Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Combined mutation in Vhl, Trp53 and Rb1 causes clear cell renal cell carcinoma in mice


Clear cell renal cell carcinomas (ccRCCs) frequently exhibit inactivation of the von Hippel–Lindau tumor-suppressor gene, VHL, and often harbor multiple copy-number alterations in genes that regulate cell cycle progression. We show here that modeling these genetic alterations by combined deletion of Vhl, Trp53 and Rb1 specifically in renal epithelial cells in mice caused ccRCC. These tumors arose from proximal tubule epithelial cells and shared molecular markers and mRNA expression profiles with human ccRCC. Exome sequencing revealed that mouse and human ccRCCs exhibit recurrent mutations in genes associated with the primary cilium, uncovering a mutational convergence on this organelle and implicating a subset of ccRCCs as genetic ciliopathies. Different mouse tumors responded differently to standard therapies for advanced human ccRCC, mimicking the range of clinical behaviors in the human disease. Inhibition of hypoxia-inducible factor (HIF)-α transcription factors with acriflavine as third-line therapy had therapeutic effects in some tumors, providing preclinical evidence for further investigation of HIF-α inhibition as a ccRCC treatment. This autochthonous mouse ccRCC model represents a tool to investigate the biology of ccRCC and to identify new treatment strategies.


Kidney cancers represent approximately 2–3% of all human cancers, and ccRCC accounts for roughly 70% of all renal malignancies1. Metastatic spread of ccRCC occurs in about half of all affected individuals, and, despite ongoing improvements in clinical management due to the availability of several therapeutic options in the form of targeted therapies and immune-checkpoint-blocking agents, 5-year survival rates for individuals with metastatic disease are still only around 8–12% (refs. 2,3). Between 82–92% of ccRCC tumors harbor biallelic inactivation of VHL4,5,6, and VHL mutations occur at the earliest stage of tumor formation7. Many individuals with von Hippel–Lindau disease (VHL) inherit a mutant VHL allele that predisposes them to develop ccRCC. However, second-hit loss-of-function mutations in VHL in the renal epithelial cells of individuals with VHL are insufficient to produce ccRCC in humans8 and numerous mice with renal epithelial cell–specific Vhl knockout also failed to develop ccRCC (reviewed in ref. 9), arguing that ccRCC formation requires mutations in addition to those in VHL. The presence of recurrent mutations in PBRM1, BAP1, SETD2, KDM5C, PIK3CA, PTEN, MTOR and TP53, as well as copy-number losses of CDKN2A and RB1 and gains of MDM4 and MYC, in human ccRCCs6,10 provides evidence that these genetic alterations may act cooperatively with VHL loss to induce ccRCC formation. In accordance with this hypothesis, kidney epithelial cell–specific co-deletion of Vhl with Pten in mice reduced the frequency of ciliated epithelial cells and caused the formation of simple and atypical cystic lesions11, which mimic the proposed precursor lesions observed in a subset of ccRCCs. Deletion of Vhl together with Kif3a, to genetically ablate primary cilia, caused a similar phenotype12. Deletion of Vhl and Trp53 gave rise to simple and atypical cystic lesions, as well as small tumors containing cells that displayed cytoplasmic clearing and elevated mechanistic target of rapamycin complex 1 (mTORC1) activity13, recapitulating some of the cellular and molecular changes that are characteristic of human ccRCC. Similar phenotypes were observed in mice with combined Vhl deletion and heterozygous loss of Bap1 (ref. 14). Although these mouse models reproduce precursor lesions of ccRCC and small tumors with some of the features of ccRCC, they do not fully reproduce all of the characteristics of human ccRCC. A very recent study15 showed that combined deletion of Vhl and Pbrm1 causes renal cysts with subsequent development of ccRCCs after approximately 10 months, providing an autochthonous model that reflects the genetic subset of human ccRCC with mutations in both VHL and PBRM1. In this study, we identify another genetic combination that gives rise to ccRCC, furthering understanding of the spectrum of molecular causes of this disease and providing an autochthonous mouse model to test new therapeutic strategies.


Human ccRCCs exhibit recurrent copy-number gains and losses of genes regulating p53 and the G1/S cell cycle checkpoint

To gain further insight into the genetic changes that arise in ccRCC, we utilized cBioPortal16,17 to reanalyze genomic data from 448 sporadic ccRCCs from The Cancer Genome Atlas (TCGA) data set10. We focused on alterations in the network of genes that regulate p53 and the G1/S cell cycle transition, including TP53, MDM2, MDM4, cyclin-dependent kinase (CDK)-inhibitor-family genes, RB-family genes, cyclins and CDKs. While loss- or gain-of-function single-nucleotide mutations in these genes rarely occurred in ccRCC, copy-number losses of negative regulators or gains of positive regulators of the network were common. Sixty-eight percent of human ccRCCs harbored a chromosomal copy-number alteration in at least one of these genes, and most (78%) of these tumors harbored multiple, simultaneous alterations (Supplementary Fig. 1a). Individuals with tumors that displayed alteration in at least one gene in the p53–G1/S network signature had a worse outcome than those without these genetic alterations (Supplementary Fig. 1b). Alterations in other frequently mutated ccRCC-associated genes, including VHL, PBRM1, BAP1 and SETD2, were not enriched in tumors with a p53–G1/S network signature (Supplementary Fig. 1a), providing evidence that alterations of the genes in this network arise independently of other putative mutational selection pressures during ccRCC evolution. These data give rise to the hypothesis that during the evolution of the majority of ccRCCs there is selection for multiple copy-number alterations that are predicted to alter the integrity of the G1/S cell cycle checkpoint, promoting tumor initiation and progression.

