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STAT3 Polymorphism Predicts Interferon-Alfa Response in Patients With Metastatic Renal Cell Carcinoma

Noriyuki Ito, Masatoshi Eto, Eijiro Nakamura, Atsushi Takahashi, Taiji Tsukamoto, Hiroshi Toma, Hayakazu Nakazawa, Yoshihiko Hirao, Hirotsugu Uemura, Susumu Kagawa, Hiroomi Kanayama, Yoshiaki Nose, Naoko Kinukawa, Tsuyoshi Nakamura, Nobuyoshi Jinnai, Toyokazu Seki, Masanobu Takamatsu, Yoshihiro Masui, Seiji Naito, Osamu Ogawa

From the Department of Urology, Graduate School of Medicine, Kyoto University, Kyoto Department of Urology, Graduate School of Medical Sciences, Kyushu University; Department of Medical Informatics, Graduate School of Medical Sciences, Kyushu University, Fukuoka; Department of Urologic Surgery and Andrology, Sapporo Medical University School of Medicine, Hokkaido; Department of Urology, Tokyo Women's Medical University; Tokyo Women's Medical University Medical Center East; Therapeutic Application Development Department, Gastroenterology/Oncology Group, Otsuka Pharmaceutical Co Ltd, Tokyo; Department of Urology, Nara Medical University, Nara; Department of Urology, Kinki University School of Medicine, Osaka; National University Corporation Tokushima University Hospita; Department of Urology, Faculty of Medicine, the Tokushima University; Theranostics Research Center, Otsuka Pharmaceutical Co Ltd, Tokushima; and the Faculty of Environment Studies, Nagasaki University, Nagasaki, Japan

Address reprint requests to Osamu Ogawa, MD, Department of Urology, Graduate School of Medicine, Kyoto University, 54, Shogoin, Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan; e-mail: ogawao{at}kuhp.kyoto-u.ac.jp

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Purpose To clarify the effect of genetic polymorphisms on the response to interferon alfa (IFN-) for metastatic renal cell carcinoma (MRCC), and to find a reliable molecular marker to select those patients with MRCC who would benefit from IFN- immunotherapy.

Patients and Methods We carried out an association study in which 463 single nucleotide polymorphisms (SNPs) in 33 candidate genes were genotyped in 75 Japanese patients who had received IFN- for MRCC.

Results After adjusting for lung metastasis, stepwise logistic regression analysis revealed that the SNPs in signal transducer and activator 3 (STAT3) were most significantly associated with better response to IFN-. Linkage disequilibrium mapping revealed that the SNP in the 5' region of STAT3, rs4796793, was the most significant predictor of IFN- response (odds ratio [OR] = 2.73; 95% CI, 1.38 to 5.78). The highest OR was shown in the CC genotype at rs4796793 compared to the GG + GC genotypes (OR = 8.38, 95% CI, 1.63 to 42.96). Genotype-dependent expressions of STAT3 in B lymphocyte cell lines and the enhanced growth inhibitory effects of IFN- by STAT3 suppression in an RCC cell line supported the results of the present association study.

Conclusion The present study suggested that the STAT3 polymorphism is a useful diagnostic marker to predict the response to IFN- therapy in patients with MRCC. An efficient response marker for IFN- needs to be utilized to establish individual optimal treatment strategies, even when newer drug therapies are used as first line treatments for MRCC.

	INTRODUCTION

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Renal cell carcinoma (RCC) accounts for approximately 3% of all malignancies. Its age-adjusted incidence rate per 100,000 individuals was 12.4 in the United States in 2002¹; in Japan in 1997, the rate was 4.9 and 1.8 in the male and female populations, respectively.² Conventional chemotherapy is not effective in patients with metastatic RCC (MRCC); therefore, immunotherapy with interferon alfa (IFN-) and/or interleukin-2 (IL-2) has been employed to improve survival. However, the response rate of MRCC to IFN- therapy is 5% to 20%, and median survival is reported to be approximately 67.6 weeks.³ Considering the low response rate and substantial adverse effects associated with IFN- therapy, identification of reliable predictive markers for response to IFN- is essential for establishing optimal treatment strategies for patients with MRCC.

