Background Papillary renal cell carcinoma, accounting for 15% of renal cell carcinoma, is a heterogeneous disease consisting of different types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal cell carcinoma; no effective forms of therapy for advanced disease exist. Methods We performed comprehensive molecular characterization utilizing whole-exome sequencing, copy number, mRNA, microRNA, methylation and proteomic analyses of 161 primary papillary renal cell carcinomas. Results Type 1 and Type 2 papillary renal cell carcinomas were found to be different types of renal cancer characterized by specific genetic alterations, with Type 2 further classified into three individual subgroups based on molecular differences that influenced patient survival. MET alterations were associated with Type 1 tumors, whereas Type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-ARE pathway. A CpG island methylator phenotype (CIMP) was found in a distinct subset of Type 2 papillary renal cell carcinoma characterized by poor survival and mutation of the fumarate hydratase (FH) gene. Conclusions Type 1 and Type 2 papillary renal cell carcinomas are clinically and biologically distinct. Alterations in the MET pathway are associated with Type 1 and activation of the NRF2-ARE pathway with Type 2; CDKN2A loss and CIMP in Type 2 convey a poor prognosis. Furthermore, Type 2 papillary renal cell carcinoma consists of at least 3 subtypes based upon molecular and phenotypic features.

Clear-cell renal carcinoma is associated with inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene. VHL is the substrate recognition subunit of an E3 ligase, known to target the alpha subunits of the HIF heterodimeric transcription factor for ubiquitin-mediated degradation under normoxic conditions. We demonstrate that competitive inhibition of the VHL substrate recognition site with a peptide derived from the oxygen degradation domain of HIF1alpha recapitulates the tumorigenic phenotype of VHL-deficient tumor cells. These studies prove that VHL substrate recognition is essential to the tumor suppressor function of VHL. We further demonstrate that normoxic stabilization of HIF1alpha alone, while capable of mimicking some aspects of VHL loss, is not sufficient to reproduce tumorigenesis, indicating that it is not the critical oncogenic substrate of VHL.

Background Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting. Objective To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification. Design, setting, and participants A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina. Outcome measurements and statistical analysis Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence. Results and limitations The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms. Conclusions The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients. Patient summary We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

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Hypoxia and induction of hypoxia-inducible factors (HIF-1alpha and HIF-2alpha) is a hallmark of many tumors. Under normal oxygen tension HIF-alpha subunits are rapidly degraded through prolyl hydroxylase dependent interaction with the von Hippel-Lindau (VHL) tumor suppressor protein, a component of E3 ubuiquitin ligase complex. Using microarray analysis of VHL mutated and re-introduced cells, we found that one of the prolyl hydroxylases (PHD3) is coordinately expressed with known HIF target genes, while the other two family members (PHD1 and 2) did not respond to VHL. We further tested the regulation of these genes by HIF-1 and HIF-2 and found that siRNA targeted degradation of HIF-1alpha and HIF-2alpha results in decreased hypoxia-induced PHD3 expression. Ectopic overexpression of HIF-2alpha in two different cell lines provided a much better induction of PHD3 gene than HIF-1alpha. In contrast, we demonstrate that PHD2 is not affected by overexpression or downregulation of HIF-2alpha. However, induction of PHD2 by hypoxia has HIF-1-independent and -dependent components. Short-term hypoxia (4 h) results in induction of PHD2 independent of HIF-1, while PHD2 accumulation by prolonged hypoxia (16 h) was decreased by siRNA-mediated degradation of HIF-1alpha subunit. These data further advance our understanding of the differential role of HIF factors and putative feedback loop in HIF regulation.