Genomic classifications of renal cell carcinoma: a critical step towards the future application of personalized kidney cancer care with pan-omics precision

Comprehensive analysis of heat shock protein 110, 90, 70, 60 families and tumor immune microenvironment characterization in clear cell renal cell carcinoma

Heat shock proteins (HSPs) are a kind of molecular chaperone that helps protein folding, which is closely related to cancer. However, the association between HSPs and clear cell renal clear cell carcinoma (ccRCC) is uncertain. We explored the prognostic value of HSP110, HSP90, HSP70 and HSP60 families in ccRCC and their role in tumor immune microenvironment. The data obtained from the Cancer Genome Atlas (TCGA) were applied to determine the differential expression of HSPs in normal tissues and ccRCC. We comprehensively analyzed the prognostic value of HSPs in ccRCC and constructed a prognostic signature. We further explored the differences of tumor immune microenvironment and targeted therapy based on the signature. Cell proliferation, invasion and metastasis were detected by CCK8 assay, wound healing and transwell. Three clusters were identified with differences in overall survival and tumor stage. 6-gene signature (HSPA8, HSP90B1, HSPA7, HSPA12B, HSPA4L, HSPA1L) was identified to predict ccRCC patients' prognosis. The signature was confirmed in the internal cohort. Survival analysis, receiver operating characteristic (ROC) curve, univariate and multivariate COX regression analysis demonstrated the accuracy and independence of signature. The expression of HSPA7, HSPA8 and HSP90B1 were validated with quantitative real-time PCR. Our signature played a pivotal role in predicting tumor immune microenvironment, immune checkpoint gene expression, drug sensitivity, and tumor mutational burden (TMB) in patients with ccRCC. Our cellular experiments confirmed HSPA7 promotes the proliferation, invasion and metastasis of ccCRC cells. The HSPs signature identified in this study could serve as potential biomarkers for predicting prognosis and treatment response in ccRCC patients. It may provide new ideas for the current research on targeted therapy and immunotherapy strategies for ccRCC patients.

Exploring the dual role of endoplasmic reticulum stress in urological cancers: Implications for tumor progression and cell death interactions

The endoplasmic reticulum (ER) is crucial for maintaining calcium balance, lipid biosynthesis, and protein folding. Disruptions in ER homeostasis, often due to the accumulation of misfolded or unfolded proteins, lead to ER stress, which plays a significant role in various diseases, especially cancer. Urological cancers, which account for high male mortality worldwide, pose a persistent challenge due to their incurability and tendency to develop drug resistance. Among the numerous dysregulated biological mechanisms, ER stress is a key factor in the progression and treatment response of these cancers. This review highlights the dual role of aberrant ER stress activation in urologic cancers, affecting both tumor growth and therapeutic outcomes. While ER stress can support tumor growth through pro-survival autophagy, it primarily inhibits cancer progression via apoptosis and pro-death autophagy. Interestingly, ER stress can paradoxically aid cancer progression through mechanisms such as exosome-mediated immune evasion. Additionally, the review examines how pharmacological interventions, particularly with phytochemicals, can stimulate ER stress-mediated tumor suppression. Key regulators, including PERK, IRE1α, and ATF6, are discussed for their roles in upregulating CHOP levels and triggering apoptosis. In conclusion, a deeper understanding of ER stress in urological cancers not only clarifies the complex interactions between cellular stress and cancer progression but also provides new opportunities for innovative therapeutic strategies.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Radiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma

Radiogenomics, a burgeoning field in biomedical research, explores the correlation between imaging features and genomic data, aiming to link macroscopic manifestations with molecular characteristics. In this review, we examine existing radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the predominant renal cancer, and von Hippel-Lindau (VHL) gene mutation, the most frequent genetic mutation in ccRCC. A thorough examination of the literature was conducted through searches on the PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases. Inclusion criteria encompassed articles published in English between 2014 and 2022, resulting in 10 articles meeting the criteria out of 39 initially retrieved articles. Most of these studies applied computed tomography (CT) images obtained from open source and institutional databases. This literature review investigates the role of radiogenomics, with and without texture analysis, in predicting VHL gene mutation in ccRCC patients. Radiogenomics leverages imaging modalities such as CT and magnetic resonance imaging (MRI), to analyze macroscopic features and establish connections with molecular elements, providing insights into tumor heterogeneity and biological behavior. The investigations explored diverse mutations, with a specific focus on VHL mutation, and applied CT imaging features for radiogenomic analysis. Moreover, radiomics and machine learning techniques were employed to predict VHL gene mutations based on CT features, demonstrating promising results. Additional studies delved into the relationship between VHL mutation and body composition, revealing significant associations with adipose tissue distribution. The review concludes by highlighting the potential role of radiogenomics in guiding targeted and selective therapies.

The potential role of precision medicine to alleviate racial disparities in prostate, bladder and renal urological cancer care

Background

Racial disparities in oncological outcomes resulting from differences in social determinants of health (SDOH) and tumour biology are well described in prostate cancer (PCa) but similar inequities exist in bladder (BCa) and renal cancers (RCCs). Precision medicine (PM) aims to provide personalized treatment based on individual patient characteristics and has the potential to reduce these inequities in GU cancers.

Objective

This article aims to review the current evidence outlining racial disparities in GU cancers and explore studies demonstrating improved oncological outcomes when PM is applied to racially diverse patient populations.

Evidence acquisition

Evidence was obtained from Pubmed and Web of Science using keywords prostate, bladder and renal cancer, racial disparity and precision medicine. Because limited studies were found, preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were not applied but rather related articles were studied to explore existing debates, identify the current status and speculate on future applications.

Results

Evidence suggests addressing SDOH for PCa can reverse racial inequities in oncological outcomes but differences in incidence remain. Similar disparities in BCa and RCC are seen, and it would be reasonable to suggest achieving parity in SDOH for all races would do the same. Research applying a PM approach to different ethnicities is lacking although in African Americans (AAs) with metastatic castrate-resistant prostate cancer (mCRPCa) better outcomes have been shown with androgen receptor inhibitors, radium-223 and sipuleucel. Exploiting the abscopal effect with targeted radiation therapy (RT) and immunotherapy has promise but requires further study, as does defining actionable mutations in specific patient groups to tailor treatments as appropriate.

Conclusion

For all GU cancers, the historical underrepresentation of ethnic minorities in clinical trials still exists and there is an urgent need for recruitment strategies to address this. PM is a promising development with the potential to reduce inequities in GU cancers, however, both improved understanding of race-specific tumour biology, and enhanced recruitment of minority populations into clinical trials are required. Without this, the benefits of PM will be limited.

Comprehensive analysis of disulfidptosis-related genes: a prognosis model construction and tumor microenvironment characterization in clear cell renal cell carcinoma

BACKGROUND

Disulfidptosis, a form of cell death induced by abnormal intracellular accumulation of disulfides, is a newly recognized variety of cell death. Clear cell renal cell carcinoma (ccRCC) is a usual urological tumor that poses serious health risks. There are few studies of disulfidptosis-related genes (DRGs) in ccRCC so far.

METHODS

The expression, transcriptional variants, and prognostic role of DRGs were assessed. Based on DRGs, consensus unsupervised clustering analysis was performed to stratify ccRCC patients into various subtypes and constructed a DRG risk scoring model. Patients were stratified into high or low-risk groups by this model. We focused on assessing the discrepancy in prognosis, TME, chemotherapeutic susceptibility, and landscape of immune between the two risk groups. Finally, we validated the expression and explored the biological function of the risk scoring gene FLRT3 through in vitro experiments.

RESULTS

The different subtypes had significantly different gene expression, immune, and prognostic landscapes. In the two risk groups, the high-risk group had higher TME scores, more significant immune cell infiltration, and a higher probability of benefiting from immunotherapy, but had a worse prognosis. There were also remarkable differences in chemotherapeutic susceptibility between the two risk groups. In ccRCC cells, the expression of FLRT3 was shown to be lower and its overexpression caused a decrease in cell proliferation and metastatic capacity.

CONCLUSIONS

Starting from disulfidptosis, we established a new risk scoring model which can provide new ideas for doctors to forecast patient survival and determine clinical treatment plans.

The success rate of small renal mass core needle biopsy and its impact on lowering benign resection rate

BACKGROUND

Small renal mass (SRM) biopsy remains under-utilized due to stigma. Meanwhile, the alarmingly high benign findings in resected kidney masses highlight the need for improved preoperative diagnosis and patient selection.

METHODS

The purpose of this study is to review the success rate of SRM biopsy and to evaluate its impact on patient management. A total of 168 percutaneous image-guided core needle biopsies (CNBs) of SRMs were retrieved at a tertiary academic center between 2015 and 2019. Subsequent treatment choices, side effects and outcomes were retrospectively reviewed.