The human genome sequence database and high throughput methods of single nucleotide polymorphism (SNP) typing have made it possible to quickly and easily analyze a large number of genomic polymorphisms. To identify a genetic marker to predict response to IFN- therapy, we performed an association study of 463 SNPs on 33 candidate genes, and found that SNPs in the 5'-flanking region of signal transducer and activator 3 (STAT3) had the most significant association with responsiveness. We also showed that STAT3 expression is reduced in the genotype responding to IFN-, and suppression of STAT3 by siRNA enhanced the inhibitory effect of IFN- on cell growth in vitro.

	PATIENTS AND METHODS

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Case-Control Association Study
This study was approved by the ethics committees of the all clinical sites and Otsuka Pharmaceutical (Tokushima, Japan). All patients gave written informed consent. Patients were classified as complete response (CR), partial response (PR), no change, or progressive disease, on the basis of the percentage reduction of the tumor mass in response to three dosages of 3 to 6 million U per week of IFN- treatment.⁴ CR and PR with a sustained response for 4 weeks were pooled into the responders group and the others into the nonresponders group. To determine the sample size, it was assumed that the proportion of responders among patients with a minor allele (a) of a specific SNP was 50%, whereas that with a major allele (A) was 12.5%. The proportion of type a in patients was assumed to be 20% and that of type A was 80%. These assumptions led to sample sizes of 29 being required for both case and control groups if the power of the test for the association of the SNPs with the end point was to be 80%.

Polymorphism Genotyping
Genomic DNA was extracted from whole blood of RCC patients using PUREGENE Blood DNA Isolation Kit (Gentra Systems, Minneapolis, MN). The SNP genotyping was based on the invader assay (Third Wave Technologies, Madison, WI) combined with a polymerase chain reaction (PCR).⁵ Genotyping procedures by invader assay were performed in 384-well plates using an automated robotic SNP typing system constructed with SAMI/Biomek FX robotics (Beckman Coulter, Fullerton, CA). The SNPs that failed to be genotyped by the invader assay were retyped using PCR-restriction fragment-length polymorphism analysis.

Statistical Analysis
Association between response to IFN- and genetic polymorphism were analyzed using Pearson's ² test for the contingency table. Here, P .10 was regarded as significant, instead of .05, as the power of the test was likely to decrease considerably, because of patient heterogeneity.⁶ All P values were two sided. In the multivariate analysis, P .05 was considered significant. Stepwise logistic regression analysis was performed, to evaluate the predictive significance of the SNPs after adjusting for clinical factors, until nonconvergence in the iteration algorithm for the maximum likelihood estimate was observed. This phenomenon indicated that it was highly correlated with those already in the model,⁷ and thus should not be included. Statistical analyses were performed with SAS 8.2 (SAS Institute, Cary, NC).

Linkage Disequilibrium Analysis
Pairwise linkage disequilibrium (LD) was estimated using SNPAlyze v3.2 software (DYNACOM, Chiba, Japan) as D = x₁₁-p₁q₁, where x₁₁ is the frequency of haplotype A₁B₁, and p₁ and q₁ are the frequencies of alleles A₁ and B₁ at loci A and B, respectively. A standardized LD coefficient, r, is given by D/(p₁p₂q₁q₂)^1/2, where p₂ and q₂ are the frequencies of the other alleles at loci A and B, respectively.⁸ Lewontin's coefficient, D', is given by D' = D/D_max, where D_max = min (p₁q₂, p₂q₁) when D is less than 0 or D_max = min (p₁q₁, p₂q₂) when D is more than 0.⁹

Cell Lines
Thirty-two B-lymphocyte cell lines transformed by Epstein-Barr virus were established from healthy volunteers or were purchased from the Health Science Research Resources Bank at the Japan Health Sciences Foundation (Tokyo, Japan).

Real-Time PCR
Total RNA was extracted from cells using an RNeasy Mini Kit (Qiagen, Valencia, CA) and reverse transcribed using the oligo dT₂₀ primer and Superscript reverse transcriptase (Invitrogen, Carlsbad, CA). Synthesized cDNA was used for real-time PCR. Real-time PCR experiments were performed with ABI PRISM 7000 Sequence Detection System device (Applied BioSystems [ABI], Foster City, CA) using TaqMan Universal PCR Master Mix (ABI) and TaqMan Gene Expression Assays designed for STAT3 (Hs00234174, ABI) or SOCS3 expression (Hs00269575, ABI). The correlation was tested by Pearson's correlation analysis using JMP 3.21J (SAS Institute).

