The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Prognostic factors for the survival of patients with papillary renal cell carcinoma after surgical management

PURPOSE

To examine the possible prognostic factors in patients with type 1 and type 2 papillary renal cell carcinoma (pRCC) after surgical management and to identify the independent predictive factors of the prognosis.

METHODS

From 2010 to 2017, 1405 patients underwent surgery for renal cell carcinoma, of whom 114 had type 1 or type 2 pRCC and follow-up data were available for 88 patients. Clinicopathological and prognostic parameters were compared between type 1 and type 2 pRCC. Possible prognostic factors were retrospectively analyzed by univariate and multivariate analyses with Cox regression.

RESULTS

The study included 63 males and 25 females with a mean age of 54.27 ± 12.91. 53 patients were diagnosed by regular physical examination and others presented with hematuria or lumbago. 53 (60.2%) underwent radical nephrectomy and 35 (39.8%) underwent nephron sparing surgery. After a mean follow-up of 46.08 ± 22.65 months, 16 patients died of pRCC metastasis and the 5-year disease-specific survival was 79.3%. The comparison of the 39 (44.3%) type 1 and 49 (55.7%) type 2 pRCCs revealed that type 2 pRCC had significantly higher grade and worse prognosis. Univariate analysis showed that symptomatic diagnosis, type, grade, and tumor stage were prognostic factors. Multivariate analysis identified that type and tumor stage were independent factors of the prognosis.

CONCLUSIONS

Pathological type and tumor stage could serve as independent factors for the prognosis of patients with pRCC.

SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation-maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

An integrative sparse boosting analysis of cancer genomic commonality and difference

In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has been conducted. Such analysis has the potential to reveal the genomic commonality as well as difference across groups/subgroups. However, in the existing literature, methods with a special attention to the genomic commonality and difference are very limited. In this study, a novel estimation and marker selection method based on the sparse boosting technique is developed to address the commonality/difference problem. In terms of technical innovation, a new penalty and computation of increments are introduced. The proposed method can also effectively accommodate the grouping structure of covariates. Simulation shows that it can outperform direct competitors under a wide spectrum of settings. The analysis of two TCGA (The Cancer Genome Atlas) datasets is conducted, showing that the proposed analysis can identify markers with important biological implications and have satisfactory prediction and stability.

Beyond growth signaling: apoptotic sensor MERTK activates AKT by a novel mechanism

Canonically the oncogenic kinase AKT is activated by growth signals. Our work suggests apoptotic materials, abundant in tumors, also contribute to AKT activation by stimulating MERTK that in turn phosphorylates Y26 in the AKT PH domain. pY26 reverses binding of an AKT endogenous, WW-domain containing inhibitor, SAV1, allowing AKT responsiveness to classic growth signals. This novel mechanism may contribute to drug resistance.

Targeting the Tumor Microenvironment in Renal Cell Cancer Biology and Therapy

Renal cell cancer (RCC) is a highly vascularized and immunogenic tumor type. The inhibition of vessel formation by anti-angiogenic therapies, as well as the stimulation of the immune system by immunotherapy has revolutionized the therapeutic landscape of RCC in recent years. Nevertheless, both therapies are associated with therapy resistance due to a highly dynamic, adaptive and heterogeneous tumor microenvironment (TME). The aim of this short review article is to provide an overview of the components of the RCC TME as well as to discuss their contribution to disease progression. In addition, we report on preclinical and clinical findings and how the different TME components can be modulated to impede treatment progression as well as to overcome therapy resistance to anti-angiogenic or immunomodulating therapy concepts. Furthermore, we discuss the predictive and prognostic role of the TME in RCC therapy. We also report on the concept of combinational targeting of anti-angiogenic therapies and immune checkpoint inhibitor therapy, also including the latest results of clinical studies discussed at recent oncological meetings. Finally, promising new therapeutic targets within the TME are mentioned.

