Kidney cancer is among the top ten cancers, both in incidence and mortality in both men and women. Inactivation of the VHL tumor suppressor gene is a causal event in the pathogenesis of clear cell Renal Cell Carcinoma (ccRCC), the most frequent subtype of kidney cancer. Approximately 70 – 80% of ccRCC are sporadic tumors that harbor biallelic inactivation of VHL (Linehan et al., 2004). In the rare disease of hereditary kidney cancer, germline VHL mutation leads to early-onset bilateral kidney tumors. Biochemically, the protein product of the VHL tumor suppressor gene pVHL acts as the substrate recognition module of an E3 ubiquitin ligase complex. This complex targets the α subunits of the heterodimeric transcription factor Hypoxia-Inducible Factor (HIF) for poly-ubiquitylation and proteasomal degradation (Zhang and Yang, 2012). When HIFα is hydroxylated on either of two prolyl residues by members of the EglN family (also called PHDs or HPHs) under normal oxygen tension, it is recognized by pVHL. Without pVHL, HIFα protein accumulates and activates the hypoxia response transcriptional program. This constitutively active HIF subsequently drives ccRCC tumorigenesis and tumor growth (Kaelin, 2005). Interestingly, HIF targets include both tumor-promoting and tumor-suppressive genes, but its overall activity is potently oncogenic (Zhang et al., 2013).

Restoration of pVHL in VHL-/- ccRCC cells suppresses their ability to form tumors in immune-compromised mice, while stabilization of HIF2α overrides the effect of pVHL (Kondo et al., 2002). Conversely, HIF2α suppression in VHL-/- ccRCC cells cripples their ability to form tumors (Zimmer et al., 2004). The current standard of care for metastatic ccRCC focuses on inhibiting the VEGF receptor, yet drug resistance eventually develops in most cases. Thus it is urgent to identify new drug targets that operate in a high percentage of RCC tumors, with the expectation that the dual actions on VEGFR and a new target might prevent drug resistance.

Most types of solid tumors harbor multiple, sometimes dozens of mutations, in cancer genes to establish the hallmarks of cancer (Hanahan and Weinberg, 2011; Vogelstein et al., 2013). In addition to VHL, several frequently mutated genes were identified in sporadic ccRCC. Varela et al. reported that 41% of ccRCC tumors had inactivating mutations in the PBRM1 gene. PBRM1 is a specificity subunit of the SWI/SNF chromatin-remodeling complex (Varela et al., 2011). The high mutation rate of PBRM1 in ccRCC has been confirmed by multiple studies, together with mutations in other genes such as BAP1, SETD2, KDM5C/JARID1C, PTEN and UTX (Dalgliesh et al., 2010; Guo et al., 2012; Peña-Llopis et al., 2012; Cancer Genome Atlas Research Network, 2013; Sato et al., 2013). However, the mutation rates of the other genes are much lower than that of PBRM1 (Liao et al., 2015).

Multiple lines of evidence suggest that PBRM1 is a key tumor suppressor. Its mutations are predominantly inactivating in both alleles. PBRM1 suppression causes changes in pathways regulating chromosome instability and cell proliferation (Varela et al., 2011). Like VHL mutations, many PBRM1 mutations occur early in tumorigenesis, unlike the other secondary mutations (Gerlinger et al., 2012). Recently a PBRM1 germline mutation was reported to predispose patients to ccRCC (Benusiglio et al., 2015). PBRM1 was also found to amplify a HIF signature (Gao et al., 2017) and genetic ablation of both Vhl and Pbrm1 in mouse kidneys leads to ccRCC while single loss fails to do so (Nargund et al., 2017; Gu et al., 2017).

KDM5C/JARID1C is a histone demethylase that removes methyl groups from tri-methylated lysine four on histone H3 (H3K4me3). H3K4me3 is a histone mark that is tightly linked to actively transcribed genes (Barski et al., 2007). KDM5C mutations occur in 3–7% of ccRCC tumors (Varela et al., 2011; Dalgliesh et al., 2010; Cancer Genome Atlas Research Network, 2013; Sato et al., 2013). Its mutations are mostly subclonal and happen later during tumor development (Gerlinger et al., 2012; Gerlinger et al., 2014). HIF increases KDM5C levels and activity, and the overall level of H3K4me3 is elevated when KDM5C is suppressed in VHL-defective kidney cancer cells (Niu et al., 2012). In addition, evidence suggests that HIF-induced KDM5C is tumor-suppressive, and this constitutes a negative feedback loop in regard to tumor growth (Niu et al., 2012).