Vhl, Trp53 and Rb1 deletion allows the evolution of ccRCC in mice

To functionally test this idea in mice, we deleted Vhl together with two tumor-suppressor genes that encode proteins that function as the key controllers of cell cycle entry in the p53–G1/S network, namely Trp53 (encoding p53) and Rb1 (encoding retinoblastoma protein (pRB)). We generated mice that allow inducible renal epithelial cell–specific (Ksp1.3-CreERT2)18 homozygous deletion of loxP-flanked alleles of Rb1, Vhl/Rb1, Trp53/Rb1 and Vhl/Trp53/Rb1 to complement our previous analyses of the effects of Vhl and Vhl/Trp53 deletion11,13. Epithelial cell–specific gene deletion throughout the nephron, including in proximal tubule epithelial cells18, the likely cell of origin of ccRCC19, was induced in pups by injecting nursing dams with tamoxifen or by feeding mice aged 6 weeks with tamoxifen-containing food. These two treatments yielded identical results, and data from them have been pooled for the purposes of the following descriptions. Hereafter, Δ/Δ refers to deleted alleles. Littermate mice lacking the Ksp1.3-CreERT2 transgene served as wild-type controls, denoted by +/+. p53 was not detectable by immunohistochemical staining, loss of immunoreactivity for pRB confirmed Rb1 deletion in all genotypes, and the nuclear accumulation of HIF-1α in the kidneys of VhlΔ/ΔRb1Δ/Δ and VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice confirmed Vhl deletion (Supplementary Fig. 2a). The kidneys of Rb1Δ/Δ (n = 35) and VhlΔ/ΔRb1Δ/Δ (n = 29) mice displayed occasional sites of subtle disorganization of renal tubular epithelia 50–57 weeks after gene deletion (Fig. 1a,b). The kidneys of Trp53Δ/ΔRb1Δ/Δ (n = 12) mice displayed small cysts (1–5 cysts per kidney section) (Fig. 1c), as well as occasional sites of cystic or tubular dysplasia (Fig. 1d,e), within 46–54 weeks of gene deletion. In contrast, within 30–47 weeks of gene deletion, 10 of 25 VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice developed a total of 64 tumors (Fig. 1f–i). Mice that did not develop tumors showed an equivalent extent of tubular immunoreactivity to carbonic anhydrase 9 (CA9) (Supplementary Fig. 2b), a marker of Vhl deletion and HIF-α activation, as histologically normal tubules in kidneys with tumors, showing that the absence of tumors in these mice was not caused by failure of Cre activation. The presence of putative small precursor lesions in these mice (Supplementary Fig. 2b) implies that tumors may potentially have developed at later time points. To follow up these observations in new cohorts of mice, we first established that the emergence of tumors in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice could be monitored using contrast-assisted micro computed tomography (μCT) imaging (Fig. 1j) and followed tumor onset over time in larger cohorts of Trp53Δ/ΔRb1Δ/Δ and VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice (Fig. 1k). Thirty-one of 38 (82%) VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice that were fed tamoxifen at 6 weeks of age developed a total of 159 tumors within 25–61 weeks of tamoxifen treatment. In contrast, 6 of 25 (24%) Trp53Δ/ΔRb1Δ/Δ mice developed a total of 13 tumors within 50–70 weeks of tamoxifen treatment. We conclude that Vhl deletion accelerates the onset and increases the incidence of tumor formation, as well as increases the number of tumors per mouse. Interestingly, male VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice developed tumors at earlier time points and developed more tumors in comparison to female VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice (Supplementary Fig. 3a,b), which is consistent with the fact that human ccRCC is almost twice as likely to occur in males than in females20.

Figure 1: Renal epithelial cell–specific co-deletion of Vhl, Trp53 and Rb1 permits the evolution of ccRCCs.

(a,b) Examples of mild renal tubular disorganization in an Rb1Δ/Δ mouse aged 12 months (a) and a VhlΔ/ΔRb1Δ/Δ mouse aged 12 months (b). (ce) Examples of a simple cyst (c), dysplasia associated with cysts (d) and solid dysplasia (e) in a Trp53Δ/ΔRb1Δ/Δ mouse aged 12 months. (f) Kidneys from a VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mouse aged 10 months; arrowheads point to tumors. (gi) Examples of histological sections from VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice aged 10 months, with tumors outlined with dashed lines. (j) Example of longitudinal μCT imaging of tumor development in a VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mouse; the time points are days following tamoxifen feeding. (k) Tumor onset in cohorts of VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ and Trp53Δ/ΔRb1Δ/Δ mice. The two-sided P value was calculated by log-rank Mantel–Cox test. (lo) Histological appearances of tumors 1–4, marked in gi, respectively; the tumors show pure clear cytoplasm (l), clear cytoplasm with weak eosinophilic staining (m), strong cytoplasmic eosinophilic staining (n) and a mixture of clear and eosinophilic cytoplasm (o). (p) Example of a papillary-like ccRCC. (q,r) Sinusoidal vascular networks in a tumor with a clear cell appearance (q) and a tumor with an eosinophilic appearance (r); endothelial cells are highlighted by arrowheads. (s,t) CD31 (s) and vWF (t) immunohistochemical staining in ccRCCs from VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice. (u) Example of a tumor margin of a mouse ccRCC. Scale bars: 50 μm in ae and lu, and 5 mm in fj.

On the basis of the World Health Organization (WHO) 2016 criteria, all tumors in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice were classified as grade 3 or grade 4 ccRCCs growing in acinar, solid or pseudopapillary growth patterns (Fig. 1l–p). Some tumors exhibited a cystic component (Fig. 1h, tumor 2). Eosin staining revealed that 60% of tumors contained cells with either completely optically clear cytoplasm (Fig. 1l) or weakly stained cytoplasm (Fig. 1m). Twenty-eight percent of tumors exhibited more cytoplasmic eosin staining (Fig. 1n) and resembled the eosinophilic variant of ccRCC. Five tumors had regions with obvious transitions between clear cell and eosinophilic morphology, providing evidence that these cytoplasmic appearances represent a continuous phenotypic spectrum (Fig. 1o). Indeed, similar eosinophilic cells can be found in high-grade human ccRCC or in hypoxic regions of human ccRCC tumors. Regions of necrosis (data not shown) were observed in many of the mouse tumors, and two tumors exhibited intratumoral hemorrhage (Fig. 1h, tumor 3). Necrosis and hemorrhage are also common features of human ccRCC. In addition, two clear cell tumors showed papillary-like features similar to tumors that rarely arise in individuals with VHL (Fig. 1p) but did not show features of the newly described clear cell papillary renal cell carcinoma entity. These tumors were also distinguished from true papillary renal cell carcinomas by the absence of a fibrovascular core and cytokeratin 7 (CK7) staining (data not shown). Irrespective of cytoplasmic morphology, all tumors displayed a highly developed vascular network of CD31- and von Willebrand factor (vWF)-positive thin-walled blood vessels enveloping clusters of carcinoma cells in a pseudoalveolar fashion (Fig. 1q–t). These sinusoidal vascular structures are a characteristic diagnostic feature of human ccRCC. All tumors were confined to the kidney and exhibited pushing, rather than infiltrative, margins (Fig. 1u). There was no evidence of invasion into blood vessels, perirenal fat or the renal pelvis. No metastases were observed in the lungs, liver, spleen, bones or brain of tumor-bearing mice (n = 19). In contrast to the ccRCC tumors in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ kidneys, the tumors that arose in Trp53Δ/ΔRb1Δ/Δ kidneys exhibited a range of non-ccRCC phenotypes and were variously characterized by sarcomatoid and rhabdoid tumor cell morphologies, eosinophilic cytoplasm, comedonecrosis, atypical giant tumor cells and aberrant mitosis (Supplementary Fig. 4).

