Long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to exert significant roles in regulating the expression of mRNAs by sequestering and binding miRNAs. To elucidate the functional roles and regulatory mechanism of lncRNAs in papillary renal cell cancer (pRCC), we conducted a comprehensive analysis of ceRNA network and constructed a mRNA signature to predict prognosis of pRCC.We collected mRNAs and lncRNAs expression profiles of 289 pRCC samples and 32 normal renal tissues, and miRNA expression profiles of 292 pRCC samples and 34 normal samples from The Cancer Genome Atlas (TCGA) database. Differential expressions of RNAs were evaluated by the "edgeR" package in R. Functional enrichment analysis of DEmRNA was performed by DAVID 6.8 and KEGG, while PPI network of top 200 DEmRNAs was conducted using the STRING database. The univariate and multivariate Cox regression were conducted to figure out the candidate DEmRNAs with predictive values in prognosis. Receiver operator characteristic (ROC) curve estimation was performed to achieve the area under the curve (AUC) of the ROC curve to judge mRNA-associated prognosic model. A ceRNA network was established relying on the basis of combination of lncRNA-miRNA interactions and miRNA-mRNA interactions.A total of 1928 DEmRNAs, 981 DElncRNAs, and 52 DEmiRNAs were identified at significance level of |log2Fold Change |>2 and adjusted P-value < .01. A 3-mRNA signatures consisting of ERG, RRM2, and EGF was constructed to predict survival in pRCC. Moreover, a pRCC-associated ceRNA network was constructed, with 57 lncRNAs, 11 miRNAs, and 28 mRNAs.Our study illustrated the regulatory mechanism of ceRNA network in papillary renal cancer. The identified mRNA signatures could be used to predict survival of pRCC.

BACKGROUND: High-grade prostate cancer (PCa) has a poor prognosis, and up to 15% of patients worldwide experience lymph node invasion (LNI). To further improve the prediction lymph node invasion in prostate cancer, we adopted risk scores of the genes expression based on the nomogram in guidelines.
METHODS: We analyzed clinical data from 320 PCa patients from the Cancer Genome Atlas database. Weighted gene coexpression network analysis was used to identify the genes that were significantly associated with LNI in PCa (n = 390). Analyses using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were performed to identify the activated signaling pathways. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for the presence of LNI.
RESULTS: We found that patients with actual LNI and predicted LNI had the worst survival outcomes. The 7 most significant genes (CTNNAL1, ENSA, MAP6D1, MBD4, PRCC, SF3B2, TREML1) were selected for further analysis. Pathways in the cell cycle, DNA replication, oocyte meiosis, and 9 other pathways were dramatically activated during LNI in PCa. Multivariate analyses identified that the risk score (odds ratio [OR] = 1.05 for 1% increase, 95% confidence interval [CI]: 1.04-1.07, P < .001), serum PSA level, clinical stage, primary biopsy Gleason grade (OR = 2.52 for a grade increase, 95% CI: 1.27-5.22, P = .096), and secondary biopsy Gleason grade were independent predictors of LNI. A nomogram built using these predictive variables showed good calibration and a net clinical benefit, with an area under the curve (AUC) value of 90.2%.
CONCLUSIONS: In clinical practice, the application of our nomogram might contribute significantly to the selection of patients who are good candidates for surgery with extended pelvic lymph node dissection.

Xp11.2 translocation renal cell carcinoma (Xp11 tRCC) is a rare sporadic pediatric kidney cancer caused by constitutively active TFE3 fusion proteins. Tumors in patients with Xp11 tRCC tend to recur and undergo frequent metastasis, in part due to lack of methods available to detect early-stage disease. Here we generated transgenic (Tg) mice overexpressing the human PRCC-TFE3 fusion gene in renal tubular epithelial cells, as an Xp11 tRCC mouse model. At 20 weeks of age, mice showed no histological abnormalities in kidney but by 40 weeks showed Xp11 tRCC development and related morphological and histological changes. MicroRNA (miR)-204-5p levels in urinary exosomes of 40-week-old Tg mice showing tRCC were significantly elevated compared with levels in control mice. MicroRNA-204-5p expression also significantly increased in primary renal cell carcinoma cell lines established both from Tg mouse tumors and from tumor tissue from 2 Xp11 tRCC patients. All of these lines secreted miR-204-5p-containing exosomes. Notably, we also observed increased miR-204-5p levels in urinary exosomes in 20-week-old renal PRCC-TFE3 Tg mice prior to tRCC development, and those levels were equivalent to those in 40-week-old Tg mice, suggesting that miR-204-5p increases follow expression of constitutively active TFE3 fusion proteins in renal tubular epithelial cells prior to overt tRCC development. Finally, we confirmed that miR-204-5p expression significantly increases in noncancerous human kidney cells after overexpression of a PRCC-TFE3 fusion gene. These findings suggest that miR-204-5p in urinary exosomes could be a useful biomarker for early diagnosis of patients with Xp11 tRCC.

