed the amplification and deletion profiles of the 12-chemokine gene signature for TLS to identify CNVs. B–C Profiles of heterozygous and hemozygous CNVs of the 12-chemokine gene signature for ccRCC are shown. D–E Kaplan–Meier analysis showed that patients with deletion (n = 153) of the TLS gene set showed worse PFS and OS compared with patients with amplification (n = 28) or the wild-type (n = 319) gene signature. *P<0.05; **P<0.01

Identification and oncogenic predictive value of TLS clusters in ccRCC

To further explore the value of the TLS gene set in ccRCC, we established four TLS clusters (C1–4) using unsupervised cluster analysis and examined the different pathogenic mechanisms and clinical prognostic characteristics of the different clusters (Figure 3A; Table S3). The heatmap shows hierarchical clustering of the 12 TLS gene expression profiles among the different clusters (Figure 3B).

Fig. 3.

Fig. 3

Open in a new tab

Identification and oncogenic predictive value of TLS clusters in ccRCC. A Establishment of four TLS clusters (C1–4) based on unsupervised cluster analysis. B The heatmap shows hierarchical clustering of the 12 TLS gene expression profiles among different clusters. C TLS clusters signature clarify progression for patients with ccRCC using Kaplan–Meier survival curve analysis. D The mRNA expression-based stemness index (mRNAsi) was calculated to evaluate the stem cell-like characteristics of the samples using the transcriptome and OCLR algorithm, a logistic regression machine learning method

We next examined the prognostic role and TME characteristics of TLS clusters in ccRCC. We found that the TLS clusters were able to discriminate occurrence for patients with ccRCC (P=0.017; Figure 3C). Patients in the TLS-excluded C4 cluster had the best prognosis; patients in the TLS-infiltrated C1 and C2 cluster had better prognosis; and patients in the C3 cluster showed intermediate prognoses.

Cancer progression involves the gradual loss of differentiated cell phenotypes and the acquisition of cells with progenitor/stem cell-like characteristics. Therefore, on the basis of the transcriptome and OCLR algorithm, a logistic regression machine learning method, we calculated the mRNA expression-based stemness index (mRNAsi) to evaluate the stem cell-like characteristics of the samples. As shown in Figure 3D, the TLS-excluded C4 cluster showed the lowest mRNAsi score, indicating that this cluster exhibits elevated cell proliferation and invasion capacities.

Identification and functional annotations for differential expressed genes according to TLS infiltration in ccRCC

We next used the Limma package (version: 3.40.2) of R software to identify the DEGs between the TLS-excluded group C4 (with the worst prognosis) and the TLS-infiltrated groups C1 and C2 (with better prognosis) (Figure S1). The results identified 7 up- and 90 down-regulated DEGs (Table S4), including CCL19 and CCL21, which were significantly down-regulated in the TLS-excluded C4 group compared with levels in the C1 and C2 groups (Figure 4A).

Fig. 4.

Fig. 4

Open in a new tab

Identification and functional annotations for DEGs according to differential TLS infiltration and tumor immune microenvironment characterizations of TLS clusters in ccRCC. A DEGs were identified in the C4 group compared with C1 and C2 groups using Limma R package. B–C KEGG and GO functional enrichment between the TLS-infiltrated C1 and C2 clusters and TLS-excluded C4 cluster. D CIBERSORT algorithm was used to examine the differences in immune cell infiltrations in different TLS clusters. **P<0.01; ***P<0.001

To further explore the functional differences in malignancy, we investigated the metabolic biological process and functional enrichment between the TLS-infiltrated C1 and C2 clusters and TLS-excluded C4 cluster. The results suggested that DEGs were involved in protein digestion and absorption, complement and coagulation cascades, and PI3K-Akt and NF-kappa B signaling pathways (Figure 4B). The down-regulated DEGs were also significantly involved in functions such as extracellular structure and matrix organization (Figure 4C).

