The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Systematic analysis of the expression profiles and prognostic significance of the MED gene family in renal clear cell carcinoma

The mediator complex (MED) family is a contributing factor in the regulation of transcription and proliferation of cells, and is closely associated with the development of various types of cancer. However, the significance of the expression levels and prognostic value of MED genes in kidney renal clear cell carcinoma (KIRC) have rarely been reported. The present study analyzed the expression and prognostic potential of MED genes in KIRC. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein-protein interaction network (PPI), the Assistant for Clinical Bioinformatics database was used to perform correlation analysis, GEPIA 2 was utilized to draw the Kaplan-Meier plot and analyze prognostic significance and the Tumor Immune Estimation Resource was used to assess the association of MED genes with the infiltration of immune cells in patients with KIRC. A total of 30 MED genes were identified, and among these genes, 11 were selected for the creation of a prognostic gene signature based on the results of a LASSO Cox regression analysis. Furthermore, according to univariate and multivariate analyses, MED7, MED16, MED21, MED25 and MED29 may be valuable independent predictive biomarkers for the prognosis of individuals with KIRC. Furthermore, there were significant differences in the expression levels of MED7, MED21 and MED25 in KIRC among different tumor grades. Additionally, patients with KIRC with high transcription levels of MED7, MED21 and MED29 had considerably longer overall survival times. The expression levels of MED genes were also linked to the infiltration of several immune cells. Overall, MED genes may have potential significance in predicting the prognosis of patients with KIRC.

Immune analysis of urine and plasma samples from patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the third most common type of urological malignancy worldwide, and it is associated with a silent progression and late manifestation. Patients with a metastatic form of ccRCC have a poor prognosis; however, when the disease is diagnosed early, it is largely curable. Currently, there are no biomarkers available in clinical practice for ccRCC. Thus, the aim of the present study was to measure 27 biologically relevant cytokines in preoperative and postoperative urine samples, and in preoperative plasma samples from 34 patients with ccRCC, and to evaluate their diagnostic significance. The concentrations of cytokines were assessed by multiplex immune assay. The results showed significantly higher levels of IL-1 receptor antagonist, IL-6, IL-15, chemokine (C-C motif) ligand (CCL)2, CCL3, CCL4, C-X-C motif ligand (CXCL)10, granulocyte-macrophage colony stimulating factor (GM-CSF) and platelet-derived growth factor-BB (PDGF-BB), and lower levels of granulocyte colony stimulating factor (G-CSF) in urine samples from patients prior to surgery compared with those in the controls. Notably, the urine levels of G-CSF, IL-5 and vascular endothelial growth factor differed following tumor removal compared with the preoperative urine levels. In addition, urinary G-CSF, GM-CSF, IL-6, CXCL10, CCL5 and PDGF-BB appeared to be potential markers of tumor grade. Plasma from patients with ccRCC contained significantly higher levels of IL-6 and lower levels of CCL2 than control plasma. In conclusion, the present findings indicated that urinary and circulating cytokines may represent a promising novel tool for the early diagnosis of ccRCC and/or prediction of tumor grade.

Discovery and Validation of Survival-Specific Genes in Papillary Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel

PURPOSE

Papillary renal cell carcinoma (PRCC), the second most common kidney cancer, is morphologically, genetically, and molecularly heterogeneous with diverse clinical manifestations. Genetic variations of PRCC and their association with survival are not yet well-understood. This study aimed to identify and validate survival-specific genes in PRCC and explore their clinical utility.

MATERIALS AND METHODS

Using machine learning, 293 patients from the Cancer Genome Atlas-Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP) database were analyzed to derive genes associated with survival. To validate these genes, DNAs were extracted from the tissues of 60 Korean PRCC patients. Next generation sequencing was conducted using a customized PRCC gene panel of 202 genes, including 171 survival-specific genes. Kaplan-Meier and Log-rank tests were used for survival analysis. Fisher's exact test was performed to assess the clinical utility of variant genes.

RESULTS

A total of 40 survival-specific genes were identified in the TCGA-KIRP database through machine learning and statistical analysis. Of them, 10 (BAP1, BRAF, CFDP1, EGFR, ITM2B, JAK1, NODAL, PCSK2, SPATA13, and SYT5) were validated in the Korean-KIRP database. Among these survival gene signatures, three genes (BAP1, PCSK2, and SPATA13) showed survival specificity in both overall survival (OS) (p = 0.00004, p = 1.38 × 10-7, and p = 0.026, respectively) and disease-free survival (DFS) (p = 0.00002, p = 1.21 × 10-7, and p = 0.036, respectively). Notably, the PCSK2 mutation demonstrated survival specificity uniquely in both the TCGA-KIRP (OS: p = 0.010 and DFS: p = 0.301) and Korean-KIRP (OS: p = 1.38 × 10-7 and DFS: p = 1.21 × 10-7) databases.

CONCLUSIONS

We discovered and verified genes specific for the survival of PRCC patients in the TCGA-KIRP and Korean-KIRP databases. The survival gene signature, including PCSK2 commonly obtained from the 40 gene signature of TCGA and the 10 gene signature of the Korean database, is expected to provide insight into predicting the survival of PRCC patients and developing new treatment.

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma

PURPOSE

Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761).

RESULTS

After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score.

CONCLUSION

We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.

Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma

Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.

Development and validation of a nomogram for predicting the impact of tumor size on cancer-specific survival of locally advanced renal cell carcinoma: a SEER-based study

This study was aimed to integrate tumor size with other prognostic factors into a prognostic nomogram to predict cancer-specific survival (CSS) in locally advanced (≥pT3a Nany M0) renal cell carcinoma (RCC) patients. Based on the Surveillance, Epidemiology, and End Results (SEER) database, 10,800 patients diagnosed with locally advanced RCC were collected. They were randomly divided into a training cohort (n = 7,056) and a validation cohort (n = 3,024). X-tile program was used to identify the optimal cut-off value of tumor size and age. The cut-off of age at diagnosis was 65 years old and 75 years old. The cut-off of tumor size was 54 mm and 119 mm. Univariate and multivariate Cox regression analyses were performed in the training cohort to identify independent prognostic factors for construction of nomogram. Then, the nomogram was used to predict the 1-, 3- and 5-year CSS. The performance of nomogram was evaluated by using concordance index (C-index), area under the Subject operating curve (AUC) and decision curve analysis (DCA). Moreover, the nomogram and tumor node metastasis (TNM) staging system (AJCC 8th edition) were compared. 10 variables were screened to develop the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) indicated satisfactory ability of the nomogram. Compared with the AJCC 8th edition of TNM stage, DCA showed that the nomogram had improved performance. We developed and validated a nomogram for predicting the CSS of patients with locally advanced RCC, which was more precise than the AJCC 8th edition of TNM staging system.

Single-cell combined bioinformatics analysis: construction of immune cluster and risk prognostic model in kidney renal clear cells based on CD8+ T cell-associated genes

BACKGROUND

Kidney cancer is an immunogenic solid tumor, characterized by high tumor burden and infiltration of CD8+ T cells. Although immunotherapy targeting the PD1/CTLA-4 axis has demonstrated excellent clinical efficacy, clinical outcomes in most patients are poor.

METHODS

We used the RNA sequencing data from the GEO database for KIRC GSE121636 and normal kidney tissue GSE131685, and performed single-cell analysis for cluster identification, pathway enrichment, and CD8+ T cell-associated gene identification. Subsequently, the significance of different CD8+ T-cell associated gene subtypes was elucidated by consensus clustering, pathway analysis, mutated gene analysis, and KIRC immune microenvironment analysis in the TCGA-KIRC disease cohort. Single gene analysis identified LAG3 as the most critical CD8+ T-cell-associated gene and its function was verified by cell phenotype and immunohistochemistry in KIRC.

RESULTS

In the present study, CD8+ T-cell associated genes in KIRC were screened, including GZMK, CD27, CCL4L2, FXYD2, LAG3, RGS1, CST7, DUSP4, CD8A, and TRBV20-1 and an immunological risk prognostic model was constructed (risk score =  - 0.291858656434841*GZMK - 0.192758342489394*FXYD2 + 0.625023643446193*LAG3 + 0.161324477181591*RGS1 - 0.380169045328895*DUSP4 - 0.107221347575037*TRBV20-1). LAG3 was identified and proved as the most critical CD8+ T cell-associated gene in KIRC.

CONCLUSION

We proposed and constructed an immunological risk prognostic model for CD8+ T cell-associated genes and identified LAG3 as a pivotal gene for KIRC progression and CD8+ T-cell infiltration. The model comprehensively explained the immune microenvironment and provided novel immune-related therapeutic targets and biomarkers in KIRC.

