Targeting renal cell carcinoma with a HIF-2 antagonist (original) (raw)

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References

1. Gnarra, J. R. et al. Mutations of the VHL tumour suppressor gene in renal carcinoma. Nat. Genet. 7, 85–90 (1994)
   Article CAS Google Scholar
2. Herman, J. G. et al. Silencing of the VHL tumor-suppressor gene by DNA methylation in renal carcinoma. Proc. Natl Acad. Sci. USA 91, 9700–9704 (1994)
   Article ADS CAS Google Scholar
3. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012)
   Article CAS Google Scholar
4. Kaelin, W. G., Jr & Ratcliffe, P. J. Oxygen sensing by metazoans: the central role of the HIF hydroxylase pathway. Mol. Cell 30, 393–402 (2008)
   Article CAS Google Scholar
5. Kondo, K., Klco, J., Nakamura, E., Lechpammer, M. & Kaelin, W. G., Jr Inhibition of HIF is necessary for tumor suppression by the von Hippel-Lindau protein. Cancer Cell 1, 237–246 (2002)
   Article CAS Google Scholar
6. Bertout, J. A. et al. HIF2α inhibition promotes p53 pathway activity, tumor cell death, and radiation responses. Proc. Natl Acad. Sci. USA 106, 14391–14396 (2009)
   Article ADS CAS Google Scholar
7. Covello, K. L. et al. HIF-2α regulates Oct-4: effects of hypoxia on stem cell function, embryonic development, and tumor growth. Genes Dev. 20, 557–570 (2006)
   Article CAS Google Scholar
8. Ema, M. et al. A novel bHLH-PAS factor with close sequence similarity to hypoxia-inducible factor 1α regulates the VEGF expression and is potentially involved in lung and vascular development. Proc. Natl Acad. Sci. USA 94, 4273–4278 (1997)
   Article ADS CAS Google Scholar
9. Gordan, J. D., Bertout, J. A., Hu, C. J., Diehl, J. A. & Simon, M. C. HIF-2α promotes hypoxic cell proliferation by enhancing c-myc transcriptional activity. Cancer Cell 11, 335–347 (2007)
   Article CAS Google Scholar
10. Mendel, D. B. et al. In vivo antitumor activity of SU11248, a novel tyrosine kinase inhibitor targeting vascular endothelial growth factor and platelet-derived growth factor receptors: determination of a pharmacokinetic/pharmacodynamic relationship. Clin. Cancer Res. 9, 327–337 (2003)
    CAS PubMed Google Scholar
11. Koehler, A. N. A complex task? Direct modulation of transcription factors with small molecules. Curr. Opin. Chem. Biol. 14, 331–340 (2010)
    Article CAS Google Scholar
12. Pavía-Jiménez, A., Tcheuyap, V. T. & Brugarolas, J. Establishing a human renal cell carcinoma tumorgraft platform for preclinical drug testing. Nat. Protocols 9, 1848–1859 (2014)
    Article Google Scholar
13. Sivanand, S. et al. A validated tumorgraft model reveals activity of dovitinib against renal cell carcinoma. Sci. Transl. Med. 4, 137ra75 (2012)
    Article Google Scholar
14. Tian, H., McKnight, S. L. & Russell, D. W. Endothelial PAS domain protein 1 (EPAS1), a transcription factor selectively expressed in endothelial cells. Genes Dev. 11, 72–82 (1997)
    Article CAS Google Scholar
15. Rankin, E. B. et al. Hypoxia-inducible factor-2 (HIF-2) regulates hepatic erythropoietin in vivo. J. Clin. Invest. 117, 1068–1077 (2007)
    Article CAS Google Scholar
16. Erbel, P. J., Card, P. B., Karakuzu, O., Bruick, R. K. & Gardner, K. H. Structural basis for PAS domain heterodimerization in the basic helix–loop–helix-PAS transcription factor hypoxia-inducible factor. Proc. Natl Acad. Sci. USA 100, 15504–15509 (2003)