Tumor immune microenvironment characterizations of TLS clusters in ccRCC

The biological network results prompt a deeper tumor immune and matrix microenvironment characterizations altered by the TLS using bioinformatics TME composition estimation algorithm. As shown in Figure 4D and Figure S2, we used the CIBERSORT algorithm to examine differences in immune cell infiltration in the TLS clusters. The findings revealed significant infiltration of M2 macrophages, memory-resting CD4+ T cells, resting NK cells, B cell naïve and activated mast cells in the TLS-excluded C4 group, suggesting an immune-suppressive TME with predicted malignant biological behaviors of ccRCC cells. The C1 and C3 groups shared similar immune cell infiltration patterns, with elevated Treg, activated NK cell, CD8+ T cell, Tfh cell, M1 macrophage, and plasma B cell enrichment, revealing an immunosuppressive microenvironment of ccRCC.

TLS clusters predict responses of ccRCC patients to immunotherapy

We next evaluated the immunosuppressive microenvironment in ccRCC tissues to explore new directions for the development of future ccRCC immunotherapy. We assessed the TIDE score of the TLS clusters, which provides signatures of both T cell dysfunction in immunologically hot tumors and T cell exclusion in cold tumors. As shown in Figure 5A, a significantly elevated TIDE score was found in the C4 group (Kruskal–Wallis test, P=4.7e-11). These results indicated T cell exclusion in an immune-cold TME of ccRCC. The mutation frequency of PBRM1, a gene encoding a component of the SWI/SNF complex that predicts favorable responses to ICIs for ccRCC patients, was assessed in different TLS clusters (Figure 5B). The PBRM1 mutation frequency showed significant differences among the different TLS clusters (Chi-square test, P=0.0056). The C4 cluster showed a lower PBRM1 mutation frequency, which predicts interfering with tumor responses to immunotherapy. The mutation frequencies of VHL and BAP1 also differed significantly among the different groups (Chi-square test, P<0.05; Figure S3).

Fig. 5.

Fig. 5

Open in a new tab

TLS clusters predict responses of ccRCC patients to immunotherapy. (A) The TIDE score was assessed in the TLS clusters. B The PBRM1 mutation frequency was evaluated among different groups using Chi-square test. C The expression of CXCL13, CD28, CD47 and CD226 that select patients and predict anti-cancer immunotherapy assessed by RNA sequence data were evaluated using Kruskal–Wallis test. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001

We then evaluated the expressions of biomarkers that predict response to ICIs using RNA sequencing data, as shown in Figure 5C. We found markedly decreased expression of CXCL13, CD28, CD47 and CD226 in the immune-cold TLS-excluded C4 group compared with other groups (Kruskal–Wallis test, P<1.0e-7).

TLS clusters predict responses of ccRCC patients to VEGF-targeted therapies

Numerous VEGF-targeted therapeutic agents have been approved as first-line clinical strategies in advanced RCC, such as sunitinib, sorafenib, pazopanib and axitinib. Therefore, we evaluated the efficacy of VEGF-targeted drugs on the basis of individual transcriptomes and IC50, which reflects the drug response of each ccRCC sample. As shown in Figure 6A–D, the IC50 trends for VEGF-targeted therapeutic agents were significantly consistent among the first-in-class targeted drugs, including sunitinib, sorafenib, pazopanib and axitinib, and the IC50 value was markedly lower in the immune-cold TLS-excluded C4 group compared with those in the other groups (Kruskal–Wallis test, P<0.01). These results suggest elevated sensitivity and effectiveness of conventional molecular VEGF-targeted therapy for the immune-cold TLS-excluded C4 group.

Fig. 6.

Fig. 6

Open in a new tab

TLS clusters predict responses of ccRCC patients to VEGF-targeted therapies. A–D Genomics of Drug Sensitivity in Cancer was used to evaluate the efficacy of VEGF-targeted drugs (sunitinib, sorafenib, pazopanib and axitinib) on the basis of individual transcriptomes and half inhibitory concentration (IC50) of each ccRCC sample. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001

Presence of TLS and prognostic role of CXCL13 in 232 patients with ccRCC from the real-world cohort

Histopathological records of unspecific lymphocytic infiltration on hematoxylin and eosin (H&E) slides have lasts for 40 years [31]. Therefore, we detected the lymphocyte aggregation status and the presence of TLS in 232 patients with ccRCC from FUSCC cohort. As displayed in Figure 7A, mature TLS corresponding to lymphoid follicles including a dense cellular aggregate resembling germinal centers were found in secondary lymphoid structures. Less differentiated structures such as lymphoid aggregates and immature TLS without germinal centers were also observed. Among 100 cases with cancer and para-carcinoma normal tissues on pathological HE slides, 38 cases showed lymphocyte aggregation (38%), 24 cases showed immature TLS (24%) and 16 cases showed mature TLS (16%) (Figure 7B). In samples from the 232 patients with ccRCC, we assessed the expression pattern and prognostic role of CXCL13, which regulates B cell and Tfh cell chemotaxis, germinal center formation, lymph node development, humoral immunity and cell proliferation. Kaplan–Meier survival analysis in the 232 ccRCC patients from the FUSCC cohort demonstrated that elevated CXCL13 expression significantly predicts poor PFS (HR=2.218, P=0.0015) and poor OS (HR=2.218, P=0.0139) (Figure 7C). Subsequently, to portray TLS-associated tumor immune microenvironment of ccRCC, we assessed the expression of PD-L1 among 100 patients with ccRCC after the presence and density of TLS was determined using pathological HE staining. The relative expression of PD-L1 was defined as strong, moderate and absent/weak staining according to the density and intensity (Figure 7E). Twenty-four cases with immature TLS and 16 cases with mature TLS were allocated to the TLS-present group; 38 cases with lymphocyte aggregation and 2 cases with immune-desert TME were allocated to the TLS-absent group. Interestingly, significantly higher PD-L1 expression was found in the TLS-present group compared with 60 patients in the TLS-absent group (P<0.01; Figure 7F), showing 25% cases with strong and 30% cases with moderate expression levels. Overall, these findings also revealed an interplay between the infiltrated TLS within the intratumoral microenvironment, reflecting an active immune reaction, and response to immunotherapy.

Fig. 7.

Fig. 7

Open in a new tab

Presence of TLS and prognostic role of CXCL13 in 232 ccRCC patients from the FUSCC cohort. A Hematoxylin and eosin (HE) slides revealed that mature TLS corresponding to lymphoid follicles including a dense cellular aggregate resembling germinal centers was found in secondary lymphoid structures; less differentiated structures such as lymphoid aggregates and immature TLS without germinal center were also observed. B Cases and percentages of lymphocyte aggregation, immature TLS, and mature TLS in 100 cases with cancer and para-carcinoma normal tissues in HE slides. C IHC staining of CXCL13. D Kaplan–Meier survival curve analysis showed that elevated CXCL13 expression predicted poor PFS and OS in 232 ccRCC patients from the FUSCC cohort. E The expression of PD-L1 in 100 patients with ccRCC was determined and defined as strong, moderate, and absent/weak staining using IHC analysis. F Twenty-four cases with immature TLS and 16 cases with mature TLS were allocated to the TLS-present group; 38 cases with lymphocyte aggregation; and 2 cases with immune-desert TME were allocated to the TLS-absent group. Chi-square test was utilized to assess differential PD-L1 expression in the TLS-present group and TLS-absent group. *P<0.05; **P<0.01

Discussion

ccRCC is the most common type of malignant kidney cancer and has been the subject of intensive research [32, 33]. Currently approved targeted therapies and ICIs have improved the management of patients with metastatic RCC [34], and numerous targeted agents and immune-related therapies have been developed for the treatment of advanced ccRCC [35–37]. However, the therapeutic effect of immunotherapy is not consistent among patients and there is a lack of relevant biomarkers; therefore, predicting treatment response and long-term prognosis has remained challenging [38, 39]. In this study, we examined the role of a 12-chemokine gene signature for TLS in ccRCC with the aim of providing new insights for the personalized treatment and management of ccRCC.

The TME plays critical roles in regulating tumor occurrence and development and reflects the anti-tumor responses [40–42]. Recent studies have shown that the immune system plays an important role in cancer response. Cancer immunotherapy involves the activation of the immune system to expand and infiltrate effector cells into tumor tissues to destroy tumor cells. The TLS can recruit lymphocytes into the TME to activate TILs, initiating the anti-tumor immune response [43]. Therefore, the TLS plays key roles in stimulating lymphocytes to exert anti-tumor immune function and is an important source of TILs in TME [31].