Single-cell analysis of immune and stroma cell remodeling in clear cell renal cell carcinoma primary tumors and bone metastatic lesions

BACKGROUND

Despite therapeutic advances, once a cancer has metastasized to the bone, it represents a highly morbid and lethal disease. One third of patients with advanced clear cell renal cell carcinoma (ccRCC) present with bone metastasis at the time of diagnosis. However, the bone metastatic niche in humans, including the immune and stromal microenvironments, has not been well-defined, hindering progress towards identification of therapeutic targets.

METHODS

We collected fresh patient samples and performed single-cell transcriptomic profiling of solid metastatic tissue (Bone Met), liquid bone marrow at the vertebral level of spinal cord compression (Involved), and liquid bone marrow from a different vertebral body distant from the tumor site but within the surgical field (Distal), as well as bone marrow from patients undergoing hip replacement surgery (Benign). In addition, we incorporated single-cell data from primary ccRCC tumors (ccRCC Primary) for comparative analysis.

RESULTS

The bone marrow of metastatic patients is immune-suppressive, featuring increased, exhausted CD8 + cytotoxic T cells, T regulatory cells, and tumor-associated macrophages (TAM) with distinct transcriptional states in metastatic lesions. Bone marrow stroma from tumor samples demonstrated a tumor-associated mesenchymal stromal cell population (TA-MSC) that appears to be supportive of epithelial-to mesenchymal transition (EMT), bone remodeling, and a cancer-associated fibroblast (CAFs) phenotype. This stromal subset is associated with poor progression-free and overall survival and also markedly upregulates bone remodeling through the dysregulation of RANK/RANKL/OPG signaling activity in bone cells, ultimately leading to bone resorption.

CONCLUSIONS

These results provide a comprehensive analysis of the bone marrow niche in the setting of human metastatic cancer and highlight potential therapeutic targets for both cell populations and communication channels.

The Role of the Complement in Clear Cell Renal Carcinoma (ccRCC)-What Future Prospects Are There for Its Use in Clinical Practice?

In recent years, the first-line available therapeutic options for metastatic renal cell carcinoma (mRCC) have radically changed with the introduction into clinical practice of new immune checkpoint inhibitor (ICI)-based combinations. Many efforts are focusing on identifying novel prognostic and predictive markers in this setting. The complement system (CS) plays a central role in promoting the growth and progression of mRCC. In particular, mRCC has been defined as an "aggressive complement tumor", which encompasses a group of malignancies with poor prognosie and highly expressed complement components. Several preclinical and retrospective studies have demonstrated the negative prognostic role of the complement in mRCC; however, there is little evidence on its possible role as a predictor of the response to ICIs. The purpose of this review is to explore more deeply the physio-pathological role of the complement in the development of RCC and its possible future use in clinical practice as a prognostic and predictive factor.

Bioinformatics analysis identifies GLUD1 as a prognostic indicator for clear cell renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a common primary tumor of the kidney and is divided into three major subtypes, of which clear cell renal cell carcinoma (ccRCC) has the highest incidence. Glutamate dehydrogenase 1 (GLUD1) encodes glutamate dehydrogenase 1, which catalyzes the oxidative deamination of glutamate.

METHODS

We analyzed TCGA data using R language software and used multiple online databases to explore the relationship of GLUD1 with signaling pathways and drug sensitivity as well as GLUD1 protein expression and methylation.

RESULTS

The results showed that GLUD1 mRNA expression was reduced in tumor tissues and correlated with the progression of ccRCC. Univariate and multivariate Cox analysis showed that GLUD1 could be used as a prognostic marker for ccRCC. GLUD1 expression in ccRCC was associated with immune cells infiltration and multiple classical signaling pathways. In addition, GLUD1 mRNA expression was related to drug sensitivity.

CONCLUSIONS

These findings provide new ideas for finding new prognostic molecular markers and therapeutic targets for ccRCC.

The utilization of single-cell sequencing technology in investigating the immune microenvironment of ccRCC

The growth and advancement of ccRCC are strongly associated with the presence of immune infiltration and the tumor microenvironment, comprising tumor cells, immune cells, stromal cells, vascular cells, myeloid-derived cells, and extracellular matrix (ECM). Nevertheless, as a result of the diverse and constantly evolving characteristics of the tumor microenvironment, prior advanced sequencing methods have frequently disregarded specific less prevalent cellular traits at varying intervals, thereby concealing their significance. The advancement and widespread use of single-cell sequencing technology enable us to comprehend the source of individual tumor cells and the characteristics of a greater number of individual cells. This, in turn, minimizes the impact of intercellular heterogeneity and temporal heterogeneity of the same cell on experimental outcomes. This review examines the attributes of the tumor microenvironment in ccRCC and provides an overview of the progress made in single-cell sequencing technology and its particular uses in the current focus of immune infiltration in ccRCC.

Multi-omics Analysis of Prognostic Significance and Immune Infiltration of FASTK Family Members in Kidney Renal Clear Cell Carcinoma

Objective

The Fas-activated serine/threonine kinase (FASTK) family of proteins has been recently found to be able to regulate mitochondrial gene expression post-transcriptionally. Nonetheless, there is a paucity of study about the role of the FASTK family in kidney renal clear cell carcinoma (KIRC). This study was conducted to explore the correlation of FASTK family genes with expression, prognosis, and immune infiltration in KIRC.

Methods

We collected the data from the UALCAN, GeneMANIA, STRING, CancerSEA, cBioPortal, Kaplan-Meier plotter, GEPIA, TISIDB and TIMER databases to evaluate the genetic alterations, differential expression, prognostic significance, and immune cell infiltration of FASTKs in patients with KIRC.

Results

In tumor tissues of KIRC, the mRNA expression level of FASTK and TBRG4 was elevated, whereas that of FASTKD1, FASTKD2, and FASTKD5 was lowered compared with normal tissues (P < .05). Patients with KIRC and high FASTK and Transforming growth factor β regulator 4 (TBRG4) expression had worse overall survival (OS) and disease specific survival (DFS), while those with lower expression of FASTKD2/3/5 had worse outcomes. FASTK was positively correlated with DNA damage. FASTKD1 was positively related to differentiation. FASTKD2 was inversely related to proliferation and FASTKD5 was inversely related to invasion and EMT in KIRC cells. FASTK expression in KIRC was inversely linked to the presence of several immune cells including Tgd, macrophages, Tcm, and Mast cells (P < .05).

Conclusions

Our research provided fresh insight and in-depth analysis to the selection of prognostic biological markers of FASTK family members in KIRC.

Construction of EMT related prognostic signature for kidney renal clear cell carcinoma, through integrating bulk and single-cell gene expression profiles

Introduction: Kidney renal clear cell carcinoma (KIRC), as a main type of malignant kidney cancers, has a poor prognosis. Epithelial-mesenchymal transformation (EMT) exerts indispensable role in tumor progression and metastasis, including in KIRC. This study aimed to mine more EMT related details and build prognostic signature for KIRC. Methods: The KIRC scRNA-seq data and bulk data were downloaded from GEO and TCGA databases, respectively. The cell composition in KIRC was calculated using CIBERSORT. Univariate Cox regression analysis and LASSO Cox regression analysis were combined to determine the prognostic genes. Gene set variation analysis and cell-cell communication analysis were conducted to obtain more functional information. Additionally, functional analyses were conducted to determine the biological roles of si-LGALS1 in vitro. Results: We totally identified 2,249 significant differentially expressed genes (DEGs) in KIRC samples, meanwhile a significant distinct expression pattern was found in KIRC, involving Epithelial Mesenchymal Transition pathway. Among all cell types, significantly higher proportion of epithelial cells were observed in KIRC, and 289 DEGs were identified in epithelial cells. After cross analysis of all DEGs and 970 EMT related genes, SPARC, TMSB10, LGALS1, and VEGFA were optimal to build prognostic model. Our EMT related showed good predictive performance in KIRC. Remarkably, si-LGALS1 could inhibit migration and invasion ability of KIRC cells, which might be involved in suppressing EMT process. Conclusion: A novel powerful EMT related prognostic signature was built for KIRC patients, based on SPARC, TMSB10, LGALS1, and VEGFA. Of which, si-LGALS1 could inhibit migration and invasion ability of KIRC cells, which might be involved in suppressing EMT process.

Comprehensive analyses of immune tumor microenvironment in papillary renal cell carcinoma

BACKGROUND

Papillary renal cell carcinoma (pRCC) is the most common non-clear cell RCC, and associated with poor outcomes in the metastatic setting. In this study, we aimed to comprehensively evaluate the immune tumor microenvironment (TME), largely unknown, of patients with metastatic pRCC and identify potential therapeutic targets.

METHODS

We performed quantitative gene expression analysis of TME using Microenvironment Cell Populations-counter (MCP-counter) methodology, on two independent cohorts of localized pRCC (n=271 and n=98). We then characterized the TME, using immunohistochemistry (n=38) and RNA-sequencing (RNA-seq) (n=30) on metastatic pRCC from the prospective AXIPAP trial cohort.