    Article ADS CAS Google Scholar
17. Scheuermann, T. H. et al. Artificial ligand binding within the HIF2α PAS-B domain of the HIF2 transcription factor. Proc. Natl Acad. Sci. USA 106, 450–455 (2009)
    Article ADS CAS Google Scholar
18. Scheuermann, T. H. et al. Allosteric inhibition of hypoxia inducible factor-2 with small molecules. Nat. Chem. Biol. 9, 271–276 (2013)
    Article CAS Google Scholar
19. Cho, H. et al. On-target efficacy of a HIF2α antagonist in preclinical kidney cancer models. Nature http://dx.doi.org/10.1038/nature19795 (2016)
20. Wallace, E. M. et al. A small-molecule antagonist of HIF-2α is efficacious in preclinical models of renal cell carcinoma. Cancer Res. 76, 5491–5500 (2016)
    Article CAS Google Scholar
21. Hu, C. J. et al. Differential regulation of the transcriptional activities of hypoxia-inducible factor 1 alpha (HIF-1α) and HIF-2α in stem cells. Mol. Cell. Biol. 26, 3514–3526 (2006)
    Article CAS Google Scholar
22. Raval, R. R. et al. Contrasting properties of hypoxia-inducible factor 1 (HIF-1) and HIF-2 in von Hippel-Lindau-associated renal cell carcinoma. Mol. Cell. Biol. 25, 5675–5686 (2005)
    Article CAS Google Scholar
23. Keith, B., Johnson, R. S. & Simon, M. C. HIF1α and HIF2α: sibling rivalry in hypoxic tumour growth and progression. Nat. Rev. Cancer 12, 9–22 (2011)
    Article Google Scholar
24. Peña-Llopis, S. et al. BAP1 loss defines a new class of renal cell carcinoma. Nat. Genet. 44, 751–759 (2012)
    Article Google Scholar
25. Li, B. et al. Fructose-1,6-bisphosphatase opposes renal carcinoma progression. Nature 513, 251–255 (2014)
    Article ADS CAS Google Scholar
26. Brugarolas, J. Molecular genetics of clear-cell renal cell carcinoma. J. Clin. Oncol. 32, 1968–1976 (2014)
    Article CAS Google Scholar
27. Pedrosa, I. et al. MR classification of renal masses with pathologic correlation. Eur. Radiol. 18, 365–375 (2008)
    Article Google Scholar
28. Wu, D., Potluri, N., Lu, J., Kim, Y. & Rastinejad, F. Structural integration in hypoxia-inducible factors. Nature 524, 303–308 (2015)
    Article ADS CAS Google Scholar
29. Rogers, J. L. et al. Development of inhibitors of the PAS-B domain of the HIF-2α transcription factor. J. Med. Chem. 56, 1739–1747 (2013)
    Article CAS Google Scholar
30. Courtney, K. D. et al. A phase I dose escalation trial of PT2385, a first-in-class oral HIF-2α inhibitor, in patients with advanced clear cell renal cell carcinoma. J. Clin. Oncol. 34, Suppl. Abstract 2506 (2016)
    Article Google Scholar
31. Peña-Llopis, S. & Brugarolas, J. Simultaneous isolation of high-quality DNA, RNA, miRNA and proteins from tissues for genomic applications. Nat. Protocols 8, 2240–2255 (2013)
    Article Google Scholar
32. Bentley, D. R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008)
    Article ADS CAS Google Scholar
33. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013)
    Article CAS Google Scholar
34. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)
    Article CAS Google Scholar
35. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012)
    Article CAS Google Scholar
36. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010)
    Article CAS Google Scholar
37. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995)
    MathSciNet MATH Google Scholar