RESULTS

The diagnostic rate of CNB was 86.9%. Benign neoplasms accounted for a significant portion (14.3%) of SRM. Renal cell carcinomas (RCCs) were the most common diagnoses (69.6%) as expected. In biopsy-resection correlation, the positive predictive value of CNB was 100%. Tumor typing and subtyping by CNB were highly accurate, 100% and 98.3% respectively. Nuclear grading for clear cell RCC was accurate in 83.8% cases. The CNB results had significant impact on treatment. Most patients with RCCs underwent either resection (54.1%) or ablation (33.9%), in contrast to observation in benign neoplasms (90.5%). Most importantly, the benign resection rate (3.2%) in this series was much lower than the national average.

CONCLUSION

CNB provided accurate diagnoses for the majority of SRMs and revealed benign diagnoses in a subset of clinically suspicious lesions. Employment of CNB in suspicious SRM may help avoid overtreatment for benign lesions.

A Differential Transcriptional Regulome Approach to Unpack Cancer Biology: Insights on Renal Cell Carcinoma Subtypes

Cancer research calls for new approaches that account for the regulatory complexities of biology. We present, in this study, the differential transcriptional regulome (DIFFREG) approach for the identification and prioritization of key transcriptional regulators and apply it to the case of renal cell carcinoma (RCC) biology. Of note, RCC has a poor prognosis and the biomarker and drug discovery studies to date have tended to focus on gene expression independent from mutations and/or post-translational modifications. DIFFREG focuses on the differential regulation between transcription factors (TFs) and their target genes rather than differential gene expression and integrates transcriptome profiling with the human transcriptional regulatory network to analyze differential gene regulation between healthy and RCC cases. In this study, RNA-seq tissue samples (n = 1020) from the Cancer Genome Atlas (TCGA), including healthy and tumor subjects, were integrated with a comprehensive human TF-gene interactome dataset (1122603 interactions between 1289 TFs and 25177 genes). Comparative analysis of DIFFREG profiles, consisting of perturbed TF-gene interactions, from three common subtypes (clear cell RCC, papillary RCC and chromophobe RCC) revealed subtype-specific alterations, supporting the hypothesis that these signatures in the transcriptional regulome profiles may be considered potential biomarkers that may play an important role in elucidating the molecular mechanisms of RCC development and translating knowledge about the genetic basis of RCC into the clinic. In addition, these indicators may help oncologists make the best decisions for diagnosis and prognosis management.

Socioeconomic determinants of racial disparities in survival outcomes among patients with renal cell carcinoma

PURPOSE

Racially driven outcomes in cancer are challenging to study. Studies evaluating the impact of race in renal cell carcinoma (RCC) outcomes are inconsistent and unable to disentangle socioeconomic disparities from inherent biological differences. We therefore seek to investigate socioeconomic determinants of racial disparities with respect to overall survival (OS) when comparing Black and White patients with RCC.

METHODS

We queried the National Cancer Database (NCDB) for patients diagnosed with RCC between 2004 and 2017 with complete clinicodemographic data. Patients were examined across various stages (all, cT1aN0M0, and cM1) and subtypes (all, clear cell, or papillary). We performed Cox proportional hazards regression with adjustment for socioeconomic and disease factors.

RESULTS

There were 386,589 patients with RCC, of whom 46,507 (12.0%) were Black. Black patients were generally younger, had more comorbid conditions, less likely to be insured, in a lower income quartile, had lower rates of high school completion, were more likely to have papillary RCC histology, and more likely to be diagnosed at a lower stage of RCC than their white counterparts. By stage, Black patients demonstrated a 16% (any stage), 22.5% (small renal mass [SRM]), and 15% (metastatic) higher risk of mortality than White patients. Survival differences were also evident in histology-specific subanalyses. Socioeconomic factors played a larger role in predicting OS among patients with SRMs than in patients with metastasis.

CONCLUSIONS

Black patients with RCC demonstrate worse survival outcomes compared to White patients across all stages. Socioeconomic disparities between races play a significant role in influencing survival in RCC.

Biomarkers in renal cell carcinoma and their targeted therapies: a review

Renal cell carcinoma (RCC) is one of the most life-threatening urinary malignancies displaying poor response to radiotherapy and chemotherapy. Although in the recent past there have been tremendous advancements in using targeted therapies for RCC, despite that it remains the most lethal urogenital cancer with a 5-year survival rate of roughly 76%. Timely diagnosis is still the key to prevent the progression of RCC into metastatic stages as well as to treat it. But due to the lack of definitive and specific diagnostic biomarkers for RCC and its asymptomatic nature in its early stages, it becomes very difficult to diagnose it. Reliable and distinct molecular markers can not only refine the diagnosis but also classifies the tumors into thier sub-types which can escort subsequent management and possible treatment for patients. Potential biomarkers can permit a greater degree of stratification of patients affected by RCC and help tailor novel targeted therapies. The review summarizes the most promising epigenetic [DNA methylation, microRNA (miRNA; miR), and long noncoding RNA (lncRNA)] and protein biomarkers that have been known to be specifically involved in diagnosis, cancer progression, and metastasis of RCC, thereby highlighting their utilization as non-invasive molecular markers in RCC. Also, the rationale and development of novel molecular targeted drugs and immunotherapy drugs [such as tyrosine kinase inhibitors and immune checkpoint inhibitors (ICIs)] as potential RCC therapeutics along with the proposed implication of these biomarkers in predicting response to targeted therapies will be discussed.

Establishment of a new prognostic risk model of MAPK pathway-related molecules in kidney renal clear cell carcinoma based on genomes and transcriptomes analysis

Purpose

The mitogen-activated protein kinase (MAPK) signaling pathway is often studied in oncology as the most easily mentioned signaling pathway. This study aims to establish a new prognostic risk model of MAPK pathway related molecules in kidney renal clear cell carcinoma (KIRC) based on genome and transcriptome analysis.

Methods

In our study, RNA-seq data were acquired from the KIRC dataset of The Cancer Genome Atlas (TCGA) database. MAPK signaling pathway-related genes were obtained from the gene enrichment analysis (GSEA) database. We used “glmnet” and the “survival” extension package for LASSO (Least absolute shrinkage and selection operator) regression curve analysis and constructed a prognosis-related risk model. The survival curve and the COX regression analysis were used the “survival” expansion packages. The ROC curve was plotted using the “survival ROC” extension package. We then used the “rms” expansion package to construct a nomogram plot. We performed a pan-cancer analysis of CNV (copy number variation), SNV (single nucleotide variant), drug sensitivity, immune infiltration, and overall survival (OS) of 14 MAPK signaling pathway-related genes using several analysis websites, such as GEPIA website and TIMER database. Besides, the immunohistochemistry and pathway enrichment analysis used The Human Protein Atlas (THPA) database and the GSEA method. Finally, the mRNA expression of risk model genes in clinical renal cancer tissues versus adjacent normal tissues was further verified by real-time quantitative reverse transcription (qRT-PCR).

Results

We performed Lasso regression analysis using 14 genes and created a new KIRC prognosis-related risk model. High-risk scores suggested that KIRC patients with lower-risk scores had a significantly worse prognosis. Based on the multivariate Cox analysis, we found that the risk score of this model could serve as an independent risk factor for KIRC patients. In addition, we used the THPA database to verify the differential expression of proteins between normal kidney tissues and KIRC tumor tissues. Finally, the results of qRT-PCR experiments suggested large differences in the mRNA expression of risk model genes.

Conclusions

This study constructs a KIRC prognosis prediction model involving 14 MAPK signaling pathway-related genes, which is essential for exploring potential biomarkers for KIRC diagnosis.

Role of γδ T Cells in Cancer Progression and Therapy

γδ T cells signify a foundational group of immune cells that infiltrate tumors early on, engaging in combat against cancer cells. The buildup of γδ T cells as cancer advances underscores their significance. Initially, these cells infiltrate and enact cytotoxic effects within the tumor tissue. However, in later stages, the predominant phenotype of γδ T cells undergoes changes in numerous cancers, fostering tumor growth and metastasis. Different mechanisms induced by cancer cell suppress effector action of γδ T cells and even sometimes promote cancer progression. In the early stages, stopping this mechanism clears this challenge and enables γδ T cells to effectively remove cancer cells. Given this context, it becomes imperative to delve into the mechanisms of how γδ T cells function in tumor microenvironment. This review discusses γδ T cells' role across different cancer types.

Protein Kinase C (PKC) Isozymes as Diagnostic and Prognostic Biomarkers and Therapeutic Targets for Cancer

Protein kinase C (PKC) is a large family of calcium- and phospholipid-dependent serine/threonine kinases that consists of at least 11 isozymes. Based on their structural characteristics and mode of activation, the PKC family is classified into three subfamilies: conventional or classic (cPKCs; α, βI, βII, and γ), novel or non-classic (nPKCs; δ, ε, η, and θ), and atypical (aPKCs; ζ, ι, and λ) (PKCλ is the mouse homolog of PKCι) PKC isozymes. PKC isozymes play important roles in proliferation, differentiation, survival, migration, invasion, apoptosis, and anticancer drug resistance in cancer cells. Several studies have shown a positive relationship between PKC isozymes and poor disease-free survival, poor survival following anticancer drug treatment, and increased recurrence. Furthermore, a higher level of PKC activation has been reported in cancer tissues compared to that in normal tissues. These data suggest that PKC isozymes represent potential diagnostic and prognostic biomarkers and therapeutic targets for cancer. This review summarizes the current knowledge and discusses the potential of PKC isozymes as biomarkers in the diagnosis, prognosis, and treatment of cancers.