RESULTS

Unsupervised clustering identified two "TME subtypes", in each of the cohorts: the "immune-enriched" and the "immune-low". Within AXIPAP trial cohort, the "immune-enriched" cluster was significantly associated with a worse prognosis according to the median overall survival to 8 months (95% CI, 6 to 29) versus 37 months (95% CI, 20 to NA, p=0.001). The two immune signatures, Teff and JAVELIN Renal 101 Immuno signature, predictive of response to immune checkpoint inhibitors (CPI) in clear cell RCC, were significantly higher in the "immune-enriched" group (adjusted p<0.05). Finally, five differentially overexpressed genes were identified, corresponding mainly to B lymphocyte populations.

CONCLUSION

For the first time, using RNA-seq and immunohistochemistry, we have highlighted a specific immune TME subtype of metastatic pRCC, significantly more infiltrated with T and B immune population. This "immune-enriched" group appears to have a worse prognosis and could have a potential predictive value for response to immunotherapy, justifying the confirmation of these results in a cohort of metastatic pRCC treated with CPI and in combination with targeted therapies.

TRIAL REGISTRATION NUMBER

NCT02489695.

Regulation of HHLA2 expression in kidney cancer and myeloid cells

BACKGROUND

The immune checkpoint HERV-H LTR-associating 2 (HHLA2) is expressed in kidney cancer and various other tumor types. Therapeutics targeting HHLA2 or its inhibitory receptor KIR3DL3 are being developed for solid tumors, including renal cell carcinoma (RCC). However, the regulation of HHLA2 expression remains poorly understood. A better understanding of HHLA2 regulation in tumor cells and the tumor microenvironment is crucial for the successful translation of these therapeutic agents into clinical applications.

METHODS

Flow cytometry and quantitative real-time PCR were used to analyze HHLA2 expression in primary kidney tumors ex vivo and during in vitro culture. HHLA2 expression in A498 and 786-O ccRCC cell lines was examined in vitro and in subcutaneous tumor xenografts in NSG mice. Monocytes and dendritic cells were analyzed for HHLA2 expression. We tested a range of cytokines and culture conditions, including hypoxia, to induce HHLA2 expression.

RESULTS

Analysis of HHLA2 expression revealed that HHLA2 is expressed on tumor cells in primary kidney tumors ex vivo; however, its expression gradually diminishes during a 4-week in vitro culture period. A498 and 786-O ccRCC tumor cell lines do not express HHLA2 in vitro, but HHLA2 expression was observed when grown as subcutaneous xenografts in NSG immunodeficient mice. Induction experiments using various cytokines and culture conditions failed to induce HHLA2 expression in A498 and 786-O tumor cell lines in vitro. Analysis of HHLA2 expression in monocytes and dendritic cells demonstrated that only IL-10 and BMP4, along with IL-1β and IL-6 to a lesser extent, modestly enhanced HHLA2 protein and mRNA expression.

CONCLUSIONS

HHLA2 expression is induced on kidney cancer cells in vivo by a tumor microenvironmental signal that is not present in vitro. HHLA2 expression is differentially regulated in kidney cancer epithelial cells and monocytes. Cytokines, particularly IL10, that induce HHLA2 expression in monocytes fail to upregulate HHLA2 expression in tumor cell lines in vitro. These findings underscore the importance of the interplay between tumor cell and tumor microenvironmental signals in the regulation of HHLA2. Further investigation is warranted to elucidate the mechanisms involved in HHLA2 regulation and its implications for therapeutic development.

GNE-235: A Lead Compound Selective for the Second Bromodomain of PBRM1

Bromodomains are acetyl-lysine binding modules that are found in different classes of chromatin-interacting proteins. Among these are large chromatin remodeling complexes such as BAF and PBAF (variants of human SWI/SNF). Previous work has identified chemical probes targeting a subset of the bromodomains present in the BAF and PBAF complexes. Selective inhibitors of the individual bromodomains have proven challenging to discover, as the domains are highly similar. Here, elaboration of an aminopyridazine scaffold used previously to develop probes for the bromodomains of SMARCA2, SMARCA4, and the fifth bromodomain of PBRM1 yielded compounds with both potency and unusual selectivity for the second bromodomain of PBRM1. One of these, GNE-235, and its enantiomer control GNE-234 are suggested for initial cellular investigations of the function of the second bromodomain of PBRM1.

Renal Neoplasia Occurring in Patients With PTEN Hamartoma Tumor Syndrome : Clinicopathologic Study of 12 Renal Cell Carcinomas From 9 Patients and Association With Intrarenal "Lipomas"

The aim of this study was to assess the histopathologic spectrum of renal tumors in patients with PTEN hamartoma tumor syndrome (PHTS), with a specific focus on potential features predictive of the underlying syndrome. A multi-institutional study was conducted to obtain clinical and pathologic data on renal tumors arising in patients with PHTS, either diagnosed by germline mutational analysis or clinical criteria for Cowden syndrome. Histologic sections of the renal tumors were re-reviewed for classification. Twelve renal epithelial tumors from 9 patients were identified (4 males and 5 females, with a mean age of 41.8 y), 7 of whom carried germline PTEN mutations. All 12 renal epithelial tumors were renal cell carcinomas (RCCs): 5 were chromophobe RCCs, 4 papillary RCCs, and 3 RCC not otherwise specified. Pathologic stage distribution was: 7 (59%) pT1a, 2 (17%) pT1b, 1 (8%) pT2a, 1 (8%) pT2b, and 1 (8%) pT3a. World Health Organization/International Society of Urological Pathology (WHO/ISUP) histologic grade was applicable in 7 (54%) nonchromophobe tumors: 4 (57%) G2, 2 (29%) G3, and 1 (14%) G4. An unexpected histologic finding was the presence of 2 patients with incidental microscopic collections of intrarenal adipocytes that had no features of angiomyolipoma (and were negative with 2 sensitive PEComa markers: cathepsin-K and GPNMB); both were classified as lipoma/"lipomatous hamartomas." The average follow-up interval was 67.8 months (13 to 172 mo): 5 patients had no evidence of disease, 2 were lost to follow-up, 1 died of other (non-PHTS) causes (ie, prostate cancer), and 1 was alive with metastatic RCC to the lung (RCC not otherwise specified with rhabdoid differentiation). All tumors showed loss of nuclear PTEN staining by immunohistochemistry. Fumarate hydratase was retained and 2SC was negative in all papillary RCCs. CK7 was moderate-strong/diffuse positive in 4 of 5 chromophobe RCCs and in 3 of 4 papillary RCCs. Renal epithelial tumors associated with PHTS represent a heterogeneous group of RCCs, but classic chromophobe and papillary RCC are most common. The majority have a favorable clinical behavior as would be predicted by subtype. In contrast to other hereditary renal neoplasia syndromes, morphologic features of the RCCs do not allow identification of PHTS-associated neoplasia with any degree of specificity in the absence of clinical setting and/or prior history, but the presence of microscopic "lipomas" within the kidney may provide a clue in rare cases. Therefore, clinical suspicion and genetic counseling with germline testing remain necessary for identifying PHTS-associated RCC.

ARHGAP11A Is a Novel Prognostic and Predictive Biomarker Correlated with Immunosuppressive Microenvironment in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a highly immunogenic tumor and immune dysfunction is associated with ccRCC poor prognosis. The RhoGTPase-activating proteins (RhoGAPs) family was reported to affect ccRCC development, but its role in immunity and prognosis prediction for ccRCC remain unknown. In the current study, we found ARHGAP11A was the only independent risk factor among 33 RhoGAPs (hazard ratio [HR] 1.949, 95% confidence interval [CI] 1.364-2.785). High ARHGAP11A level was associated with shorter overall survival (OS, HR 2.040, 95% CI 1.646-3.417) and ARHGAP11A is a prognostic biomarker for ccRCC. ARHGAP11A knockdown suppressed renal cell carcinoma (RCC) cell proliferation, colony formation, and migration, suggesting the promoting role of ARHGAP11A on RCC development. Mechanistically, ARHGAP11A might contribute to the suppressive tumor immune microenvironment (TIME). High ARHGAP11A level was correlated with infiltration of immunosuppressive cells (including T helper 2 (Th2) cells, regulatory T (Treg) cells, myeloid derived suppressor cells (MDSC), and M2 macrophage cells), activation of immunosuppressive pathways (IL6-JAK-STAT3 signaling and IFNγ response), and expression of inhibitory immune checkpoints (ICs). ARHGAP11A could promote T cell exhaustion and induce immune escape. ccRCC patients with low ARHGAP11A level were more suitable for immune checkpoint inhibitors (ICIs) therapy, while those with high ARHGAP11A level might benefit from a combination of ARHGAP11A blockade and ICIs. In all, ARHGAP11A might serve as a novel prognostic marker, therapeutic target, and predictor in the clinical response to ICIs therapy for ccRCC.

