T and NK cell abundance defines two distinct subgroups of renal cell carcinoma

Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the largest subtype of kidney tumour, with inflammatory responses characterising all stages of the tumour. Establishing the relationship between the genes related to inflammatory responses and ccRCC may help the diagnosis and treatment of patients with ccRCC.

Methods

First, we obtained the data for this study from a public database. After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC. As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable. Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation. The data used for modelling and internal validation came from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (GSE29609) databases. ccRCC data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Tumour data from the E-MTAB-1980 cohort were used for external validation. The GSE40453 and GSE53757 datasets were used to verify the differential expression of inflammation-related gene model signatures (IRGMS). The immunohistochemistry of IRGMS was queried through the Human Protein Atlas (HPA) database. After the adequate validation of the IRGM, we further explored its application by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification.

Results

The IRGM can robustly predict the prognosis of samples from patients with ccRCC from different databases. The verification results show that nomogram can accurately predict the survival rate of patients. Pathway enrichment analysis showed that patients in the high-risk (HR) group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggested that patients with higher IRGM scores had more treatment options.

Conclusions

The IRGMS can effectively predict the prognosis of ccRCC. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.

Development and validation of a novel anoikis-related gene signature in clear cell renal cell carcinoma

Background

Despite numerous treatments available, clear cell renal cell carcinoma (ccRCC) remains a deadly and invasive cancer. Anoikis-related genes (ARGs) are essential regulators of tumor metastasis and development. However, the potential roles of ARGs in ccRCC remain unclear.

Methods

Based on the TCGA-KIRC cohort and GeneCards database, we identified differentially expressed ARGs in ccRCC. Then a 4 ARGs risk model was created by Cox regression and LASSO. The Kaplan-Meier and receiver operating characteristic (ROC) curves were utilized to verify the predictive efficacy of the prognostic signature. Subsequently, the possible molecular mechanism of ARGs was investigated by functional enrichment analysis. To assess the immune infiltration, immune checkpoint genes, and immune function in various risk groups, single sample gene set enrichment (ssGSEA) algorithm was employed. Furthermore, the low-risk and high-risk groups were compared in terms of tumor mutation burden (TMB). Ultimately, we analyzed the protein expression of these four ARGs utilizing the western blot test.

Results

Four genes were utilized to create a risk signature that may predict prognosis, enabling the classification of KIRC patients into groups with low or high risk. The reliability of the signature was examined utilizing survival analysis and ROC analysis. According to the multivariate Cox regression result, the risk score was a reliable independent prognostic predictor for KIRC patients. The novel risk model could differentiate between KIRC patients with various clinical outcomes and represent KIRC's specific immune status. An analysis of the correlation of TMB and risk score indicated a positive correlation between them, with high TMB being potentially linked to worse outcomes.

Conclusion

Based on our findings, the prognostic signature of ARGs may be employed as an independent prognostic factor for ccRCC patients. It may introduce alternative perspectives on prognosis evaluation and serve as a prominent reference for personalized and precise therapy in KIRC.

Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints

BACKGROUND

Technical factors can bias H&E digital slides potentially compromising computational histopathology studies. Here, we hypothesised that sample quality and sampling variation can introduce even greater and undocumented technical fallacy.

METHODS

Using The Cancer Genome Atlas (TCGA) clear-cell renal cell carcinoma (ccRCC) as a model disease, we annotated ~78,000 image tiles and trained deep learning models to detect histological textures and lymphocyte infiltration at the tumour core and its surrounding margin and correlated these with clinical, immunological, genomic, and transcriptomic profiles.

RESULTS

The models reached 95% validation accuracy for classifying textures and 95% for lymphocyte infiltration enabling reliable profiling of ccRCC samples. We validated the lymphocyte-per-texture distributions in the Helsinki dataset (n = 64). Texture analysis indicated constitutive sampling bias by TCGA clinical centres and technically suboptimal samples. We demonstrate how computational texture mapping (CTM) can abrogate these issues by normalising textural variance. CTM-harmonised histopathological architecture resonated with both expected associations and novel molecular fingerprints. For instance, tumour fibrosis associated with histological grade, epithelial-to-mesenchymal transition, low mutation burden and metastasis.