Exosomal AP000439.2 from clear cell renal cell carcinoma induces M2 macrophage polarization to promote tumor progression through activation of STAT3

Background

Tumorigenic phenotype of M2 tumor-associated macrophages promote tumor progression in response to exosomes cues imposed by tumor cells. However, the effect and underlying mechanisms of clear cell renal cell carcinoma (ccRCC)-derived exosomes (ccRCC-exo) on instructing macrophages phenotype remains unclear.

Methods

Macrophages were cocultured with ccRCC-exo and then evaluate the polarization of macrophages and migration of ccRCC cells. The effect and mechanism of lncRNA AP000439.2 overexpressed or deleted exosomes on macrophages M2 polarization were examined. Xenograft tumor mice model was used for in vivo validation.

Results

The ccRCC-exo significantly activated macrophages to M2 phenotype presented by increased expression of transforming growth factor-beta (TGF-β) and interleukin 10 (IL-10) at mRNA and protein levels, and these M2 macrophages in turn facilitating the migration of ccRCC cells. LncRNA AP000439.2 was highly enriched in the ccRCC-exo. Overexpression of exosomal AP000439.2 promoted M2 macrophage polarization whereas AP000439.2-deficient exosome had the opposite effects. Nuclear-localized AP000439.2 directly interacted with signal transducer and activator of transcription 3 (STAT3) proteins and phosphorylated STAT3 in macrophages. RNA-Seq results showed overexpression of AP000439.2 activated NF-κB signaling pathway. Silencing of STAT3 suppressed overexpression of AP000439.2-induced up-regulation of TGF-β and IL-10 expression, and p65 phosphorylation. AP000439.2-deleted exosome inhibited tumor growth in vivo.

Conclusion

Exosomes from ccRCC deliver AP000439.2 to promote M2 macrophage polarization via STAT3, thus enhancing ccRCC progression, indicating exosomal AP000439.2 might be a novel therapeutic target in ccRCC.

Curcumin in the treatment of urological cancers: Therapeutic targets, challenges and prospects

Urological cancers include bladder, prostate and renal cancers that can cause death in males and females. Patients with urological cancers are mainly diagnosed at an advanced disease stage when they also develop resistance to therapy or poor response. The use of natural products in the treatment of urological cancers has shown a significant increase. Curcumin has been widely used in cancer treatment due to its ability to trigger cell death and suppress metastasis. The beneficial effects of curcumin in the treatment of urological cancers is the focus of current review. Curcumin can induce apoptosis in the three types of urological cancers limiting their proliferative potential. Furthermore, curcumin can suppress invasion of urological cancers through EMT inhibition. Notably, curcumin decreases the expression of MMPs, therefore interfering with urological cancer metastasis. When used in combination with chemotherapy agents, curcumin displays synergistic effects in suppressing cancer progression. It can also be used as a chemosensitizer. Based on pre-clinical studies, curcumin administration is beneficial in the treatment of urological cancers and future clinical applications might be considered upon solving problems related to the poor bioavailability of the compound. To improve the bioavailability of curcumin and increase its therapeutic index in urological cancer suppression, nanostructures have been developed to favor targeted delivery.

PLK4 Is a Potential Biomarker for Abnormal Tumor Proliferation, Immune Infiltration, and Prognosis in ccRCC

Background

PLK4 is highly expressed and associated with poor prognosis in various malignancies. However, the role of PLK4 in clear cell renal cell carcinoma (ccRCC) is still unclear. This study is aimed at analyzing the expression, the potential regulating mechanism, and the role of PLK4 in the ccRCC by bioinformatics.

Methods

PLK4 mRNA expression data and methylation levels in ccRCC were examined using TIMER, UALCAN, MethSurv, NCBI-GEO, and UCSC databases. Quantitative real-time PCR verifies the regulatory relationship between PLK4 and has-miR-214-3p. The GEPIA2 and STRING databases were used to find similar genes of PLK4 and then enriched with R language to analyze their similar genes. Correlations between PLK4 and tumor-infiltrating immune cells and cytokines exerting immunosuppression were analyzed using the TIMER database and the TISIDB databases.

Results

PLK4 mRNA expression levels were significantly higher in ccRCC tissues than in paracancerous tissues. ccRCC tissues had lower DNA methylation levels of PLK4 than normal tissues. Importantly, the high PLK4 expression was associated with poor prognosis in ccRCC patients. The has-miR-214-3p negatively regulates the expression of PLK4. GO and KEGG pathway analysis showed that PLK4 coexpressed genes were mainly associated with multiple immune-related pathways, including cytokinesis, sister chromatid adhesions, and mitotic nuclear division. Our data suggest that the PLK4 expression is closely related to the level of immune infiltration and the cytokines that exert immune suppression, and IPS was significantly higher in the PLK4 low expression group.

Conclusion

The PLK4 expression is associated with the prognosis of ccRCC patients and affects the immune microenvironment of ccRCC, and PLK4 is expected to be a new target for the diagnosis and treatment of ccRCC.

Comparative Analysis for the Distinction of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma in Computed Tomography Imaging Using Machine Learning Radiomics Analysis

Background: ChRCC and RO are two types of rarely occurring renal tumors that are difficult to distinguish from one another based on morphological features alone. They differ in prognosis, with ChRCC capable of progressing and metastasizing, but RO is benign. This means discrimination of the two tumors is of crucial importance. Objectives: The purpose of this research was to develop and comprehensively evaluate predictive models that can discriminate between ChRCC and RO tumors using Computed Tomography (CT) scans and ML-Radiomics texture analysis methods. Methods: Data were obtained from 78 pathologically confirmed renal masses, scanned at two institutions. Data from the two institutions were combined to form a third set resulting in three data cohorts, i.e., cohort 1, 2 and combined. Contrast-enhanced scans were used and the axial cross-sectional slices of each tumor were extracted from the 3D data using a semi-automatic segmentation technique for both 2D and 3D scans. Radiomics features were extracted before and after applying filters and the dimensions of the radiomic features reduced using the least absolute shrinkage and selection operator (LASSO) method. Synthetic minority oversampling technique (SMOTE) was applied to avoid class imbalance. Five ML algorithms were used to train models for predictive classification and evaluated using 5-fold cross-validation. Results: The number of selected features with good model performance was 20, 40 and 6 for cohorts 1, 2 and combined, respectively. The best model performance in cohorts 1, 2 and combined had an excellent Area Under the Curve (AUC) of 1.00 ± 0.000, 1.00 ± 0.000 and 0.87 ± 0.073, respectively. Conclusions: ML-based radiomics signatures are potentially useful for distinguishing ChRCC and RO tumors, with a reliable level of performance for both 2D and 3D scanning.

COLGALT1 is a potential biomarker for predicting prognosis and immune responses for kidney renal clear cell carcinoma and its mechanisms of ceRNA networks

Background

As precision medicine gradually played an inaccessible role in cancer treatment, there was an urgent need to explore biomarkers or signatures for predicting cancer prognosis. Currently, little was known about the associations between COLGALT1 and kidney renal clear cell carcinoma (KIRC). Hence, this study was performed to reveal its roles in KIRC and to identify potential mechanisms of competing endogenous RNA (ceRNA) networks.

Methods

R 4.1.1 software was utilized to conduct bioinformatics analyses with the data derived from online databases. Difference analysis, survival analysis, univariate/multivariate cox regression analysis and correlation analysis were carried out successively in this article. Besides, we also investigated potential effects and mechanisms of COLGALT1 in KIRC.

Results

COLGALT1 expression was overexpressed in KIRC samples compared with the normal samples and it was associated with poor OS (P < 0.001). COLGALT1 was also found to be significantly related to clinicopathological characteristics such as grade, T, N, M, stage and Cox regression analysis with univariate and multivariate data suggested it might be an independent prognostic parameter in KIRC (P < 0.001). Furthermore, Seven significantly enriched pathways were identified. Interestingly, correlation analyses revealed an association between COLGALT1 and microsatellite instability (MSI), tumor mutational burden (TMB) and immunity (P < 0.001). In addition, we used TIDE and TCIA databases to predict the immune response of COLGALT1 in KIRC and it suggested low expression of COLGALT1 is more likely to benefit from immunotherapy. Besides, we identified a ceRNA network of SLC16A1-AS1/hsa-mir-502-3p/COLGALT1 for its potential mechanism. Finally, experiments in vitro indicated that COLGALT1 was significantly related to cell proliferation.