APPL1 Is a Prognostic Biomarker and Correlated with Treg Cell Infiltration via Oxygen-Consuming Metabolism in Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most hazardous tumors in the urinary system. The regulation of oxygen consumption in renal clear cell carcinoma is a consequence of adaptive reprogramming of oxidative metabolism in tumor cells. APPL1 is a signaling adaptor involved in cell survival, oxidative stress, inflammation, and energy metabolism. However, the correlation of APPL1 with regulatory T cell (Treg) infiltration and prognostic value in KIRC remain unclear. In this study, we comprehensively predicted the potential function and prognostic value of APPL1 in KIRC. For KIRC patients, relatively low expression of APPL1 was associated with high degree of metastasis, pathological stage, and shorter overall time or poor prognosis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggested that low expression of APPL1 may be adapted to the malignant progression of tumors via affecting oxygen-consuming metabolism. In addition, the expression level of APPL1 was negatively correlated with Treg cell infiltration and chemotherapy sensitivity, which indicated that APPL1 may regulate the tumor immune infiltration and chemotherapy resistance by decrease oxygen-consuming metabolic process in KIRC. Therefore, APPL1 may become one of the important prognostic factors, and it may serve as a candidate prognostic biomarker in KIRC.

Gene-Transcript Expression in Urine Supernatant and Urine Cell-Sediment Are Different but Equally Useful for Detecting Prostate Cancer

There is considerable interest in urine as a non-invasive liquid biopsy to detect prostate cancer (PCa). PCa-specific transcripts such as the TMPRSS2:ERG fusion gene can be found in both urine extracellular vesicles (EVs) and urine cell-sediment (Cell) but the relative usefulness of these and other genes in each fraction in PCa detection has not been fully elucidated. Urine samples from 76 men (PCa n = 40, non-cancer n = 36) were analysed by NanoString for 154 PCa-associated genes-probes, 11 tissue-specific, and six housekeeping. Comparison to qRT-PCR data for four genes (PCA3, OR51E2, FOLH1, and RPLP2) was strong (r = 0.51-0.95, Spearman p < 0.00001). Comparing EV to Cells, differential gene expression analysis found 57 gene-probes significantly more highly expressed in 100 ng of amplified cDNA products from the EV fraction, and 26 in Cells (p < 0.05; edgeR). Expression levels of prostate-specific genes (KLK2, KLK3) measured were ~20× higher in EVs, while PTPRC (white-blood Cells) was ~1000× higher in Cells. Boruta analysis identified 11 gene-probes as useful in detecting PCa: two were useful in both fractions (PCA3, HOXC6), five in EVs alone (GJB1, RPS10, TMPRSS2:ERG, ERG_Exons_4-5, HPN) and four from Cell (ERG_Exons_6-7, OR51E2, SPINK1, IMPDH2), suggesting that it is beneficial to fractionate whole urine prior to analysis. The five housekeeping genes were not significantly differentially expressed between PCa and non-cancer samples. Expression signatures from Cell, EV and combined data did not show evidence for one fraction providing superior information over the other.

Epigenomic charting and functional annotation of risk loci in renal cell carcinoma

While the mutational and transcriptional landscapes of renal cell carcinoma (RCC) are well-known, the epigenome is poorly understood. We characterize the epigenome of clear cell (ccRCC), papillary (pRCC), and chromophobe RCC (chRCC) by using ChIP-seq, ATAC-Seq, RNA-seq, and SNP arrays. We integrate 153 individual data sets from 42 patients and nominate 50 histology-specific master transcription factors (MTF) to define RCC histologic subtypes, including EPAS1 and ETS-1 in ccRCC, HNF1B in pRCC, and FOXI1 in chRCC. We confirm histology-specific MTFs via immunohistochemistry including a ccRCC-specific TF, BHLHE41. FOXI1 overexpression with knock-down of EPAS1 in the 786-O ccRCC cell line induces transcriptional upregulation of chRCC-specific genes, TFCP2L1, ATP6V0D2, KIT, and INSRR, implicating FOXI1 as a MTF for chRCC. Integrating RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data reveals that risk-variants are significantly enriched in allelically-imbalanced peaks. This epigenomic atlas in primary human samples provides a resource for future investigation.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

Genomic features of renal cell carcinoma developed during end-stage renal disease and dialysis

Patients with end-stage renal disease (ESRD) or receiving dialysis have a much higher risk for renal cell carcinoma (RCC), but carcinogenic mechanisms and genomic features remain little explored and undefined. This study's goal was to identify the genomic features of ESRD RCC and characterize them for associations with tumor histology and dialysis exposure. In this study, we obtained 33 RCCs, with various histological subtypes, that developed in ESRD patients receiving dialysis and performed whole-genome sequencing and transcriptome analyses. Driver events, copy-number alteration (CNA) analysis and mutational signature profiling were performed using an analysis pipeline that integrated data from germline and somatic SNVs, Indels and structural variants as well as CNAs, while transcriptome data were analyzed for differentially expressed genes and through gene set enrichment analysis. ESRD related clear cell RCCs' driver genes and mutations mirrored those in sporadic ccRCCs. Longer dialysis periods significantly correlated with a rare mutational signature SBS23, whose etiology is unknown, and increased mitochondrial copy number. All acquired cystic disease (ACD)-RCCs, which developed specifically in ESRD patients, showed chromosome 16q amplification. Gene expression analysis suggests similarity between certain ACD-RCCs and papillary RCCs and in TCGA papillary RCCs with chromosome 16 gain identified enrichment for genes related to DNA repair, as well as pathways related to reactive oxygen species, oxidative phosphorylation and targets of Myc. This analysis suggests that ESRD or dialysis could induce types of cellular stress that impact some specific types of genomic damage leading to oncogenesis.

Gene mutation profiling and clinical significances in patients with renal cell carcinoma

OBJECTIVES

The pathological mechanisms of patients with Renal Cell Carcinoma (RCC) remain defined. This study aimed to evaluate relationships between the landscape of gene mutations and their clinical significance in RCC patients.

METHODS

Tissue and peripheral blood samples of 42 patients with RCC were collected and performed for the Next Generation Sequencing (NGS) with Geneseeq PrimeTM 425-gene panel probes. Their landscapes of gene mutation were analyzed. We also carried out an evaluation of Tumor-Node-Metastasis (TNM) staging, RENAL nephelometry score, surgery, and targeted drug treatment of patients. Then we compared the correlations of landscape in gene mutations and the prognosis.

RESULTS

The most common gene alternations, including BAP1, PBRM1, SETD2, CSF1R, NPM1, EGFR, POLE, RB1, and VHL genes, were identified in tissue and blood samples of 75% of patients. EGFR, POLE, and RB1 gene mutations frequently occurred in relapsed and metastatic patients. BAP1, CCND2, KRAS, PTPN11, ERBB2/3, JAK2, and POLE were presented in the patients with > 9 RENAL nephelometry score. Univariable analysis indicated that SETD2, BAP1, and PBRM1 genes were key factors for Disease-Free Survival (DFS). Multivariable analysis confirmed that mutated SETD1, NPM1, and CSF1R were critical factors for the Progression Free Survival (PFS) of RCC patients with target therapy.

CONCLUSIONS

Wild-type PBRM1 and mutated BAP1 in patients with RCC were strongly associated with the outcomes of the patient. The PFS of the patients with SETD2, NPM1, and CSF1R mutations were significantly shorter than those patients without variants.

Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

The iron-modulating hormone hepcidin is upregulated and associated with poor survival outcomes in renal clear cell carcinoma

Background: Reliable biomarkers are rare for renal cell carcinoma (RCC) treatment selection. We aimed to discover novel biomarkers for precision medicine. The iron-regulating hormone hepcidin (HAMP) was reportedly increased in RCC patient sera and tissues. However, its potential implication as a prognostic biomarker remains exclusive. Methods: Multiple RNA-seq and cDNA microarray datasets were utilized to analyze gene expression profiles. Hepcidin protein expression was assessed using an ELISA assay in cell culture models. Comparisons of gene expression profiles and patient survival outcomes were conducted using the R package bioinformatics software. Results: Five (HAMP, HBS, ISCA2, STEAP2, and STEAP3) out of 71 iron-modulating genes exhibited consistent changes along with tumor stage, lymph node invasion, distal metastasis, tumor cell grade, progression-free interval, overall survival, and disease-specific survival. Of which HAMP upregulation exerted as a superior factor (AUC = 0.911) over the other four genes in distinguishing ccRCC tissue from normal renal tissue. HAMP upregulation was tightly associated with its promoter hypomethylation and immune checkpoint factors (PDCD1, LAG3, TIGIT, and CTLA4). Interleukin-34 (IL34) treatment strongly enhanced hepcidin expression in renal cancer Caki-1 cells. Patients with higher levels of HAMP expression experienced worse survival outcomes. Conclusion: These data suggest that HAMP upregulation is a potent prognostic factor of poor survival outcomes and a novel immunotherapeutic biomarker for ccRCC patients.