BAP1 in solid tumors

One of the most attractive cancer-related genes under investigation is BAP1. Reasons of this growing interest are related to the wide spectrum of pathways directly or indirectly modulated by this gene and shared by several solid tumors. Programmed cell-death, cell metabolisms, immune cells development, ferroptosis and defects in DNA damage response are only some of the multitude of processes depending on BAP1. Loss of this gene seems to occur in different times of tumor history. Moreover, times of BAP1 loss strongly diverge among primary tumors suggesting the presence of several and different triggering factors. Regardless of when it happens, BAP1 loss usually results in prognosis worsening and in the acquisition of more aggressive clinical features by cancer cells.

Analysis of Expression Patterns of MicroRNAs That Are Closely Associated With Renal Carcinogenesis

Background: MicroRNAs (miRNA) are frequently dysregulated in clear cell renal cell carcinoma (ccRCC). Objective: This study aimed to elucidate the role of miRNA expression patterns in renal carcinogenesis and to identify the specific miRNAs that exhibit expression patterns closely associated with patient outcomes. Methods: We examined the expression patterns of selected miRNAs, including miRNA-155-5p, miRNA-122-5p, miRNA-21-5p, miRNA-185-5p, miRNA-106a-5p, miRNA-106b-3p, miRNA-34b-3p, miRNA-210-3p, miRNA-141-3p, miRNA-200c-3p, miRNA-135a-5p, miRNA-30a-5p, miRNA-218-5p, miRNA-429, miRNA-200a-3p and miRNA-200b-3p, in 96 samples of ccRCCs using the TaqMan real-time PCR method. In addition, cluster analysis was performed to stratify expression patterns of multiple miRNAs. Results: In the present study, three distinct subgroups could be clearly stratified in ccRCCs. Subgroup 1 was characterized by upregulation of miRNA-155-5p, miRNA-122-5p, miRNA-21-5p, miRNA-185-5p, miRNA-106a-5p, miRNA-106b-3p, miRNA-34b-3p and miRNA-210-3p. Subgroup 2 was closely associated with downregulation of miRNA-141-3p, miRNA200c-3p, miRNA-30a-5p, miRNA-218-5p, miRNA-429, miRNA-200a-3p and miRNA-200b-3p. Moreover, significant lower expression of miRNA-135a-5p was a distinctive feature of subgroup 3, which was correlated with metachronous metastasis. Among the individual markers in subgroup 3, miRNA-135a-5p was retained in multivariate analysis. The cutoff value of miRNA-135a-5p expression to identify the association of an altered level of miRNA-135a-5p with metachronous metastasis in ccRCCs was determined and showed excellent specificity. Conclusion: We suggest that the expression pattern of the chosen miRNAs is useful to identify renal carcinogenesis and to help identify the association of such expression patterns with metachronous metastasis in ccRCCs. In addition, miRNA-135a-5p was an excellent marker for prediction of metachronous metastasis.

Metabolomics informs common patterns of molecular dysfunction across histologies of renal cell carcinoma

The last 30 years of research in renal cell carcinoma (RCC) has revealed that the vast majority of RCC histologies share a recurrent pattern of mutations to metabolic genes, including VHL, MTOR, ELOC, TSC1/2, FH, SDH, and mitochondrial DNA. This has prompted intense study of the consequences of these mutations on cellular metabolism and physiology in vivo by leveraging high-throughput technologies to measure small-molecule metabolites (i.e., metabolomics). The purpose of this review is to give a broad and integrated view on the discoveries made in RCC with metabolomics, and to give a basic understanding of the experimental design of metabolomic studies. Our discussion is organized around five concepts which synthesize discoveries from genomics and metabolomics into the molecular basis of RCC and transcend the different RCC histologies: (1) metabolic phenotypes unique to certain genotypes, (2) mitochondrial dysfunction, (3) the oxidative stress response, (4) epigenetics, and (5) therapy targeted to metabolism. We conclude by proposing several promising lines of investigation that intersect metabolism with emerging ideas in RCC biology.