Conclusions

COLGALT1 could act as a valid immune-related prognostic indicator for KIRC and participated in a ceRNA network of SLC16A1-AS1/hsa-mir-502-3p/COLGALT1, offering one potential biomarker to investigate the mechanism and clinical therapeutic value of KIRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-022-00745-5.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

p53 and Its Isoforms in Renal Cell Carcinoma—Do They Matter?

p53 is a transcription al factor responsible for the maintenance of cellular homeostasis. It has been shown that more than 50% of tumors are connected with mutations in the Tp53 gene. These mutations cause a disturbance in cellular response to stress, and eventually, cancer development. Apart from the full-length p53, at least twelve isoforms of p53 have been characterized. They are able to modulate p53 activity under stress conditions. In 2020, almost a half of million people around the world were diagnosed with renal cancer. One genetic disturbance which is linked to the most common type of kidney cancer, renal cell carcinoma, RCC, occurs from mutations in the VHL gene. Recent data has revealed that the VHL protein is needed to fully activate p53. Disturbance of the interplay between p53 and VHL seems to explain the lack of efficient response to chemotherapy in RCC. Moreover, it has been observed that changes in the expression of p53 isoforms are associated with different stages of RCC and overall survival. Thus, herein, an attempt was made to answer the question whether p53 and its isoforms are important factors in the development of RCC on the one hand, and in positive response to anti-RCC therapy on the other hand.

The genomic landscape of pediatric renal cell carcinomas

Pediatric renal cell carcinomas (RCC) differ from their adult counterparts not only in histologic subtypes but also in clinical characteristics and outcome. However, the underlying biology is still largely unclear. For this reason, we performed whole-exome and transcriptome sequencing analyses on a cohort of 25 pediatric RCC patients with various histologic subtypes, including 10 MiT family translocation (MiT) and 10 papillary RCCs. In this cohort of pediatric RCC, we find only limited genomic overlap with adult RCC, even within the same histologic subtype. Recurrent somatic mutations in genes not previously reported in RCC were detected, such as in CCDC168, PLEKHA1, VWF, and MAP3K9. Our papillary pediatric RCCs, which represent the largest cohort to date with comprehensive molecular profiling in this age group, appeared as a distinct genomic subtype differing in terms of gene mutations and gene expression patterns not only from MiT-RCC but also from their adult counterparts.

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WES and RNA-seq of 25 pediatric RCCs with various histologic subtypes

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Detected only limited genomic overlap with adult RCC

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Revealed recurrent somatic mutations in genes not previously reported in RCC

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Discovery of a CRK-PITPNA fusion gene in a pediatric papillary RCC

A Novel Nomogram for Prediction and Evaluation of Lymphatic Metastasis in Patients With Renal Cell Carcinoma

Background

Lymphatic metastasis is an important mechanism of renal cell carcinoma (RCC) dissemination and is an indicator of poor prognosis. Therefore, we aimed to identify predictors of lymphatic metastases (LMs) in RCC patients and to develop a new nomogram to assess the risk of LMs.

Methods

This study included patients with RCC from 2010 to 2018 in the Surveillance, Epidemiology, and Final Results (SEER) database into the training cohort and included the RCC patients diagnosed during the same period in the Second Affiliated Hospital of Dalian Medical University into the validation cohort. Univariate and multivariate logistic regression analysis were performed to identify risk factors for LM, constructing a nomogram. The receiver operating characteristic (ROC) curves were generated to assess the nomogram’s performance, and the concordance index (C-index), area under curve value (AUC), and calibration plots were used to evaluate the discrimination and calibration of the nomogram. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA), probability density function (PDF) and clinical utility curve (CUC). Furthermore, Kaplan-Meier curves were performed in the training and the validation cohort to evaluate the survival risk of the patients with lymphatic metastasis or not. Additionally, on the basis of the constructed nomogram, we obtained a convenient and intuitive network calculator.

Results

A total of 41837 patients were included for analysis, including 41,018 in the training group and 819 in the validation group. Eleven risk factors were considered as predictor variables in the nomogram. The nomogram displayed excellent discrimination power, with AUC both reached 0.916 in the training group (95% confidence interval (CI) 0.913 to 0.918) and the validation group (95% CI 0.895 to 0.934). The calibration curves presented that the nomogram-based prediction had good consistency with practical application. Moreover, Kaplan-Meier curves analysis showed that RCC patients with LMs had worse survival outcomes compared with patients without LMs.

Conclusions

The nomogram and web calculator (https://liwenle0910.shinyapps.io/DynNomapp/) may be a useful tool to quantify the risk of LMs in patients with RCC, which may provide guidance for clinicians, such as identifying high-risk patients, performing surgery, and establishing personalized treatment as soon as possible.

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating the gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction. In this study, 5,198 differentially expressed genes (DEGs) and 270 DNA methylation-driven genes were selected to obtain 146 differentially expressed DNA methylation-driven genes (DEMDGs). Two clusters were distinguished by consensus clustering using 146 DEMDGs. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Cluster-based immune status analysis and functional enrichment analysis of 106 DEGs provide new insights for the development of RCC. To predict the prognosis of patients with RCC, a prognostic model based on eight DEMDGs was constructed. The patients were divided into high-risk groups and low-risk groups based on their risk scores. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:3496) were built to validate the predictive accuracy of the prognostic model. Gene set enrichment analysis was performed to annotate the signaling pathways in which the genes are enriched. The correlation of the risk score with clinical features, immune status, and drug susceptibility was also evaluated. These results suggested that the prognostic model might be a promising prognostic tool for RCC and might facilitate the management of patients with RCC.

Functions, Roles, and Biological Processes of Ferroptosis-Related Genes in Renal Cancer: A Pan-Renal Cancer Analysis

Ferroptosis is a cell death process discovered in recent years, highly related to cancer, acute kidney injury, and other diseases. In this study, a pan-renal cancer analysis of ferroptosis-associated genes in renal cancer was performed to construct a multigene joint signature for predicting prognosis in renal cancer patients. First, gene expression profiles were downloaded from the TCGA and GTEx databases to search for genes significantly associated with renal cancer prognosis through differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis. Thereafter, the gene-set enrichment analysis (GSEA) was used to identify the biological processes in which ferroptosis-associated genes might be involved. Weighted gene co-expression network analysis resulted in 4,434 differentially expressed genes (DEGs) and 42 co-expression modules, among which ferroptosis-related genes were distributed in 11 gene modules. The survival analysis screening resulted in three DEGs associated with renal cancer prognosis, namely SLC7A11, HMOX1, and MT1G. Specifically, SLC7A11 and HMOX1 were upregulated in renal cancer tissues, while MT1G was downregulated. Receiver operating characteristic (ROC) curves, combined with Kaplan–Meier and Cox regression analysis, revealed that high expression of SLC7A11 was a prognostic risk factor for four different renal cancers, that low expression of HMOX1 was a poor prognostic marker for patients, and that increased expression of MT1G increased the prognostic risk for three additional classes of renal cancer patients, except for renal papillary cell carcinoma. The GSEA results showed that the ferroptosis-related genes from these screens were mainly associated with signaling pathways related to tumor progression and tumor immunity. This study provides potential biological markers for prognosis prediction in renal cancer patients with different subtypes, and these results imply that ferroptosis is highly associated with renal carcinogenesis progression.

A retrospective single-centered, comprehensive targeted genetic sequencing analysis of prognostic survival using tissues from Korean patients with metastatic renal cell carcinoma after targeted therapy

Purpose

To identify candidate gene mutations to significantly predict the risk of survival prognosis after treatment with systemic first-line targeted therapy (TT) in metastatic renal cell carcinoma (mRCC) patients.

Materials and Methods

Between 2005 and 2017, 168 triplet-tissue block samples from 56 mRCC patients were selected for targeted gene sequencing (TGS). Fifty-six patients’ medical records including overall survival (OS) and progression-free survival (PFS) at the time of mRCC diagnosis were evaluated. The patients were grouped into favorable (>12 months/>3 years), intermediate (3–12/12–36 months), and poor groups according to their PFS/OS (<3 months/<12 months). We identified any significant therapeutic targeted genes relating to the survival with a significance at p<0.050.

Results

The first line therapeutic response showed 1.8% complete remission, 14.2% partial response, 42.9% stable disease, and 41.1% progressive disease. Among the overall TGS results, the cumulative effect of CDH1, and/or PTK2 genes significantly reflected the therapeutic responses in terms of PFS/OS; CDH1 and PTK2 mutations were associated with poor prognostic outcomes (p<0.050). Among only triplet-quality check passed tissues, the SGO2, BRAF, URB1, and NEDD1 mutated genes significantly correlated with OS. Regarding metastasis, patients with liver metastasis had the worst OS (p=0.050). The combinational mutation number from these two candidate genes in the liver metastatic samples with mutated EGFR2 and FABP7 also showed a significantly worse OS than those with other metastatic lesions (p<0.050).