In addition to ccRCCs, the kidneys from VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice exhibited a spectrum of lesions (Supplementary Fig. 5) that recapitulated the putative precursor lesions that are found in the kidneys of individuals with VHL, namely cysts with a single epithelial layer and cysts with proliferation of atypical cells growing in multilayered structures in the cystic lumen, as well as small solid lesions that appeared to have no cystic component. ccRCCs in this model therefore appear to arise via both cystic and solid precursor lesions.

Mouse ccRCCs are molecularly similar to human ccRCCs

Immunohistochemical staining revealed numerous similarities between VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mouse ccRCCs and human ccRCCs (Fig. 2). All mouse ccRCCs displayed nuclear accumulation of HIF-2α (n = 41) and 75% displayed nuclear accumulation of HIF-1α (n = 47), which is consistent with the fact that, although most human ccRCCs express both HIF-1α and HIF-2α, approximately 30% express only HIF-2α and not HIF-1α (ref. 21). All tumors stained positively for the HIF-α target CA9 (n = 46). It is noteworthy that histologically normal tubules in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ kidneys frequently exhibited HIF-1α, HIF-2α and CA9 immunoreactivity (Fig. 2), demonstrating that these mutant cells had not become tumors within 9–11 months of gene deletion. All tumors had strong staining for 4E-BP1 phosphorylated at Thr37/Thr46 (n = 44), which is indicative of strong mTORC1 activation, a common feature of human ccRCC. In contrast, antibodies against ERK1 and ERK2 (ERK1/2) phosphorylated at Thr202/Tyr204, a marker of activation of the RAS–mitogen-activated protein kinase (MAPK) pathway, labeled only rare cells in most tumors (n = 44). All tumors showed strong immunoreactivity for paired-box gene 8 (PAX8) (n = 47) (Fig. 2) and pan-cytokeratin (n = 47) (Supplementary Fig. 6), which are clinical diagnostic markers for ccRCC. Thirty-nine of 43 tumors stained positively for CD10, 37 of 43 stained positively for aquaporin 1 (AQP1) and 14 of 43 stained positively for NAPI2A (Supplementary Fig. 6). All tumors were positive for at least one of these proximal tubule marker proteins, and no tumor (n = 43) displayed immunoreactivity for markers of loop of Henle (THP), distal tubule (NCC) or collecting duct (AQP2) (Supplementary Fig. 6). We conclude that, even though the Cre driver induces gene deletion widely throughout the nephron, VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mouse ccRCCs arise from proximal tubule epithelial cells.

Figure 2: ccRCCs in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice exhibit HIF-α and mTORC1 pathway activation.

Examples of immunohistochemical staining using antibodies against the indicated proteins in normal kidney tissue from a wild-type (WT) mouse, histologically normal tissue from a tumor-bearing VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mouse and ccRCCs from three different VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice. All panels are the same magnification; scale bar, 50 μm. P-4E-BP1, phosphorylated 4E-BP1; P-ERK, phosphorylated ERK1/2.

In contrast to tumors in the kidneys of VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice, which exhibited uniform positive staining for HIF-1α and CA9, tumors in Trp53Δ/ΔRb1Δ/Δ kidneys stained positively only in specific regions, often bordering on necrotic areas, which are typically hypoxic in most types of tumors (Supplementary Fig. 4). Eleven of 11 tumors were positive for the proximal tubule marker CD10, 2 of 11 were positive for NAPI2A and 0 of 11 were positive for THP, implicating the proximal tubule as the likely cell of origin (Supplementary Fig. 4).

We next used RNA sequencing to compare global mRNA abundance between six mouse ccRCCs and three nontransgenic kidney cortices from littermate Vhlfl/flTrp53fl/flRb1fl/fl mice. Multidimensional scaling analysis demonstrated a clear segregation of global gene expression profiles between normal kidney and tumors (Supplementary Fig. 7a,b), with 1,852 genes significantly upregulated more than twofold and 2,010 genes significantly downregulated more than twofold (Fig. 3a and Supplementary Table 1). Of the human orthologs corresponding to these differentially expressed genes in mouse ccRCC, 37% were also present in a list of differentially expressed genes identified by comparing 72 samples from normal human kidneys and 533 human ccRCC samples (Fig. 3b). Moreover, correlation analysis of the average expression values for all unique orthologous gene pairs between human ccRCC and mouse ccRCC revealed a strong correlation in global transcriptional profiles, particularly for those genes that were highly expressed (Fig. 3c). These data demonstrate that there are strong transcriptional similarities between the mouse ccRCC model and human ccRCCs. Predominant transcriptional signatures in mouse ccRCCs included upregulation of a set of HIF-1α and HIF-2α target genes that we previously identified as being upregulated following deletion of Vhl in primary renal epithelial cells22 (Fig. 3d) and upregulation of numerous genes that regulate cell cycle progression, DNA replication and mitosis (Fig. 3e), as well as upregulation of a set of genes that regulate immune responses and inflammation (Fig. 3f). Global comparisons of mRNA expression in mouse ccRCCs with the gene expression profiles of microdissected normal mouse nephron segments23 revealed strong expression correlations of all tumors with S1 and S3 proximal tubule segments, but not with other nephron segments (Fig. 3g). These data provide strong evidence that the proximal tubule is the origin of ccRCCs in this model, which is consistent with similar gene expression correlation analyses of human ccRCC19 and with our immunohistochemical stainings.

Figure 3: ccRCCs in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice exhibit global transcriptional similarities to human ccRCCs.

(a) Volcano plot showing significantly differentially regulated genes (orange dots; false discovery rate < 0.01 (horizontal line) and >2-fold up- or downregulated (vertical lines)) between six mouse ccRCCs and three normal kidney cortex samples. Blue, black and green dots represent genes that did not meet either or both criteria of being significantly up- or downregulated. (b) Venn diagram showing overlap of the human orthologs of the set of genes differentially expressed in mouse ccRCC with the set of differentially expressed genes derived from analysis of human normal kidney (n = 72) and ccRCC (n = 533) samples using the same criteria as for the analyses in a. (c) Sample-averaged, normalized and log-transformed gene expression values for all unique orthologous gene pairs in human and mouse ccRCC (black dots). The red line represents perfect correlation and the blue line results from a smooth parametric regression, with the confidence band depicted in gray. The 95% confidence interval for the Pearson coefficients is 0.65–0.67 (Pearson correlation test, P < 1 × 10−16). (df) Mouse ccRCCs (column numbers indicate individual mice from which samples were obtained; 2017-1 and 2017-2 were obtained from the same mouse) show upregulation of mRNA expression of HIF-1α and HIF-2α target genes (d), genes that regulate cell cycle progression, DNA replication and mitosis (e) and genes involved in immune responses (f). (g) Normalized Pearson correlation scores depicting global mRNA expression similarities between mouse ccRCCs and normal nephron segments. Glom, glomeruli; S1, proximal tubule S1 segments; S3, proximal tubule S3 segments; mTAL, medullary thick ascending limbs of Henle's loop; cTAL, cortical thick ascending limbs of Henle's loop; DCT, distal convoluted tubules; CCD, cortical collecting ducts; OMCD, outer medullary collecting ducts.