Fumarate hydratase in cancer: A multifaceted tumour suppressor

Cancer is now considered a multifactorial disorder with different aetiologies and outcomes. Yet, all cancers share some common molecular features. Among these, the reprogramming of cellular metabolism has emerged as a key player in tumour initiation and progression. The finding that metabolic enzymes such as fumarate hydratase (FH), succinate dehydrogenase (SDH) and isocitrate dehydrogenase (IDH), when mutated, cause cancer suggested that metabolic dysregulation is not only a consequence of oncogenic transformation but that it can act as cancer driver. However, the mechanisms underpinning the link between metabolic dysregulation and cancer remain only partially understood. In this review we discuss the role of FH loss in tumorigenesis, focusing on the role of fumarate as a key activator of a variety of oncogenic cascades. We also discuss how these alterations are integrated and converge towards common biological processes. This review highlights the complexity of the signals elicited by FH loss, describes that fumarate can act as a bona fide oncogenic event, and provides a compelling hypothesis of the stepwise neoplastic progression after FH loss.

The Metabolic Basis of Kidney Cancer

Kidney cancer is not a single disease but represents several distinct types of cancer that have defining histologies and genetic alterations and that follow different clinical courses and have different responses to therapy. Mutation of genes associated with kidney cancer, such as VHL, FLCN, TFE3, FH, or SDHB, dysregulates the tumor's responses to changes in oxygen, iron, nutrient, or energy levels. The identification of these varying genetic bases of kidney cancer has increased our understanding of the biology of this cancer, allowing the development of targeted therapies and the appreciation that it is a cancer driven by metabolic alterations. SIGNIFICANCE: Kidney cancer is a complex disease composed of different types of cancer that present with different histologies, clinical courses, genetic changes, and responses to therapy. This review describes the known genetic changes within kidney cancer, how they alter tumor metabolism, and how these metabolic changes can be therapeutically targeted.

The Complete Spectrum of Infiltrative Renal Masses: Clinical Characteristics and Prognostic Implications

OBJECTIVE

To analyze the full spectrum of patients presenting with radiologically-identified infiltrative renal masses (IRMs), including those managed surgically or otherwise, with focus on clinical presentation/prognosis.

METHODS

All 280 patients presenting with radiologically-identified renal mass with infiltrative features (2008-2017) were retrospectively reviewed. Poorly-defined interface between tumor and parenchyma and irregular shape (nonelliptical) in one or more distinct/unequivocal areas were required for classification as IRM. IRM was confirmed in 265 and clinical characteristics and outcomes were assessed.

RESULTS

Median age/tumor size were 65-years/6.9 cm, respectively, and 225 patients (85%) were R.E.N.A.L. = 10-12. Overall, 181 patients (68%) presented symptomatically, locally-advanced cancer (cT3-T4) was observed in 176 (66%) and disseminated disease and/or lymphadenopathy (>2 cm) in 181(68%). Clinical/radiographic findings were suggestive of etiology and could direct evaluation, but were nonspecific for definitive diagnosis. Renal-mass biopsy was performed in 103 patients and diagnostic in 97 (94%). Renal surgery was only performed in 82 patients (31%) and partial nephrectomy in 3 (1.1%). Overall, 72 patients (27%) received systemic chemotherapy and 59 (22%) targeted therapy. Final-diagnosis was renal cell carcinoma in 94 patients (35%), including 49 with highly-aggressive histology (sarcomatoid/rhabdoid/collecting-duct/medullary/unclassified). High-grade urothelial-carcinoma was found in 70 (26%), and lymphoma/metastatic cancer in 26 (10%)/25 (9%), respectively. Overall, 153 patients (58%) died; 138 (52%) cancer-related at median of 5 months. The majority of patients with renal cell carcinoma, urothelial-carcinoma, and renal metastasis died, almost exclusively cancer-related, at medians of 8, 3, and 2 months, respectively.

CONCLUSION

Our series includes the full spectrum of IRMs and confirms predominance of symptomatic, poorly-differentiated, highly-lethal malignancies. Our study highlights the overriding importance of identifying infiltrative features, a simple radiologic diagnosis, during assessment of renal masses.