Conclusions

This study reports several significant mutated genes related to the survival prognosis in mRCC patients treated with first-line TT.

G6PD upregulates Cyclin E1 and MMP9 to promote clear cell renal cell carcinoma progression

Background: Clear cell renal cell carcinoma (ccRCC) is a cell metabolic disease with high metastasis rate and poor prognosis. Our previous studies demonstrate that glucose-6-phosphate dehydrogenase (G6PD), the first and rate-limiting enzyme of the pentose phosphate pathway, is highly expressed in ccRCC and predicts poor outcomes of ccRCC patients. The aims of this study were to confirm the oncogenic role of G6PD in ccRCC and unravels novel mechanisms involving Cyclin E1 and MMP9 in G6PD-mediated ccRCC progression. Methods: Real-time RT-PCR, Western blot and immunohistochemistry were used to determine the expression patterns of G6PD, Cyclin E1 and MMP9 in ccRCC. TCGA dataset mining was used to identify Cyclin E1 and MMP9 correlations with G6PD expression, relationships between clinicopathological characteristics of ccRCC and the genes of interest, as well as the prognosis of ccRCC patients. The role of G6PD in ccRCC progression and the regulatory effect of G6PD on Cyclin E1 and MMP9 expression were investigated by using a series of cytological function assays in vitro. To verify this mechanism in vivo, xenografted mice models were established. Results: G6PD, Cyclin E1 and MMP9 were overexpressed and positively correlated in ccRCC, and they were associated with poor prognosis of ccRCC patients. Moreover, G6PD changed cell cycle dynamics, facilitated cells proliferation, promoted migration in vitro, and enhanced ccRCC development in vivo, more likely through enhancing Cyclin E1 and MMP9 expression. Conclusion: These findings present G6PD, Cyclin E1 and MMP9, which contribute to ccRCC progression, as novel biomarkers and potential therapeutic targets for ccRCC treatment.

Camrelizumab plus Famitinib in Patients with Advanced or Metastatic Renal Cell Carcinoma: Data from an Open-label, Multicenter Phase II Basket Study

PURPOSE

Blockade of immune checkpoint and angiogenesis is an effective treatment strategy for advanced or metastatic renal cell carcinoma (RCC). We report the results of camrelizumab plus famitinib in the RCC cohort of an open-label, multicenter, phase II basket study.

PATIENTS AND METHODS

Eligible patients were enrolled to receive camrelizumab (200 mg i.v. every 3 weeks) and famitinib (20 mg orally once daily). Primary endpoint was objective response rate (ORR) per RECIST version 1.1.

RESULTS

Totally, 38 patients were recruited, including 13 (34.2%) treatment-naïve and 25 (65.8%) previously treated patients. With a median duration from enrollment to data cutoff of 16.5 months (range, 6.1-20.4), 23 patients achieved a confirmed objective response, and ORR was 60.5% [95% confidence interval (CI), 43.4-76.0]. Responses in 18 (78.3%) responders were still ongoing, and Kaplan-Meier estimated median duration of response had not been reached yet (range, 1.0⁺-14.8⁺ months). Median progression-free survival (PFS) was 14.6 months (95% CI, 6.2-not reached). ORR was 84.6% (95% CI, 54.6-98.1) in treatment-naïve patients and 48.0% (95% CI, 27.8-68.7) in pretreated patients; median PFS had not been reached and was 13.4 months (95% CI, 4.1-not reached), respectively. Most common grade 3 or 4 treatment-related adverse events included proteinuria (18.4%), hypertension (18.4%), decreased neutrophil count (13.2%), palmar-plantar erythrodysesthesia syndrome (10.5%), and hypertriglyceridemia (10.5%). No treatment-related deaths occurred, and no new safety signals were observed.

CONCLUSIONS

Camrelizumab plus famitinib showed potent and enduring antitumor activity in patients with advanced or metastatic RCC, both in treatment-naïve and previously treated population.

Moving pan-cancer studies from basic research toward the clinic

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

Chromophobe Renal Cell Carcinoma with Radiologic-Pathologic Correlation

Renal cell carcinoma (RCC) is a heterogeneous group of neoplasms derived from the renal tubular epithelial cells. Chromophobe RCC (chRCC) is the third most common subtype of RCC, accounting for 5% of cases. chRCC may be detected as an incidental finding or less commonly may manifest with clinical symptoms. The mainstay of therapy for chRCC is surgical resection. chRCC has a better prognosis compared with the more common clear cell RCC. At gross pathologic analysis, chRCC is a solid well-defined mass with lobulated borders. Histologic findings vary by subtype but include large pale polygonal cells with abundant transparent cytoplasm, crinkled "raisinoid" nuclei with perinuclear halos, and prominent cell membranes. Pathologic analysis reveals only moderate vascularity. The most common imaging pattern is a predominantly solid renal mass with circumscribed margins and enhancement less than that of the renal cortex. The authors discuss chRCC with emphasis on correlative pathologic findings and illustrate the multimodality imaging appearances of chRCC by using cases from the Radiologic Pathology Archives of the American Institute for Radiologic Pathology. ^©RSNA, 2021.

Kidney cancer biomarkers and targets for therapeutics: survivin (BIRC5), XIAP, MCL-1, HIF1α, HIF2α, NRF2, MDM2, MDM4, p53, KRAS and AKT in renal cell carcinoma

The incidence of renal cell carcinoma (RCC) is increasing worldwide with an approximate 20% mortality rate. The challenge in RCC is the therapy-resistance. Cancer resistance to treatment employs multiple mechanisms due to cancer heterogeneity with multiple genetic and epigenetic alterations. These changes include aberrant overexpression of (1) anticancer cell death proteins (e.g., survivin/BIRC5), (2) DNA repair regulators (e.g., ERCC6) and (3) efflux pump proteins (e.g., ABCG2/BCRP); mutations and/or deregulation of key (4) oncogenes (e.g., MDM2, KRAS) and/or (5) tumor suppressor genes (e.g., TP5/p53); and (6) deregulation of redox-sensitive regulators (e.g., HIF, NRF2). Foci of tumor cells that have these genetic alterations and/or deregulation possess survival advantages and are selected for survival during treatment. We will review the significance of survivin (BIRC5), XIAP, MCL-1, HIF1α, HIF2α, NRF2, MDM2, MDM4, TP5/p53, KRAS and AKT in treatment resistance as the potential therapeutic biomarkers and/or targets in RCC in parallel with our analized RCC-relevant TCGA genetic results from each of these gene/protein molecules. We then present our data to show the anticancer drug FL118 modulation of these protein targets and RCC cell/tumor growth. Finally, we include additional data to show a promising FL118 analogue (FL496) for treating the specialized type 2 papillary RCC.

Germline mutation in the NBR1 gene involved in autophagy detected in a family with renal tumors

Hereditary Renal Cell Carcinomas (RCC) are caused by mutations in predisposing genes, the major ones including VHL, FLCN, FH and MET. However, many families with inherited RCC have no germline mutation in these genes. Using Whole Exome Sequencing on germline DNA from a family presenting three different histological renal tumors (an angiomyolipoma, a clear-cell RCC and an oncocytic papillary RCC), we identified a frameshift mutation in the Neighbor of BRCA1 gene 1 (NBR1), segregating with the tumors. NBR1 encodes a cargo receptor protein involved in autophagy. Genetic and functional analyses suggested a pathogenic impact of the mutation. Indeed, functional study performed in renal cell lines showed that the mutation alters NBR1 interactions with some of its partners (such as p62/SQSTM1), leading to a dominant negative effect. This results in an altered autophagic process and an increased proliferative capacity in renal cell lines. Our study suggests that NBR1 may be a new predisposing gene for RCC, however its characterization needs to be further investigated in order to confirm its role in renal carcinogenesis.

Circ_RPL23A acts as a miR-1233 sponge to suppress the progression of clear cell renal cell carcinoma by promoting ACAT2

Clear cell renal cell carcinoma (ccRCC) is a prevalent urological carcinoma with high metastatic risk. Circular RNAs (circRNAs) have been identified as effective diagnostic and therapeutic biomarkers for ccRCC. This research aims to disclose the effect and regulatory mechanism of circRNA ribosomal protein L23a (circ_RPL23A) in ccRCC. We performed quantitative real-time polymerase chain reaction (qRT-PCR) to examine circ_RPL23A, microRNA-1233 (miR-1233) and acetyl-coenzyme A acetyltransferase 2 (ACAT2). Cell cycle progression, apoptosis, cell viability, invasion and migration, which were respectively conducted by using flow cytometry, 3-(4, 5-dimethylthiazol-2-y1)-2, 5-diphenyl tetrazolium bromide (MTT), transwell assays. The levels of ACAT2 protein and cell cycle proteins, proliferation-associated protein, and epithelial-mesenchymal transition (EMT) associated proteins were measured by western blot. Target relationship was analyzed via dual-luciferase reporter assay and RNA pull down assay. The animal model was used to study how circ_RPL23A affects in vivo. Circ_RPL23A was lower expressed in ccRCC tissues and cells. The elevated circ_RPL23A suppressed cell cycle progression, proliferation, migration and invasion but promoted apoptosis in ccRCC cells. MiR-1233 was a target of circ_RPL23A and direct targeted to ACAT2. Besides, circ_RPL23A exerted its anti-tumor effect by sponging miR-1233, and then relieved the inhibition effect of miR-1233 on ACAT2. Overexpression of circ_RPL23A also curbed ccRCC tumor growth in vivo. Circ_RPL23A inhibited ccRCC progression by upregulating ACAT2 expression by competitively binding miR-1233, which might provide an in-depth cognition for ccRCC pathogenesis and circ_RPL23A might be a promising biomarker in ccRCC diagnosis and treatment.

An Integrated Deep Network for Cancer Survival Prediction Using Omics Data

As a highly sophisticated disease that humanity faces, cancer is known to be associated with dysregulation of cellular mechanisms in different levels, which demands novel paradigms to capture informative features from different omics modalities in an integrated way. Successful stratification of patients with respect to their molecular profiles is a key step in precision medicine and in tailoring personalized treatment for critically ill patients. In this article, we use an integrated deep belief network to differentiate high-risk cancer patients from the low-risk ones in terms of the overall survival. Our study analyzes RNA, miRNA, and methylation molecular data modalities from both labeled and unlabeled samples to predict cancer survival and subsequently to provide risk stratification. To assess the robustness of our novel integrative analytics, we utilize datasets of three cancer types with 836 patients and show that our approach outperforms the most successful supervised and semi-supervised classification techniques applied to the same cancer prediction problems. In addition, despite the preconception that deep learning techniques require large size datasets for proper training, we have illustrated that our model can achieve better results for moderately sized cancer datasets.

Diagnostic Utility of RNA-Seq for Evaluation of PD-L1 Expression in Clear Cell Renal Cell Carcinoma

BACKGROUND

Although there are immune checkpoint inhibitors (ICIs) available for the treatment of renal cell carcinoma (RCC), the utility of PD-L1 detection by immunohistochemistry (IHC) as a predictive biomarker in clear cell RCC (ccRCC) remains controversial. Nevertheless, alternative methods for PD-L1 detection, such as RNA sequencing (RNA-Seq), may be clinically useful in ccRCC; therefore, we sought to determine the ability of RNA-Seq to accurately and sensitively detect PD-L1 expression across different ccRCC clinical samples in comparison with IHC.

PATIENTS AND METHODS

Patients with ccRCC (n=127) who received treatment from Washington University in St. Louis between 2018 and 2020 were identified. Tumors from these patients were analyzed using RNA-Seq and IHC.

RESULTS

PD-L1 detection by RNA-Seq strongly correlated with IHC (P < .001), which was further validated using two independent datasets. Furthermore, RNA-Seq analysis identified an immune-enriched (higher PD-L1 positivity) and an immune-desert (lower PD-L1 positivity) microenvironment of ccRCC, which also correlated with IHC (P < .00001).

CONCLUSION

The results demonstrate the ability of RNA-Seq to detect PD-L1 in various ccRCC clinical samples compared to IHC. Ultimately, these findings suggest that PD-L1 detection by RNA-Seq can be further developed to determine the clinical utility of this methodology in ccRCC.

NHERF4 hijacks Mas-mediated PLC/AKT signaling to suppress the invasive potential of clear cell renal cell carcinoma cells

The Mas receptor has been reported to promote migration and invasion of clear cell renal cell carcinoma (ccRCC) cells via Ang-(1-7)-dependent AKT signaling. However, the mechanism underlying the regulation of Mas function remains unknown. Here, eight PDZ domain-containing proteins were identified as Mas interactors using surface plasmon resonance (SPR) coupled to mass spectrometry (MS). NHERF4 was the only downregulated gene across multiple independent ccRCC datasets. GST pull-down and co-immunoprecipitation assays confirmed physical interaction between NHERF4 and Mas. Using NHERF4 overexpression and knockdown assays, we found that NHERF4 inhibited Mas-induced migration, invasion and in vivo metastasis of ccRCC cells. Mechanistically, NHERF4 suppressed Mas-stimulated AKT phosphorylation and the PLC/Ca²⁺ response. We further demonstrated that NHERF4 compromised Mas-mediated migration and invasion of ccRCC cells via regulation of the PLC/AKT signaling axis. Analysis of the ccRCC dataset revealed that low levels of NHERF4 expression were correlated with higher TNM stage, and independently predicted poor prognosis of ccRCC patients. Overall, our study identified NHERF4 as a novel regulator of ccRCC invasiveness, and a prognostic biomarker, which may be beneficial for determining optimal therapeutic strategies for ccRCC patients.

The Therapeutic Landscape of Renal Cell Carcinoma: From the Dark Age to the Golden Age

Oncologic treatments for renal cell carcinoma (RCC) have undergone a major revolution in the past 2 decades, moving away from the pre-2004 Dark Age during which interleukin 2 and interferon-α were the only therapeutic options and induced treatment responses in only 5% to 10% of patients with metastatic disease. The development of anti-angiogenic tyrosine kinase inhibitors against vascular endothelial growth factor receptor 2 and inhibitors of mammalian target of rapamycin complex 1 in 2005 introduced the Modern Age with better overall and progression-free survival and a greater number of patients (30%-40%) responding to and (∼80%) benefiting from these targeted therapeutic agents. The coming of age of the immuno-oncology era with the use of immune checkpoint inhibitors (ICIs) have ushered us into the Golden Age of metastatic RCC care, in which combined administrations of two ICIs (anti-programmed cell death protein 1/programmed death-ligand 1 and anti-cytotoxic T-lymphocyte-associated protein 4 or one tyrosine kinase inhibitor plus one ICI (anti-programmed cell death protein 1/programmed death-ligand 1) have recast the treatment landscape of clear cell RCC, the most common RCC subtype, with an approximately 60% response rate and an approximately 90% disease control rate that further improves metastatic RCC survival. Exciting clinical trials are in the pipeline investigating complementary/synergistic molecular mechanisms, based on studies investigating the biology, pathology, and genomics of renal carcinoma and the respective treatment outcome. This will enable us to enter the Diamond Age of precision medicine in which a specific treatment can be tailored to the specific biological and pathologic circumstance of an individual kidney tumor to offer more effective yet less toxic therapy.

Integrated Analysis to Identify a Redox-Related Prognostic Signature for Clear Cell Renal Cell Carcinoma

The imbalance of the redox system has been shown to be closely related to the occurrence and progression of many cancers. However, the biological function and clinical significance of redox-related genes (RRGs) in clear cell renal cell carcinoma (ccRCC) are unclear. In our current study, we downloaded transcriptome data from The Cancer Genome Atlas (TCGA) database of ccRCC patients and identified the differential expression of RRGs in tumor and normal kidney tissues. Then, we identified a total of 344 differentially expressed RRGs, including 234 upregulated and 110 downregulated RRGs. Fourteen prognosis-related RRGs (ADAM8, CGN, EIF4EBP1, FOXM1, G6PC, HAMP, HTR2C, ITIH4, LTB4R, MMP3, PLG, PRKCG, SAA1, and VWF) were selected out, and a prognosis-related signature was constructed based on these RRGs. Survival analysis showed that overall survival was lower in the high-risk group than in the low-risk group. The area under the receiver operating characteristic curve of the risk score signature was 0.728 at three years and 0.759 at five years in the TCGA cohort and 0.804 at three years and 0.829 at five years in the E-MTAB-1980 cohort, showing good predictive performance. In addition, we explored the regulatory relationships of these RRGs with upstream miRNA, their biological functions and molecular mechanisms, and their relationship with immune cell infiltration. We also established a nomogram based on these prognostic RRGs and performed internal and external validation in the TCGA and E-MTAB-1980 cohorts, respectively, showing an accurate prediction of ccRCC prognosis. Moreover, a stratified analysis showed a significant correlation between the prognostic signature and ccRCC progression.

Novel insights into clear cell renal cell carcinoma prognosis by comprehensive characterization of aberrant alternative splicing signature: a study based on large-scale sequencing data

Clear cell renal cell carcinoma (ccRCC) is the most common type with poor prognosis in kidney tumor. Growing evidence has indicated that aberrant alternative splicing (AS) events are efficacious signatures for tumor prognosis prediction and therapeutic targets. However, the detailed roles of AS events in ccRCC are largely unknown. In our study, level 3 RNA-seq data was acquired from The Cancer Genome Atlas dataset and corresponding AS profiles were detected with the assistance of SpliceSeq software. A total of 2100 aberrant survival-associated AS events were identified via differential expression and univariate cox regression analysis. The final prognostic panel formed by 17 specific events was developed by stepwise least absolute shrinkage and selection operator (LASSO) penalty, with the area under curve (AUC) values of receiver operator characteristic (ROC) curves keeping above 0.7 spanning 1 year to 5 years. And the results from functional enrichment analyses are unanimous that autophagy could be a potential mechanism of splicing regulation in ccRCC. Furthermore, splicing regulatory network was constructed via Spearman correlation between splicing factors and AS events. Finally, unsupervised clustering analysis revealed three clusters with distinct survival patterns, and associated with specific clinicopathological phenotypes. In overall, we developed a robust and individualized predictive model based on large-scale sequencing data. The identified AS events and splicing network may be valuable in deciphering the crucial posttranscriptional mechanisms on tumorigenesis of ccRCC.