SETD2 is a histone-modifying enzyme that tri-methylates histone H3 at Lysine 36 (H3K36me3). It is mutated in 10 – 15% of ccRCC tumors (Dalgliesh et al., 2010; Cancer Genome Atlas Research Network, 2013; Sato et al., 2013; Hakimi et al., 2013). It is also located on chromosome 3 p like VHL and PBRM1. SETD2 mutations are subclonalin ccRCC tumors (Gerlinger et al., 2012; Gerlinger et al., 2014; Sankin et al., 2014), and are associated with worse patient survival (Hakimi et al., 2013). SETD2 deficiency was reported to be associated with alternative splicing and transcriptional repression (Wagner and Carpenter, 2012). Indeed, SETD2 mutations in ccRCC tumors are associated with changes in chromatin accessibility and DNA methylation (Buck et al., 2014) or widespread RNA processing defects (Simon et al., 2014). Recently, SETD2 was shown to regulate interferon signaling by methylating STAT1 (Chen et al., 2017), and to maintain mitosis and cytokinesis through methylating α-tubulin (Park et al., 2016). The relevance of these SETD2 functions to tumor suppression in ccRCC remains to be elucidated.

Like KDM5C and SETD2, BAP1 is also a histone modifying enzyme. BAP1 is inactivated in 10 – 15% of ccRCC (Guo et al., 2012; Peña-Llopis et al., 2012). The BAP1 gene is located on chromosome 3 at band 3p21, which is very close to the PBRM1 gene. As with VHL, PBRM1 and SETD2, biallelic inactivation of BAP1 via mutation and loss of heterozygosity fit a two-hit tumor suppressor profile. Germline mutations of BAP1 predispose patients to ccRCC, uveal melanomas, mesothelioma, lung adenocarcinoma, meningioma, breast carcinoma and paraganglioma (Abdel-Rahman et al., 2011; Wadt et al., 2012; Cheung et al., 2013; Pilarski et al., 2014). Loss of BAP1 is associated with worse patient survival in ccRCC and seems to be mutually exclusive withPBRM1 mutations (Joseph et al., 2014; Joseph et al., 2016). BAP1 protein was reported to form a complex with host cell factor-1 (HCF-1) and promotes DNA double-strand break repair (Yu et al., 2010; Yu et al., 2014). It also forms a complex with ASXL1 which is essential for its ability to deubiquitinate histone H2A on lysine 119, and this function regulates cell proliferation (Daou et al., 2015). Recently it was shown to regulate IP3R3-mediated Ca²⁺ to mitochondria to suppress cell transformation (Bononi et al., 2017). How these functions contribute to its tumor suppressor role in ccRCC is unclear.

These secondary tumor suppressors each have their own distinct biological functions and are associated with different prognoses for the patients, and yet they all regulate chromatin biology. Thus, we investigated whether they share a common tumor suppressor pathway in this study.

Although the overall activity of HIF is strongly oncogenic, our previous results indicate that it also triggers a significant anti-tumor response. For instance, KDM5C is induced by HIF, and its demethylase activity against global H4K4me3 mark is dependent on HIF in VHL-/-ccRCC cells, and yet it clearly plays an anti-tumor role in ccRCC (Niu et al., 2012). In addition, some other HIF downstream targets, such as VEGF or Cyclin D1, are strongly pro-tumor growth, whereas other HIF downstream targets such as GLUT1 or IGFBP3, are obviously tumor suppressive (Zhang et al., 2013). Consistent with this, Vhl conditional knockout in mouse kidneys alone is not sufficient to generate ccRCC despite fully activated HIF. Only after combination with Pbrm1 or Bap1 conditional knockout can Vhl loss lead to kidney tumors (Nargund et al., 2017; Gu et al., 2017). Thus HIF could activate ISGF3, which constitutes a negative feedback loop in ccRCC. Its inhibitory effect on tumor growth can be relieved by the loss of PBRM1, KDM5C, SETD2 or BAP1 to promote robust tumor growth (Figure 9E).