Mouse ccRCCs exhibit genetic mutational profiles similar to those of human ccRCCs

The observation that ccRCCs arise in vivo after a relatively long latency implies that additional mutational events likely accumulate to allow tumor formation. To identify such cooperating genetic alterations, we conducted exome sequencing of DNA isolated from seven mouse ccRCCs and normal liver tissue from these six mice. An average of 4.52 ± 0.56 × 107 total reads were obtained per sample, giving mean target coverages of 56- ± 5-fold. Specific losses of sequencing coverage of the loxP-flanked regions of Vhl, Trp53 and Rb1 confirmed that all three genes were mutated in the tumors (Supplementary Fig. 7c). Copy-number variants in tumors were determined by comparison with the matched normal liver samples. All tumors exhibited a normal autosomal karyotype without evidence of whole-chromosome aneuploidy. Large regions of gain and loss were rare, but a total of 55 regions of copy-number variation were identified. We focused analyses on named genes that were present in the minimal overlapping chromosomal regions of gain or loss between different tumors or that were amplified (present in four or more copies) in individual tumors (Fig. 4a and Supplementary Table 2). Notably, two tumors harbored amplifications (estimated copy numbers of 59 and 66) of the Myc oncogene (Fig. 4a and Supplementary Fig. 7d), and these tumors exhibited very high levels of Myc mRNA (Fig. 3e). Copy-number gains or amplifications of MYC occur in 8–15% of human ccRCCs and are associated with poor survival10,24,25 (Fig. 4c). We identified regions of human–mouse synteny and investigated whether similar copy-number variations arise in human ccRCCs. Interestingly, when human tumors exhibited copy-number gains of genes that were gained in the mouse ccRCCs or losses of genes that were lost in the mouse ccRCCs, these tumors almost invariably (with the exception of the DYNLL1 (DYNTL1) syntenic region) also displayed alteration in at least one gene in the p53–G1/S network signature (Fig. 4b), providing evidence that the copy-number alterations in the mouse model are nonrandom and that they may participate in the evolution and progression of a subset of ccRCCs. In accordance with this notion, copy-number gain of SYCP1 in human ccRCC predicts poor survival (Fig. 4c).

Figure 4: Copy-number variations in mouse ccRCCs are also present in the subset of human ccRCCs with p53–G1/S alterations.

(a) Focal copy-number alterations of the indicated genes and chromosomal regions in mouse ccRCCs. The Myc and Pvt1 genes are present in the Myc/Pvt1 region, and numbers in parentheses represent the number of other named candidate genes in each region; see Supplementary Table 2 for the full list of genes. (b) Distribution of copy-number gains or amplifications of the human orthologs or syntenic chromosomal regions of genes or chromosomal regions gained in mouse ccRCC and copy-number losses of genes or chromosomal regions lost in mouse ccRCC in 448 human ccRCC tumors. Numbers in parentheses represent the number of other human orthologs of the mouse genes that are found in each chromosomal region. The subset of tumors with the p53–G1/S mutation signature is indicated by a box. Classifications as amplification, gain, shallow (hemizygous) deletion and deep (homozygous) deletion are derived from cBioPortal GISTIC 2.0 analyses. (c) Survival curves of human ccRCCs harboring copy-number amplifications or gains (amp/gain) of the indicated genes. P values were calculated by log-rank Mantel–Cox test.

Single-nucleotide variants (SNVs) and insertions and deletions (indels) were identified in mouse ccRCCs versus matched liver samples. The most frequent SNVs were C>A/G>T transversions, C>T/G>A transitions and A>G/T>C transitions (Fig. 5a). These three classes of mutations are also the most frequently occurring in human ccRCC10, demonstrating that the mouse model reproduces the same classes of mutations that arise in human ccRCC. Mouse ccRCCs exhibited 161 ± 17 nonsynonymous mutations per tumor, and these were almost entirely attributable to SNVs rather than indels (Fig. 5b). Human ccRCCs exhibited 50.6 ± 20.3 nonsynonymous mutations per tumor (n = 382, cBioPortal), suggesting that VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mouse ccRCCs do not harbor fewer mutations than the human counterparts, which is a limitation of some genetically engineered mouse tumor models26. To narrow the search for potential truncal or clonal mutations that cooperate in tumor evolution, we focused further analyses on the set of truncating and frameshift mutations that were present at a variant allele frequency (VAF) greater than or equal to 5% plus nonsynonymous coding mutations that were present at a high VAF (>25%) (Supplementary Table 3). Although mutations of the mouse orthologs of commonly mutated genes in human ccRCC, including Pbrm1, Bap1 and Setd2, were not observed in these tumors, gene set enrichment analysis demonstrated striking enrichment for mutations in genes associated with Gene Ontology (GO) terms for cell projection part (P = 3.91 × 10−14), vesicle-mediated transport (P = 5.89 × 10−11) and microtubule cytoskeleton (P = 1.71 × 10−9), among other related gene sets. Further inspection revealed recurrence of mutations in genes that are involved in regulating the structure or function of the primary cilium. Each of the seven tumors exhibited one or more mutations in the 12 primary-cilium-related genes (Fig. 5c), including Kif3a and Kif3b, which encode components of the kinesin II microtubule motor complex that is necessary for the generation of primary cilia. Genetic deletion of Kif3a cooperates with loss of Vhl or Vhl and Trp53 to promote the formation of precursor lesions to ccRCC in mice12,27. Eleven of 12 of the human orthologs of these genes are mutated in small percentages of human ccRCC, and, after extending the list to include other known human ciliopathy-related genes and genes for which there is evidence of a function in primary cilium biology, we found that 40% of human ccRCCs harbored one or more mutations in primary-cilium-related genes (Supplementary Fig. 8). Notably, these mutations were largely mutually exclusive of one another and of PTEN mutations. We have previously shown that mutated Pten cooperates with mutated Vhl to reduce the frequency of ciliated renal epithelial cells and to induce renal cysts11. Similar to human ccRCCs28, all mouse ccRCCs exhibited a reduced frequency of ciliated tumor cells (Fig. 5d). These data uncover a mutational convergence on genes associated with the primary cilium in mouse and human ccRCCs.