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Acknowledgements

We thank the patients who generously provided tissues and participated in our studies. PT2399 was provided by Peloton Therapeutics, Inc. Funding was provided by Peloton Therapeutics, Inc. (OTD-105466), CPRIT (RP160440) and philanthropy, including the Tom Green memorial. W.C. is supported by grants from the National Natural Science Foundation of China (No. 811011934) and the Science and Technology Program of Guangzhou, China (No. 2012J5100031). M.S.K. and H.Z. are supported by a grant from CPRIT (RP150596). I.P. is supported by grants from the NIH (R01CA154475, P50CA196516). X.S. is supported by a grant from CPRIT (RP110771). J.B. is a Virginia Murchison Linthicum endowed scholar and is supported by grants from the NIH (R01CA175754, P50CA196516, P30CA142543) and CPRIT (RP130603). R.K.B. is the Michael L. Rosenberg Scholar in Medical Research and was supported by CPRIT (RP130513). Histology equipment was purchased with funding from the National Center for Advancing Translational Sciences (Center for Translational Medicine UL1TR001105).

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Author notes

1. Wenfang Chen, Haley Hill, Alana Christie and Min Soo Kim: These authors contributed equally to this work.

Authors and Affiliations

1. Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
   Wenfang Chen, Haley Hill, Alana Christie, Min Soo Kim, Eboni Holloman, Andrea Pavia-Jimenez, Farrah Homayoun, Yuanqing Ma, Nirav Patel, Qurratulain Yousuf, Allison Joyce, Ivan Pedrosa, He Zhang, Jenny Chang, Richard K. Bruick, Tae Hyun Hwang, Kevin Courtney, Eugene Frenkel, Xiankai Sun, Yang Xie, Xian-Jin Xie, Payal Kapur, Renée M. McKay & James Brugarolas
2. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
   Wenfang Chen, Haley Hill, Eboni Holloman, Andrea Pavia-Jimenez, Farrah Homayoun, Yuanqing Ma, Nirav Patel, Qurratulain Yousuf, Allison Joyce, Kevin Courtney, Eugene Frenkel, Renée M. McKay & James Brugarolas
3. Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China
   Wenfang Chen
4. Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
   Min Soo Kim, He Zhang, Tae Hyun Hwang, Yang Xie & Xian-Jin Xie
5. Parkland Health and Hospital System, Dallas, 75235, Texas, USA
   Paul Yell
6. Department of Radiology, University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
   Guiyang Hao, Ivan Pedrosa & Xiankai Sun
7. New York Genome Center, New York, 10013, New York, USA
   Heather Geiger & Catherine Reeves
8. Structural Biology Initiative, CUNY Advanced Science Research Center, New York, 10031, New York, USA
   Kevin H. Gardner
9. Department of Chemistry and Biochemistry, City College of New York, New York, 10031, New York, USA
   Kevin H. Gardner
10. Biochemistry, Chemistry and Biology Ph.D. Programs, Graduate Center, City University of New York, New York, 10016, New York, USA
    Kevin H. Gardner
11. Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
    Richard K. Bruick
12. Peloton Therapeutics Inc., Dallas, 75235, Texas, USA
    Naseem Zojwalla, Tai Wong, James P. Rizzi, Eli M. Wallace & John A. Josey
13. Department of Pathology, University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
    Payal Kapur

Authors

1. Wenfang Chen
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2. Haley Hill
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3. Alana Christie
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4. Min Soo Kim
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5. Eboni Holloman
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6. Andrea Pavia-Jimenez
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7. Farrah Homayoun
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8. Yuanqing Ma
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9. Nirav Patel
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10. Paul Yell
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11. Guiyang Hao
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12. Qurratulain Yousuf
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13. Allison Joyce
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14. Ivan Pedrosa
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15. Heather Geiger
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16. He Zhang
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17. Jenny Chang
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18. Kevin H. Gardner
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19. Richard K. Bruick
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20. Catherine Reeves
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21. Tae Hyun Hwang
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22. Kevin Courtney
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23. Eugene Frenkel
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24. Xiankai Sun
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25. Naseem Zojwalla
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26. Tai Wong
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27. James P. Rizzi
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28. Eli M. Wallace
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29. John A. Josey
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30. Yang Xie
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31. Xian-Jin Xie
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32. Payal Kapur
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33. Renée M. McKay
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34. James Brugarolas
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Contributions

W.C. designed and performed biochemical experiments; H.H., E.H., A.P.-J., Q.Y., and A.J. performed tumourgraft experiments; A.C. performed extensive statistical analyses with the supervision of X.-J.X.; M.S.K. performed RNA-seq analyses under the supervision of T.H.H. and Y.X., who also supervised H.Z. on sequencing analysis; Y.M. and N.P. performed experiments; F.H. and P.Y. performed histological analyses under the supervision of P.K.; G.H. performed PET studies under the supervision of X.S.; I.P. performed patient imaging analyses; H.G. and C.R. performed RNA-seq validation studies at the New York Genome Center; J.C. is the clinical research coordinator of the PT2385 phase 1 trial overseen by K.C. and N.Z.; K.H.G., R.K.B., and E.F. participated in discussions; T.W., J.P.R., E.M.W., and J.A.J. oversaw the development and characterization of PT2399 and provided the drug; R.M.M. assisted with manuscript preparation, writing and submission; and J.B. conceived and supervised the project, and wrote the manuscript with input from R.M.M. and the other authors.

Corresponding author

Correspondence toJames Brugarolas.