ISGF3 is traditionally activated by interferon stimulation through phosphorylation of STAT1 and STAT2 (Borden et al., 2007). However, days after the initial activation, the posttranslational modifications (PTMs) disappear, yet ISGF3 can still maintain its activity as unphosphorylated ISGF3 (U-ISGF3) with greatly enhanced protein levels (Cheon et al., 2013). U-ISGF3 activates fewer targets than phosphorylated ISGF3, but it still potently antagonizes viral infection and DNA damage. In oncology, ISGF3 is known to be a predictive biomarker for chemotherapy and radiation in breast cancer (Weichselbaum et al., 2008), and it is also associated with drug-resistance to immune checkpoint blockade (Benci et al., 2016). Recently, immune checkpoint inhibitors, specifically those that target Programmed Death 1 (PD-1), Programmed Death Ligand 1 (PD-L1), and Cytotoxic T-Lymphocyte Associated 4 (CTLA-4) have gained traction in metastatic RCC (mRCC). The FDA has granted priority review for ipilimumab/nivolumab in intermediate and poor-risk mRCC (Abernathy, 2017), with RCC experts declaring this combination the new standard of care in the treatment of mRCC (Chustecka, 2017; Rini, 2017). Loss-of-function mutations in PBRM1 were discovered to confer clinical benefit to ccRCC patients treated with anti-PD-1 alone or in combination with anti-CTLA-4 therapies (Miao et al., 2018). In addition, PBRM1 mutations were found to enhance T-cell-mediated killing in an unbiased, high-throughput screen in a melanoma model (Pan et al., 2018). As PBRM1 and other ccRCC tumor suppressor genes (TSGs) converge on ISGF3, it will be very interesting to examine whether ISGF3 status is a predictive biomarker for response to immunotherapy. The fact that PBRM1 mutations, but not those of BAP1, SETD2, and KDM5C, are a predictive biomarker for response to immunotherapy is likely because PBRM1 and BAP1 mutations are truncal (root change) in many cases while the mutations of the other two are subclonal (Gerlinger et al., 2012; Gerlinger et al., 2014). BAP1 mutations might also elicit an additional change that cancels their immunotherapy-sensitizing effect.

ccRCC is known to harbor rampant intra-tumoral heterogeneity (Gerlinger et al., 2012; Gerlinger et al., 2014; Jiang et al., 2016; Jiang et al., 2017). Inactivating mutations in PBRM1, SETD2, KDM5C and BAP1 tend to be focal in the tumors, so therapies that are designed to exploit the weakness induced by mutation of an individual gene could be problematic since it cannot treat the remaining cancer cells free of that mutation. Since the suppression of PBRM1, KDM5C, SETD2 or BAP1 all lead to the reduction of ISGF3, a potent tumor suppressor, it is possible that any effective therapeutic intervention that restores ISGF3 function could benefit ccRCC patients with a wide variety of mutations.

Pbrm1 loss in mice and PBRM1-/- vs PBRM1 re-expressed human A704 cells showed that PBRM1 deficiency was linked to the activation of the JAK/STAT3 pathway (Nargund et al., 2017; Miao et al., 2018). STAT3 and ISGF3 are two different, often times opposing, pathways: ISGF3 is activated by type I IFN (Cheon et al., 2013), while STAT3 is activated by the IL6 family, oncogenes, and growth factors (Heinrich et al., 1998). They have very different gene targets. STAT3 is often considered an oncogene (Yuan et al., 2016), while STAT1 is usually considered a TSG (Stephanou and Latchman, 2003), and thus they appear to play opposing biological roles in tumorigenesis (Avalle et al., 2012). In human T lymphoma cells or mouse cells, loss of STAT3 leads to prolonged activation of STAT1 (Regis et al., 2008; Costa-Pereira et al., 2002; Maritano et al., 2004). In addition, STAT3 negatively regulates the type I IFN signaling pathway by inhibiting expression of IRF7, IRF9, STAT1 in diffuse large B cell lymphoma (Lu et al., 2018). Thus STAT3 and ISGF3 are distinct and activated STAT3 might suppress ISGF3 in ccRCC cells, and this deserves further investigation.

In a recent publication, SETD2 was shown to methylate STAT1 to promote ISGF3 function (Chen et al., 2017). However, this requirement of SETD2 is not observed unless IFN is added to activate ISGF3. In our system, no exogenous IFN was present, and HIF activation seemed to be sufficient to increase the protein levels of STAT2 and IRF9, thus it might activate U-ISGF3 (Figure 5—figure supplement 1). In 786-O cells with or without PBRM1 or KDM5C suppression, the mRNA levels of STAT1, STAT2 and IRF9 mirror that of the proteins (data not shown), suggesting that the regulation of U-ISFG3 occurred at the transcriptional level. Interestingly, BAP1 loss reduced the protein levels of STAT2 and IRF9 independent of PBRM1 status, since RCC4 cells express no endogenous PBRM1 (Figure 6C).

Why ISGF3 is a potent tumor suppressor in ccRCC tumors is currently not well understood. One possibility is that interferon stimulated genes (ISG) have anti-proliferative and pro-apoptotic function in ccRCC cancer cells. ISGs activated by IFN do have such activity to curb the spread of virus in the infected cells (Borden et al., 2007), and the U-ISGF3 was thought to be weaker in this regard with a much smaller target pool (Cheon et al., 2013). However, the U-ISGF3 may still possess sufficient anti-proliferative/pro-apoptotic activity to impair the fitness of the cancer cells. Our current evidence suggests that this does not apply in our model. Alternatively, U-ISGF3 may change how the cancer cells interact with the immune system. The nude mice used in our study do not have adaptive immune system but the innate immune response remains intact. The heightened U-ISGF3 may attract more innate immune cells such as macrophages and NK cells to attack the tumors and curb tumor growth, as a previous report showed that over-expression of IFN-β recruited macrophages which suppressed tumor growth and metastasis in nude mice (Zhang et al., 2002). However, further studies to closely monitor the cancer and host cells are needed to provide a definitive answer to this question. It will be important to identify the mechanism of how ISGF3 curbs tumor growth in ccRCC with human tumor samples and immune-competent mouse models so we can exploit this pathway to treat ccRCC patients.