Long noncoding RNAs (lncRNAs) function as competing endogenous RNAs (ceRNAs). ceRNA networks may serve important roles in various tumors, as demonstrated by an increasing number of studies; however, papillary renal cell carcinoma (PRCC)‑associated ceRNA networks mediated by lncRNAs remain unknown. Increased knowledge of ceRNA networks in PRCC may aid the identification of novel targets and biomarkers in the treatment of PRCC. In the present study, a comprehensive investigation of mRNA, lncRNA, and microRNA (miRNA) expression in PRCC was conducted using sequencing data from The Cancer Genome Atlas. Differential expression (DE) profiles of mRNAs, lncRNAs and miRNAs were evaluated, with 1,970 mRNAs, 1,201 lncRNAs and 96 miRNAs identified as genes with significantly different expression between PRCC and control paracancerous tissues. Based on the identified DEmRNAs, a protein‑protein interaction network was generated using the STRING database. Furthermore, a ceRNA network for PRCC was determined using a targeted assay combined with the DE of miRNAs, mRNAs and lncRNAs, enabling the identification of important lncRNA‑miRNA and miRNA‑mRNA pairs. Analysis of the ceRNA network led to the extraction of a subnetwork and the identification of lncRNA maternally expressed 3 (MEG3), lncRNA PWRN1, miRNA (miR)‑508, miR‑21 and miR519 as important genes. Reverse transcription‑quantitative polymerase chain reaction analysis was conducted to validate the results of the bioinformatics analyses; it was revealed that lncRNA MEG3 expression levels were downregulated in PRCC tumor tissues compared with adjacent non‑tumor tissues. In addition, survival analysis was conducted to investigate the association between identified genes and the prognosis of patients with PRCC, indicating the potential involvement of 13 mRNAs, 15 lncRNAs and six miRNAs. In conclusion, the present study may improve understanding of the regulatory mechanisms of ceRNA networks in PRCC and provide novel insight for future studies of prognostic biomarkers and potential therapeutic targets.

PURPOSE: Papillary renal cell carcinoma (
MATERIALS AND METHODS: We performed immunohistochemistry analysis with a tissue array containing 161 primary NSCLCs. Small interfering RNA targeting PRCC (siPRCC) was transfected into two lung cancer cell lines (NCI-H358 and A549), after which tumor growth, migration, and invasion were observed. Expressions of cell proliferation-, cell cycle-, and metastasis-related molecules were examined by Western blot analysis. We also explored the
RESULTS: PRCC overexpression was recurrently observed in NSCLCs (95/161, 59%). After siPRCC treatment, tumor cell proliferation, colony formation, and anchorage independent growth were significantly reduced (
CONCLUSION: The present data provide evidence that PRCC overexpression is involved in the tumorigenesis and progression of lung cancer.

Papillary renal cell carcinoma (PRCC) accounts for 15‑20% of all kidney neoplasms and continually attracts attention due to the increase in the incidents in which it occurs. The molecular mechanism of PRCC remains unclear and the efficacy of drugs that treat PRCC lacks sufficient evidence in clinical trials. Therefore, it is necessary to investigate the underlying mechanism in the development of PRCC and identify additional potential anti‑PRCC drugs for its treatment. The differently expressed genes (DEGs) of PRCC were identified, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional annotation. Then, potential drugs for PRCC treatment were predicted by Connectivity Map (Cmap) based on DEGs. Furthermore, the latent function of query drugs in PRCC was explored by integrating drug‑target, drug‑pathway and drug‑protein interactions. In total, 627 genes were screened as DEGs, and these DEGs were annotated using KEGG pathway analyses and were clearly associated with the complement and coagulation cascades, amongst others. Then, 60 candidate drugs, as predicted based on DEGs, were obtained from the Cmap database. Vorinostat was considered as the most promising drug for detailed discussion. Following protein‑protein interaction (PPI) analysis and molecular docking, vorinostat was observed to interact with C3 and ANXN1 proteins, which are the upregulated hub genes and may serve as oncologic therapeutic targets in PRCC. Among the top 20 metabolic pathways, several significant pathways, such as complement and coagulation cascades and cell adhesion molecules, may greatly contribute to the development and progression of PRCC. Following the performance of the PPI network and molecular docking tests, vorinostat exhibited a considerable and promising application in PRCC treatment by targeting C3 and ANXN1.

Papillary renal cell carcinoma (PRCC) is the second most common type of renal carcinoma following clear cell renal cell carcinoma. Papillary renal cell carcinoma is usually divided histologically into 2 types namely, type 1 and type 2. This classification, however, is unsatisfactory as many of papillary carcinoma are unclassifiable by the existing criteria. In recent years there has been a remarkable progress in our understanding of the molecular basis of PRCC. These studies have revealed that type 2 PRCCs represent a heterogenous group which may be subdivided into additional subtypes based on the genetic and molecular make up of these tumors and reflecting different clinical course and prognosis. Some of the molecular features such a hypermethylation of CPG islands in the promotor regions of genes and over expression of the antioxidant pathways within tumor cells have been recognized as markers of poor prognosis. Targeted therapies for papillary carcinoma in the past have been unsuccessful because of lack of clear understanding of the molecular basis of these tumors. It is hoped that recent progress in our understanding of the pathogenesis of various subtypes of PRCC, effective targeted therapies will eventually emerge in due course.

BACKGROUND: Papillary renal cell carcinoma (pRCC) is the 2nd most frequent histological type of kidney cancer and accounts for approximately 15% of all renal cell carcinoma. It has a poorer prognosis than clear cell RCC (ccRCC) with a lack of standard treatments.
CASE PRESENTATION: We report the case of a 51 year old man with a metastatic pRCC (hepatic dome and left colonic peritoneal carcinomatosis) progressive after sunitinib, with a MET amplification. The patient was enrolled in the UNICANCER-sponsored AcSé crizotinib trial (NCT02034981), designed to give an access to crizotinib for patients with tumors harboring a genomic alteration on one of the biological targets of the drug. With 2nd line crizotinib (250 mg twice/day), the patient had a very good tolerance, a partial response in the target lesions using RECIST 1.1, and a 19 months' clinical efficacy.
CONCLUSIONS: In metastatic pRCC with a MET amplification, crizotinib maybe a potential met-inhibitory therapeutic option.