An update on the pathology of collecting duct & papillary renal cell carcinoma with a discussion of SNP-Arrays as an emerging laboratory technique

The diagnostic criteria and management of renal cell carcinoma (RCC) has evolved significantly in the last two decades. Increased molecular profiling of RCC has yielded further refinement of existing diagnostic categories. This is particularly true for papillary RCC, which has evolved into multiple molecularly distinct entities. Collecting duct carcinoma, on the other hand, continues to be defined based on traditional histology-based criteria, although it is now possible to exclude various categories of tumors that share overlapping morphologic features. In this context, it is important to note that pathology laboratories have varying degrees of access to complex molecular technologies required to profile tumors in routine clinical practice. Single nucleotide polymorphism (SNP) based copy number profiling represents one modality of testing which is becoming widely available and is being increasingly utilized to profile renal tumors. In this special Seminars issue, we explore the evolution of Collecting Duct & Papillary RCC as diagnostic entities, with a detailed discussion of SNP array-based copy number profiling in contemporary clinical practice.

Tumors masquerading as type 2 papillary renal cell carcinoma: pathologists' ever-expanding differential diagnosis for a heterogeneous group of entities

Although papillary renal cell carcinoma has historically been classified as either type 1 or type 2, data from The Cancer Genome Atlas (TCGA) has demonstrated significant genomic heterogeneity in tumors classified as "type 2 papillary renal cell carcinoma" (T2PRCC). Papillary renal cell carcinoma is expected to have a favorable clinical course compared to clear cell renal cell carcinoma (CCRCC). However, tumors with poor outcome more similar to CCRCC were included in the T2PRCC cohort studied by the TCGA. The differential diagnosis for T2PRCC includes a variety of other renal tumors, including aggressive entities such as TFE3 translocation-associated renal cell carcinoma, TFEB-amplified renal cell carcinoma, fumarate hydratase-deficient renal cell carcinoma, high-grade CCRCC, and collecting duct carcinoma. Accurate classification of these tumors is important for prognostication and selection of therapy.

A contemporary guide to chromosomal copy number profiling in the diagnosis of renal cell carcinoma

The routine clinical implementation of molecular methods other than fluorescence in situ hybridization in the evaluation of renal neoplasia is currently limited, as the current standard of care primarily involves a combination of morphologic and immunophenotypic analysis of such tumors. Amongst various molecular techniques, global copy number profiling using single nucleotide polymorphism-based microarrays, colloquially referred to as SNP-arrays, is being increasingly utilized to profile renal tumors, as several subtypes have characteristic recurrent patterns of copy number alterations. Recurrent copy number alterations in common tumor types include loss of chromosome 3p in clear cell renal cell carcinoma (RCC), gain of chromosomes 7 and 17 in papillary RCC and multiple losses in chromosomes 1, 2, 6, 10, 13, 17, and 21 in chromophobe RCC. Such assays are being increasingly utilized in the clinical setting. Herein, we discuss some common clinical applications of such testing that includes high yield diagnostic and prognostic applications. Diagnostic utility includes evaluation of tumor types that are primarily defined by underlying copy number alterations, establishing the underlying subtype in high grade dedifferentiated (unclassified) renal tumors, as well as assessment of loss of heterozygosity, which is an important component in the workup for germline alterations in tumor suppressor genes. Universal adoption of these techniques across clinical laboratories will likely be significantly affected by variables such as cost, reimbursement, and turnaround time.

Identification and validation of the molecular subtype and prognostic signature for clear cell renal cell carcinoma based on neutrophil extracellular traps

Background: Renal cell carcinoma (RCC) is one of the most common cancers, with an annual incidence of nearly 400,000 cases worldwide. Increasing evidence has also demonstrated the vital role of neutrophil extracellular traps (NETs) in cancer progression and metastatic dissemination. Methods: Consensus cluster analysis was performed to determine the number of ccRCC subtypes. The Kruskal-Wallis test or Student t-test was performed to evaluate the difference of infiltrating immune cell and gene expression in different groups. The Kaplan-Meier (KM) method was used to draw the survival curve. LASSO cox regression analysis was conducted to construct a NET-related prognostic signature. We also constructed a lncRNA-miRNA-mRNA regulatory axis by several miRNA and lncRNA target databases. Results: A total of 23 differentially expressed NET-related genes were obtained in ccRCC. Three clusters of ccRCC cases with significant difference in prognosis, immune infiltration, and chemotherapy and targeted therapy were identified. LASSO Cox regression analysis identified a NET-related prognostic signature including six genes (G0S2, DYSF, MMP9, SLC22A4, SELP, and KCNJ15), and this signature had a good performance in predicting the overall survival of ccRCC patients. The expression of prognostic signature genes was significantly correlated with the pTMN stage, immune infiltration, tumor mutational burdens, microsatellite instability, and drug sensitivity of ccRCC patients. MMP9 was identified as the hub gene. We also identified the lncRNA UBA6-AS1/miR-149-5p/MMP9 regulatory axis for the progression of ccRCC. Conclusion: Collectively, the current study identified three molecular clusters and a prognostic signature for ccRCC based on neutrophil extracellular traps. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.

LINC00472 inhibits cell migration by enhancing intercellular adhesion and regulates H3K27ac level via interacting with P300 in renal clear cell carcinoma

Renal clear cell carcinoma (RCCC) is the most common type of renal cell carcinoma, which is also difficult to diagnose and easy to metastasize. Currently, there is still a lack of effective clinical diagnostic indicators and treatment targets. This study aims to find effective diagnostic markers and therapeutic targets from the perspective of noncoding RNA. In this study, we found that the expression of Long noncoding RNA LINC00472 was significantly decreased in RCCC and showed a downward trend with the progression of cancer stage. Patients with low LINC00472 expression have poor prognosis. Inhibition of LINC00472 significantly increased cell proliferation and migration, while overexpression of LINC00472 obviously inhibited cell proliferation and enhanced intercellular adhesion. Transcriptome sequencing analysis demonstrated that LINC00472 was highly correlated with extracellular matrix and cell metastasis-related pathways, and the consistent results were obtained by The Cancer Genome Atlas (TCGA) data analysis. Additionally, we discovered that the integrin family protein ITGB8 is a potential target gene of LINC00472. Mechanistically, we found that the change of LINC00472 affected the acetylation level of H3K27 site in cells, and we speculate that this effect is likely to be generated through the interaction with acetyltransferase P300. In conclusion, LINC00472 has an important impact on the proliferation and metastasis of renal clear cells, and probably participate in the regulation of histone modification, and it may be used as a potential diagnostic marker of RCCC.

MUC1 Tissue Expression and Its Soluble Form CA15-3 Identify a Clear Cell Renal Cell Carcinoma with Distinct Metabolic Profile and Poor Clinical Outcome

An altered metabolism is involved in the development of clear cell renal carcinoma (ccRCC). MUC1 overexpression has been found to be associated with advanced disease and poor prognosis. In this study, we evaluated the metabolomic profile of human ccRCC, according to MUC1 expression, and integrated it with transcriptomic data. Moreover, we analyzed the role of MUC1 in sustaining ccRCC aggressiveness and the prognostic value of its soluble form CA15-3. Integrated metabolomic and transcriptomic analysis showed that MUC1-expressing ccRCC was characterized by metabolic reprogramming involving the glucose and lipid metabolism pathway. In addition, primary renal cancer cells treated with a small interfering RNA targeting MUC1 (siMUC1) migrated and proliferated at a slower rate than untreated cancer cells. After cisplatin treatment, the death rate of cancer cells treated with siMUC1 was significantly greater than that of untreated cells. Kaplan-Meier curves showed significant differences in CSS and PFS among groups of patients with high versus low levels of CA15-3. In a multivariate analysis, CA15-3 was an independent adverse prognostic factor for cancer-specific and progression-free survival. In conclusion, MUC1 expressing ccRCC is characterized by a particular metabolic reprogramming. The inhibition of MUC1 expression decreases cell motility and viability and improves cisplatin susceptibility, suggesting that this pathway can regulate de novo chemotherapy resistance in ccRCC.

WHO 2022 landscape of papillary and chromophobe renal cell carcinoma

The 5th edition of the WHO Classification of Tumours of the Urinary and Male Genital Systems contains relevant revisions and introduces a group of molecularly defined renal tumour subtypes. Herein we present the World Health Organization (WHO) 2022 perspectives on papillary and chromophobe renal cell carcinoma with emphasis on their evolving classification, differential diagnosis, and emerging entities. The WHO 2022 classification eliminated the type 1/2 papillary renal cell carcinoma (pRCC) subcategorization, given the recognition of frequent mixed tumour phenotypes and the existence of entities with a different molecular background within the type 2 pRCC category. Additionally, emerging entities such as biphasic squamoid alveolar RCC, biphasic hyalinising psammomatous RCC, papillary renal neoplasm with reverse polarity, and Warthin-like pRCC are included as part of the pRCC spectrum, while additional morphological and molecular data are being gathered. In addition to oncocytomas and chromophobe renal cell carcinoma (chRCC), a category of 'other oncocytic tumours' with oncocytoma/chRCC-like features has been introduced, including emerging entities, most with TSC/mTOR pathway alterations (eosinophilic vacuolated tumour and so-called 'low-grade' oncocytic tumour), deserving additional research. Eosinophilic solid and cystic RCC was accepted as a new and independent tumour entity. Finally, a highly reproducible and clinically relevant universal grading system for chRCC is still missing and is another niche of ongoing investigation. This review discusses these developments and highlights emerging morphological and molecular data relevant for the classification of renal cell carcinoma.