CONCLUSIONS

This study highlights texture-based standardisation to resolve technical bias in computational histopathology and understand the molecular basis of tissue architecture. All code, data and models are released as a community resource.

TLR4 predicts patient prognosis and immunotherapy efficacy in clear cell renal cell carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) constitutes the commonest kidney malignancy. Immunogenic cell death (ICD) is a type of regulated cell death (RCD), which sufficiently activates adaptive immunity. However, ICD's involvement in cancer development is unclear, as well as the associations of ICD effectors with ccRCC prognosis. Methods: RNA-sequencing expression profiles of ccRCC in The Cancer Genome Atlas (TCGA) and normal samples in Gene Expression Omnibus (GEO) were comprehensively investigated. Consensus clustering analysis was employed to determine subgroup members linked to ICD-related genes. Functional enrichment analysis was utilized for the examination of TLR4's biological role, and in vitro cellular assays were utilized for further confirmation. We also used Kaplan-Meier (KM) and Cox regression analyses to assess TLR4's prognostic value. Finally, "CIBERSORT" was employed for immune score evaluation. Results: The associations of ICD effectors with ccRCC prognosis were examined based on TCGA, and 12 genes showed upregulation in ccRCC tissue specimens. Meanwhile, ccRCC cases with upregulated ICD-related genes had increased overall survival. Among these ICD-related genes, TLR4 was selected for subsequent analysis. TLR4 was upregulated in ccRCC samples and independently predicted ccRCC. TLR4 also enhanced the proliferative, migratory and invasive abilities in cultured ccRCC cells. Moreover, TLR4 had close relationships with immune checkpoints and infiltrated immune cells. ccRCC cases with elevated TLR4 expression had prolonged overall survival, suggesting a prognostic value for TLR4. Finally, a pan-cancer analysis demonstrated TLR4 had differential expression in various malignancies in comparison with normal tissue samples. Conclusions: This study revealed prognostic values for ICD-associated genes, particularly TLR4, and experimentally validated the inducing effects of TLR4 on ccRCC progression in vitro. We also demonstrated the associations of TLR4 with immune cell infiltration, providing a novel strategy for prognostic evaluation and a novel therapeutic target in ccRCC.

Immunologic Characterization and T cell Receptor Repertoires of Expanded Tumor-infiltrating Lymphocytes in Patients with Renal Cell Carcinoma

The successful use of expanded tumor-infiltrating lymphocytes (TIL) in adoptive TIL therapies has been reported, but the effects of the TIL expansion, immunophenotype, function, and T cell receptor (TCR) repertoire of the infused products relative to the tumor microenvironment (TME) are not well understood. In this study, we analyzed the tumor samples (n = 58) from treatment-naïve patients with renal cell carcinoma (RCC), "pre-rapidly expanded" TILs (pre-REP TIL, n = 15) and "rapidly expanded" TILs (REP TIL, n = 25) according to a clinical-grade TIL production protocol, with single-cell RNA (scRNA)+TCRαβ-seq (TCRαβ sequencing), TCRβ-sequencing (TCRβ-seq), and flow cytometry. REP TILs encompassed a greater abundance of CD4⁺ than CD8⁺ T cells, with increased LAG-3 and low PD-1 expressions in both CD4⁺ and CD8⁺ T cell compartments compared with the pre-REP TIL and tumor T cells. The REP protocol preferentially expanded small clones of the CD4⁺ phenotype (CD4, IL7R, KLRB1) in the TME, indicating that the largest exhausted T cell clones in the tumor do not expand during the expansion protocol. In addition, by generating a catalog of RCC-associated TCR motifs from >1,000 scRNA+TCRαβ-seq and TCRβ-seq RCC, healthy and other cancer sample cohorts, we quantified the RCC-associated TCRs from the expansion protocol. Unlike the low-remaining amount of anti-viral TCRs throughout the expansion, the quantity of the RCC-associated TCRs was high in the tumors and pre-REP TILs but decreased in the REP TILs. Our results provide an in-depth understanding of the origin, phenotype, and TCR specificity of RCC TIL products, paving the way for a more rationalized production of TILs.