Subgroup-Independent Mapping of Renal Cell Carcinoma-Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries

Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.

Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).

Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.

Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

Identification of subtype specific biomarkers of clear cell renal cell carcinoma using random forest and greedy algorithm

Suitable biomarkers can be good indicator for cancer subtype. To find biomarkers that can accurately distinguish clear cell renal cell carcinoma (ccRCC) subtypes, we first determined ccRCC subtypes based on the expression of mRNA, miRNA and lncRNA, named clear cell type 1 (ccluster1) and 2 (ccluster2), using three unsupervised clustering algorithms. Besides being associated with the expression pattern derived from the single type of RNA, the differences between subtypes are relevant to the interactions between RNAs. Then, based on ceRNA network, the optimal combination features are selected using random forest and greedy algorithm. Further, in survival-related sub-ceRNA, competing gene pairs centering on miR-106a, miR-192, miR-193b, miR-454, miR-32, miR-98, miR-143, miR-145, miR-204, miR-424 and miR-1271 can also well identify ccluster1 and ccluster2 with prediction accuracy over 92%. These subtype-specific features potentially enhance the accuracy with which machine learning methods predict specific ccRCC subtypes. Simultaneously, the changes of miR-106 and OIP5-AS1 affect cell proliferation and the prognosis of ccluster1. The changes of miR-145 and FAM13A-AS1 in ccluster2 have an effect on cell invasion, apoptosis, migration and metabolism function. Here miR-192 displays a unique characteristic in both subtypes. Two subtypes also display notable differences in diverse pathways. Tumors belonging to ccluster1 are characterized by Fc gamma R-mediated phagocytosis pathway that affects tissue remodeling and repair, whereas those belonging to ccluster2 are characterized by EGFR tyrosine kinase inhibitor resistance pathway that participates in regulation of cell homeostasis. In conclusion, identifying these gene pairs can shed light on therapeutic mechanisms of ccRCC subtypes.

Diagnostic approach in TFE3-rearranged renal cell carcinoma: a multi-institutional international survey

Transcription factor E3-rearranged renal cell carcinoma (TFE3-RCC) has heterogenous morphologic and immunohistochemical (IHC) features.131 pathologists with genitourinary expertise were invited in an online survey containing 23 questions assessing their experience on TFE3-RCC diagnostic work-up.Fifty (38%) participants completed the survey. 46 of 50 participants reported multiple patterns, most commonly papillary pattern (almost always 9/46, 19.5%; frequently 29/46, 63%). Large epithelioid cells with abundant cytoplasm were the most encountered cytologic feature, with either clear (almost always 10/50, 20%; frequently 34/50, 68%) or eosinophilic (almost always 4/49, 8%; frequently 28/49, 57%) cytology. Strong (3+) or diffuse (>75% of tumour cells) nuclear TFE3 IHC expression was considered diagnostic by 13/46 (28%) and 12/47 (26%) participants, respectively. Main TFE3 IHC issues were the low specificity (16/42, 38%), unreliable staining performance (15/42, 36%) and background staining (12/42, 29%). Most preferred IHC assays other than TFE3, cathepsin K and pancytokeratin were melan A (44/50, 88%), HMB45 (43/50, 86%), carbonic anhydrase IX (41/50, 82%) and CK7 (32/50, 64%). Cut-off for positive TFE3 fluorescent in situ hybridisation (FISH) was preferably 10% (9/50, 18%), although significant variation in cut-off values was present. 23/48 (48%) participants required TFE3 FISH testing to confirm TFE3-RCC regardless of the histomorphologic and IHC assessment. 28/50 (56%) participants would request additional molecular studies other than FISH assay in selected cases, whereas 3/50 participants use additional molecular cases in all cases when TFE3-RCC is in the differential.Optimal diagnostic approach on TFE3-RCC is impacted by IHC and/or FISH assay preferences as well as their conflicting interpretation methods.

Baseline plasma levels of soluble PD-1, PD-L1, and BTN3A1 predict response to nivolumab treatment in patients with metastatic renal cell carcinoma: a step toward a biomarker for therapeutic decisions

Despite a proportion of renal cancer patients can experiment marked and durable responses to immune-checkpoint inhibitors, the treatment efficacy is widely variable and identifying the patient who will benefit from immunotherapy remains an issue. We performed a prospective study to investigate if soluble forms of the immune-checkpoints PD-1 (sPD-1), PD-L1 (sPD-L1), pan-BTN3As, BTN3A1, and BTN2A1, could be candidate to predict the response to immune-checkpoint blockade therapy. We evaluated the plasma levels in a learning cohort of metastatic clear cell renal carcinoma (mccRCC) patients treated with the anti-PD-1 agent nivolumab by ad hoc developed ELISA’s. Using specific cut-offs determined through ROC curves, we showed that high baseline levels of sPD-1 (>2.11 ng/ml), sPD-L1 (>0.66 ng/ml), and sBTN3A1 (>6.84 ng/ml) were associated with a longer progression-free survival (PFS) to nivolumab treatment [median PFS, levels above thresholds: sPD-1, 20.7 months (p < .0001); sPD-L1, 19 months (p < .0001); sBTN3A1, 17.5 months (p = .002)]. High sPD-1 and sBTN3A1 levels were also associated with best overall response by RECIST and objective response of >20%. The results were confirmed in a validation cohort of 20 mccRCC patients. The analysis of plasma dynamic changes after nivolumab showed a statistically significant decrease of sPD-1 after 2 cycles (Day 28) in the long-responder patients. Our study revealed that the plasma levels of sPD-1, sPD-L1, and sBTN3A1 can predict response to nivolumab, discriminating responders from non-responders already at therapy baseline, with the advantages of non-invasive sample collection and real-time monitoring that allow to evaluate the dynamic changes during cancer evolution and treatment.

NF-κB and pSTAT3 synergistically drive G6PD overexpression and facilitate sensitivity to G6PD inhibition in ccRCC

Background

Glucose 6-phosphate dehydrogenase (G6PD) serves key roles in cancer cell metabolic reprogramming, and has been reported to be involved in certain carcinogenesis. Previous results from our laboratory demonstrated that overexpressed G6PD was a potential prognostic biomarker in clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer. G6PD could stimulate ccRCC growth and invasion through facilitating reactive oxygen species (ROS)-phosphorylated signal transducer and activator of transcription 3 (pSTAT3) activation and ROS-MAPK-MMP2 axis pathway, respectively. However, the reasons for ectopic G6PD overexpression and the proliferation repressive effect of G6PD inhibition in ccRCC are still unclear.

Methods

The impact of ROS accumulation on NF-κB signaling pathway and G6PD expression was determined by real-time RT-PCR and Western blot in ccRCC cells following treatment with ROS stimulator or scavenger. The regulatory function of NF-κB signaling pathway in G6PD transcription was analyzed by real-time RT-PCR, Western blot, luciferase and ChIP assay in ccRCC cells following treatment with NF-κB signaling activator/inhibitor or lentivirus infection. ChIP and Co-IP assay was performed to demonstrate protein-DNA and protein-protein interaction of NF-κB and pSTAT3, respectively. MTS assay, human tissue detection and xenograft model were conducted to characterize the association between NF-κB, pSTAT3, G6PD expression level and proliferation functions.

Results

ROS-stimulated NF-κB and pSTAT3 signaling over-activation could activate each other, and exhibit cross-talks in G6PD aberrant transcriptional regulation. The underlying mechanism was that NF-κB signaling pathway facilitated G6PD transcription via direct DNA-protein interaction with p65 instead of p50. p65 and pSTAT3 formed a p65/pSTAT3 complex, occupied the pSTAT3-binding site on G6PD promoter, and contributed to ccRCC proliferation following facilitated G6PD overexpression. G6PD, pSTAT3, and p65 were highly expressed and positively correlated with each other in ccRCC tissues, confirming that NF-κB and pSTAT3 synergistically promote G6PD overexpression. Moreover, G6PD inhibitor exhibited tumor-suppressor activities in ccRCC and attenuated the growth of ccRCC cells both in vitro and in vivo.

Conclusion

ROS-stimulated aberrations of NF-κB and pSTAT3 signaling pathway synergistically drive G6PD transcription through forming a p65/pSTAT3 complex. Moreover, G6PD activity inhibition may be a promising therapeutic strategy for ccRCC treatment.

B-MYB-p53-related relevant regulator for the progression of clear cell renal cell carcinoma

PURPOSE

To investigate the mRNA expression of B-MYB and MDM2 together with their p53 relatedness in clear cell renal cell carcinoma (ccRCC).

METHODS

Genes were screened for their mRNA expression from 529 patients in a publicly available ccRCC cohort (TCGA). A cohort of 101 patients with ccRCC served as validation by qRT-PCR mRNA tissue expression analysis.

RESULTS

Expression: B-MYB expression was significantly higher in high-grade tumours (p < 0.0001 and p = 0.048) and in advanced stages (p = 0.005 and p = 0.037) in both cohorts. Correlation: p53-B-MYB as well as MDM2-B-MYB showed significant correlations in local and low-grade ccRCCs, but not in high grade tumours or advanced stages (r < 0.3 and/or p > 0.05). Survival: Multivariable Cox regression of the TCGA cohort revealed B-MYB upregulation and low MDM2 expression as predictors for an impaired overall survival (OS) (HR 1.97; p = 0.0003; HR 2.94, p < 0.0001) and progression-free survival (PFS) (HR 2.86; p = 0.0005; HR 1.58, p = 0.046). In the validation cohort, the results were confirmed for OS by univariable, but not multivariable regression: high B-MYB expression (HR = 3.05, p = 0.035) and low MDM2 expression (HR 3.81, p value 0.036).

CONCLUSION

In ccRCC patients with high-grade tumours and advanced stages, high B-MYB expression is common and is associated with poorer OS and PFS. These patients show a loss of their physiological B-MYB-p53 network correlation, suggesting an additional, alternative regulatory, oncogenic mechanism. Assuming further characterization of its signalling pathways, B-MYB could be a potential therapy target for ccRCC.

The Pan-Omics Landscape of Renal Cell Carcinoma and Its Implication on Future Clinical Practice

Renal cell carcinoma has traditionally been classified based on histological features. Contemporary studies have identified genomic, transcriptomic, epigenomic, and metabolomic signatures that correspond to or even transcend histological subtypes. Much remains to be learned about improving the algorithm of pan-omics integration for precision oncology, which will not only advance our understanding of RCC pathobiology and treatment response but also result in novel therapeutic opportunities. Accordingly, this review focuses on recent RCC multi-omics literature. Encouragingly, a few reports on omics integration into routinely employed prognostic risk models have shown early promise that could lay the foundation for future development of precision kidney cancer therapies. Hence, this article serves as a primer on what we have learned and how we might better realize the clinical potential of the burgeoning pan-omics data.

Characterization of solid renal neoplasms using MRI-based quantitative radiomics features

PURPOSE

To assess the diagnostic value of magnetic resonance imaging (MRI)-based radiomics features using machine learning (ML) models in characterizing solid renal neoplasms, in comparison/combination with qualitative radiologic evaluation.

METHODS

Retrospective analysis of 125 patients (mean age 59 years, 67% males) with solid renal neoplasms that underwent MRI before surgery. Qualitative (signal and enhancement characteristics) and quantitative radiomics analyses (histogram and texture features) were performed on T2-weighted imaging (WI), T1-WI pre- and post-contrast, and DWI. Mann-Whitney U test and receiver-operating characteristic analysis were used in a training set (n = 88) to evaluate diagnostic performance of qualitative and radiomics features for differentiation of renal cell carcinomas (RCCs) from benign lesions, and characterization of RCC subtypes (clear cell RCC [ccRCC] and papillary RCC [pRCC]). Random forest ML models were developed for discrimination between tumor types on the training set, and validated on an independent set (n = 37).

RESULTS

We assessed 104 RCCs (51 ccRCC, 29 pRCC, and 24 other subtypes) and 21 benign lesions in 125 patients. Significant qualitative and quantitative radiomics features (area under the curve [AUC] between 0.62 and 0.90) were included for ML analysis. Models with best diagnostic performance on validation sets showed AUC of 0.73 (confidence interval [CI] 0.5-0.96) for differentiating RCC from benign lesions (using combination of qualitative and radiomics features); AUC of 0.77 (CI 0.62-0.92) for diagnosing ccRCC (using radiomics features), and AUC of 0.74 (CI 0.53-0.95) for diagnosing pRCC (using qualitative features).

CONCLUSION

ML models incorporating MRI-based radiomics features and qualitative radiologic assessment can help characterize renal masses.

TRIM33 Overexpression Inhibits the Progression of Clear Cell Renal Cell Carcinoma In Vivo and In Vitro

PURPOSE

To evaluate the expression of tripartite motif-containing 33 (TRIM33) in ccRCC tissues and explore the biological effect of TRIM33 on the progress of ccRCC.

METHOD

The Cancer Genome Atlas (TCGA) database was used to examine the mRNA expression levels of TRIM33 in ccRCC tissues and its clinical relevance. Immunohistochemistry (IHC) was performed to evaluate its expression in ccRCC tissues obtained from our hospital. The correlation between TRIM33 expression and clinicopathological features of the patients was also investigated. The effects of TRIM33 on the proliferation of ccRCC cells were examined using the CCK-8 and colony formation assays. The effects of TRIM33 on the migration and invasion of ccRCC cells were explored through wound healing and transwell assays, along with the use of Wnt signaling pathway agonists in rescue experiments. Western blotting was used to explore the potential mechanism of TRIM33 in renal cancer cells. A xenograft model was used to explore the effect of TRIM33 on tumor growth.

RESULT

Bioinformatics analysis showed that TRIM33 mRNA expression in ccRCC tissues was downregulated, and low TRIM33 expression was related to poor prognosis in ccRCC patients. In agreement with this, low TRIM33 expression was detected in human ccRCC tissues. TRIM33 expression levels were correlated with clinical characteristics, including tumor size and Furman's grade. Furthermore, TRIM33 overexpression inhibited proliferation, migration, and invasion of 786-O and ACHN cell lines. The rescue experiment showed that the originally inhibited migration and invasion capabilities were restored. TRIM33 overexpression reduced the expression levels of β-catenin, cyclin D1, and c-myc, and inhibited tumor growth in ccRCC cells in vivo.

CONCLUSION

TRIM33 exhibits an abnormally low expression in human ccRCC tissues. TRIM33 may serve as a potential therapeutic target and prognostic marker for ccRCC.

Combination drug regimens for metastatic clear cell renal cell carcinoma

Renal cell carcinomas (RCC) make up about 90% of kidney cancers, of which 80% are of the clear cell subtype. About 20% of patients are already metastatic at the time of diagnosis. Initial treatment is often cytoreductive nephrectomy, but systemic therapy is required for advanced RCC. Single agent targeted therapies are moderately toxic and only somewhat effective, leading to development of immunotherapies and combination therapies. This review identifies limitations of monotherapies for metastatic renal cell carcinoma, discusses recent advances in combination therapies, and highlights therapeutic options under development. The goal behind combining various modalities of systemic therapy is to potentiate a synergistic antitumor effect. However, combining targeted therapies may cause increased toxicity. The initial attempts to create therapeutic combinations based on inhibition of the vascular endothelial growth factor or mammalian target of rapamycin pathways were largely unsuccessful in achieving a profile of increased synergy without increased toxicity. To date, five combination therapies have been approved by the U.S. Food and Drug Administration, with the most recently approved therapies being a combination of checkpoint inhibition plus targeted therapy. Several other combination therapies are under development, including some in the phase 3 stage. The new wave of combination therapies for metastatic RCC has the potential to increase response rates and improve survival outcomes while maintaining tolerable side effect profiles.

Genetic Alterations in Renal Cancers: Identification of The Mechanisms Underlying Cancer Initiation and Progression and of Therapeutic Targets

Renal cell cancer (RCC) involves three most recurrent sporadic types: clear-cell RCC (70-75%, CCRCC), papillary RCCC (10-15%, PRCC), and chromophobe RCC (5%, CHRCC). Hereditary cases account for about 5% of all cases of RCC and are caused by germline pathogenic variants. Herein, we review how a better understanding of the molecular biology of RCCs has driven the inception of new diagnostic and therapeutic approaches. Genomic research has identified relevant genetic alterations associated with each RCC subtype. Molecular studies have clearly shown that CCRCC is universally initiated by Von Hippel Lindau (VHL) gene dysregulation, followed by different types of additional genetic events involving epigenetic regulatory genes, dictating disease progression, aggressiveness, and differential response to treatments. The understanding of the molecular mechanisms that underlie the development and progression of RCC has considerably expanded treatment options; genomic data might guide treatment options by enabling patients to be matched with therapeutics that specifically target the genetic alterations present in their tumors. These new targeted treatments have led to a moderate improvement of the survival of metastatic RCC patients. Ongoing studies based on the combination of immunotherapeutic agents (immune check inhibitors) with VEGF inhibitors are expected to further improve the survival of these patients.