With our current preliminary data we think that PBRM1 and KDM5C regulate ISGF3 subunits at transcriptional level, while SETD2 and BAP1 regulate ISGF3 at post-translational modification level. They might use quite different mechanisms to achieve the same goal: maintaining the ISGF3 activity, so loss of any of them could promote tumor development. Further investigation into these mechanisms is needed.

It is highly surprising that so many major TSGs in ccRCC share the same target in ISGF3. Undoubtedly each TSG has its own unique tumor suppressing functions, as each carries different prognoses for the patients. KDM5C mutations were reported to occur in the cells with PBRM1 mutations (Hsieh et al., 2017). It is possible that their unique tumor suppressor functions are synergistic so their mutations are selected in the same cells during tumor development. However, the fact that they all converge on ISGF3 warrants further investigation into how this pathway impacts drug response, patient survival, animal modeling and drug development.

Microarray data have been deposited inn GEO under the accession code GSE108229.

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Author details

1. Lili Liao

   1. Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States
   2. Department of Pathology, Yale University, Connecticut, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Zongzhi Z Liu, Lauren Langbein, Weijia Cai and Eun-Ah Cho

   Competing interests

   No competing interests declared

2. Zongzhi Z Liu

   Department of Pathology, Yale University, Connecticut, United States

   Contribution

   Validation, Investigation, Writing—review and editing

   Contributed equally with

   Lili Liao, Lauren Langbein, Weijia Cai and Eun-Ah Cho

   Competing interests

   No competing interests declared

3. Lauren Langbein

   Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States

   Contribution

   Data curation, Formal analysis, Visualization

   Contributed equally with

   Lili Liao, Zongzhi Z Liu, Weijia Cai and Eun-Ah Cho

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3007-5287

4. Weijia Cai

   Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States

   Contribution

   Validation, Investigation, Writing—review and editing

   Contributed equally with

   Lili Liao, Zongzhi Z Liu, Lauren Langbein and Eun-Ah Cho

   Competing interests

   No competing interests declared

5. Eun-Ah Cho

   1. Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States
   2. Fox Chase Cancer Center, Pennsylvania, United States

   Contribution

   Validation, Investigation

   Contributed equally with

   Lili Liao, Zongzhi Z Liu, Lauren Langbein and Weijia Cai

   Competing interests

   No competing interests declared

6. Jie Na

   Department of Health Sciences Research, Mayo Clinic, Minnesota, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

7. Xiaohua Niu

   Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

8. Wei Jiang

   Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

9. Zhijiu Zhong

   Sidney Kimmel Cancer Center, Thomas Jefferson University, Pennsylvania, United States

   Contribution

   Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

10. Wesley L Cai

    Department of Pathology, Yale University, Connecticut, United States

    Contribution

    Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

11. Geetha Jagannathan

    Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States

    Contribution

    Formal analysis, Writing—review and editing

    Competing interests

    No competing interests declared

12. Essel Dulaimi

    Fox Chase Cancer Center, Pennsylvania, United States

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

13. Joseph R Testa

    Fox Chase Cancer Center, Pennsylvania, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

14. Robert G Uzzo

    Fox Chase Cancer Center, Pennsylvania, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

15. Yuxin Wang

    Department of Cancer Biology, Lerner Research Institute, Cleveland Clinic, Ohio, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

16. George R Stark

    Department of Cancer Biology, Lerner Research Institute, Cleveland Clinic, Ohio, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

17. Jianxin Sun

    Department of Medicine, Thomas Jefferson University, Pennsylvania, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

18. Stephen Peiper

    Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States

    Contribution

    Resources, Writing—review and editing

    Competing interests

    No competing interests declared

19. Yaomin Xu

    1. Department of Biostatistics, Vanderbilt University Medical Center, Tennessee, United States
    2. Department of Biomedical Informatics, Vanderbilt University Medical Center, Tennessee, United States

    Contribution

    Resources, Writing—review and editing

    For correspondence

    yaomin.xu@vanderbilt.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3752-4006

20. Qin Yan

    Department of Pathology, Yale University, Connecticut, United States

    Contribution

    Data curation, Formal analysis, Supervision, Methodology, Writing—review and editing, Funding Acquisition

    For correspondence

    qin.yan@yale.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4077-453X

21. Haifeng Yang

    Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Pennsylvania, United States

    Contribution

    Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Methodology, Writing—review and editing

    For correspondence

    Haifeng.yang@jefferson.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0892-9055

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