Metabolomic insights into pathophysiological mechanisms and biomarker discovery in clear cell renal cell carcinoma

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is a metabolic disease, of which the incidence rate is increasing worldwide. Renal carcinoma is characterized by mutations in target genes involved in metabolic pathways. Metabolic reprogramming covers different processes such as aerobic glycolysis, fatty acid metabolism, and the utilization of tryptophan, glutamine, and arginine. In the era of the multi-omics approach (with integrated transcriptomics, proteomics, and metabolomics), discovering biomarkers for early diagnosis is gaining renewed importance. Areas covered: In this review, we discuss the pathophysiological mechanisms underlying ccRCC metabolic reprogramming. In addition, we describe the emerging metabolomics-based biomarkers differentially expressed in ccRCC and the rationale for the recently developed drugs specifically targeting the ccRCC metabolome. Expert opinion: A number of metabolic pathways will be explored in future years, and many of these pathways are potential therapeutic targets and may serve as diagnostic and prognostic biomarkers of ccRCC.

Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations Between mRNA-Based Subtyping and CT Imaging Features

PURPOSE

To investigate associations between clear-cell renal cell carcinoma mRNA-based subtyping and CT features.

MATERIALS AND METHODS

The CT data from 177 patients generated with The Cancer Imaging Archive were reviewed. The correlation was analyzed using chi-square test and univariate regression analysis.

RESULTS

Identified were 124 (53.2%) m1, 67 (28.8%) m2, 17 (7.3%) m3, and 14 (8.7%) m4 subtypes. m1-subtype rates were significantly higher in well-defined margin lesions (p = 0.041). m3-subtype rates were significantly higher in ill-defined margin lesions (p = 0.012), in collecting system invasion lesions (p = 0.028) and collecting system invasion lesions (p = 0.026).On univariate logistic regression analysis, tumor margin (well-defined margin vs ill-defined margin, OR: 2.104; p = 0.041; 95% CI: 1.024-4.322) was associated with m1-subtype. Tumor margin (well-defined margin vs ill-defined margin, OR: 2.104; p = 0.012; 95% CI: 0.212-0.834) and collecting system invasion (yes vs no, OR: 0.421; p = 0.028; 95% CI: 0.212-0.834) and renal vein invasion (yes vs no, OR: 2.164; p = 0.026; 95% CI: 1.090-4.294) were associated with m3-subtype. There was no significant difference between mRNA-based subtyping (m2 vs other; m4 vs other) and the CT features.

CONCLUSIONS

This preliminary radiogenomics analysis of clear-cell renal cell carcinoma revealed associations between CT features and mRNA-based subtyping which warrant further investigation and validation.

Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients

MiRNAs and proteins play important roles in different stages of breast tumor development and serve as biomarkers for the early diagnosis of breast cancer. A new algorithm that combines machine learning algorithms and multilayer complex network analysis is hereby proposed to explore the potential diagnostic values of miRNAs and proteins. XGBoost and random forest algorithms were employed to screen the most important miRNAs and proteins. Maximal information coefficient was applied to assess intralayer and interlayer connection. A multilayer complex network was constructed to identify miRNAs and proteins that could serve as biomarkers for breast cancer. Proteins and miRNAs that are nodes in the network were subsequently categorized into two network layers considering their distinct functions. The betweenness centrality was used as the first measurement of the importance of the nodes within each single layer. The degree of the nodes was chosen as the second measurement to map their signalling pathways. By combining these two measurements into one score and comparing the difference of the same candidate between normal tissue and cancer tissue, this novel multilayer network analysis could be applied to successfully identify molecules associated with breast cancer.

Cell-Free SHOX2 DNA Methylation in Blood as a Molecular Staging Parameter for Risk Stratification in Renal Cell Carcinoma Patients: A Prospective Observational Cohort Study

BACKGROUND

Novel targeted treatments and immunotherapies have substantially changed therapeutic options for advanced and metastatic renal cell carcinomas (RCCs). However, accurate diagnostic tests for the identification of high-risk patients are urgently needed. Here, we analyzed SHOX2 mRNA expression in RCC tissues and SHOX2 gene body methylation quantitatively in circulating cell-free DNA (ccfDNA) and RCC tissues with regard to risk stratification.

METHODS

The clinical performance of SHOX2 methylation was tested retrospectively and prospectively in a training and testing cohort of RCC tissue samples (n = 760 in total). SHOX2 mRNA expression analysis was included in the training cohort. In matched blood plasma samples from the testing cohort (n = 100), we prospectively examined the capability of pretherapeutic quantitative SHOX2 ccfDNA methylation to assess disease stage and identify patients at high risk of death.