Increasing evidence has shown that the TLS plays an important role in controlling tumor invasion and metastasis. The TLS has a beneficial effect on the overall survival and disease-free survival of patients with lung cancer, colorectal cancer, pancreatic cancer and breast cancer [44–49]. Interestingly, pro-cancer roles of TLS were also detected in several cancers. For example, one study showed that the presence of TLS in liver tissues near liver cancer is associated with an increased risk of late recurrence [21]. In addition, the TLS can provide a favorable growth environment for malignant hepatocyte precursor cells. TLS located in non-cancerous liver tissues helps promote tumors. The inflammatory state of the tumor and the TLS in the tumor reflects the anti-tumor immunity status. We speculate that the poor prognosis and challenges in treatment of ccRCC may be related to the TLS and related inflammation. Advanced ccRCC often causes systemic inflammation because cancer cells secrete cytokines or chemokines to reshape the immune system, and the inflammation of cancer cells can trigger the metastatic cascade [50]. TLS promotes inflammation and thus affects the immune environment, which in turn controls tumor invasion and progression.

An attractive hypothesis is that the special spatial structure of TLS may be conducive to the priming activity of immunoglobulin G, so that locally produced anti-tumor immunoglobulin G enhances the antigen presentation ability through its receptor [51]. Therefore, revealing how the TLS coordinates the TME could provide a deeper understanding of the immune response in tumors. Our study indicates that the TLS may influence the prognosis of ccRCC patients by affecting the TME. Previous studies have found that in some cancers, CD8+ TILs with anti-tumor specificity are present, as well as some CD8+ TILs not related to cancer [52, 53]. We found that the immune-cold TLS-excluded C4 group exhibited poor prognosis and intolerance to ICIs, with favorable responses to VEGF-targeted therapies. These findings could provide personalized treatment guidance for ccRCC patients, but failed to evaluate the expression of chemokine signature or density of TLS in immunotherapy responsiveness, which may reduce the strength and rationality of the research evidence.

The specific genes used by TLS to regulate tumors have become a crucial issue. In our study, we found that CXCL13 plays a key role in the influence of TLS on ccRCC. Chemokine ligand 13 (CXCL13) and its cognate receptor CXCR5 (CXCL13/CXCR5 axis) represent a dysfunctional chemokine ligand/receptor axis [54]. Abnormal activation of this axis has been associated with cancer progression. We found that a higher expression of CXCL13 corresponded to a higher TLS density and worse prognosis of ccRCC patients. CXCL13 is enriched in the TLS in gastric cancer, and the development of submucosal TLS has been found to be inseparable from inflammatory factors such as IL-1, IL-22 and IL-23 [55]. Chemokines are key regulators of immune cell transport in the TME and play an important role in initiating and executing anti-tumor immune responses [56, 57]. Tumors use a variety of immunosuppressive regulators to evade host tumor immune surveillance. The recruitment of immunosuppressed cells in the TME is also mediated by different chemokine signaling networks [58, 59]. This indicates that the chemokine signals in the TME may be interrelated. Recent evidence has emphasized the “interrelated” function of the CXCL13/CXCR5 axis and characterized its involvement in anti-tumor immune surveillance and tumor immune evasion mechanisms, which might, however, explain the reasons for "interrelated" in tumors. TLS not only promotes tumor development, but it also inhibits tumor invasion, which may be from the ability of CXCL13 to promote tumor cell development and proliferation by stimulating local inflammation, germinal center formation, lymph node development, and humoral immunity. In this study, we found that a higher expression of CXCL13 corresponded to a worse prognosis in ccRCC. We thus hypothesized that CXCL13, as a key factor of the TLS, regulates the TME and inflammatory environment in ccRCC, thereby affecting the prognosis of patients.

Conclusion

This study described a 12-chemokine gene signature for TLS in ccRCC and established TLS clusters that reflected different TME immune status and corresponded to prognosis of ccRCC. We confirmed the dense presence of TILs aggregation and TLS in ccRCC and demonstrated an oncogenic role of CXCL13 expression of ccRCC, which help develop immunotherapies and provide novel insights on the management of long-term treatment of ccRCC.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (TIF 5084 kb) (5MB, tif)
Supplementary file2 (TIF 3681 kb) (3.6MB, tif)
Supplementary file3 (TIF 94 kb) (94.3KB, tif)
Supplementary file4 (XLSX 11 kb) (11.6KB, xlsx)
Supplementary file5 (XLSX 11 kb) (11.9KB, xlsx)
Supplementary file6 (XLSX 19 kb) (19.3KB, xlsx)
Supplementary file7 (CSV 1955 kb) (1.9MB, csv)
Supplementary file8 (XLSX 13 kb) (13.2KB, xlsx)

Acknowledgements

We are grateful to all patients for their dedicated participation in the current study. We expressed our sincere gratitude to Ms. ZOO for editing figures.