Proteogenomic, Epigenetic, and Clinical Implications of Recurrent Aberrant Splice Variants in Clear Cell Renal Cell Carcinoma

BACKGROUND

Alternative mRNA splicing can be dysregulated in cancer, resulting in the generation of aberrant splice variants (SVs). Given the paucity of actionable genomic mutations in clear cell renal cell carcinoma (ccRCC), aberrant SVs may be an avenue to novel mechanisms of pathogenesis.

OBJECTIVE

To identify and characterize aberrant SVs enriched in ccRCC.

DESIGN, SETTING, AND PARTICIPANTS

Using RNA-seq data from the Cancer Cell Line Encyclopedia, we identified neojunctions uniquely expressed in ccRCC. Candidate SVs were then checked for expression across normal tissue in the Genotype-Tissue Expression Project and primary tumor tissue from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and our institutional Total Cancer Care database.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Clinicopathologic, genomic, and survival data were available for all cohorts. Epigenetic data were available for the TCGA and CPTAC cohorts. Proteomic data were available for the CPTAC cohort. The association of aberrant SV expression with these variables was examined using the Kruskal-Wallis test, pairwise t test, Spearman correlation test, and Cox regression analysis.

RESULTS AND LIMITATIONS

Our pipeline identified 16 ccRCC-enriched SVs. EGFR, HPCAL1-SV and RNASET2-SV expression was negatively correlated with gene-specific CpG methylation. We derived a survival risk score based primarily on the expression of five SVs (RNASET2, FGD1, PDZD2, COBLL1, and PTPN14), which was consistent and applicable across multiple cohorts on multivariate analysis. The splicing factor RBM4, which modulates splicing of HIF-1α, exhibited significantly lower expression at the protein level in the high-risk group, as defined by our SV-based score.

CONCLUSIONS

We describe 16 aberrant SVs enriched in ccRCC, many of which are associated with disease biology and/or clinical outcomes. This study provides a novel strategy for identifying and characterizing disease-specific aberrant SVs.

PATIENT SUMMARY

We describe a method to identify disease targets and biomarkers using transcriptomic analysis beyond somatic mutations or gene expression. Kidney tumors express unique splice variants that may provide additional prognostic information following surgery.

Iron accumulation typifies renal cell carcinoma tumorigenesis but abates with pathological progression, sarcomatoid dedifferentiation, and metastasis

Iron is a potent catalyst of oxidative stress and cellular proliferation implicated in renal cell carcinoma (RCC) tumorigenesis, yet it also drives ferroptosis that suppresses cancer progression and represents a novel therapeutic target for advanced RCC. The von Hippel Lindau (VHL)/hypoxia-inducible factor-α (HIF-α) axis is a major regulator of cellular iron, and its inactivation underlying most clear cell (cc) RCC tumors introduces both iron dependency and ferroptosis susceptibility. Despite the central role for iron in VHL/HIF-α signaling and ferroptosis, RCC iron levels and their dynamics during RCC initiation/progression are poorly defined. Here, we conducted a large-scale investigation into the incidence and prognostic significance of total tissue iron in ccRCC and non-ccRCC patient primary tumor cancer cells, tumor microenvironment (TME), metastases and non-neoplastic kidneys. Prussian Blue staining was performed to detect non-heme iron accumulation in over 1600 needle-core sections across multiple tissue microarrays. We found that RCC had significantly higher iron staining scores compared with other solid cancers and, on average, >40 times higher than adjacent renal epithelium. RCC cell iron levels correlated positively with TME iron levels and inversely with RCC levels of the main iron uptake protein, transferrin receptor 1 (TfR1/TFRC/CD71). Intriguingly, RCC iron levels, including in the TME, decreased significantly with pathologic (size/stage/grade) progression, sarcomatoid dedifferentiation, and metastasis, particularly among patients with ccRCC, despite increasing TfR1 levels, consistent with an increasingly iron-deficient tumor state. Opposite to tumor iron changes, adjacent renal epithelial iron increased significantly with RCC/ccRCC progression, sarcomatoid dedifferentiation, and metastasis. Lower tumor iron and higher renal epithelial iron each predicted significantly shorter ccRCC patient metastasis-free survival. In conclusion, iron accumulation typifies RCC tumors but declines toward a relative iron-deficient tumor state during progression to metastasis, despite precisely opposite dynamics in adjacent renal epithelium. These findings raise questions regarding the historically presumed selective advantage for high iron during all phases of cancer evolution, suggesting instead distinct tissue-specific roles during RCC carcinogenesis and early tumorigenesis versus later progression. Future study is warranted to determine how the relative iron deficiency of advanced RCC contributes to ferroptosis resistance and/or introduces a heightened susceptibility to iron deprivation that might be therapeutically exploitable.

Kinase activity profiling in renal cell carcinoma, benign renal tissue and in response to four different tyrosine kinase inhibitors

Kinase activity is frequently altered in renal cell carcinoma (RCC), and tyrosine kinase inhibitors (TKIs) are part of the standard treatment strategy in patients with metastatic disease. However, there are still no established biomarkers to predict clinical benefits of a specific TKI. Here, we performed protein tyrosine kinase (PTK) profiling using PamChip^® technology. The aim of this study was to identify differences in PTK activity between normal and malignant kidney tissue obtained from the same patient, and to investigate the inhibitory effects of TKIs frequently used in the clinics: sunitinib, pazopanib, cabozantinib and tivozanib. Briefly, our results showed that 36 kinase substrates differs (FDR < 0.05) between normal and cancer kidney tissue, where members of the Src family kinases and the phosphoinositide-3-kinase (PI3K) pathway exhibit high activity in renal cancer. Furthermore, ex vivo treatment of clear cell RCC with TKIs revealed that pathways such as Rap1, Ras and PI3K pathways were strongly inhibited, whereas the neurotrophin pathway had increased activity upon TKI addition. In our assay, tivozanib and cabozantinib exhibited greater inhibitory effects on PTK activity compared to sunitinib and pazopanib, implying they might be better suitable as TKIs for selected RCC patients.

Elevated expression of LIF predicts a poor prognosis and promotes cell migration and invasion of clear cell renal cell carcinoma

Background

Renal cell carcinoma (RCC) is the seventh most common cancer in humans, of which clear cell renal cell carcinoma (ccRCC) accounts for the majority. Recently, although there have been significant breakthroughs in the treatment of ccRCC, the prognosis of targeted therapy is still poor. Leukemia inhibitory factor (LIF) is a pleiotropic protein, which is overexpressed in many cancers and plays a carcinogenic role. In this study, we explored the expression and potential role of LIF in ccRCC.

Methods

The expression levels and prognostic effects of the LIF gene in ccRCC were detected using TCGA, GEO, ICGC, and ArrayExpress databases. The function of LIF in ccRCC was investigated using a series of cell function approaches. LIF-related genes were identified by weighted gene correlation network analysis (WGCNA). GO and KEGG analyses were performed subsequently. Cox univariate and LASSO analyses were used to develop risk signatures based on LIF-related genes, and the prognostic model was validated in the ICGC and E-MTAB-1980 databases. Then, a nomogram model was constructed for survival prediction and validation of ccRCC patients. To further explore the drug sensitivity between LIF-related genes, we also conducted a drug sensitivity analysis based on the GDSC database.

Results

The mRNA and protein expression levels of LIF were significantly increased in ccRCC patients. In addition, a high expression of LIF has a poor prognostic effect in ccRCC patients. LIF knockdown can inhibit the migration and invasion of ccRCC cells. By using WGCNA, 97 LIF-related genes in ccRCC were identified. Next, a prognostic risk prediction model including eight LIF-related genes (TOB2, MEPCE, LIF, RGS2, RND3, KLF6, RRP12, and SOCS3) was developed and validated. Survival analysis and ROC curve analysis indicated that the eight LIF-related-gene predictive model had good performance in evaluating patients’ prognosis in different subgroups of ccRCC.

Conclusion

Our study revealed that LIF plays a carcinogenic role in ccRCC. In addition, we firstly integrated multiple LIF-related genes to set up a risk-predictive model. The model could accurately predict the prognosis of ccRCC, which offers clinical implications for risk stratification, drug screening, and therapeutic decision.

A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10–15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (p < 0.001; HR = 2.73, 95% CI = 1.97–3.79) and the validation cohort (p = 0.001; HR = 2.84, 95% CI = 1.50–5.38). The nomogram combined with multiple clinical information and MRSS was more effective at predicting patient outcomes than a single independent prognostic factor. The impact of metabolism on ccRCC was also assessed, and seven metabolism-related genes were established and validated as biomarkers to predict patient outcomes effectively.

A single-cell map of dynamic chromatin landscapes of immune cells in renal cell carcinoma

A complete chart of the chromatin regulatory elements of immune cells in patients with cancer and their dynamic behavior is necessary to understand the developmental fates and guide therapeutic strategies. Here, we map the single-cell chromatin landscape of immune cells from blood, normal tumor-adjacent kidney tissue and malignant tissue from patients with early-stage clear cell renal cell carcinoma (ccRCC). We catalog the T cell states dictated by tissue-specific and developmental-stage-specific chromatin accessibility patterns, infer key chromatin regulators and observe rewiring of regulatory networks in the progression to dysfunction in CD8⁺ T cells. Unexpectedly, among the transcription factors orchestrating the path to dysfunction, NF-κB is associated with a pro-apoptotic program in late stages of dysfunction in tumor-infiltrating CD8⁺ T cells. Importantly, this epigenomic profiling stratified ccRCC patients based on a NF-κB-driven pro-apoptotic signature. This study provides a rich resource for understanding the functional states and regulatory dynamics of immune cells in ccRCC.

The close interaction between hypoxia-related proteins and metastasis in pancarcinomas

Many primary-tumor subregions exhibit low levels of molecular oxygen and restricted access to nutrients due to poor vascularization in the tissue, phenomenon known as hypoxia. Hypoxic tumors are able to regulate the expression of certain genes and signaling molecules in the microenvironment that shift it towards a more aggressive phenotype. The transcriptional landscape of the tumor favors malignant transformation of neighboring cells and their migration to distant sites. Herein, we focused on identifying key proteins that participate in the signaling crossroads between hypoxic environment and metastasis progression that remain poorly defined. To shed light on these mechanisms, we performed an integrated multi-omics analysis encompassing genomic/transcriptomic alterations of hypoxia-related genes and Buffa hypoxia scores across 17 pancarcinomas taken from the PanCancer Atlas project from The Cancer Genome Atlas consortium, protein-protein interactome network, shortest paths from hypoxia-related proteins to metastatic and angiogenic phenotypes, and drugs involved in current clinical trials to treat the metastatic disease. As results, we identified 30 hypoxia-related proteins highly involved in metastasis and angiogenesis. This set of proteins, validated with the MSK-MET Project, could represent key targets for developing therapies. The upregulation of mRNA was the most prevalent alteration in all cancer types. The highest frequencies of genomic/transcriptomic alterations and hypoxia score belonged to tumor stage 4 and positive metastatic status in all pancarcinomas. The most significantly associated signaling pathways were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF. The interactome network revealed high-confidence interactions among hypoxic and metastatic proteins. The analysis of shortest paths revealed several ways to spread metastasis and angiogenesis from hypoxic proteins. Lastly, we identified 23 drugs enrolled in clinical trials focused on metastatic disease treatment. Six of them were involved in advanced-stage clinical trials: aflibercept, bevacizumab, cetuximab, erlotinib, ipatasertib, and panitumumab.

Identification and Verification of Immune Subtype-Related lncRNAs in Clear Cell Renal Cell Carcinoma

Background

According to clinical study results, immune checkpoint blockade (ICB) treatment enhances the survival outcome of patients with clear cell renal cell carcinoma (ccRCC). Previous research has divided ccRCC patients into immune subtypes with distinct ICB response rates. However, the study on the association between lncRNAs and ccRCC immune subtypes is lacking.

Methods

Differentially expressed lncRNAs/mRNAs between two major immune subgroups were calculated. A weighted gene co-expression network analysis (WGCNA) was conducted to establish the lncRNA-mRNA co-expression network and select the key lncRNAs. Then, prognostic lncRNAs were selected from the network by the bioinformatics method. Next, the risk-score was estimated by lncRNA expression and their coefficients. Finally, a nomogram based on lncRNAs and clinical parameters was created to predict the prognosis of ccRCC.

Results

LncRNAs and mRNAs associated with ccRCC immune subtypes were identified. The lncRNAs and mRNAs from a gene module closely linked to the immune subtype were used to construct a network. The KEGG pathways enriched in the network were related to immune system activation processes. These 8 lncRNAs (AL365361.1, LINC01934, AC090152.1, PCED1B-AS1, LINC00426, AC007728.2, AC243829.4, and LINC00158) were found to be positively correlated with immune cells of the tumor microenvironment. The C-index of the nomogram was 0.777, and the calibration curve data suggests that the nomogram has a high degree of discriminating capacity.

Conclusion

In summary, we discovered core lncRNAs linked with immune subtypes and created corresponding lncRNA–mRNA networks. These lncRNAs are anticipated to have predictive significance for ccRCC and may provide insight into novel biomarkers for the disease.

Signatures of copy number alterations in human cancer

Gains and losses of DNA are prevalent in cancer and emerge as a consequence of inter-related processes of replication stress, mitotic errors, spindle multipolarity and breakage-fusion-bridge cycles, among others, which may lead to chromosomal instability and aneuploidy1,2. These copy number alterations contribute to cancer initiation, progression and therapeutic resistance3-5. Here we present a conceptual framework to examine the patterns of copy number alterations in human cancer that is widely applicable to diverse data types, including whole-genome sequencing, whole-exome sequencing, reduced representation bisulfite sequencing, single-cell DNA sequencing and SNP6 microarray data. Deploying this framework to 9,873 cancers representing 33 human cancer types from The Cancer Genome Atlas6 revealed a set of 21 copy number signatures that explain the copy number patterns of 97% of samples. Seventeen copy number signatures were attributed to biological phenomena of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, chromothripsis and haploidization. The aetiologies of four copy number signatures remain unexplained. Some cancer types harbour amplicon signatures associated with extrachromosomal DNA, disease-specific survival and proto-oncogene gains such as MDM2. In contrast to base-scale mutational signatures, no copy number signature was associated with many known exogenous cancer risk factors. Our results synthesize the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes that give rise to these alterations.

Genome-wide cross-cancer analysis illustrates the critical role of bimodal miRNA in patient survival and drug responses to PI3K inhibitors

Heterogeneity of cancer means many tumorigenic genes are only aberrantly expressed in a subset of patients and thus follow a bimodal distribution, having two modes of expression within a single population. Traditional statistical techniques that compare sample means between cancer patients and healthy controls fail to detect bimodally expressed genes. We utilize a mixture modeling approach to identify bimodal microRNA (miRNA) across cancers, find consistent sources of heterogeneity, and identify potential oncogenic miRNA that may be used to guide personalized therapies. Pathway analysis was conducted using target genes of the bimodal miRNA to identify potential functional implications in cancer. In vivo overexpression experiments were conducted to elucidate the clinical importance of bimodal miRNA in chemotherapy treatments. In nine types of cancer, tumors consistently displayed greater bimodality than normal tissue. Specifically, in liver and lung cancers, high expression of miR-105 and miR-767 was indicative of poor prognosis. Functional pathway analysis identified target genes of miR-105 and miR-767 enriched in the phosphoinositide-3-kinase (PI3K) pathway, and analysis of over 200 cancer drugs in vitro showed that drugs targeting the same pathway had greater efficacy in cell lines with high miR-105 and miR-767 levels. Overexpression of the two miRNA facilitated response to PI3K inhibitor treatment. We demonstrate that while cancer is marked by considerable genetic heterogeneity, there is between-cancer concordance regarding the particular miRNA that are more variable. Bimodal miRNA are ideal biomarkers that can be used to stratify patients for prognosis and drug response in certain types of cancer.

Bimodal genes can be defined as those having two modes of expression within the same population. A variety of statistical methodologies have been employed to assess bimodal gene expression, but current methods and their applications have been limited. Given the advances in next-generation sequencing as well as the extensive regulatory role of miRNA, assessing bimodality in miRNA-seq data can greatly broaden our understanding of factors underlying tumor progression. The goal of the current study was to utilize a novel mixture modeling approach to identify bimodal miRNA and then demonstrate their importance in cancer by evaluating their ability to predict overall survival and drug response. Our results showed that high levels of bimodal miRNA expression was characteristic of cancer. Additionally, several bimodal miRNA were common to multiple cancer types, suggesting that certain miRNA consistently account for tumor heterogeneity and may be involved in general oncogenic processes. Our study points to the potential of bimodal miRNA to facilitate precise prognostic evaluation and effective treatment strategies.