RESULTS

SHOX2 gene body methylation was positively correlated with mRNA expression in RCC tissues (training cohort: Spearman ρ = 0.23, P < 0.001). SHOX2 methylation in tissue and plasma strongly correlated with an advanced disease stage (training cohort: ρ = 0.28, P < 0.001; testing cohort/tissue: ρ = 0.40, P < 0.001; testing cohort/plasma: ρ = 0.34, P = 0.001) and risk of death after initial partial or radical nephrectomy [training cohort: hazard ratio (HR) = 1.40 (95% CI, 1.24–1.57), P < 0.001; testing cohort/tissue: HR = 1.16 (95% CI, 1.07–1.27), P = 0.001; testing cohort/plasma: HR = 1.50 (95% CI, 1.29–1.74), P < 0.001].

CONCLUSIONS

Pretherapeutic SHOX2 ccfDNA methylation testing allows for the identification of RCC patients at high risk of death after nephrectomy. These patients might benefit from an adjuvant treatment or early initiation of a palliative treatment.

von Hippel-Lindau mutants in renal cell carcinoma are regulated by increased expression of RSUME

Renal cell carcinoma (RCC) is the major cause of death among patients with von Hippel-Lindau (VHL) disease. Resistance to therapies targeting tumor angiogenesis opens the question about the underlying mechanisms. Previously we have described that RWDD3 or RSUME (RWD domain-containing protein SUMO Enhancer) sumoylates and binds VHL protein and negatively regulates HIF degradation, leading to xenograft RCC tumor growth in mice. In this study, we performed a bioinformatics analysis in a ccRCC dataset showing an association of RSUME levels with VHL mutations and tumor progression, and we demonstrate the molecular mechanism by which RSUME regulates the pathologic angiogenic phenotype of VHL missense mutations. We report that VHL mutants fail to downregulate RSUME protein levels accounting for the increased RSUME expression found in RCC tumors. Furthermore, we prove that targeting RSUME in RCC cell line clones carrying missense VHL mutants results in decreased early tumor angiogenesis. The mechanism we describe is that RSUME sumoylates VHL mutants and beyond its sumoylation capacity, interacts with Type 2 VHL mutants, reduces HIF-2α-VHL mutants binding, and negatively regulates the assembly of the Type 2 VHL, Elongins and Cullins (ECV) complex. Altogether these results show RSUME involvement in VHL mutants deregulation that leads to the angiogenic phenotype of RCC tumors.

Linking Binary Gene Relationships to Drivers of Renal Cell Carcinoma Reveals Convergent Function in Alternate Tumor Progression Paths

Renal cell carcinoma (RCC) subtypes are characterized by distinct molecular profiles. Using RNA expression profiles from 1,009 RCC samples, we constructed a condition-annotated gene coexpression network (GCN). The RCC GCN contains binary gene coexpression relationships (edges) specific to conditions including RCC subtype and tumor stage. As an application of this resource, we discovered RCC GCN edges and modules that were associated with genetic lesions in known RCC driver genes, including VHL, a common initiating clear cell RCC (ccRCC) genetic lesion, and PBRM1 and BAP1 which are early genetic lesions in the Braided Cancer River Model (BCRM). Since ccRCC tumors with PBRM1 mutations respond to targeted therapy differently than tumors with BAP1 mutations, we focused on ccRCC-specific edges associated with tumors that exhibit alternate mutation profiles: VHL-PBRM1 or VHL-BAP1. We found specific blends molecular functions associated with these two mutation paths. Despite these mutation-associated edges having unique genes, they were enriched for the same immunological functions suggesting a convergent functional role for alternate gene sets consistent with the BCRM. The condition annotated RCC GCN described herein is a novel data mining resource for the assignment of polygenic biomarkers and their relationships to RCC tumors with specific molecular and mutational profiles.