Abbreviations

ccRCC

clear cell renal cell carcinoma

CI

confidence interval

FUSCC

Fudan University Shanghai Cancer Center

GDSC

Genomics of Drug Sensitivity in Cancer

GISTIC

Genomic Identification of Significant Targets in Cancer

GSVA

Gene Set Cancer Analysis

HE

Hematoxylin and eosin

HR

hazard ratio

IC50

half inhibitory concentration

ICIs

immune checkpoint inhibitor

IHC

immunohistochemistry

KICH

chromophobe renal cell carcinoma

KIRC

clear cell carcinoma

KIRP

papillary cell carcinoma

RCC

Renal cell carcinoma

TCGA

The Cancer Genome Atlas

TIDE

Tumor Immune Dysfunction and Exclusion

TILs

Tumor-infiltrating lymphocytes

TLSs

tertiary lymphoid structures

TME

tumor microenvironment

Authors’ contributions

WX, MC, WL and HZ done conceptualization. WX, MC, WL, JW, XT and WZ helped in data curation and formal analysis. WS, YQ, HZ and DY were involved in funding acquisition. WX, MC, AA, XT, GS and WL contributed to investigation and methodology. WL, GS, WS, YQ, HZ and DY done resources and software. GS, WS, JW, HZ and DY done supervision. WX, WL, CM and AA validated and visualized the study. WX, AA and WL helped in original draft.WS, YQ, HZ and DY edited the study.

Funding

This work is supported by Grants from National Key Research and Development Project (No. 2019YFC1316000), the National Natural Science Foundation of China (Nos. 81802525,82172817,81872099,82172741), the Natural Science Foundation of Shanghai (No. 20ZR1413100) and Shanghai Municipal Health Bureau (No. 2020CXJQ03).

Declarations

Conflict of interests

The author reports no conflicts of interest in this work.

Ethical approval

All the study designs and test procedures were performed in accordance with the Helsinki Declaration II. The ethics approval and participation consent of this study were approved and agreed by the ethics committee of Fudan University Shanghai Cancer Center (No: 050432-4-1805C, FUSCC, Shanghai, China).

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Wenhao Xu, Chunguang Ma, Wangrui Liu, Aihetaimujiang Anwaier have contribute equally to this work.

Contributor Information

Yuanyuan Qu, Email: quyy1987@163.com.

Shiyin Wei, Email: yjweishiyin@163.com.

Hailiang Zhang, Email: zhanghl918@163.com.

Dingwei Ye, Email: dwyelie@163.com.