RNA Interference
Validated Stealth RNAi DuoPak and Stealth RNAi Negative Control Duplexes (Invitrogen) were used in our RNAi experiments. Lipofectamine 2000 (Invitrogen) was used as the transfection reagent. Cells were seeded again 6 hours after transfection and used for growth assay, Western blotting, and quantitative real-time PCR.

Interferon-Stimulated Response Element Luciferase Reporter Assay
The HEK293 cells were cotransfected with the STAT3 siRNA reagent and the reporter vector, phosphorylated interferon-stimulated response element luciferase (pISRE-Luc; Stratagene, La Jolla, CA), or control vector, cytomegalovirus promoter-driven renilla luciferase (Promega, Madison, WI). These cotransfected cells were cultured in medium with or without interferon- (100 U/mL) for 9 hours. The luciferase activities in each cell were measured and the relative activities were presented as described earlier herein.

	RESULTS

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Selection of Candidate Genes and Polymorphisms
Based on published papers on IFN- function, 33 genes related to signal transduction via IFN- receptors (11 genes), Th1/Th2 balance (10 genes), IFN- induced gene expression (six genes), and pathogenesis of RCC were selected as candidate genes (Table 1) . In the publicly available single nucleotide polymorphisms database (National Center for Biotechnology Information [NCBI]), 1,167 SNPs were registered in the 33 genes.

The analysis indicated that the presence or absence of metastasis in the lung was the only factor that was significantly associated with IFN- response. Next, to search for a set of SNPs that have mutually independent information for discriminating between the cases and controls after adjusting for metastases, stepwise logistic regression analysis was applied. The analysis revealed that STAT3-2, STAT3-0, SOCS3-1, IL4R-34, PTGS1-4, PTGS1-5, ICSBP-38, PTGS1-3, PTGS2-12, TAP2-5, and IRF2-67 were significantly associated with the end point after adjusting for confounders; among these, STAT3-2 was the most significant.

LD Mapping of STAT3
STAT3-2, which was located in the first intron and in LD with STAT3-21, STAT3-25, and STAT3-52, was the most significant genetic factor associated with response to IFN-; therefore, we analyzed the STAT3 gene more in detail: an attempt to locate a polymorphism that affected STAT3 expression or function. In addition to searching in publicly available databases, we carried out extensive direct sequencing of STAT3 in 12 individuals that covered the entire 24 exons, along with their 100-base-pair flanking intron sequences, and the regions spanning 2.2 kb upstream of exon 1, and found an additional 59 SNPs. Among these SNPs, only two had a minor allele frequency more than 0.1, which was our criterion for use in LD block analysis. To estimate the LD block in the STAT3 gene, pairwise LD was calculated among SNPs with a minor allele frequency more than 0.1 in the STAT3 genes and the genes next to STAT3 [polymerase I and transcript release factor (PTRF) and STAT5a]. Although the LD block appeared to cover the neighboring genes, D' and r² values among SNPs in STAT3 were much higher than those in the neighboring genes (Fig 1). Moreover, SNPs in PTRF and STAT5a showed only a weak association with response to IFN-. Therefore, we concluded that STAT3 is a candidate gene for acting as a determinant factor for IFN- response. Among the SNPs in tight LD (|D'| = 1.0, r²> 0.9) with STAT3-2, rs4796793, which was located in the 5'-flanking region, showed the most significant association with IFN- response. The minor allele frequency of rs4796793 was 32.6% for nonresponders and 56.9% for responders (nonresponders v responders; ² = 8.61, P = .0033). The homozygous carriers of the minor allele also showed significant association (nonresponders v responders; ² = 8.31; P = .0039) and the highest odds ratio (OR; OR = 8.38, 95% CI, 1.63 to 42.96; Fig 2). According to the NCBI Database (http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=4796793), in Japanese, Chinese, and white populations, the proportions of CC of rs4796793 were 0.159, 0.133, and 0.034; CG were 0.500, 0,400, and 0.390; and GG were 0.341, 0.467, and 0.576, respectively. The proportion in the general Japanese population was similar to those in the RCC cases in this study (where the frequencies of CC, CG, and GG were 0.133, 0.573, and 0.293, respectively).

View larger version (32K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. Linkage disequilibrium (LD) pattern of the STAT3 gene. The upper section shows the genomic region, including STAT3 and its neighboring genes, and the locations of the SNPs with allele frequencies more than 0.1. The lower section shows pairwise LD in the region. Rs4796793, located in the 5' region upstream of the gene, was in tight LD (|D'| = 1.0, r2> 0.9) with STAT3-21 (rs1026916), STAT3-2 (rs1905341), STAT3-25 (rs744166), and STAT3-52 (rs2306581) associated with the IFN- response in the association study.

View larger version (14K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. Association of rs4796793 with interferon alfa (IFN-) response. The upper figure shows the location of rs4796793 in the STAT3 gene. The lower figure shows the significant association between rs4796793 and IFN- response. 2 testing was performed for the alleles, or the genotypes of rs4796793. NON, nonresponder; RES, responder.

Under the hypothesis that unidentified SNPs, or a combination of SNPs, in LD with rs4796793 might affect STAT3 mRNA level, we genotyped 32 B lymphocyte cell lines. We then compared STAT3 mRNA expression levels assessed by real-time PCR among the three groups; namely, major homozygote (G/G), heterozygote (G/C) and minor homozygote (C/C). Pearson's correlation analysis revealed a significant correlation between STAT3 expression and rs4796793 SNP genotype (R² = 0.14; P = .0375; Fig 3). The C allele, which was observed more frequently in IFN- responders, had a tendency for lower STAT3 expression. These results suggested that unidentified SNPs or a combination of SNPs in LD with rs4796793 might affect STAT3 expression.

View larger version (14K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 3. Endogenous STAT3 expression in human B-cell lines. STAT3 mRNA expression was measured in 32 B-lymphocyte cell lines, transformed by Epstein Barr virus, using real-time polymerase chain reaction. The STAT3 expression of each sample was described by the ratio with the GAPDH housekeeping gene expression level in the same sample. Simple linear regression analysis revealed a significant correlation between STAT3 expression and rs4796793 genotype (P = .0375, R2 = 0.14). The dotted curving lines show 95% CIs.

Enhancement of IFN-–Induced Growth Inhibition by STAT3 Suppression

View larger version (11K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 4. Enhancement of interferon alfa (IFN-) -induced cell growth inhibition by STAT3 siRNA. IFN-–induced cell growth inhibition was enhanced by STAT3 siRNA in ACHN and HEK293 cells. The open circles show the growth of cells transfected with STAT3 siRNA and the closed circles show that with negative control (NC) siRNA. Each experiment was done in triplicate, and the error bars represent standard deviation.

Enhancement of IFN-–Induced ISRE Activity by STAT3 Suppression

View larger version (12K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 5. Effect of STAT3 siRNA on interferon-stimulated response element (ISRE) -luciferase activity. Interferon alfa (IFN-) -induced ISRE activity in HEK293 was enhanced by STAT3 siRNA. The closed columns show the relative luciferase activity transfected with STAT3 siRNA and the open columns show the relative luciferase activity transfected with negative control (NC) siRNA. Experiments were performed in triplicate, and the relative luciferase activity was presented as the mean ± SE, after normalizing with Renilla luciferase activity. MCS, Firefly luciferase vector without ISRE sequence, pLuc-MCS.

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

STATs are ligand-induced transcriptional factors¹⁰ that are activated in response to growth factors and cytokines such as IFN-.^10-11 Among the STAT family, STAT3 is recognized as a primordial STAT protein because it is activated by a wide variety of cytokines, growth factors, and other stimuli,^12-13 and participates in a wide variety of physiological processes. Therefore, to discuss the involvement of STAT3 in IFN- sensitivity, we need to consider the two different aspects of STAT3: its roles in tumor cells and in the immune system.