Figure 5: Exome sequencing reveals that mouse and human ccRCCs exhibit recurrent mutations of genes associated with the primary cilium.

(a) Distributions of nucleotide transitions and transversions in mouse ccRCCs. (b) Numbers of nonsynonymous mutations caused by SNVs or indels in mouse ccRCCs. (c) Recurrent alterations in primary-cilium-associated genes in mouse ccRCCs. Variant allele frequencies (VAFs) of each mutation are shown. (d) Examples of anti-acetylated-tubulin staining to label primary cilia in normal kidney tissue and mouse ccRCCs. The frequency of tumors (n = 59) showing these observed patterns is indicated. All panels are the same magnification; scale bar, 50 μm.

Therapeutic studies using the mouse ccRCC model

Having established many molecular similarities between mouse and human ccRCC, as well as demonstrated that different mouse ccRCCs are genetically distinct from one another, we next asked whether this model is useful for preclinical therapeutic studies. Patients with metastatic ccRCC typically receive antiangiogenic therapy in the form of receptor tyrosine kinase inhibitors such as sunitinib. Up to 20% of tumors are refractory to these therapies, and the majority of patients typically develop resistance within 1 year. Second-line therapy often involves mTOR inhibition (for example, treatment with everolimus), which confers a moderate increase in progression-free survival in comparison to treatment with placebo. We first mimicked this therapeutic regime by monitoring tumor initiation and progression in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice using contrast-assisted μCT imaging, and we initiated therapy once the largest tumor in each mouse reached a volume of approximately 20–70 mm3 (Fig. 6a,b). Of 19 independent tumors in four mice under sunitinib treatment, 6 tumors grew at a rapid rate, 3 tumors regressed and 10 tumors were stable with no or slow growth (Fig. 6c). Of these latter tumors, six developed resistance within 2–3 weeks and grew rapidly. Mice undergoing second-line therapy with everolimus showed stable disease or regression in 18 of 23 tumors (several new tumors developed during sunitinib therapy), 4 of which developed resistance (Fig. 6c). Thus, individual ccRCCs in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice have different sensitivities to clinical agents used to treat human ccRCC, further validating the accuracy of the mouse model and suggesting that this model will be useful to interrogate mechanisms or identify biomarkers that are associated with therapeutic sensitivity or resistance.

Figure 6: ccRCCs in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice exhibit varying patterns of therapeutic sensitivity and resistance.

(a) Examples of development of three tumors (T1, T2, T3) in two different mice before and during therapy; tumors were visualized with contrast-assisted μCT imaging. Information regarding treatment type and dosage (in mg per kg bodyweight per day) is provided under the images. Scale bars, 5 mm. (b) Quantification of the volumes of T1, T2 and T3 from a. (c) Summary of the therapeutic effects of sunitinib, everolimus and acriflavine (each row represents an independent tumor) in four mice.

Acriflavine, a drug that was given orally as a urinary antiseptic in the 1920s, was recently identified as an inhibitor of the dimerization of HIF-1α and HIF-2α with HIF-1β, blocking transcriptional activation29. Acriflavine treatment was shown to reduce the growth of various cancer cell lines in xenograft assays and to reduce colorectal cancer growth in an autochthononous mouse model29,30. Given the important pathogenic role of dysregulated HIF-α activity in human ccRCC, we tested acriflavine in the mice described above as a third-line therapy after sunitinib and everolimus. Daily intraperitoneal injection slowed the growth of 2 of 23 tumors, and 1 large tumor that was resistant to everolimus showed initial regression but developed resistance after 3 weeks of acriflavine therapy (Fig. 6b,c). Three smaller tumors regressed during acriflavine therapy, while other similarly sized tumors in the same mice increased in size (Fig. 6c and Supplementary Fig. 9). Although these proof-of-principle studies show that this drug is effective in only a subset of tumors, these results provide further preclinical evidence to support the ongoing development and clinical testing of various HIF-α inhibitors with better specificities and pharmacological properties as therapies for ccRCC. This mouse model represents an experimental platform that should assist in the identification of biomarkers that could be used to predict which human ccRCC tumors are likely to respond to inhibition of HIF-α.


This study describes an autochthonous mouse model of ccRCC that accurately recapitulates the cellular and molecular features of human ccRCC. Although the exact combination of biallelic inactivation of VHL, TP53 and RB1 is not common in human ccRCC tumors, VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice represent a genetic background that models the predicted effects of the many combinatorial copy-number alterations in regulators of the interconnected networks governing the p53 pathway and the G1/S cell cycle control machinery that arise in human ccRCC. The VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ genetic background does not automatically confer tumor-forming capacity on renal epithelial cells in vivo but rather permits the evolution of genetically distinct ccRCC tumors. The dependency of these tumors on Vhl mutation is clearly shown by the accelerated and increased incidence of tumor formation in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice as compared to Trp53Δ/ΔRb1Δ/Δ mice, as well as the fact that only VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice develop ccRCCs. These functional genetic data are consistent with the fact that VHL is biallelically inactivated as an initiating event in the vast majority of ccRCCs6,7,10. We identified mutational convergence on genes that regulate the structure or function of the primary cilium in mouse ccRCCs and in 40% of human ccRCCs. Owing to the relative rarity of these mutations when genes are considered individually, they had previously been missed by statistical analyses to identify recurrent mutations, but, when these mutations are considered in the larger biological context, we propose that at least a subset of ccRCCs can be viewed as genetic ciliopathies. Although further functional studies will be necessary to establish whether the mutations that we identified in ccRCC play a pathogenic role in the disease, given that a common phenotypic outcome of genetic alterations in diverse genes that are important for cilia biology is the induction of renal epithelial cell proliferation and cyst formation31, it appears likely that mutations in primary-cilium-related genes might either permit or enhance the proliferation of VHL-mutant cells. In this context, our previous studies have shown cooperation between loss of Vhl and loss of primary cilia in causing uncontrolled renal epithelial cell proliferation and development of simple and atypical cystic precursor lesions in mice11,12,27,32. It is plausible that the combination of mutation in VHL, mutation in primary-cilium-related genes and additional genetic alterations—such as those in the p53 and G1/S cell cycle network or in other frequently mutated tumor-suppressor genes, like PBRM1, BAP1 or SETD2—act cooperatively to cause the evolution of ccRCC. It is noteworthy that none of the seven mouse ccRCCs examined in this study showed mutations in these tumor-suppressor genes, providing evidence that this model may reflect the approximately 50% of human ccRCCs that do not harbor mutations in other known or suspected tumor-suppressor genes associated with kidney cancer.

The VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ ccRCC mouse model will provide an opportunity to assess genetic dependencies and identify mutational modifiers that enhance tumor initiation or promote invasion and metastasis. Our copy-number analyses identified several candidate genes, including the Myc oncogene and Loxl2, that have been implicated in various aspects of the pathogenesis of ccRCC21,33,34,35,36. It is also noteworthy that all VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mouse ccRCCs showed high levels of mTORC1 activation, as evidenced by staining for phosphorylated 4E-BP1. PI3K–mTORC1 pathway activation was also observed in cysts or tumors in mice with mutations in Vhl/Pten11, Vhl/Trp53 (ref. 13), Vhl/Trp53/Kif3a27, Vhl/Bap1 (ref. 14) and Vhl/Pbrm1 (ref. 15), suggesting that PI3K–mTORC1 pathway activation might generally promote ccRCC evolution in the context of diverse cooperating mutations.

The fact that different ccRCCs in VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice are genetically distinct from one another, coupled with our observations that different ccRCCs within and between mice respond differently to the therapeutic regimes used to treat human metastatic ccRCC, suggests that this experimental system will be useful for the identification of molecular determinants of tumor sensitivity and resistance. Our observations of the therapeutic effects of the HIF-1α and HIF-2α inhibitor acriflavine on at least some tumors support recent findings that a new HIF-2α-specific inhibitor shows good therapeutic activity in various preclinical models toward approximately half of human ccRCC-derived cell lines or tumourgrafts37,38. Finally, the availability of this and other autochthonous models of ccRCC will allow investigations of the role of the tumor microenvironment in tumor progression and therapeutic responses, particularly in the context of the ongoing optimization of immune-checkpoint-blocking therapies, which have already yielded good clinical responses in subfractions of patients with ccRCC39.



Rb1fl/fl (FVB.129P2 background)40, Vhlfl/fl (C.129S background)41 and Trp53fl/fl (FVB.129P2 background)42 mice were crossed with Ksp1.3-CreERT2 (B6.Cg background)18 mice to generate Ksp1.3-CreERT2; Vhlfl/fl; Trp53fl/fl; Rb1fl/fl, Ksp1.3-CreERT2; Vhlfl/fl; Rb1fl/fl, Ksp1.3-CreERT2; Trp53fl/fl; Rb1fl/fl and Ksp1.3-CreERT2 Tg/+; Rb1fl/fl animals. Littermate mice that lacked the Cre transgene served as wild-type controls. Gene deletion in mice aged 6 weeks was achieved by feeding them food containing tamoxifen (400 ppm) for 2 weeks. For gene deletion in pups, nursing dams were injected intraperitoneally with tamoxifen (0.1 mg per gram bodyweight per day) from postnatal days 2–4. Mouse crosses and phenotyping were conducted under the breeding license of the Laboratory Animal Services Center, University of Zurich, and tumor monitoring studies were conducted under license ZH116/16 of the Canton of Zurich. Investigators were not blinded to the genotype of the mice. No statistical method was used to predetermine sample size. The experiments were not randomized.

Immunohistochemistry and immunofluorescence.

Immunohistochemistry and immunofluorescence were conducted using previously described methods11 using antibodies against the following antigens: AQP1 (1:500, Abcam, ab15080), AQP2 (1:4,000, gift from J. Loffing43), CA9 (1:2,000, Invitrogen, PA1-16592), CD10 (1:2,000, Thermo Fisher Scientific, PA5-47075), CD31 (1:2,000, Abcam, ab28364), pan-cytokeratin (1:1,000, DAKO, M3515), HIF-1α (1:20,000, Novus Biotechnologies, NB-100-105), HIF-2α (1:2,500, PM8, gift from P. Pollard44), NAPI2A (1:250, gift from J. Biber45), NCC (1:500, Millipore, AB3553), PAX8 (1:800, Protein Tech Group, 10336-1-AP), phospho-ERK1/2 (1:1,000, Thr202/Tyr204; Cell Signaling Technologies, 9101), phospho-4E-BP1 (1:800, Thr37/Thr46; Cell Signaling Technologies, 2855), pRB (1:10,000, BD Biosciences, 554136), THP (1:200, Santa Cruz Biotechnologies, sc-20631), acetylated tubulin (1:1,000, Sigma-Aldrich, T6793), vWF (1:1,000, Sigma, F3520). Validations of the primary antibodies are provided on the manufacturers' websites or in the referenced citations.

Therapeutic studies and μCT imaging.

Imaging of animals was performed as previously described12. Mice were imaged monthly, beginning 5 months after tamoxifen feeding and every week or every 2 weeks during therapeutic studies. Tumor volumes were determined from μCT images by measuring the maximum diameter of tumors in the x, y and z planes and using the formula for the volume of an ellipsoid (V = 4/3 × π × radiusx × radiusy × radiusz). As a first-line therapy, animals with tumors between 20–70 mm3 received 40 mg per kg bodyweight sunitinib (dissolved in 0.5% carboxymethyl cellulose, 5% dextrose) every day via oral gavage. If tumors did not respond or developed resistance to sunitinib, therapy was switched to daily 10 mg per kg bodyweight everolimus (dissolved in 5% polyethylene glycol (PEG), 5% Tween80, 5% ethanol (EtOH) and 85% D5W) via oral gavage. As a third-line therapy, 2 mg per kg bodyweight acriflavine dissolved in PBS was injected intraperitoneally every day.

RNA sequencing.