Novel Diagnostic Value of Driver Gene Transcription Signatures to Characterise Clear Cell Renal Cell Carcinoma, ccRCC

Routine molecular tumour diagnostics are augmented by DNA-based qualitative and quantitative molecular techniques detecting mutations of DNA. However, in the past decade, it has been unravelled that the phenotype of cancer, as it’s an extremely complex disease, cannot be fully described and explained by single or multiple genetic variants affecting only the coding regions of the genes. Moreover, studying the manifestation of these somatic mutations and the altered transcription programming—driven by genomic rearrangements, dysregulation of DNA methylation and epigenetic landscape—standing behind the tumorigenesis and detecting these changes could provide a more detailed characterisation of the tumour phenotype. Consequently, novel comparative cancer diagnostic pipelines, including DNA- and RNA-based approaches, are needed for a global assessment of cancer patients. Here we report, that by monitoring the expression patterns of key tumour driver genes by qPCR, the normal and the tumorous samples can be separated into distinct categories. Furthermore, we also prove that by examining the transcription signatures of frequently affected genes at 3p25, 3p21 and 9p21.3 genomic regions, the ccRCC (clear cell renal cell carcinoma) and non-tumorous kidney tissues can be distinguished based on the mRNA level of the selected genes. Our results open new diagnostics possibilities where the mRNA signatures of tumour drivers can supplement the DNA-based approaches providing a more precise diagnostics opportunity leading to determine more precise therapeutic protocols.

Pathological and genetic markers improve recurrence prognostication with the University of California Los Angeles Integrated Staging System for patients with clear cell renal cell carcinoma

PURPOSE

To elucidate which patients with clear cell renal cell carcinoma have the highest risk for disease relapse after curative nephrectomy is challenging but is acutely relevant in the era of approved adjuvant therapies. Pathological and genetic markers were used to improve the University of California Los Angeles Integrated Staging System (UISS) for the risk stratification and prognostication of recurrence free survival (RFS).

PATIENTS AND METHODS

Necrosis, sarcomatoid features, Rhabdoid features, chromosomal loss 9p, combined chromosomal loss 3p14q and microvascular invasion (MVI) were tested in univariable and multivariable analyses for their ability to improve the discriminatory ability of the UISS.

RESULTS

In the development cohort, during the median follow-up time of 43.4 months (±SD 54.1 months), 50/240 (21%) patients developed disease recurrence. MVI (HR: 2.22; p = 0.013) and the combined loss of chromosome 3p/14q (HR: 2.89; p = 0.004) demonstrated independent association with RFS and were used to improve the assignment to the UISS risk category. In the current UISS high-risk group, only 7/50 (14%) recurrence cases were correctly identified; while in the improved system, 23/50 (45%) were correctly prognosticated. The concordance index meaningfully improved from 0.55 to 0.68 to distinguish patients at intermediate risk versus high risk. Internal validation demonstrated a robust prognostication of RFS. In the external validation cohort, there was no case with disease recurrence in the low-risk group, and the mean RFS times were 13.2 (±1.8) and 8.2 (±0.8) years in the intermediate and high-risk groups, respectively.

CONCLUSIONS

Adding MVI and combined chromosomal loss3p/14q to the UISS improves the ability to define the patient group with clear cell renal cell carcinomawho are at the highest risk for disease relapse after surgical treatment.

METTL14-mediated N6-methyladenosine modification of ITGB4 mRNA inhibits metastasis of clear cell renal cell carcinoma

Background

Integrin β4 (ITGB4) participates in tumorigenesis and progression of several malignancies, but its role and related mechanisms in clear cell renal cell carcinoma (ccRCC) remain unclear.

Methods

Quantitative real-time PCR (qRT-PCR), western blot and immunohistochemistry were used to detect mRNA and protein levels of relevant genes. Biological functions of ITGB4 and methyltransferase-like 14 (METTL14) were determined by in vitro and in vivo experiments. The levels of N6-methyladenosine (m6A) in ccRCC tissues and adjacent normal tissues were calculated via total RNA m6A quantification assay. The m6A modification of ITGB4 was demonstrated via m6A RNA immunoprecipitation (MeRIP), RIP and luciferase reporter assays.

Results

ITGB4 was significantly overexpressed in ccRCC tissues and high level of ITGB4 predicted poor prognosis as well as metastasis. Functionally, ITGB4 stimulated ccRCC cell migration and invasion in vitro and metastasis in vivo with epithelial–mesenchymal transition (EMT) strengthened. Mechanically, the total levels of m6A were reduced in ccRCC tissues. METTL14, a favorable factor for ccRCC patients’ prognosis, facilitated m6A modification on ITGB4 3′UTR and subsequently accelerated ITGB4 mRNA degradation, leading to its declined expression. Furthermore, the METTL14-mediated inhibition of ITGB4 expression was dependent on the YTH domain family protein 2 (YTHDF2), which acted as an m6A reader to bind to ITGB4 mRNA and to promote its decay. In addition, we demonstrated that knockdown of METTL14 promoted ccRCC cell migration, invasiveness and metastasis as well as stimulating the EMT process and the PI3K/AKT signal by overexpressing ITGB4.

Conclusion

Our study reveals that METTL14 inhibits ITGB4 expression via m6A modification to attenuate metastasis and EMT of ccRCC cells, suggesting the METTL14/ITGB4 axis as a potential prognostic biomarker and therapeutic target for ccRCC.

Clear cell renal cell carcinoma with stage IV cavoatrial tumour thrombus extension and rapid metastatic reoccurrence postsurgical treatment with review of current treatment strategies

Renal cell carcinoma (RCC) is the most aggressive urological malignancy, with a high recurrence rate. Despite the rapid evolution of the treatment of RCC from non-specific cytotoxic therapies to specific novel combination therapies, the general prognosis for advanced RCC remains poor because patients' responses to these therapies vary. Herein, we present the case of a male in early forties who was diagnosed with a right lower pole renal mass with a level IV tumour thrombus, which was later confirmed as stage IIIc clear cell RCC. About 19 months after radical nephrectomy (curative surgery), the patient was diagnosed with a biopsy-proven metastatic disease, which was not responsive to first-line treatment owing to insufficient data on the best treatment regimen. Herein, we also present a literature review on the pathological impact of genomic alterations in tumour suppressors and highlight emerging paradigm shifts in the treatment of RCC.

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically relevant driver genes are reflected in the histological phenotype of solid tumors and can be inferred by analysing routine Haematoxylin and Eosin (H&E) stained tissue sections with Deep Learning. However, these studies mostly focused on selected individual genes in selected tumor types. In addition, genetic changes in solid tumors primarily act by changing signaling pathways that regulate cell behaviour. In this study, we hypothesized that Deep Learning networks can be trained to directly predict alterations of genes and pathways across a spectrum of solid tumors. We manually outlined tumor tissue in H&E-stained tissue sections from 7,829 patients with 23 different tumor types from The Cancer Genome Atlas. We then trained convolutional neural networks in an end-to-end way to detect alterations in the most clinically relevant pathways or genes, directly from histology images. Using this automatic approach, we found that alterations in 12 out of 14 clinically relevant pathways and numerous single gene alterations appear to be detectable in tissue sections, many of which have not been reported before. Interestingly, we show that the prediction performance for single gene alterations is better than that for pathway alterations. Collectively, these data demonstrate the predictability of genetic alterations directly from routine cancer histology images and show that individual genes leave a stronger morphological signature than genetic pathways.

Systematic Review of Treatment of Metastatic Non-Clear Cell Renal Cell Carcinoma

Background: Metastatic and unresectable non-clear cell renal cell carcinoma comprises more than a quarter of kidney cancers but does not have standardized treatment. Non-clear renal carcinoma consists of a variety of diverse histologic subtypes, including papillary, chromophobe, collecting duct, translocation, and medullary histologies, many of which carry a poor prognosis. Many prospective clinical trials exclude these kidney cancers, and for most clinical trials of non-clear cell renal cell carcinoma, only a small number of patients are enrolled. Objective: To perform a systematic review of recently published and currently enrolling prospective clinical trials for advanced non-clear cell renal cell carcinoma. Methods: A systematic search of Pubmed and MEDLINE (Ovid) was conducted as per PRISMA guidelines to identify recent prospective clinical trials in non-clear cell renal cell carcinoma. To ensure a thorough search, terms not only included non-clear cell renal carcinoma but also molecular subtypes. A review of currently enrolling clinical trials was conducted on Clinicaltrials.gov and the EU Clinical Trials Register as well. Results: A total of 33 prospective clinical trials with published results and 10 currently enrolling clinicals trials were identified. About half (48.5%) of these studies were reported in 2020 or 2021, and 36.4% were in the first-line setting. Treatments investigated in these trials included mTOR inhibitors, VEGF- and MET-targeted tyrosine kinase inhibitors, immune checkpoint inhibitors, and combinatorial strategies. Outcomes from these data revealed a wide range of response rate and progression free survival, favoring TKIs and immune checkpoint inhibitors -based combination regimens. Conclusions: Novel targeted therapies and immunotherapies have changed the landscape of treatment for advanced non-clear cell renal cell carcinoma. Combination regimens may provide even further clinical benefit and warrant further investigation in larger, randomized prospective clinical trials.