References

  • 1.Weiss RH. Metabolomics and metabolic reprogramming in kidney cancer. Semin Nephrol. 2018;38(2):175–182. doi: 10.1016/j.semnephrol.2018.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30. doi: 10.3322/caac.21590. [DOI] [PubMed] [Google Scholar]
  • 3.Xu W, et al. m(6)A Regulator-Mediated Methylation Modification Model Predicts Prognosis, Tumor Microenvironment Characterizations and Response to Immunotherapies of Clear Cell Renal Cell Carcinoma. Front Oncol. 2021;11:709579. doi: 10.3389/fonc.2021.709579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Xu W, et al. Hexokinase 3 dysfunction promotes tumorigenesis and immune escape by upregulating monocyte/macrophage infiltration into the clear cell renal cell carcinoma microenvironment. Int J Biol Sci. 2021;17(9):2205–2222. doi: 10.7150/ijbs.58295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Atkins MB, Tannir NM. Current and emerging therapies for first-line treatment of metastatic clear cell renal cell carcinoma. Cancer Treat Rev. 2018;70:127–137. doi: 10.1016/j.ctrv.2018.07.009. [DOI] [PubMed] [Google Scholar]
  • 6.Elaidi R, et al. Outcomes from second-line therapy in long-term responders to first-line tyrosine kinase inhibitor in clear-cell metastatic renal cell carcinoma. Ann Oncol. 2015;26(2):378–85. doi: 10.1093/annonc/mdu552. [DOI] [PubMed] [Google Scholar]
  • 7.Wang L, et al. Molecular subtyping of metastatic renal cell carcinoma: implications for targeted therapy. Mol Cancer. 2014;13:39. doi: 10.1186/1476-4598-13-39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Zheng, J., et al. (2021) Traditional Chinese medicine Bu-Shen-Jian-Pi-Fang attenuates glycolysis and immune escape in clear cell renal cell carcinoma: results based on network pharmacology. Biosci Rep. 41(6) [DOI] [PMC free article] [PubMed]
  • 9.Galon J, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313(5795):1960–4. doi: 10.1126/science.1129139. [DOI] [PubMed] [Google Scholar]
  • 10.Fridman WH, et al. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol. 2017;14(12):717–734. doi: 10.1038/nrclinonc.2017.101. [DOI] [PubMed] [Google Scholar]
  • 11.Cabrita R, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561–565. doi: 10.1038/s41586-019-1914-8. [DOI] [PubMed] [Google Scholar]
  • 12.Groeneveld CS, et al. Tertiary lymphoid structures marker CXCL13 is associated with better survival for patients with advanced-stage bladder cancer treated with immunotherapy. Eur J Cancer. 2021;148:181–189. doi: 10.1016/j.ejca.2021.01.036. [DOI] [PubMed] [Google Scholar]
  • 13.Petitprez F, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature. 2020;577(7791):556–560. doi: 10.1038/s41586-019-1906-8. [DOI] [PubMed] [Google Scholar]
  • 14.Dieu-Nosjean MC, et al. Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol Rev. 2016;271(1):260–75. doi: 10.1111/imr.12405. [DOI] [PubMed] [Google Scholar]
  • 15.Sautès-Fridman C, et al. Tertiary lymphoid structures in cancers: prognostic value, regulation, and manipulation for therapeutic intervention. Front Immunol. 2016;7:407. doi: 10.3389/fimmu.2016.00407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Martinet L, et al. Human solid tumors contain high endothelial venules: association with T- and B-lymphocyte infiltration and favorable prognosis in breast cancer. Cancer Res. 2011;71(17):5678–87. doi: 10.1158/0008-5472.CAN-11-0431. [DOI] [PubMed] [Google Scholar]
  • 17.Germain C, et al. Presence of B cells in tertiary lymphoid structures is associated with a protective immunity in patients with lung cancer. Am J Respir Crit Care Med. 2014;189(7):832–44. doi: 10.1164/rccm.201309-1611OC. [DOI] [PubMed] [Google Scholar]
  • 18.Kroeger DR, Milne K, Nelson BH. Tumor-infiltrating plasma cells are associated with tertiary lymphoid structures, cytolytic t-cell responses, and superior prognosis in ovarian cancer. Clin Cancer Res. 2016;22(12):3005–15. doi: 10.1158/1078-0432.CCR-15-2762. [DOI] [PubMed] [Google Scholar]
  • 19.Goc J, et al. Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res. 2014;74(3):705–15. doi: 10.1158/0008-5472.CAN-13-1342. [DOI] [PubMed] [Google Scholar]
  • 20.Di Caro G, et al. Occurrence of tertiary lymphoid tissue is associated with T-cell infiltration and predicts better prognosis in early-stage colorectal cancers. Clin Cancer Res. 2014;20(8):2147–58. doi: 10.1158/1078-0432.CCR-13-2590. [DOI] [PubMed] [Google Scholar]