Significance

TILs are a heterogenous group of immune cells that recognize and attack the tumor, thus are utilized in various clinical trials. In our study, we explored the TILs in patients with kidney cancer by expanding the TILs using a clinical-grade protocol, as well as observed their characteristics and ability to recognize the tumor using in-depth experimental and computational tools.

Molecular mechanisms of immunotherapy resistance in triple-negative breast cancer

The emergence of immunotherapy has profoundly changed the treatment model for triple-negative breast cancer (TNBC). But the heterogeneity of this disease resulted in significant differences in immunotherapy efficacy, and only some patients are able to benefit from this therapeutic modality. With the recent explosion in studies on the mechanism of cancer immunotherapy drug resistance, this article will focus on the processes of the immune response; summarize the immune evasion mechanisms in TNBC into three categories: loss of tumor-specific antigen, antigen presentation deficiency, and failure to initiate an immune response; together with the aberrant activation of a series of immune-critical signaling pathways, we will discuss how these activities jointly shape the immunosuppressive landscape within the tumor microenvironment. This review will attempt to elucidate the molecular mechanism of drug resistance in TNBC, identify potential targets that may assist in reversing drug resistance, and lay a foundation for research on identifying biomarkers for predicting immune efficacy and selection of breast cancer populations that may benefit from immunotherapy.

Natural Killer Cells, as the Rising Point in Tissues, Are Forgotten in the Kidney

Natural killer (NK) cells are members of a rapidly expanding family of innate lymphoid cells (ILCs). NK cells play roles in the spleen, periphery, and in many tissues, such as the liver, uterine, lung, adipose, and so on. While the immunological functions of NK cells are well established in these organs, comparatively little is known about NK cells in the kidney. Our understanding of NK cells is rapidly rising, with more and more studies highlighting the functional significance of NK cells in different types of kidney diseases. Recent progress has been made in translating these findings to clinical diseases that occur in the kidney, with indications of subset-specific roles of NK cells in the kidney. For the development of targeted therapeutics to delay kidney disease progression, a better understanding of the NK cell with respect to the mechanisms of kidney diseases is necessary. In order to promote the targeted treatment ability of NK cells in clinical diseases, in this paper we demonstrate the roles that NK cells play in different organs, especially the functions of NK cells in the kidney.

Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

The natural killer cell immunotherapy platform: an overview of the landscape of clinical trials in liquid and solid tumors

The translation of natural killer (NK) cells to the treatment of malignant disease has made significant progress in the last few decades. With a variety of available sources and improvements in both in vitro and in vivo NK cell expansion, the NK cell immunotherapy platform has come into its own. The enormous effort continues to further optimize this platform, including ways to enhance NK cell persistence, trafficking to the tumor microenvironment, and cytotoxicity. As this effort bears fruit, it is translated into a plethora of clinical trials in patients with advanced malignancies. The adoptive transfer of NK cells, either as a standalone therapy or in combination with other immunotherapies, has been applied for the treatment of both liquid and solid tumors, with numerous early-phase trials showing promising results. This review aims to summarize the key advantages of NK cell immunotherapy, highlight several of the current approaches being taken for its optimization, and give an overview of the landscape of clinical trials translating this platform into clinic.

Identification of exosomes-related lncRNAs in clear cell renal cell carcinoma based on Bayesian spike-and-slab lasso approach

Exosomes-related long non-coding RNAs (lncRNAs) have been reported to play significant roles in clear cell renal cell carcinoma (ccRCC). However, there is little known about the relationship between exosomes-related lncRNAs and ccRCC. This study aimed to select optimal prognostic model based on exosomes-related lncRNAs to provide a methodological reference for high-dimensional data. Based on the Cancer Genome Atlas (TCGA) database of 515 ccRCC patients, two risk score models were generated underlying Bayesian spike-and-slab lasso and lasso regression. The optimal model was determined by calculating the area of time-dependent receiver-operating characteristic (ROC) curves in the TCGA and ArrayExpress databases. The immune patterns and sensitivity of immunotherapy between the high and low groups were further explored. Initially, we constructed two risk score models containing 11 and 7 exosomes-related lncRNAs according to Bayesian spike-and-slab lasso and lasso regression respectively. ROC curves revealed that the model constructed by Bayesian spike-and-slab lasso regression was more reliable in predicting survival at 1, 3, and 5 years, yielding an area under the curves (AUCs) of 0.796, 0.732, and 0.742, respectively. Kaplan-Meier (K-M) curves presented that prognosis was poorer in the high-risk score group (P < 0.001). Additionally, the high-risk score group patients were enriched in immune-activating phenotypes and more sensitive to immunotherapy. The exosomes-related lncRNAs model constructed with Bayesian spike-and-slab lasso regression has higher predictive power for ccRCC patients' prognosis, which provides methodological reference for the analysis of high-dimensional data in bioinformatics and guides the tailored treatment of ccRCC patients.

Can microRNAs be utilized as tumor markers for recurrence following nephrectomy in renal cell carcinoma patients? A meta-analysis provides the answer

INTRODUCTION

Renal cell carcinoma (RCC) is an aggressive tumor. Many studies investigated microRNAs (miRs) as RCC prognostic biomarkers, often reporting inconsistent findings. We present a meta-analysis to identify if tissue-derived miRs can be used as a prognostic factor in patients after nephrectomy.

METHODS

Data were obtained from PubMed, Embase, and Web of Science. The hazard ratio with 95% confidence intervals assessed the prognostic value of microRNAs. Outcomes of interest included the prognosis role of microRNAs in overall survival (OS), recurrence-free survival (RFS), and cancer-specific survival (CSS) in nephrectomy patients.

RESULTS

Nine retrospective studies that evaluated microRNAs in 1,541 nephrectomy patients were collected. There were heterogeneities across studies for microRNAs in the 15 studies examining OS, RFS, and CSS (I² = 84.51%; P < 0.01); the random-effect model was calculated (HR = 1.371; (95% CI: 0.831-2.260); P = 0.216).

CONCLUSION

Our study indicated that miRNAs cannot be used as a marker for recurrence in RCC patients after nephrectomy, and researchers shouldn't make the mistake that if miRs can be used as a biomarker in RCC, they cannot be used as a marker after nephrectomy in RCC. As all of these findings were from retrospective studies, further studies are needed to verify the role of microRNAs in clinical trials.

Identification of an immune subtype-related prognostic signature of clear cell renal cell carcinoma based on single-cell sequencing analysis

Background

There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies.

Methods

Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays.

Result

Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis.

Conclusion

Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.

Identification of a Prognostic Model Based on Immune Cell Signatures in Clear Cell Renal Cell Carcinoma

Background. Accumulating evidence substantiated that the immune cells were intricately intertwined with the prognosis and therapy of clear cell renal cell carcinoma (ccRCC). We aimed to construct an immune cell signatures (ICS) score model to predict the prognosis of ccRCC patients and furnish guidance for finding appropriate treatment strategies. Methods. Based on The Cancer Genome Atlas (TCGA) database, the normalized enrichment score (NES) of 184 ICSf was calculated using single-sample gene set enrichment analysis (ssGSEA). An ICS score model was generated in light of univariate Cox regression and Least absolute shrinkage and selection operator (Lasso)-Cox regression, which was independently validated in ArrayExpress database. In addition, we appraised the predictive power of the model via Kaplan-Meier (K-M) curves and time-dependent receiver operating characteristic (ROC) curves. Eventually, immune infiltration, genomic alterations and immunotherapy were analyzed between high and low ICS score groups. Results. Initially, we screened 11 ICS with prognostic impact based on 515 ccRCC patients. K-M curves presented that the high ICS score group experienced a poorer prognosis ( $P<0.001$ ). In parallel, ROC curves revealed a satisfactory reliability of model to predict individual survival at 1, 3, and 5 years, with area under the curves (AUCs) of 0.744, 0.713, and 0.742, respectively. In addition, we revealed that the high ICS score group was characterized by increased infiltration of immune cells, strengthened BAP1 mutation frequency, and enhanced expression of immune checkpoint genes. Conclusion. The ICS score model has higher predictive power for patients’ prognosis and can instruct ccRCC patients in seeking suitable treatment.