RNA isolated from six ccRCCs from VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice (two tumors were from the same mouse) and from renal cortex samples from three non-transgenic Vhlfl/flTrp53fl/flRb1fl/fl littermate mice was subjected to RNA sequencing by Otogenetics Corporation (USA). Starting from the raw .fastq files (2 × 150 bp), the reads were mapped against the mouse reference genome GRCm38 (with Ensembl annotation GRCm38.p4, release 84) using the STAR aligner (version 2.4.2a)46. Standard quality control (Picard and RSeQC)47 was run to assess the quality of the resulting alignments. Subsequently, strand-specific (reverse-stranded) read counting was performed using featureCount (subread, version 1.5.0)48, yielding count tables for 47,729 transcripts per sample. From 1.9–3.8 × 107 mapped reads were obtained per sample. To avoid technical artifacts, the data were filtered such that genes with an average over all samples that was less than 5 counts were removed. This resulted in 29,111 transcripts being removed out of 47,729 total transcripts, leaving 18,618 transcripts for further analysis. Differential expression was modeled using generalized linear regression of the negative binomial family, as implemented in the R package DESeq2 (ref. 49). TCGA RNASeqV2 mRNA data for human ccRCCs and normal kidneys were downloaded using the TCGA2STAT package. Limma/voom50 was used to identify differentially expressed genes in this data set. Comparisons were made using only mouse and human genes with one-to-one orthology. Similarity to normal nephron segment expression profiles was assessed following the described procedure of Davis et al.19. Briefly, the Pearson correlation coefficients between each mouse tumor sample and the nephron-segment-specific reference data sets were calculated over all common genes23. Independently, analysis of competitive enrichment of nephron-segment-specific gene sets was performed using Fisher's exact test. The negative logarithmic P value can serve as a similarity score and gave results analogous to the Pearson coefficient. Global similarities in gene expression between mouse and human ccRCC were determined using Pearson correlation analysis of the normalized log-transformed gene expression values for all unique orthologous gene pairs in human (RNA sequencing data from TCGA studies) and mouse (this study) ccRCCs.

Exome sequencing.

Genomic DNA isolated from seven ccRCCs from VhlΔ/ΔTrp53Δ/ΔRb1Δ/Δ mice (two tumors were from the same mouse), from six normal liver samples from the tumor-bearing mice and from renal cortex samples from three wild-type mice was subjected to exome sequencing by Otogenetics Corporation (USA) using Agilent's SureSelect Mouse All Exon V1 kit. The raw fastq files were mapped against the mouse reference (GRCm38/mm10) using BWA-MEM ( Subsequently, the Picard tool MarkDuplicates and the GATK IndelRealigner51 were used to improve the final alignments. The Picard tools AlignmentSummaryMetrics and HsMetrics were used to perform quality control on the raw sequencing data and the alignments. Somatic SNVs were called for each tumor–normal combination using two tools: mutect and strelka. Small somatic indels were called using strelka. Merging and filtering of variant calls was performed using GATK51, and only variants with variant allele frequencies that were greater than 5% were included in the analyses. SnpEff52, SnpSift53 and BEDTools54 were used for annotations. EXCAVATOR55 was used to call copy-number variations with a 95% probability cutoff in tumors against the matched normal liver samples. Lists of copy-number variants were manually filtered to remove calls in which multiple tumors showed the exact start and end points of the gain or loss, as these are highly likely to represent artifacts of the calling algorithm. Result files were annotated with mouse and human genes using BEDTools54. We used the non-mouse refgenes track (xenoRefGene) from the UCSC Genome Browser56 ( to map human genes to the mouse genome. Gene set enrichment analysis was conducted using the online GSEA software (

Survival analyses.

Human ccRCC Kaplan–Meier survival analyses were conducted using the online tools of the cBioPortal for Cancer Genomics (


All sample size (n) values used for statistical analyses are provided in the relevant figures and supplementary figures. Differences in tumor onset between different mouse genotypes and sexes were assessed using the log-rank Mantel–Cox test, and differences in tumor numbers between mice were assessed using Student's one-tailed unpaired t-test. Pearson correlation analyses were used to compare global mouse and human ccRCC mRNA expression as well as to compare mouse ccRCC mRNA expression with nephron-segment-specific mRNA expression profiles.

Data availability.

Exome sequencing or RNA sequencing data sets (beyond the summaries that are deposited as supplementary information) are available from the corresponding author upon reasonable request.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1

    Hsieh, J.J. et al. Renal cell carcinoma. Nat Rev Dis Primers 3, 17009 (2017).

    PubMed  PubMed Central  Google Scholar 

  2. 2

    Ghatalia, P. et al. Checkpoint inhibitors for the treatment of renal cell carcinoma. Curr. Treat. Options Oncol. 18, 7 (2017).

    Article  PubMed  Google Scholar 

  3. 3

    Choueiri, T.K. & Motzer, R.J. Systemic therapy for metastatic renal-cell carcinoma. N. Engl. J. Med. 376, 354–366 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. 4

    Moore, L.E. et al. Von Hippel–Lindau (VHL) inactivation in sporadic clear cell renal cancer: associations with germline VHL polymorphisms and etiologic risk factors. PLoS Genet. 7, e1002312 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Nickerson, M.L. et al. Improved identification of von Hippel–Lindau gene alterations in clear cell renal tumors. Clin. Cancer Res. 14, 4726–4734 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Sato, Y. et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat. Genet. 45, 860–867 (2013).

    Article  CAS  Google Scholar 

  7. 7

    Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Mandriota, S.J. et al. HIF activation identifies early lesions in VHL kidneys: evidence for site-specific tumor suppressor function in the nephron. Cancer Cell 1, 459–468 (2002).

    Article  CAS  PubMed  Google Scholar 

  9. 9

    Frew, I.J. & Moch, H. A clearer view of the molecular complexity of clear cell renal cell carcinoma. Annu. Rev. Pathol. 10, 263–289 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. 10

    Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

  11. 11

    Frew, I.J. et al. pVHL and PTEN tumour suppressor proteins cooperatively suppress kidney cyst formation. EMBO J. 27, 1747–1757 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Lehmann, H., Vicari, D., Wild, P.J. & Frew, I.J. Combined deletion of Vhl and Kif3a accelerates renal cyst formation. J. Am. Soc. Nephrol. 26, 2778–2788 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Albers, J. et al. Combined mutation of Vhl and Trp53 causes renal cysts and tumours in mice. EMBO Mol. Med. 5, 949–964 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Wang, S.-S. et al. Bap1 is essential for kidney function and cooperates with Vhl in renal tumorigenesis. Proc. Natl. Acad. Sci. USA 111, 16538–16543 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. 15

    Nargund, A.M. et al. The SWI/SNF protein PBRM1 restrains VHL-loss-driven clear cell renal cell carcinoma. Cell Rep. 18, 2893–2906 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18

    Patel, V. et al. Acute kidney injury and aberrant planar cell polarity induce cyst formation in mice lacking renal cilia. Hum. Mol. Genet. 17, 1578–1590 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Davis, C.F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Ricketts, C.J. & Linehan, W.M. Gender specific mutation incidence and survival associations in clear cell renal cell carcinoma (CCRCC). PLoS One 10, e0140257 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Gordan, J.D. et al. HIF-α effects on c-Myc distinguish two subtypes of sporadic VHL-deficient clear cell renal carcinoma. Cancer Cell 14, 435–446 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Schönenberger, D. et al. Formation of renal cysts and tumors in Vhl/Trp53-deficient mice requires HIF1α and HIF2α. Cancer Res. 76, 2025–2036 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. 23

    Cheval, L., Pierrat, F., Rajerison, R., Piquemal, D. & Doucet, A. Of mice and men: divergence of gene expression patterns in kidney. PLoS One 7, e46876 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Klatte, T. et al. Gain of chromosome 8q is associated with metastases and poor survival of patients with clear cell renal cell carcinoma. Cancer 118, 5777–5782 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. 25

    Beroukhim, R. et al. Patterns of gene expression and copy-number alterations in von-Hippel Lindau disease-associated and sporadic clear cell carcinoma of the kidney. Cancer Res. 69, 4674–4681 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Westcott, P.M.K. et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517, 489–492 (2015).