  • 21.Finkin S, et al. Ectopic lymphoid structures function as microniches for tumor progenitor cells in hepatocellular carcinoma. Nat Immunol. 2015;16(12):1235–44. doi: 10.1038/ni.3290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Coppola D, et al. Unique ectopic lymph node-like structures present in human primary colorectal carcinoma are identified by immune gene array profiling. Am J Pathol. 2011;179(1):37–45. doi: 10.1016/j.ajpath.2011.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Messina JL, et al. 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep. 2012;2:765. doi: 10.1038/srep00765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Prabhakaran S, et al. Evaluation of invasive breast cancer samples using a 12-chemokine gene expression score: correlation with clinical outcomes. Breast Cancer Res. 2017;19(1):71. doi: 10.1186/s13058-017-0864-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Helmink BA, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 2020;577(7791):549–555. doi: 10.1038/s41586-019-1922-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Remark R, et al. Characteristics and clinical impacts of the immune environments in colorectal and renal cell carcinoma lung metastases: influence of tumor origin. Clin Cancer Res. 2013;19(15):4079–91. doi: 10.1158/1078-0432.CCR-12-3847. [DOI] [PubMed] [Google Scholar]
  • 27.Koti M, et al. Tertiary lymphoid structures associate with tumour stage in urothelial bladder cancer. Bladder Cancer. 2017;3(4):259–267. doi: 10.3233/BLC-170120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Liu X, et al. Distinct Tertiary Lymphoid Structure Associations and Their Prognostic Relevance in HER2 Positive and Negative Breast Cancers. Oncologist. 2017;22(11):1316–1324. doi: 10.1634/theoncologist.2017-0029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Gu-Trantien C, et al. CD4(+) follicular helper T cell infiltration predicts breast cancer survival. J Clin Invest. 2013;123(7):2873–92. doi: 10.1172/JCI67428. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Jiang P, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24(10):1550–1558. doi: 10.1038/s41591-018-0136-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Sautes-Fridman C, et al. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer. 2019;19(6):307–325. doi: 10.1038/s41568-019-0144-6. [DOI] [PubMed] [Google Scholar]
  • 32.Xu WH, et al. Large-scale transcriptome profiles reveal robust 20-signatures metabolic prediction models and novel role of G6PC in clear cell renal cell carcinoma. J Cell Mol Med. 2020;24(16):9012–9027. doi: 10.1111/jcmm.15536. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Ma CG, et al. Identification and validation of novel metastasis-related signatures of clear cell renal cell carcinoma using gene expression databases. Am J Transl Res. 2020;12(8):4108–4126. [PMC free article] [PubMed] [Google Scholar]
  • 34.Miao D, et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science. 2018;359(6377):801–806. doi: 10.1126/science.aan5951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Li Q, et al. Single-cell sequencing to identify six heat shock protein (HSP) genes-mediated progression subtypes of clear cell renal cell carcinoma. Int J Gen Med. 2021;14:3761–3773. doi: 10.2147/IJGM.S318271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Armesto, M., et al., Integrated mRNA and miRNA Transcriptomic Analyses Reveals Divergent Mechanisms of Sunitinib Resistance in Clear Cell Renal Cell Carcinoma (ccRCC). Cancers (Basel), 2021. 13(17) [DOI] [PMC free article] [PubMed]
  • 37.He W, et al. NUPR1 is a novel potential biomarker and confers resistance to sorafenib in clear cell renal cell carcinoma by increasing stemness and targeting the PTEN/AKT/mTOR pathway. Aging (Albany NY) 2021;13(10):14015–14038. doi: 10.18632/aging.203012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Hua X, et al. A costimulatory molecule-related signature in regard to evaluation of prognosis and immune features for clear cell renal cell carcinoma. Cell Death Discov. 2021;7(1):252. doi: 10.1038/s41420-021-00646-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Suryanti S, et al. High immunoexpression of COX-2 as a metastatic risk factor in ccRCC without PD-L1 involvement. Res Rep Urol. 2021;13:623–630. doi: 10.2147/RRU.S324510. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Xu WH, et al. Prognostic value and immune infiltration of novel signatures in clear cell renal cell carcinoma microenvironment. Aging (Albany NY) 2019;11(17):6999–7020. doi: 10.18632/aging.102233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Wolf MM, Kimryn Rathmell W, Beckermann KE. Modeling clear cell renal cell carcinoma and therapeutic implications. Oncogene. 2020;39(17):3413–3426. doi: 10.1038/s41388-020-1234-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Xu, W., et al., Systematic Genome-Wide Profiles Reveal Alternative Splicing Landscape and Implications of Splicing Regulator DExD-Box Helicase 21 in Aggressive Progression of Adrenocortical Carcinoma. Phenomics, 2021 [DOI] [PMC free article] [PubMed]