BACKGROUND: Karyopherin α2 (KPNA2) is involved in the nucleocytoplasmic transport system and is functionally involved in the pathogenesis of various solid tumors by the translocation of cancer associated cargo proteins. However, the role of KPNA2 in renal-cell carcinoma (RCC) is still unknown. The aim of the present study was to investigate the protein expression of KPNA2 in cancerous and healthy renal tissues to evaluate its prognostic value in RCC.
PATIENTS AND METHODS: We assessed KPNA2 protein expression via immunohistochemistry in a well-characterized cohort of 240 RCC patients by using a quantitative image analysis software. In addition, we analyzed publicly available gene expression data from The Cancer Genome Atlas (TCGA).
RESULTS: A subgroup of clear-cell RCC (ccRCC) showed elevated protein expression levels of KPNA2. Most remarkably, we detected a correlation between high KPNA2 protein expression and shorter overall survival times as well as higher tumor stage and International Society of Urologic Pathology grade in ccRCC. However, the prognostic value of KPNA2 was not confirmed by multivariate Cox regression analysis when tested together with strong prognostic factors like tumor stage, lymph node metastasis, International Society of Urologic Pathology grade, and resection status. The results of the TCGA gene expression data analysis confirmed the prognostic value of KPNA2 in ccRCC. Additionally, KPNA2 expression was identified as an adverse factor in papillary RCC at the transcript level.
CONCLUSION: KPNA2 appears to be involved in the carcinogenesis of RCC and functions as a novel prognostic indicator.

Monensin is a metal ionophore used as anticancer agent in many types of cancer cells. In this study, therapeutic potential of monensin was evaluated in TFE3 translocated renal cell carcinoma (RCC) cell line UOK146. UOK146 cells were treated with different concentrations of monensin, and cell death was induced as shown by MTT assay. Autophagy was studied by LC3 western, FACS and LC3 puncta formation after monensin treatment. Mitochondrial potential was studied by staining with TMRM and FACS. Antimetastatic potential of monensin was checked by inhibition of wound closure and MMP7 expression at RNA level. Dead and floating cells after the 10 μM monensin treatment were observed under phase contrast microscope. FACS analysis following TMRM staining showed that mitochondrial membrane gets depolarized after monensin treatment. FACS analysis after acridine orange staining showed increased double positive (green and red) cells, and LC3 upregulation and increased LC3 punta displayed autophagy activation in UOK146 cell line after monensin treatment. These findings showed that monensin acts as antiproliferative agent, activating autophagy and downregulates PRCC-TFE3 fusion transcript in Xp11.2 translocated tumor cell line.

BACKGROUND: We present a series of papillary renal cell carcinomas (PRCC) reminiscent of so-called "oncocytic variant of papillary renal cell carcinoma" (OPRCC), included in the 2016 WHO classification as a potential type 3 PRCC. OPRCC is a poorly understood entity, cytologically characterized by oncocytic cells with non-overlapping low grade nuclei. OPRCC is not genotypically distinct and the studies concerning this variant have shown an inconsistent genetic profile. The tumors presented herein demonstrated predominantly papillary/tubulopapillary architecture and differed from OPRCC by pseudostratification and grade 2-3 nuclei (Fuhrman/ISUP). Because there is a morphologic overlap between renal oncocytoma (RO) and PRCC in the cases included in this study, the most frequently affected chromosomes in RO and PRCC were analyzed.
MATERIALS AND METHODS: 147 PRCC composed of oncocytic cells were retrieved from our registry in order to select a group of morphologically uniform tumors. 10 cases with predominantly papillary, tubulopapillary or solid architectural patterns were identified. For immunohistochemical analysis, the following antibodies were used: vimentin, antimitochondrial antigene (MIA), AMACR, PAX8, CK7, CK20, AE1-3, CAM5.2, OSCAR, Cathepsin K, HMB45, SDHB, CD10, and CD117. Enumeration changes of locus 1p36, chromosomes 7, 14, 17, X, Y and rearrangement of CCND1 were examined by FISH. For further study, only tumors showing karyotype similar to that of RO were selected. The tumors exhibiting either trisomy of chromosomes 7, 17 or gain of Y, thus abnormalities characteristic for PRCC, were excluded.
RESULTS: There were 5 males and 5 females, with patient age ranging from 56 to 79 years (mean 66.8 years). The tumor size ranged from 2 to 10 cm (mean 5.1 cm). Follow-up was available for 8/10 patients (mean 5.2 years); one patient died of the disease, while 7 of 8 are alive and well. Immunohistochemically, all cases were reactive for AMACR, vimentin, PAX8, OSCAR, CAM5.2, and MIA. SDHB was retained in all cases. 9/10 cases were positive for CD10, 7/10 cases reacted with CK7, 4/10 with Cathepsin K, and 2/10 with AE1-3. None of the cases were positive for CD117, HMB45 and CK20. All 10 cases were analyzable by FISH and showed chromosomal abnormalities similar to that usually seen in RO (i.e. loss of 1p36 gene loci, loss of chromosome Y, rearrangement of CCND1 and numerical changes of chromosome 14).
CONCLUSIONS: We analyzed a series of renal tumors combining the features of PRCC/OPRCC and RO, that included pseudostratification and mostly high grade oncocytic cells lining papillary/tubulopapillary structures, karyotype characterized by loss of 1p36, loss of chromosome Y, rearrangement of CCND1 gene and numerical changes of chromosome 14. Despite the chromosomal numerical abnormalities typical of RO, we classified these tumors as part of the spectrum of PRCC because of their predominant papillary/tubulopapillary architecture, immunoprofile that included reactivity for AMACR, vimentin and lack of reactivity for CD117, all of which is incompatible with the diagnosis of RO. This study expands the morphological spectrum of PRCC by adding a cohort of diagnostically challenging cases, which may be potentially aggressive.

Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop a predictive model. In this study, we have adopted a machine learning approach to identify biomarkers and build classifiers to discriminate between early and late stages of PRCC from gene expression profiles. A machine learning pipeline incorporating different feature selection algorithms and classification models is developed to analyse RNA sequencing dataset (RNASeq). Further, to get a reliable feature set, we extracted features from different partitions of the training dataset and aggregated them into feature sets for classification. We evaluated the performance of different algorithms on the basis of 10-fold cross validation and independent test dataset. 10-fold cross validation was also performed on a microarray dataset of PRCC. A random forest based feature selection (varSelRF) yielded minimum number of features (104) and a best performance with area under Precision Recall curve (PR-AUC) of 0.804, MCC (Matthews Correlation Coefficient) of 0.711 and accuracy of 88% with Shrunken Centroid classifier on a test dataset. We identified 80 genes that are consistently altered between stages by different feature selection algorithms. The extracted features are related to cellular components - centromere, kinetochore and spindle, and biological process mitotic cell cycle. These observations reveal potential mechanisms for an increase in chromosome instability in the late stage of PRCC. Our study demonstrates that the gene expression profiles can be used to classify stages of PRCC.

Postoperative recurrence for papillary renal cell carcinoma (PRCC) remains a tough problem in clinic. Previous studies have shown that general control nonderepressible kinase 2 (GCN2) was critically involved in tumour development. However, its function and clinical significance in renal cancer remain unknown. In this study, we investigated the role of GCN2 in PRCC. GCN2 silencing suppressed the viability and proliferation, promoted apoptosis of renal cancer cells. We found that the protein level of GCN2 was increased in PRCC tissues. Immunohistochemistry was performed in 84 patients with PRCC to explore the association of GCN2 level with clinical significance. High GCN2 protein level was observed to be significantly correlated with adverse clinicopathological parameters, such as larger tumor size, higher TNM stage, higher Fuhurman Grade, and lymph node metastasis. We evaluated patient outcomes according to various clinical parameters as well as GCN2 expression by Kaplan-Meier curves. Multivariate analysis revealed that GCN2 overexpression can be a predictive factor correlated with reduced OS and PFS of postoperative PRCC patients. Collectively, GCN2 is potentially to play crucial roles in PRCC progression, and its overexpression may be used to predict poor prognosis and promising therapeutic strategy for PRCC patients.

The nucleotide degrading enzyme gene

Both Xp11 translocation renal cell carcinomas and the corresponding mesenchymal neoplasms are characterized by a variety of gene fusions involving TFE3. It has been known that tumors with different gene fusions may have different clinicopathologic features; however, further in-depth investigations of subtyping Xp11 translocation-associated cancers are needed in order to explore more meaningful clinicopathologic correlations. A total of 22 unusual cases of Xp11 translocation-associated cancers were selected for the current study; 20 cases were further analyzed by RNA sequencing to explore their TFE3 gene fusion partners. RNA sequencing identified 17 of 20 cases (85%) with TFE3-associated gene fusions, including 4 ASPSCR1/ASPL-TFE3, 3 PRCC-TFE3, 3 SFPQ/PSF-TFE3, 1 NONO-TFE3, 4 MED15-TFE3, 1 MATR3-TFE3, and 1 FUBP1-TFE3. The results have been verified by fusion fluorescence in situ hybridization (FISH) assays or reverse transcriptase polymerase chain reaction (RT-PCR). The remaining 2 cases with specific pathologic features highly suggestive of MED15-TFE3 renal cell carcinoma were identified by fusion FISH assay. We provide the detailed morphologic and immunophenotypic description of the MED15-TFE3 renal cell carcinomas, which frequently demonstrate extensively cystic architecture, similar to multilocular cystic renal neoplasm of low malignant potential, and expressed cathepsin K and melanotic biomarker Melan A. This is the first time to correlate the MED15-TFE3 renal cell carcinoma with specific clinicopathologic features. We also report the first case of the corresponding mesenchymal neoplasm with MED15-TFE3 gene fusion. Additional novel TFE3 gene fusion partners, MATR3 and FUBP1, were identified. Cases with ASPSCR1-TFE3, SFPQ-TFE3, PRCC-TFE3, and NONO-TFE3 gene fusion showed a wide variability in morphologic features, including invasive tubulopapillary pattern simulating collecting duct carcinoma, extensive calcification and ossification, and overlapping and high columnar cells with nuclear grooves mimicking tall cell variant of papillary thyroid carcinoma. Furthermore, we respectively evaluated the ability of TFE3 immunohistochemistry, TFE3 FISH, RT-PCR, and RNA sequencing to subclassify Xp11 translocation-associated cancers. In summary, our study expands the list of TFE3 gene fusion partners and the clinicopathologic features of Xp11 translocation-associated cancers, and highlights the importance of subtyping Xp11 translocation-associated cancers combining morphology, immunohistochemistry, and multiple molecular techniques.

BACKGROUND: The mediator complex consists of 33 subunits and plays a central role in transcription. Studies have already described the involvement of individual subunits, especially in carcinogenesis. With regard to the subunit MED30, this has, so far, only been confirmed in gastric and breast carcinoma. The role of MED30 in urological tumours is unknown.
MATERIALS AND METHODS: First, a database analysis using cBioPortal was performed for the mRNA expression and survival analysis of MED30 in clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC). The immunohistochemical analysis (IHC) against MED30 was performed on tissue microarrays (TMA), with benign, ccRCC, pRCC samples, and ccRCC-metastases. Intensity evaluation was performed using the IRS (Immunoreactive Score). The ccRCC cell lines ACHN and A-498 were used for the functional investigation of proliferation, migration, and invasion after the knockdown of MED30 by siRNA.
RESULTS: In a database analysis by cBioPortal, it was shown that mRNA overexpression of MED30 in the pRCC was significantly associated with a poorer overall survival and progression-free survival. In the IHC, pRCC showed the highest level of MED30 expression, unfortunately without significant results in the survival analysis. The knockdown of MED30 resulted in a significant decrease in proliferation, migration, and invasion in ccRCC.
CONCLUSION: In summary, MED30 seems to be involved in the progression of the RCC.

Mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney is a rare type of renal cell carcinoma that frequently exhibits histologic and immunophenotypic features overlapping with type 1 papillary renal cell carcinoma (PRCC). To clarify molecular attributes that can be used for this difficult differential diagnosis, we sought to delineate the genome-wide copy number alterations in tumors displaying classic histologic features of MTSCC in comparison to the solid variant of type 1 PRCC and indeterminate cases with overlapping histologic features. The study included 11 histologically typical MTSCC, 9 tumors with overlapping features between MTSCC and PRCC, and 6 cases of solid variant of type 1 PRCC. DNA samples extracted from macrodissected or microdissected tumor areas were analyzed for genome-wide copy number alterations using an SNP-array platform suitable for clinical archival material. All cases in the MTSCC group exhibited multiple chromosomal losses, most frequently involving chromosomes 1, 4, 6, 8, 9, 13, 14, 15, and 22, while lacking trisomy 7 or 17. In contrast, cases with overlapping morphologic features of MTSCC and PRCC predominantly showed multiple chromosomal gains, most frequently involving chromosomes 7, 16, 17, and 20, similar to the chromosomal alteration pattern that was seen in the solid variant of type 1 PRCC cases. Morphologic comparison of these molecularly characterized tumors identified histologic features that help to distinguish MTSCC from PRCC, but immunohistochemical profiles of these tumors remained overlapping, including a marker for Hippo-Yes-associated protein signaling. Characteristic patterns of genome-wide copy number alterations strongly support MTSCC and PRCC as distinct entities despite their immunohistochemical and certain morphologic overlap, and help define histologic features useful for the classification of questionable cases.

AIMS: MITF, TFE3, TFEB and TFEC belong to the same microphthalmia-associated transcription factor family (MiT). Two transcription factors in this family have been identified in two unusual types of renal cell carcinoma (RCC): Xp11 translocation RCC harbouring TFE3 gene fusions and t(6;11) RCC harbouring a MALAT1-TFEB gene fusion. The 2016 World Health Organisation classification of renal neoplasia grouped these two neoplasms together under the category of MiT family translocation RCC. RCCs associated with the other two MiT family members, MITF and TFEC, have rarely been reported. Herein, we identify a case of MITF translocation RCC with the novel PRCC-MITF gene fusion by RNA sequencing.
METHODS AND RESULTS: Histological examination of the present tumour showed typical features of MiT family translocation RCCs, overlapping with Xp11 translocation RCC and t(6;11) RCC. However, this tumour showed negative results in TFE3 and TFEB immunochemistry and split fluorescence in-situ hybridisation (FISH) assays. The other MiT family members, MITF and TFEC, were tested further immunochemically and also showed negative results. RNA sequencing and reverse transcription-polymerase chain reaction confirmed the presence of a PRCC-MITF gene fusion: a fusion of PRCC exon 5 to MITF exon 4. We then developed FISH assays covering MITF break-apart probes and PRCC-MITF fusion probes to detect the MITF gene rearrangement.
CONCLUSIONS: This study both proves the recurring existence of MITF translocation RCC and expands the genotype spectrum of MiT family translocation RCCs.

Increasing detection of small renal masses by imaging techniques entails the need for accurate discrimination between benign and malignant renal cell tumors (RCTs) as well as among malignant RCTs, owing to differential risk of progression through metastization. Although histone methylation has been implicated in renal tumorigenesis, its potential as biomarker for renal cell carcinoma (RCC) progression remains largely unexplored. Thus, we aimed to characterize the differential expression of histone methyltransferases (HMTs) and histone demethylases (HDMs) in RCTs to assess their potential as metastasis biomarkers. We found that SETDB2 and RIOX2 (encoding for an HMT and an HDM, respectively) expression levels was significantly altered in RCTs; these genes were further selected for validation by quantitative RT-PCR in 160 RCTs. Moreover, SETDB2, RIOX2, and three genes encoding for enzymes involved in histone methylation (NO66, SETD3, and SMYD2), previously reported by our group, were quantified (RT-PCR) in an independent series of 62 clear cell renal cell carcinoma (ccRCC) to assess its potential role in ccRCC metastasis development. Additional validation was performed using TCGA dataset. SETDB2 and RIOX2 transcripts were overexpressed in RCTs compared to renal normal tissues (RNTs) and in oncocytomas vs. RCCs, with ccRCC and papillary renal cell carcinoma (pRCC) displaying the lowest levels. Low SETDB2 expression levels and higher stage independently predicted shorter disease-free survival. In our 62 ccRCC cohort, significantly higher RIOX2, but not SETDB2, expression levels were depicted in cases that developed metastasis during follow-up. These findings were not apparent in TCGA dataset. We concluded that SETDB2 and RIOX2 might be involved in renal tumorigenesis and RCC progression, especially in metastatic spread. Moreover, SETDB2 expression levels might independently discriminate among RCC subgroups with distinct outcome, whereas higher RIOX2 transcript levels might identify ccRCC cases with more propensity to endure metastatic dissemination.

Tubulocystic renal cell carcinoma (TC-RCC) is a rare recently described renal neoplasm characterized by gross, microscopic, and immunohistochemical differences from other renal tumor types and was recently classified as a distinct entity. However, this distinction remains controversial particularly because some genetic studies suggest a close relationship with papillary RCC (PRCC). The molecular basis of this disease remains largely unexplored. We therefore performed noncoding (nc) RNA/miRNA expression analysis and targeted next-generation sequencing mutational profiling on 13 TC-RCC cases (11 pure, two mixed TC-RCC/PRCC) and compared with other renal neoplasms. The expression profile of miRNAs and other ncRNAs in TC-RCC was distinct and validated 10 differentially expressed miRNAs by quantitative RT-PCR, including miR-155 and miR-34a, that were significantly down-regulated compared with PRCC cases (n = 22). With the use of targeted next-generation sequencing we identified mutations in 14 different genes, most frequently (>60% of TC-RCC cases) in ABL1 and PDFGRA genes. These mutations were present in <5% of clear cell RCC, PRCC, or chromophobe RCC cases (n > 600) of The Cancer Genome Atlas database. In summary, this study is by far the largest molecular study of TC-RCC cases and the first to investigate either ncRNA expression or their genomic profile. These results add molecular evidence that TC-RCC is indeed a distinct entity from PRCC and other renal neoplasms.

Papillary renal cell carcinoma (PRCC) has 2 histologic subtypes. Almost half of the cases fail to meet all morphologic criteria for either type, hence are characterized as PRCC not otherwise specified (NOS). There are yet no markers to resolve the PRCC NOS category. Accurate classification can better guide the management of these patients. In our previous PRCC study we identified markers that can distinguish between the subtypes. A PRCC patient cohort of 108 cases was selected for the current study. A panel of potentially distinguishing markers was chosen from our previous genomic analysis, and assessed by immunohistochemistry. The panel exhibited distinct staining patterns between the 2 classic PRCC subtypes; and successfully reclassified the NOS (45%) cases. Moreover, these immunomarkers revealed a third subtype, PRCC3 (35% of the cohort). Molecular testing using miRNA expression and copy number variation analysis confirmed the presence of 3 distinct molecular signatures corresponding to the 3 subtypes. Disease-free survival was significantly enhanced in PRCC1 versus 2 and 3 (P=0.047) on univariate analysis. The subtypes stratification was also significant on multivariate analysis (P=0.025; hazard ratio, 6; 95% confidence interval, 1.25-32.2). We propose a new classification system of PRCC integrating morphologic, immunophenotypical, and molecular analysis. The newly described PRCC3 has overlapping morphology between PRCC1 and PRCC2, hence would be subtyped as NOS in the current classification. Molecularly PRCC3 has a distinct signature and clinically it behaves similar to PRCC2. The new classification stratifies PRCC patients into clinically relevant subgroups and has significant implications on the management of PRCC.

BACKGROUND: Two histologic subtypes are recognized for papillary renal cell carcinoma (PRCC). Studies have shown that the subtypes differ in characteristic genetic alterations and clinical behavior. Clinically, the subtypes are managed similarly.
OBJECTIVES: To analyze the biological differences between the two PRCC histological subtypes, in order to further guide their clinical management.
DESIGN, SETTING, AND PARTICIPANTS: PRCC cohort consisting of 317 patients from the Cancer Genome Atlas database and our institution. Patients were stratified according to histologic criteria as type 1, type 2, or not otherwise specified (NOS). Gene and miRNA expression data for the cohort were examined via unsupervised and supervised clustering.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Significant molecular signatures for each subtype were used to unravel the implicated molecular pathways via bioinformatics analysis. Survival was compared between the subtypes. Newly discovered biomarkers were used to further stratify survival of patients in the NOS category.
RESULTS AND LIMITATIONS: Tumor genotyping revealed two distinct PRCC subtypes. The top molecular pathways enriched in PRCC1 were WNT, Hedgehog, and Notch signaling (p=0.001-0.01); highlighting an embryonic developmental theme to the pathogenesis of this subtype. PRCC2 showed enrichment in the mTOR, VEGF (p=7.49E-09) and HIF (p=7.63E-05) signaling pathways. Overall survival and disease-free survival significantly differed between the types. ABCC2 expression was identified as a significant prognostic biomarker for the NOS group in univariate (log rank p<0.0001; hazard ratio [HR] >11.63) and multivariate analysis (p=0.003; HR >2.12). ABCC2 expression and its effect on survival should be further validated at the protein level.
CONCLUSIONS: The classical PRCC types 1 and 2 have two distinct genotypes. We unraveled pathways that indicate that the two types could potentially respond differently to current therapies. We also identified biomarkers that stratify tumors within the PRCC NOS category into prognostic subgroups. Our findings highlight the need for molecular markers to accurately subtype PRCC and guide clinical management.
PATIENT SUMMARY: The two types of papillary renal cancer are treated similarly. We show that the two types have a different genetic makeup, and hence they should be considered two different tumors. There is a different biology underlying each tumor type that can potentially affect the way they respond to treatment. We uncovered genes that can be tested for to guide therapy in some problematic cases for which it hard to define the tumor type.

Purpose Patients with advanced papillary renal cell carcinoma (PRCC) have limited therapeutic options. PRCC may involve activation of the MET pathway, for example, through gene amplification or mutations. Savolitinib (AZD6094, HMPL-504, volitinib) is a highly selective MET tyrosine kinase inhibitor. We report results of a single-arm, multicenter, phase II study evaluating the safety and efficacy of savolitinib in patients with PRCC according to MET status. Patients and Methods Patients with histologically confirmed locally advanced or metastatic PRCC were enrolled and received savolitinib 600 mg orally once daily. MET-driven PRCC was defined as any of the following: chromosome 7 copy gain, focal MET or HGF gene amplification, or MET kinase domain mutations. Efficacy was assessed according to MET status. Safety, toxicity, and patient-reported health-related quality-of-life outcomes were assessed in all patients. Results Of 109 patients treated, PRCC was MET driven in 44 (40%) and MET independent in 46 (42%); MET status was unknown in 19 (17%). MET-driven PRCC was strongly associated with response; there were eight confirmed partial responders with MET-driven disease (18%), but none with MET-independent disease ( P = .002). Median progression-free survival for patients with MET-driven and MET-independent PRCC was 6.2 months (95% CI, 4.1 to 7.0 months) and 1.4 months (95% CI, 1.4 to 2.7 months), respectively (hazard ratio, 0.33; 95% CI, 0.20 to 0.52; log-rank P < .001). The most frequent adverse events associated with savolitinib were nausea, fatigue, vomiting, and peripheral edema. Conclusion These data show activity and tolerability of savolitinib in the subgroup of patients with MET-driven PRCC. Furthermore, molecular characterization of MET status was more predictive of response to savolitinib than a classification based on pathology. These findings justify investigating savolitinib in MET-driven PRCC.

Renal carcinoma is a common and aggressive malignancy whose histopathogenesis is incompletely understood and that is largely resistant to cytotoxic chemotherapy. We present two mouse models of kidney cancer that recapitulate the genomic alterations found in human papillary (pRCC) and clear cell RCC (ccRCC), the most common RCC subtypes. MYC activation results in highly penetrant pRCC tumours (MYC), while MYC activation, when combined with Vhl and Cdkn2a (Ink4a/Arf) deletion (VIM), produce kidney tumours that approximate human ccRCC. RNAseq of the mouse tumours demonstrate that MYC tumours resemble Type 2 pRCC, which are known to harbour MYC activation. Furthermore, VIM tumours more closely simulate human ccRCC. Based on their high penetrance, short latency, and histologic fidelity, these models of papillary and clear cell RCC should be significant contributions to the field of kidney cancer research.

BACKGROUND: Papillary renal cell carcinoma (PRCC) is a rare subset of RCC. The Cancer Genome Atlas (TCGA) data largely reflect localized disease, and there are limited data for advanced PRCC.
OBJECTIVE: To characterize the frequency of genomic alterations (GAs) in patients with advanced PRCC for whom comprehensive genomic profiling (CGP) was performed in the context of routine clinical care.
DESIGN, SETTING, AND PARTICIPANTS: Formalin-fixed, paraffin-embedded tissue was obtained for 169 consecutive patients with confirmed PRCC. DNA was extracted and comprehensive genomic profiling was performed in a certified central laboratory.
MEASUREMENTS: Hybrid-capture, adaptor ligation-based libraries of up to 315 genes were sequenced to a median coverage of 648×. All classes of GAs were identified, including substitutions, insertions/deletions, copy number alterations, and rearrangements.
RESULTS AND LIMITATIONS: From 169 patients, either primary tumor tissue (102 patients, 60%) or metastatic tissue (67 patients, 40%) was collected. In patients with type 1 PRCC, commonly altered genes were MET (33%; 8 activating mutations, 5 amplifications at >6 copies), TERT (30%), CDKN2A/B (13%), and EGFR (8%). In patients with type 2 PRCC, commonly altered genes were CDKN2A/B (18%), TERT (18%), NF2 (13%), and FH (13%); MET GAs (5 mutations, 3 amplifications) were observed in 7% of type 2 cases. Notable differences from TCGA data include higher frequencies of MET, NF2, and CDKN2A/B GAs, association of alterations in SWI/SNF complex genes with type 2 PRCC, and observation of frequent CDKN2A/B alterations in both type 1 and type 2 disease.
CONCLUSIONS: Both the current study and the TCGA experience represent similarly sized cohorts of patients with PRCC. Key differences in GA frequency probably underscore the marked difference in stage distribution between these data sets. These results may inform planned precision medicine trials for metastatic PRCC.
PATIENT SUMMARY: Papillary renal cell carcinoma (PRCC) is a rare subtype of kidney cancer, and understanding of the biology of advanced PRCC is limited. This report highlights some of the unique biologic features of PRCC that may inform on future use of targeted therapies for the treatment of metastatic disease.

The utility of cancer cell lines is affected by the similarity to endogenous tumour cells. Here we compare genomic data from 65 kidney-derived cell lines from the Cancer Cell Line Encyclopedia and the COSMIC Cell Lines Project to three renal cancer subtypes from The Cancer Genome Atlas: clear cell renal cell carcinoma (ccRCC, also known as kidney renal clear cell carcinoma), papillary (pRCC, also known as kidney papillary) and chromophobe (chRCC, also known as kidney chromophobe) renal cell carcinoma. Clustering copy number alterations shows that most cell lines resemble ccRCC, a few (including some often used as models of ccRCC) resemble pRCC, and none resemble chRCC. Human ccRCC tumours clustering with cell lines display clinical and genomic features of more aggressive disease, suggesting that cell lines best represent aggressive tumours. We stratify mutations and copy number alterations for important kidney cancer genes by the consistency between databases, and classify cell lines into established gene expression-based indolent and aggressive subtypes. Our results could aid investigators in analysing appropriate renal cancer cell lines.

Although papillary renal cell carcinoma (PRCC) accounts for 10%-15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01).The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.

We evaluated the frequency of translocation renal cell carcinoma (RCC) by reverse transcription polymerase chain reaction (RT-PCR) and how well the TFE3 immunoreactivity is concordant with TFE3 gene translocation status proved by fluorescence in situ hybridization (FISH) assay and RT-PCR. TFE3 and Cathepsin K expression was analyzed by immunohistochemistry in 185 RCC cases, and 48 cases either of more than weak expression of TFE3 or of positivity for Cathepsin K were done for FISH analysis and RT-PCR. All the RT-PCR positive cases were confirmed by cloning and sequencing. Of the 14 cases with strong nuclear TFE3 expression, 12 showed a break-apart signal by FISH. ASPL- and PRCC-TFE3 translocations were detected in 13 and one case, respectively, by RT-PCR. Of 21 cases with weak TFE3 expression, five were translocation-positive by FISH. ASPL-, PRCC-, and PSF-TFE3 translocations were detected by RT-PCR (n=3, 3, and 1, respectively). All 13 TFE3-negative/cathepsin K-positive cases were negative by FISH and two each harbored ASPL- and PRCC-TFE3 translocations that were detected by RT-PCR. A high rate of TFE3 immunoreactivity (8.6%) was confirmed by RT-PCR (13.5%) and FISH (9.7%). Higher translocation rate of RT-PCR means RT-PCR detected translocation in TFE3 weak expression group and only cathepsin K positive group more specifically than FISH. Thus, RT-PCR would complement FISH analysis for detecting translocation RCC with fusion partners.

Renal cell carcinomas (RCCs) with Xp11 translocation (Xp11 RCC) constitute a distinctive molecular subtype characterized by chromosomal translocations involving the Xp11.2 locus, resulting in gene fusions between the TFE3 transcription factor with a second gene (usually ASPSCR1, PRCC, NONO, or SFPQ). RCCs with Xp11 translocations comprise up to 1% to 4% of adult cases, frequently displaying papillary architecture with epithelioid clear cells. To better understand the biology of this molecularly distinct tumor subtype, we analyze the microRNA (miRNA) expression profiles of Xp11 RCC compared with normal renal parenchyma using microarray and quantitative reverse-transcription polymerase chain reaction. We further compare Xp11 RCC with other RCC histologic subtypes using publically available data sets, identifying common and distinctive miRNA signatures along with the associated signaling pathways and biological processes. Overall, Xp11 RCC more closely resembles clear cell rather than papillary RCC. Furthermore, among the most differentially expressed miRNAs specific for Xp11 RCC, we identify miR-148a-3p, miR-221-3p, miR-185-5p, miR-196b-5p, and miR-642a-5p to be up-regulated, whereas miR-133b and miR-658 were down-regulated. Finally, Xp11 RCC is most strongly associated with miRNA expression profiles modulating DNA damage responses, cell cycle progression and apoptosis, and the Hedgehog signaling pathway. In summary, we describe here for the first time the miRNA expression profiles of a molecularly distinct type of renal cancer associated with Xp11.2 translocations involving the TFE3 gene. Our results might help understanding the molecular underpinning of Xp11 RCC, assisting in developing targeted treatments for this disease.

To date, studies on papillary renal-cell carcinoma (pRCC) have largely focused on coding alterations in traditional drivers, particularly the tyrosine-kinase, Met. However, for a significant fraction of tumors, researchers have been unable to determine a clear molecular etiology. To address this, we perform the first whole-genome analysis of pRCC. Elaborating on previous results on MET, we find a germline SNP (rs11762213) in this gene predicting prognosis. Surprisingly, we detect no enrichment for small structural variants disrupting MET. Next, we scrutinize noncoding mutations, discovering potentially impactful ones associated with MET. Many of these are in an intron connected to a known, oncogenic alternative-splicing event; moreover, we find methylation dysregulation nearby, leading to a cryptic promoter activation. We also notice an elevation of mutations in the long noncoding RNA NEAT1, and these mutations are associated with increased expression and unfavorable outcome. Finally, to address the origin of pRCC heterogeneity, we carry out whole-genome analyses of mutational processes. First, we investigate genome-wide mutational patterns, finding they are governed mostly by methylation-associated C-to-T transitions. We also observe significantly more mutations in open chromatin and early-replicating regions in tumors with chromatin-modifier alterations. Finally, we reconstruct cancer-evolutionary trees, which have markedly different topologies and suggested evolutionary trajectories for the different subtypes of pRCC.