Accuracy of renal tumour biopsy for the diagnosis and subtyping of papillary renal cell carcinoma: analysis of paired biopsy and nephrectomy specimens with focus on discordant cases

Aims

Renal tumour biopsy (RTB) is increasingly recognised as a useful diagnostic tool in the management of small renal masses, particularly those that are incidentally found. Intratumoural heterogeneity with respect to morphology, grade and molecular features represents a frequently identified limitation to the use of RTB. While previous studies have evaluated pathological correlation between RTB and nephrectomy, no studies to date have focused specifically on the role of RTB for the diagnosis of papillary renal cell carcinoma (PRCC) and its further subclassification into clinically relevant subtypes.

Methods

This single-institution study evaluated 60 cases of PRCC for concordance between RTB and nephrectomy with respect to diagnosis, grading and subtyping (type 1/type 2).

Results

We observed 93% concordance (55 of 59 evaluable cases) between RTB and nephrectomy for the diagnosis of PRCC, although seven tumours (12%) were undergraded on RTB. Subtyping of PRCC on RTB was concordant with nephrectomy in 89% of cases reported as type 1 PRCC on RTB (31/35), but only 40% of cases reported as type 2 PRCC on RTB (4/10). Morphological misclassification of PRCC on RTB was most likely to occur in tumours showing a solid growth pattern. Discordant PRCC subtyping most often occurred in tumours with eosinophilia/oncocytic change.

Conclusion

There was good concordance between RTB and nephrectomy for the primary diagnosis of PRCC. Although further subtyping of PRCC can aid therapeutic stratification, this can be challenging on RTB and tumours with overlapping or ambiguous features are best reported as PRCC not otherwise specified pending development of more robust methods to facilitate definitive subclassification.

Predictive genomic markers of response to VEGF targeted therapy in metastatic renal cell carcinoma

Background

First-line treatment for metastatic renal cell carcinoma (mRCC) is rapidly changing. It currently includes VEGF targeted therapies (TT), multi-target tyrosine kinase inhibitors (TKIs), mTOR inhibitors, and immunotherapy. To optimize outcomes for individual patients, genomic markers of response to therapy are needed. Here, we aim to identify tumor-based genomic markers of response to VEGF TT to optimize treatment selection.

Methods

From an institutional database, primary tumor tissue was obtained from 79 patients with clear cell mRCC, and targeted sequencing was performed. Clinical outcomes were obtained retrospectively. Progression-free survival (PFS) on first-line VEGF TT was correlated to genomic alterations (GAs) using Kaplan-Meier methodology and Cox proportional hazard models. A composite model of significant GAs predicting PFS in the first-line setting was developed.

Results

Absence of VHL mutation was associated with inferior PFS on first-line VEGF TT. A trend for inferior PFS was observed with GAs in TP53 and FLT1 C/C variant. A composite model of these 3 GAs was associated with inferior PFS in a dose-dependent manner.

Conclusion

In mRCC, a composite model of TP53 mutation, wild type VHL, and FLT1 C/C variant strongly predicted PFS on first-line VEGF TT in a dose-dependent manner. These findings require external validation.

The molecular characterization and therapeutic strategies of papillary renal cell carcinoma

Introduction: Papillary renal cell carcinoma (pRCC) is an important subtype of kidney cancer with a problematic pathological classification and highly variable clinical behavior. In this review, we summarize the current progression on pRCC in molecular level. Our findings highlight the need for molecular markers to accurately subtype pRCC and may lead to the development of more targeted agents and better patient stratification in clinical trials for pRCC. Areas covered: This review highlights the need for molecular markers to accurately subtype PRCC and may lead to the development of more targeted agents and better patient stratification in clinical trials for pRCC. Expert commentary: There are mainly two subtypes of pRCC based on histology. However, little is known about the genetic characterization of the sporadic forms of pRCC and there are currently no standard forms of therapy for patients with advanced disease. Both MET inhibitors and immunotherapy may be effective in advanced pRCC treatment. Therefore, understanding the molecular basis of pRCC and identifying the main goal of treatment is crucial for the selection of the best strategy.