  • 43.Kinker GS, et al. B cell orchestration of anti-tumor immune responses: a matter of cell localization and communication. Front Cell Dev Biol. 2021;9:678127. doi: 10.3389/fcell.2021.678127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Siliņa K, et al. Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 2018;78(5):1308–1320. doi: 10.1158/0008-5472.CAN-17-1987. [DOI] [PubMed] [Google Scholar]
  • 45.Neesse A, et al. Stromal biology and therapy in pancreatic cancer. Gut. 2011;60(6):861–8. doi: 10.1136/gut.2010.226092. [DOI] [PubMed] [Google Scholar]
  • 46.Posch, F., et al., Maturation of tertiary lymphoid structures and recurrence of stage II and III colorectal cancer. Oncoimmunology, 2018. 7(2): p. e1378844. [DOI] [PMC free article] [PubMed]
  • 47.Hiraoka N, et al. Intratumoral tertiary lymphoid organ is a favourable prognosticator in patients with pancreatic cancer. Br J Cancer. 2015;112(11):1782–90. doi: 10.1038/bjc.2015.145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Castino, G.F., et al., Spatial distribution of B cells predicts prognosis in human pancreatic adenocarcinoma. Oncoimmunology, 2016. 5(4): p. e1085147. [DOI] [PMC free article] [PubMed]
  • 49.Wirsing AM, et al. Presence of high-endothelial venules correlates with a favorable immune microenvironment in oral squamous cell carcinoma. Mod Pathol. 2018;31(6):910–922. doi: 10.1038/s41379-018-0019-5. [DOI] [PubMed] [Google Scholar]
  • 50.Nishida J, et al. Epigenetic remodelling shapes inflammatory renal cancer and neutrophil-dependent metastasis. Nat Cell Biol. 2020;22(4):465–475. doi: 10.1038/s41556-020-0491-2. [DOI] [PubMed] [Google Scholar]
  • 51.Montfort A, et al. A strong B-cell response is part of the immune landscape in human high-grade serous ovarian metastases. Clin Cancer Res. 2017;23(1):250–262. doi: 10.1158/1078-0432.CCR-16-0081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Scheper W, et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat Med. 2019;25(1):89–94. doi: 10.1038/s41591-018-0266-5. [DOI] [PubMed] [Google Scholar]
  • 53.Simoni Y, et al. Bystander CD8(+) T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature. 2018;557(7706):575–579. doi: 10.1038/s41586-018-0130-2. [DOI] [PubMed] [Google Scholar]
  • 54.Hussain M, et al. CXCL13/CXCR5 signaling axis in cancer. Life Sci. 2019;227:175–186. doi: 10.1016/j.lfs.2019.04.053. [DOI] [PubMed] [Google Scholar]
  • 55.Kennedy CL, et al. The molecular pathogenesis of STAT3-driven gastric tumourigenesis in mice is independent of IL-17. J Pathol. 2011;225(2):255–64. doi: 10.1002/path.2933. [DOI] [PubMed] [Google Scholar]
  • 56.Nagarsheth N, Wicha MS, Zou W. Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy. Nat Rev Immunol. 2017;17(9):559–572. doi: 10.1038/nri.2017.49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Krieg C, Boyman O. The role of chemokines in cancer immune surveillance by the adaptive immune system. Semin Cancer Biol. 2009;19(2):76–83. doi: 10.1016/j.semcancer.2008.10.011. [DOI] [PubMed] [Google Scholar]
  • 58.Lo Presti E, et al. γδ T cells and tumor microenvironment: from immunosurveillance to tumor evasion. Front Immunol. 2018;9:1395. doi: 10.3389/fimmu.2018.01395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Seliger B. Strategies of tumor immune evasion. BioDrugs. 2005;19(6):347–54. doi: 10.2165/00063030-200519060-00002. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (TIF 5084 kb) (5MB, tif)
Supplementary file2 (TIF 3681 kb) (3.6MB, tif)
Supplementary file3 (TIF 94 kb) (94.3KB, tif)
Supplementary file4 (XLSX 11 kb) (11.6KB, xlsx)
Supplementary file5 (XLSX 11 kb) (11.9KB, xlsx)
Supplementary file6 (XLSX 19 kb) (19.3KB, xlsx)
Supplementary file7 (CSV 1955 kb) (1.9MB, csv)
Supplementary file8 (XLSX 13 kb) (13.2KB, xlsx)

Articles from Cancer Immunology, Immunotherapy : CII are provided here courtesy of Springer

RESOURCES