STAT3 has been reported to be frequently overexpressed in various cancers¹⁴ and has, therefore, been recognized as a type of oncogene. Additionally, laboratory-induced mutation, resulting in constitutive STAT3 activation, can transform normal cells,¹⁵ whereas no naturally occurring mutations of STAT3 have been reported.¹⁶ In previous studies on RCC, the most frequently activated STAT was reported to be STAT3,¹⁷ and the expression of pSTAT3 was associated with clinical outcome.¹⁸ These findings suggest that inherited STAT3 polymorphisms, which correlate with STAT3 expression, might have a substantial effect on the progression or survival of cancer cells. In this context, SOCS3, a negative regulator of the Janus kinase (Jak)-STAT signaling pathways may have some role in the response because SOCS3 expression was decreased by STAT3 suppression (Fig A3, online only). For example, constitutive SOCS3 expression in cutaneous T-cell lymphoma cell lines,^19,20 as well as in several chronic myelogenous leukemia (CML) cell lines and blast cells of CML patients,²¹ seems to be involved in the respective resistances to IFN- treatment.

Research on STAT3 in respect to tumor immunity has been limited because ablating STAT3 leads to embryonic lethality. However, a recent report showed that conditional STAT3 knockout in hematopoietic cells results in enhanced intrinsic antitumor immunity in vivo.²² This report demonstrates that STAT3 activation in tumor-enhancing immune cells is an important contributor to impaired antitumor immunity. Also, that marked activation of dendritic cells (DCs), natural killer (NK) cells and neutrophils in tumor-bearing mice with STAT3^–/– hematopoietic cells is responsible for enhanced antitumor immunity.²² Because IFN- is thought to elicit its therapeutic effect via enhancing antitumor immunity mediated by NK and T lymph cells, it is probable that patients with a minor rs4796793 allele have better intrinsic tumor immune surveillance, which can be activated by IFN- administration, and so respond well to IFN- therapy.

A relationship between STAT3 activation in tumor cells and antitumor immunity has also been reported.²³ Activation of STAT3 in tumor cells promotes the expression of factors that inhibit the functional differentiation and mutation of DCs, resulting in suppression of proinflammatory cytokines and the chemokines necessary for antitumor immunity.²³ Collectively, these reports support the idea that the inherited genetic polymorphism in STAT3, which affects gene transcription, might influence the outcome of IFN- therapy for RCC via antitumor immunity.

This study is exploratory in nature because the number of samples was limited and no statistical adjustment was done to avoid false-positive results caused by multiple testing. However, the SNPs in IL4R were shown to be associated with the response to IFN- in RCC in this study, which is consistent with our previous study that showed an association between IL4R polymorphisms and long survival in RCC patients.²⁴ Because IL4 is widely used for DC differentiation,²⁵ SNPs in IL4R may be correlated with DC differentiation in patients.

We have now launched a prospective cohort study in Japan to confirm the association with the SNP in STAT3 and the response to IFN- therapy; however, confirmatory results are expected to be a few years away. We also hope that the association with the SNP in STAT3 and the response to IFN- therapy will be tested in other case-control studies in different races.

In conclusion, we showed that rs4796793 in STAT3 is a strong candidate genetic marker for predicting response to IFN- therapy in RCC. STAT3 expression was correlated with the SNP genotype and suppression of STAT3 led to enhanced sensitivity to IFN- in vitro. These findings, together with recent research on STAT3, suggested that SNPs might influence the sensitivity of tumor cells to IFN- and/or the activation status of antitumor immunity. Although further studies using other validation sets will be necessary to confirm the involvement of SNPs in the response to IFN-, the present study provides important evidence for recognizing STAT3 as a diagnostic marker for IFN- sensitivity. Several novel agents that are believed to improve RCC treatments are now being developed.²⁶ As well, the simultaneous or sequential combination of IFN- with tyrosine kinase inhibitors needs to be the subject of future investigation. However, even when these new-type drugs such as TKIs are used as first-line treatments, the identification of reliable predictive markers of response to IFN- is essential for establishing optimal treatment strategies for patients with MRCC.

	AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

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Although all authors completed the disclosure declaration, the following authors or their immediate family members indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment: Nobuyoshi Jinnai, Otsuka Pharmaceutical Co Ltd; Toyokazu Seki, Otsuka Pharmaceutical Co Ltd; Masanobu Takamatsu, Otsuka Pharmaceutical Co Ltd; Yoshihiro Masui, Otsuka Pharmaceutical Co Ltd Leadership: N/A Consultant: N/A Stock: N/A Honoraria: N/A Research Funds: N/A Testimony: N/A Other: N/A

	AUTHOR CONTRIBUTIONS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Conception and design: Noriyuki Ito, Masatoshi Eto, Eijiro Nakamura, Atsushi Takahashi, Taiji Tsukamoto, Hiroshi Toma, Hayakazu Nakazawa, Yoshihiko Hirao, Hirotsugu Uemura, Susumu Kagawa, Hiroomi Kanayama, Yoshiaki Nose, Naoko Kinukawa, Tsuyoshi Nakamura, Nobuyoshi Jinnai, Toyokazu Seki, Masanobu Takamatsu, Yoshihiro Masui, Seiji Naito, Osamu Ogawa

Provision of study materials or patients: Noriyuki Ito, Masatoshi Eto, Eijiro Nakamura, Atsushi Takahashi, Taiji Tsukamoto, Hiroshi Toma, Hayakazu Nakazawa, Yoshihiko Hirao, Hirotsugu Uemura, Susumu Kagawa, Hiroomi Kanayama, Nobuyoshi Jinnai, Toyokazu Seki, Masanobu Takamatsu, Yoshihiro Masui, Seiji Naito, Osamu Ogawa

Collection and assembly of data: Noriyuki Ito, Masatoshi Eto, Eijiro Nakamura, Atsushi Takahashi, Taiji Tsukamoto, Hiroshi Toma, Hayakazu Nakazawa, Yoshihiko Hirao, Hirotsugu Uemura, Susumu Kagawa, Hiroomi Kanayama, Seiji Naito, Osamu Ogawa

Data analysis and interpretation: Noriyuki Ito, Masatoshi Eto, Yoshiaki Nose, Naoko Kinukawa, Tsuyoshi Nakamura, Nobuyoshi Jinnai, Toyokazu Seki, Masanobu Takamatsu, Yoshihiro Masui, Osamu Ogawa

Manuscript writing: Noriyuki Ito, Osamu Ogawa

Final approval of manuscript: Noriyuki Ito, Masatoshi Eto, Eijiro Nakamura, Atsushi Takahashi, Taiji Tsukamoto, Hiroshi Toma, Hayakazu Nakazawa, Yoshihiko Hirao, Hirotsugu Uemura, Susumu Kagawa, Hiroomi Kanayama, Yoshiaki Nose, Naoko Kinukawa, Tsuyoshi Nakamura, Nobuyoshi Jinnai, Toyokazu Seki, Masanobu Takamatsu, Yoshihiro Masui, Seiji Naito, Osamu Ogawa

	Appendix

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 

View larger version (16K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A1. STAT3 luciferase assay. We cloned the two types of 2.2-kb allelic sequence of STAT3 promoter region, including the SBE and rs4796793 site, upstream of the luciferase reporter gene. The dual-luciferase reporter assay system revealed no significant effects on transcriptional activity of the allelic sequences of rs4796793 in ACHN, HEK293 and Caki-2 (interferon resistant) cell lines (data not shown). Experiments were performed in triplicate, and the relative luciferase activity was expressed as the mean ± SE, after normalizing with Renilla luciferase activity.

View larger version (16K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A2. Knockdown of STAT3 by siRNA. Transfection of STAT3 siRNA suppressed STAT3 mRNA, as assessed by quantitative real-time polymerase chain reaction in ACHN and HEK293 cells. Protein and phosphorylated forms of STAT3 were also suppressed by STAT3 siRNA in ACHN cells. NC, negative control.

View larger version (14K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A3. Effect of STAT3 siRNA on SOCS3 expression. Transfection of STAT3 siRNA suppressed SOCS3 expression by > 50% in ACHN and HEK293 cells. Experiments were performed in triplicate, and SOCS3 expression was expressed as the mean ± SEM, after normalizing with GAPDH. NC, negative control.

	ACKNOWLEDGMENTS

 
We thank K. Nakajima for helpful discussion in preparing the article.

	NOTES

 
Supported by a grant-in-aid for scientific research from the Ministry of Education, Culture, Sports, Science and Technology, Japan Grant No. 18209049, 18014013, and a research grant from Japan Immunotherapy SNPs-Study Group for Kidney Cancer.

N.I. and M.E. contributed equally to this work.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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Submitted November 17, 2006; accepted April 10, 2007.

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