    Article  CAS  Google Scholar 

  27. 27

    Guinot, A., Lehmann, H., Wild, P.J. & Frew, I.J. Combined deletion of Vhl, Trp53 and Kif3a causes cystic and neoplastic renal lesions. J. Pathol. 239, 365–373 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. 28

    Schraml, P. et al. Sporadic clear cell renal cell carcinoma but not the papillary type is characterized by severely reduced frequency of primary cilia. Mod. Pathol. 22, 31–36 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. 29

    Lee, K. et al. Acriflavine inhibits HIF-1 dimerization, tumor growth, and vascularization. Proc. Natl. Acad. Sci. USA 106, 17910–17915 (2009).

    Article  PubMed  Google Scholar 

  30. 30

    Shay, J.E.S. et al. Inhibition of hypoxia-inducible factors limits tumor progression in a mouse model of colorectal cancer. Carcinogenesis 35, 1067–1077 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Pan, J., Seeger-Nukpezah, T. & Golemis, E.A. The role of the cilium in normal and abnormal cell cycles: emphasis on renal cystic pathologies. Cell. Mol. Life Sci. 70, 1849–1874 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. 32

    Thoma, C.R. et al. pVHL and GSK3β are components of a primary cilium–maintenance signalling network. Nat. Cell Biol. 9, 588–595 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. 33

    Gordan, J.D., Thompson, C.B. & Simon, M.C. HIF and c-Myc: sibling rivals for control of cancer cell metabolism and proliferation. Cancer Cell 12, 108–113 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Hase, H. et al. LOXL2 status correlates with tumor stage and regulates integrin levels to promote tumor progression in ccRCC. Mol. Cancer Res. 12, 1807–1817 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. 35

    Nishikawa, R. et al. Tumour-suppressive microRNA-29s directly regulate LOXL2 expression and inhibit cancer cell migration and invasion in renal cell carcinoma. FEBS Lett. 589, 2136–2145 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. 36

    Kurozumi, A. et al. Regulation of the collagen cross-linking enzymes LOXL2 and PLOD2 by tumor-suppressive microRNA-26a/b in renal cell carcinoma. Int. J. Oncol. 48, 1837–1846 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Cho, H. et al. On-target efficacy of a HIF-2α antagonist in preclinical kidney cancer models. Nature 539, 107–111 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Chen, W. et al. Targeting renal cell carcinoma with a HIF-2 antagonist. Nature 539, 112–117 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Motzer, R.J. et al. Nivolumab versus Everolimus in advanced renal-cell carcinoma. N. Engl. J. Med. 373, 1803–1813 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Marino, S., Vooijs, M., van Der Gulden, H., Jonkers, J. & Berns, A. Induction of medulloblastomas in p53-null mutant mice by somatic inactivation of Rb in the external granular layer cells of the cerebellum. Genes Dev. 14, 994–1004 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Haase, V.H., Glickman, J.N., Socolovsky, M. & Jaenisch, R. Vascular tumors in livers with targeted inactivation of the von Hippel–Lindau tumor suppressor. Proc. Natl. Acad. Sci. USA 98, 1583–1588 (2001).

    Article  CAS  PubMed  Google Scholar 

  42. 42

    Jonkers, J. et al. Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer. Nat. Genet. 29, 418–425 (2001).

    Article  CAS  Google Scholar 

  43. 43

    Wagner, C.A. et al. Mouse model of type II Bartter's syndrome. II. Altered expression of renal sodium- and water-transporting proteins. Am. J. Physiol. Renal Physiol. 294, F1373–F1380 (2008).

    Article  CAS  PubMed  Google Scholar 

  44. 44

    Pollard, P.J. et al. Targeted inactivation of Fh1 causes proliferative renal cyst development and activation of the hypoxia pathway. Cancer Cell 11, 311–319 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. 45

    Custer, M., Lötscher, M., Biber, J., Murer, H. & Kaissling, B. Expression of Na–Pi cotransport in rat kidney: localization by RT–PCR and immunohistochemistry. Am. J. Physiol. 266, F767–F774 (1994).

    CAS  PubMed  Google Scholar 

  46. 46

    Dobin, A. et al. STAR: ultrafast universal RNA–seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA–seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. 48

    Liao, Y., Smyth, G.K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  Google Scholar 

  49. 49

    Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA–seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. 50

    Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  53. 53

    Cingolani, P. et al. Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift. Front. Genet. 3, 35 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54

    Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Magi, A. et al. EXCAVATOR: detecting copy number variants from whole-exome sequencing data. Genome Biol. 14, R120 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  56. 56

    Rosenbloom, K.R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).

    Article  CAS  PubMed  Google Scholar 

Download references


This work was supported by grants to I.J.F. from the Swiss National Science Foundation (PP00P3_128257), the European Research Council (260316) and the VHL Family Alliance. We are most grateful to Johannes Loffing (University of Zurich), Jürg Biber (University of Zurich) and the late Patrick Pollard (University of Oxford) for providing antibodies.

Author information




I.J.F. and S.H. designed the study; S.H., D.S., A.C. and L.B. conducted experiments; N.C.T., M.P., I.J.F. and S.H. conducted bioinformatic analyses; P.J.W. and H.M. conducted pathological analyses; I.J.F. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Ian J Frew.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9. (PDF 18589 kb)

Supplementary Table 1

RNA sequencing analysis of normal kidney cortices and mouse ccRCCs. (XLSX 4548 kb)

Supplementary Table 2

Copy number variations in mouse ccRCCs. (XLSX 68 kb)

Supplementary Table 3

Collation of high impact mutations and high VAF SNVs in mouse ccRCC tumours. (XLSX 68 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Harlander, S., Schönenberger, D., Toussaint, N. et al. Combined mutation in Vhl, Trp53 and Rb1 causes clear cell renal cell carcinoma in mice. Nat Med 23, 869–877 (2017).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing