Spatial clustering of CD68+ tumor associated macrophages with tumor cells is associated with worse overall survival in metastatic clear cell renal cell carcinoma

Soluble CD206 in metastatic renal cell carcinoma: Relation to clinical-biochemical parameters and patient outcome

The mannose receptor (MR/CD206) is a marker of M2-like tumor-associated macrophages. Membrane CD206 can be shed, releasing the receptor as a soluble protein (sCD206), which can be measured in serum. Here, we investigated the biomarker potential of sCD206 in patients with metastatic renal cell carcinoma (mRCC). Serum sCD206 was measured by an enzyme-linked immunosorbent assay in 88 mRCC patients and 20 healthy controls (HCs). At diagnosis, serum sCD206 was elevated in patients with intermediate-risk mRCC according to the Memorial Sloan Kettering Cancer Center (MSKCC) risk score, compared to both HCs and patients with favorable MSKCC risk score. Furthermore, sCD206 levels correlated with both sCD163 and C-reactive protein. Soluble CD206 levels decreased after treatment initiation (p < .0001 at 5 weeks) but with a tendency toward elevated levels at time of progression, compared to baseline (p = .06). In univariate survival analysis, high levels of serum sCD206 at baseline was a significant risk factor associated with reduced overall survival (hazard ratio [HR] = 1.37, 95% confidence interval: 1.12-1.67, p = .002). Stratified by clinical risk scores, increased sCD206 was still a statistically significant risk factor of overall mortality (p < .01) in the intermediate-risk group by both the MSKCC (HR = 1.48) and the newer International Metastatic RCC Database Consortium (IMDC) score (HR = 1.53). Furthermore, addition of sCD206 as a dichotomized variable to the IMDC risk score enabled separation of the intermediate-risk group into two groups with survival comparable to those with favorable and poor risk, respectively. Overall, sCD206 is a potential add-on biomarker for mRCC patients in the intermediate-risk group of the current clinical risk scores.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Tumor macrophage functional heterogeneity can inform the development of novel cancer therapies

Macrophages represent a key component of the tumor microenvironment (TME) and are largely associated with poor prognosis. Therapeutic targeting of macrophages has historically focused on inhibiting their recruitment or reprogramming their phenotype from a protumor (M2-like) to an antitumor (M1-like) one. Unfortunately, this approach has not provided clinical breakthroughs that have changed practice. Emerging studies utilizing single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics have improved our understanding of the ontogeny, phenotype, and functional plasticity of macrophages. Overlaying the wealth of current information regarding macrophage molecular subtypes and functions has also identified novel therapeutic vulnerabilities that might drive better control of tumor-associated macrophages (TAMs). Here, we discuss the functional profiling of macrophages and provide an update of novel macrophage-targeted therapies in development.

Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

INTRODUCTION

Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME.

METHODS

TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering.

RESULTS

The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively.

CONCLUSION

Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies.

Spatial heterogeneity of tumor microenvironment influences the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is an immunologically and histologically diverse tumor. However, how the structural heterogeneity of tumor microenvironment (TME) affects cancer progression and treatment response remains unclear. Hence, we characterized the TME architectures of ccRCC tissues using imaging mass cytometry (IMC) and explored their associations with clinical outcome and therapeutic response.

METHODS

Using IMC, we profiled the TME landscape of ccRCC and paracancerous tissue by measuring 17 markers involved in tissue architecture, immune cell and immune activation. In the ccRCC tissue, we identified distinct immune architectures of ccRCC tissue based on the mix score and performed cellular neighborhood (CN) analysis to subdivide TME phenotypes. Moreover, we assessed the relationship between the different TME phenotypes and ccRCC patient survival, clinical features and treatment response.

RESULTS

We found that ccRCC tissues had higher levels of CD8⁺ T cells, CD163^- macrophages, Treg cells, endothelial cells, and fibroblasts than paracancerous tissues. Immune infiltrates in ccRCC tissues distinctly showed clustered and scattered patterns. Within the clustered pattern, we identified two subtypes with different clinical outcomes based on CN analysis. The TLS-like phenotype had cell communities resembling tertiary lymphoid structures, characterized by cell-cell interactions of CD8⁺ T cells-B cells and GZMB⁺CD8⁺ T cells-B cells, which exhibited anti-tumor features and favorable outcomes, while the Macrophage/T-clustered phenotype with macrophage- or T cell-dominated cell communities had a poor prognosis. Patients with scattered immune architecture could be further divided into scattered-CN-hot and scattered-CN-cold phenotypes based on the presence or absence of immune CNs, but both had a better prognosis than the macrophage/T-clustered phenotype. We further analyzed the relationship between the TME phenotypes and treatment response in five metastatic ccRCC patients treated with sunitinib, and found that all three responders were scattered-CN-hot phenotype while both non-responders were macrophage/T-clustered phenotype.

CONCLUSION

Our study revealed the structural heterogeneity of TME in ccRCC and its impact on clinical outcome and personalized treatment. These findings highlight the potential of IMC and CN analysis for characterizing TME structural units in cancer research.

Bioinformatic analyses reveal the prognostic significance and potential role of ankyrin 3 (ANK3) in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most aggressive cancer type of the urinary system. Metastatic KIRC patients have poor prognosis and limited therapeutic options. Ankyrin 3 (ANK3) is a scaffold protein that plays important roles in maintaining physiological function of the kidney and its alteration is implicated in many cancers. In this study, we investigated differential expression of ANK3 in KIRC using GEPIA2, UALCAN, and HPA databases. Survival analysis was performed by GEPIA2, Kaplan-Meier plotter, and OSkirc databases. Genetic alterations of ANK3 in KIRC were assessed using cBioPortal database. Interaction network and functional enrichment analyses of ANK3-correlated genes in KIRC were performed using GeneMANIA and Shiny GO, respectively. Finally, the TIMER2.0 database was used to assess correlation between ANK3 expression and immune infiltration in KIRC. We found that ANK3 expression was significantly decreased in KIRC compared to normal tissues. The KIRC patients with low ANK3 expression had poorer survival outcomes than those with high ANK3 expression. ANK3 mutations were found in 2.4% of KIRC patients and were frequently co-mutated with several genes with a prognostic significance. ANK3-correlated genes were significantly enriched in various biological processes, mainly involved in peroxisome proliferator-activated receptor (PPAR) signaling pathway, in which positive correlations of ANK3 with PPARA and PPARG expressions were confirmed. Expression of ANK3 in KIRC was significantly correlated with infiltration level of B cell, CD8+ T cell, macrophage, and neutrophil. These findings suggested that ANK3 could serve as a prognostic biomarker and promising therapeutic target for KIRC.

Geospatial characterization of immune cell distributions and dynamics across the microenvironment in clear cell renal cell carcinoma

INTRODUCTION

In clear cell renal cell carcinoma (ccRCC), tumor-associated macrophage (TAM) induction of CD8+T cells into a terminally exhausted state has been implicated as a major mechanism of immunotherapy resistance, but a deeper biological understanding is necessary.

METHODS

Primary ccRCC tumor samples were obtained from 97 patients between 2004 and 2018. Multiplex immunofluorescence using lymphoid and myeloid markers was performed in seven regions of interest per patient across three predefined zones, and geospatial analysis was performed using Ripley's K analysis, a methodology adapted from ecology.

RESULTS

Clustering of CD163+M2 like TAMs into the stromal compartment at the tumor-stroma interface was associated with worse clinical stage (tumor/CD163+nK(75): stage I/II: 4.4 (IQR -0.5 to 5.1); stage III: 1.4 (IQR -0.3 to 3.5); stage IV: 0.6 (IQR -2.1 to 2.1); p=0.04 between stage I/II and stage IV), and worse overall survival (OS) and cancer-specific survival (CSS) (tumor/CD163+nK(75): median OS-hi=149 months, lo=86 months, false-discovery rate (FDR)-adj. Cox p<0.001; median CSS-hi=174 months, lo=85 months; FDR-adj. Cox p<0.001). An RNA-seq differential gene expression score was developed using this geospatial metric, and was externally validated in multiple independent cohorts of patients with ccRCC including: TCGA KIRC, and the IMmotion151, IMmotion150, and JAVELIN Renal 101 clinical trials. In addition, this CD163+ geospatial pattern was found to be associated with a higher TIM-3+ proportion of CD8+T cells, indicative of terminal exhaustion (tumor-core: 0.07 (IQR 0.04-0.14) vs 0.40 (IQR 0.15-0.66), p=0.05).

CONCLUSIONS

Geospatial clustering of CD163+M2 like TAMs into the stromal compartment at the tumor-stromal interface was associated with poor clinical outcomes and CD8+T cell terminal exhaustion.

A Cuproptosis-Related lncRNAs Signature Could Accurately Predict Prognosis in Patients with Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers. As cuproptosis, a new cell death mechanism proposed recently, differs from all other known mechanisms regulating cell death, we aimed to create prognostic markers using cuproptosis-related long non-coding ribonucleic acids (RNAs; lncRNAs) and elucidate the molecular mechanism.

Methods

Data from transcriptome RNA sequencing of ccRCC samples and the relevant clinical data were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was implemented to obtain the cuproptosis-related lncRNAs. Then, univariate Cox, multivariate Cox, and Least Absolute Shrinkage and Selection Operator Cox analyses were performed to construct the risk signatures. The cuproptosis-related lncRNAs predictive signature was evaluated with receiver operating characteristic curves and subgroup analysis. Finally, Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), tumor immune microenvironment (TIME), and immune checkpoints were performed to explore the relationship between immunity and patient prognosis.

Results

Five cuproptosis-related lncRNAs, including FOXD2-AS1, LINC00460, AC091212.1, AC007365.1, and AC026401.3, were used to construct the signature. In the training and test sets, low-risk groups (as identified by a risk score lower than the median) demonstrated a better prognosis with an area under the curve for 1-, 3-, and 5-year survival being 0.793, 0.716, and 0.719, respectively. GSEA analysis suggested significant enrichment of the tricarboxylic acid cycle and metabolism-related pathways in the low-risk group. Besides, both ssGSEA and TIME suggested that the high-risk group exhibited more active immune infiltration.

Conclusion

We proposed a cuproptosis-related lncRNAs signature, which had the potential for prognoses and prediction. Our findings might contribute to elucidating potential genomic biomarkers and targets for future therapies in the cuproptosis-related signaling pathways.

2022 WUOF/SIU International Consultation on Urological Diseases: Genetics and Tumor Microenvironment of Renal Cell Carcinoma

Renal cell carcinoma is a diverse group of diseases that can be distinguished by distinct histopathologic and genomic features. In this comprehensive review, we highlight recent advancements in our understanding of the genetic and microenvironmental hallmarks of kidney cancer. We begin with clear cell renal cell carcinoma (ccRCC), the most common subtype of this disease. We review the chromosomal and genetic alterations that drive initiation and progression of ccRCC, which has recently been shown to follow multiple highly conserved evolutionary trajectories that in turn impact disease progression and prognosis. We also review the diverse genetic events that define the many recently recognized rare subtypes within non-clear cell RCC. Finally, we discuss our evolving understanding of the ccRCC microenvironment, which has been revolutionized by recent bulk and single-cell transcriptomic analyses, suggesting potential biomarkers for guiding systemic therapy in the management of advanced ccRCC.

From Basic Science to Clinical Translation in Kidney Cancer: A Report from the Second Kidney Cancer Research Summit

The second Kidney Cancer Research Summit was held virtually in October 2020. The meeting gathered worldwide experts in the field of kidney cancer, including basic, translational, and clinical scientists as well as patient advocates. Novel studies were presented, addressing areas of unmet need related to different topics. These include novel metabolic targets, promising immunotherapeutic regimens, predictive genomic and transcriptomic biomarkers, and variant histologies of renal cell carcinoma (RCC). With the development of pioneering technologies, and an unprecedented commitment to kidney cancer research, the field has tremendously evolved. This perspective aims to summarize the different sessions of the conference, outline major advances in the understanding of RCC and discuss current challenges faced by the field.

Geospatial Cellular Distribution of Cancer-Associated Fibroblasts Significantly Impacts Clinical Outcomes in Metastatic Clear Cell Renal Cell Carcinoma

Simple Summary

Cancer-associated fibroblasts (CAFs) are highly prevalent cells in the clear cell renal cell carcinoma (ccRCC) tumor immune microenvironment. CAFs are thought to potentiate tumor proliferation primarily through paracrine interactions, as evidenced by laboratory-based studies. We sought to corroborate these findings using surgically removed tissue samples from 96 patients with metastatic ccRCC and associate geospatial relationships between CAFs and rapidly proliferating tumor cells with survival outcomes. We found that CAFs exhibited more geospatial clustering with proliferating tumor cells than with dying tumor cells, and patients whose samples exhibited higher tumor cell proliferation had worse overall survival and were more likely to be resistant to systemic tyrosine-kinase-inhibiting targeted therapies. Immunotherapy resistance was not associated with the geospatial metrics measured in this analysis. Overall, these findings suggest that close proximity to CAFs potentiates tumor cell proliferation, worsening survival and conferring resistance to targeted therapies.

Abstract

Cancer-associated fibroblasts (CAF) are highly prevalent cells in the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). CAFs exhibit a pro-tumor effect in vitro and have been implicated in tumor cell proliferation, metastasis, and treatment resistance. Our objective is to analyze the geospatial distribution of CAFs with proliferating and apoptotic tumor cells in the ccRCC tumor microenvironment and determine associations with survival and systemic treatment. Pre-treatment primary tumor samples were collected from 96 patients with metastatic ccRCC. Three adjacent slices were obtained from 2 tumor-core regions of interest (ROI) per patient, and immunohistochemistry (IHC) staining was performed for αSMA, Ki-67, and caspase-3 to detect CAFs, proliferating cells, and apoptotic cells, respectively. H-scores and cellular density were generated for each marker. ROIs were aligned, and spatial point patterns were generated, which were then used to perform spatial analyses using a normalized Ripley’s K function at a radius of 25 μm (nK(25)). The survival analyses used an optimal cut-point method, maximizing the log-rank statistic, to stratify the IHC-derived metrics into high and low groups. Multivariable Cox regression analyses were performed accounting for age and International Metastatic RCC Database Consortium (IMDC) risk category. Survival outcomes included overall survival (OS) from the date of diagnosis, OS from the date of immunotherapy initiation (OS-IT), and OS from the date of targeted therapy initiation (OS-TT). Therapy resistance was defined as progression-free survival (PFS) <6 months, and therapy response was defined as PFS >9 months. CAFs exhibited higher cellular clustering with Ki-67⁺ cells than with caspase-3⁺ cells (nK(25): Ki-67 1.19; caspase-3 1.05; p = 0.04). The median nearest neighbor (NN) distance from CAFs to Ki-67⁺ cells was shorter compared to caspase-3⁺ cells (15 μm vs. 37 μm, respectively; p < 0.001). Multivariable Cox regression analyses demonstrated that both high Ki-67⁺ density and H-score were associated with worse OS, OS-IT, and OS-TT. Regarding αSMA+CAFs, only a high H-score was associated with worse OS, OS-IT, and OS-TT. For caspase-3⁺, high H-score and density were associated with worse OS and OS-TT. Patients whose tumors were resistant to targeted therapy (TT) had higher Ki-67 density and H-scores than those who had TT responses. Overall, this ex vivo geospatial analysis of CAF distribution suggests that close proximity clustering of tumor cells and CAFs potentiates tumor cell proliferation, resulting in worse OS and resistance to TT in metastatic ccRCC.