Chromosome 3p Loss-Orchestrated VHL, HIF, and Epigenetic Deregulation in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype, and metastatic ccRCC is associated with 5-year survival rates of 10% to 20%. Genetically, ccRCC originates from sequential losses of multiple tumor suppressor genes. Remarkably, chromosome 3p loss occurs in more than 90% of sporadic ccRCCs. This results in concurrent one-copy loss of four tumor suppressor genes that are also mutated individually at high frequency in ccRCC (ie, VHL, 80%; PBRM1, 29% to 46%; BAP1, 6% to 19%; and SETD2, 8% to 30%). Pathogenically, 3p loss probably represents the first genetic event that occurs in sporadic ccRCC and the second genetic event in VHL-mutated hereditary ccRCC. VHL constitutes the substrate recognition module of the VCB-Cul2 E3 ligase that degrades HIF1/2α, whereas PBRM1, BAP1, and SETD2 are epigenetic modulators that regulate gene transcription. Because 3p loss and VHL inactivation are nearly universal truncal events in ccRCC, the resulting HIF1/2 signaling overdrive and accompanied tumor hypervascularization probably underlie the therapeutic benefits observed with vascular endothelial growth factor receptor inhibitors, including sorafenib, sunitinib, pazopanib, axitinib, bevacizumab, cabozantinib, and lenvatinib. Furthermore, recent marked advances in ccRCC genomics, transcriptomics, proteomics, metabolomics, molecular mechanisms, mouse models, prognostic and predictive biomarkers, and clinical trials have rendered invaluable translational insights concerning precision kidney cancer therapeutics. With an armamentarium encompassing 13 drugs that exploit seven unique therapeutic mechanisms (ie, cytokines, vascular endothelial growth factor receptor, mTORC1, cMET/AXL, fibroblast growth factor receptor, programmed cell death-1 and programmed death-ligand 1, and cytotoxic T-cell lymphocyte associated-4) to treat metastatic renal cell carcinoma, one of the imminent clinical questions concerning care of patients with metastatic ccRCC is how a personalized treatment strategy, through rationally combining and sequencing different therapeutic modalities, can be formulated to offer the best clinical outcome for individual patients. Here, we attempt to integrate recent discoveries of immediate translational impacts and discuss future translational challenges and opportunities.

Recent advances in understanding the complexities of metastasis

Tumour metastasis is a dynamic and systemic process. It is no longer seen as a tumour cell-autonomous program but as a multifaceted and complex series of events, which is influenced by the intrinsic cellular mutational burden of cancer cells and the numerous bidirectional interactions between malignant and non-malignant cells and fine-tuned by the various extrinsic cues of the extracellular matrix. In cancer biology, metastasis as a process is one of the most technically challenging aspects of cancer biology to study. As a result, new platforms and technologies are continually being developed to better understand this process. In this review, we discuss some of the recent advances in metastasis and how the information gleaned is re-shaping our understanding of metastatic dissemination.

The Role of DNA Methylation in Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. While survival rates are high if RCC is diagnosed when still confined to the kidney and treated definitively, there are no specific diagnostic screening tests available and symptoms are rare in early stages of the disease. Management of advanced RCC has changed significantly with the advent of targeted therapies, yet survival is usually increased by months due to acquired resistance to these therapies. DNA methylation, the covalent addition of a methyl group to a cytosine, is essential for normal development and transcriptional regulation, but becomes altered commonly in cancer. These alterations result in broad transcriptional changes, including in tumor suppressor genes. Because DNA methylation is one of the earliest molecular changes in cancer and is both widespread and stable, its role in cancer biology, including RCC, has been extensively studied. In this review, we examine the role of DNA methylation in RCC disease etiology and progression, the preclinical use of DNA methylation alterations as diagnostic, prognostic and predictive biomarkers, and the potential for DNA methylation-directed therapies.

Recent advances in understanding the complexities of metastasis

Tumour metastasis is a dynamic and systemic process. It is no longer seen as a tumour cell-autonomous program but as a multifaceted and complex series of events, which is influenced by the intrinsic cellular mutational burden of cancer cells and the numerous bidirectional interactions between malignant and non-malignant cells and fine-tuned by the various extrinsic cues of the extracellular matrix. In cancer biology, metastasis as a process is one of the most technically challenging aspects of cancer biology to study. As a result, new platforms and technologies are continually being developed to better understand this process. In this review, we discuss some of the recent advances in metastasis and how the information gleaned is re-shaping our understanding of metastatic dissemination.

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.