Heterogeneity in NK Cell Subpopulations May Be Involved in Kidney Cancer Metastasis

Although substantial progress has been made in the immunotherapy of kidney cancer, its efficacy varies from patient to patient, with many responding suboptimally or even developing metastases. Thus, research on the tumour immune microenvironment and immune cell heterogeneity is essential for kidney cancer treatment. In this study, natural killer (NK) cell populations were isolated using signature genes from the single-cell sequencing data of clear cell renal cell carcinoma (ccRCC) and normal kidney tissues and divided into three subpopulations according to the differences in gene expression profiles: NK(GZMH), NK(EGR1), and NK(CAPG). Gene set enrichment analysis revealed that NK(EGR1) and NK(CAPG) were closely related to tumour metastasis, as shown by kidney cancer metastasis to Hodgkin lymphoma, T-cell leukaemia, and Ki-1+ anaplastic large cell lymphoma. Thus, these two NK cell subpopulations are promising targets for inhibiting metastasis in ccRCC. Our findings revealed heterogeneity in the infiltrating NK cells of kidney cancer, which can serve as a reference for the mechanisms underlying metastasis in kidney cancer.

The tumor and plasma cytokine profiles of renal cell carcinoma patients

Renal cell carcinoma (RCC) accounts for 90% of all renal cancers and is considered highly immunogenic. Although many studies have reported the circulating peripheral cytokine profiles, the signatures between the tumor tissue and matching healthy adjacent renal tissue counterparts have not been explored. We aimed to comprehensively investigate the cytokine landscape of RCC tumors and its correlation between the amount and phenotype of the tumor infiltrating lymphocytes (TILs). We analyzed the secretion of 42 cytokines from the tumor (n = 46), adjacent healthy kidney tissues (n = 23) and matching plasma samples (n = 33) with a Luminex-based assay. We further explored the differences between the tissue types, as well as correlated the findings with clinical data and detailed immunophenotyping of the TILs. Using an unsupervised clustering approach, we observed distinct differences in the cytokine profiles between the tumor and adjacent renal tissue samples. The tumor samples clustered into three distinct profiles based on the cytokine expressions: high (52.2% of the tumors), intermediate (26.1%), and low (21.7%). Most of the tumor cytokines positively correlated with each other, except for IL-8 that showed no correlation with any of the measured cytokine expressions. Furthermore, the quantity of lymphocytes in the tumor samples analyzed with flow cytometry positively correlated with the chemokine-family of cytokines, CXCL10 (IP-10) and CXCL9 (MIG). No significant correlations were found between the tumor and matching plasma cytokines, suggesting that circulating cytokines poorly mirror the tumor cytokine environment. Our study highlights distinct cytokine profiles in the RCC tumor microenvironment and provides insights to potential biomarkers for the treatment of RCC.

Turning cold tumors hot: from molecular mechanisms to clinical applications

Immune checkpoint blockade (ICB) therapies have achieved clinical benefit, but most 'immune-cold' solid tumors are not responsive. The diversity of immune evasion mechanisms remains a key obstacle in turning nonresponsive 'cold' tumors into responsive 'hot' ones. Therefore, exploring the mechanisms of such transitions and tumor immunotyping can provide significant insights into designing effective therapeutic strategies against cancer. Here, we focus on the latest advances regarding local and systemic regulatory mechanisms of immune responses in cold and hot tumors. We also highlight the necessity for tumor immunotyping through the assessment of multiple immunological variables using various diagnostic techniques and biomarkers. Finally, we discuss the challenges and potential clinical applications of immunophenotyping to turn cold tumors hot, which may further guide combined immunotherapies.