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Competing interests

T.W., J.P.R., E.M.W., N.Z. and J.A.J. are employees and own equity in Peloton Therapeutics, Inc. and K.H.G. and R.K.B. have licensed intellectual property, consult for and own equity in Peloton Therapeutics, Inc. M.S.K., T.H.H, Y.X. and J.B. are authors on a filed patent pertaining to a biomarker of PT2399. J.B. is a member of the advisory board for Bethyl Laboratories.

Additional information

RNAseq was released to NCBI Sequence Read Archive (SRA). ID: SRP073253.

Extended data figures and tables

Extended Data Figure 1 Effects of PT2399 on human RCC-bearing mice.

a, Platelet, white blood cell, neutrophil, and lymphocyte counts from tumourgraft-bearing mice treated with vehicle (n = 52), PT2399 (n = 58), or sunitinib (n = 53) at the end of the drug trial period (~28 days). Low lymphocyte levels throughout are consistent with expected levels in age- and sex-matched NOD/SCID mice. b, Tumour growth trend lines for sensitive, intermediate, and resistant groups after controlling for baseline tumour volume (refer to Fig. 1d for individual curves). c, Representative gross images of tumours from sensitive (XP164 and XP373; green) and resistant (XP169 and XP490; red) lines at the end of the drug trial. d, Representative haematoxylin and eosin-stained images illustrating different effects of PT2399 on sensitive tumours including patchy intercellular fibrosis and hyalinization (open arrow heads), reduced tumour necrosis (red arrows), decreased tumour cell density (XP164 and XP469), reduced nuclear-to-cytoplasmic ratio (XP469), cell ballooning (filled arrow), and dystrophic calcification (blue stars). Scale bars, 50 μm. e, Summary of histopathological changes induced by PT2399 in 10 sensitive tumourgraft lines represented as number of tumours (n) compared to the total or as mean ± s.e. in 28 vehicle-treated tumours compared to 31 PT2399-treated tumours. MVD, microvessel density per mm2; MLA, mean lumen area (μm2). PT2399 collapsed tumour vasculature without decreasing the number of CD31-expressing endothelial cells. f, Top, IHC for Ki67 in tumours harvested from sensitive (XP144 and XP373) or resistant (XP530 and XP506) tumours following treatment with vehicle or PT2399. Bottom, haematoxylin and eosin staining and IHC for CD31 in sensitive tumours (XP373 and XP469) treated with vehicle or PT2399. Scale bars, 100 μm. g, Representative [18F]FLT-PET/CT images of mice with subcutaneous tumourgrafts treated with either vehicle or PT2399. Yellow arrows point to tumours. h, Representative [18F]FLT-PET/CT images of XP144 mice with orthotopic tumours before and after treatment with PT2399 for 10 days. Yellow arrowheads, kidney tumours. White asterisks, intestine. FLT uptake in tumour compared to normal kidney reduced by 19% after 10-day treatment (n = 3; paired _t_-test, P = 0.001). i, Human and mouse VEGF levels in plasma as determined by ELISA in different treatment groups (vehicle: n = 63; PT2399: n = 74; sunitinib: n = 61). a, i, Tests completed using a mixed model analysis with compound symmetrical covariance structure for mice in the same tumourgraft line using vehicle as the reference group. b, Trend lines were obtained from a mixed model analysis for each response group using an autoregressive (1) covariance structure for the longitudinal measurements on each mouse, compound symmetry for mice within the same tumourgraft line, and controlled for baseline volume. e, Continuous measures were analysed using a mixed model with compound symmetrical covariance structure for mice in the same tumourgraft line and using vehicle treatment as the reference group. Specifically for categorical variables, a binomial test was used to test whether the proportion of tumours affected by PT2399 compared to vehicle was different from 10%. hVEGF and mVEGF levels were Box-Cox transformed; raw values depicted in all graphs. All boxplots have median centre values. *P < 0.05; ***P < 0.001; ****P < 0.0001.

Extended Data Figure 2 Evaluation of the effects of PT2399 on tumours progressing on sunitinib.

a, Tumour volumes in mice from sensitive lines (XP374 or XP144) switched from vehicle or sunitinib to PT2399 as indicated (bottom black arrows). b, Circulating tumour-produced hVEGF levels in mice treated with vehicle, sunitinib, or sunitinib followed by PT2399. The Wilcoxon rank-sum test was used to determine whether sunitinib (n = 4) or sunitinib followed by PT2399 (n = 6) were different from vehicle (n = 4). *P < 0.05. Boxplots have median centre values. c, Representative images of haematoxylin and eosin and Ki67 staining of tumours from mice (XP144) treated with vehicle or sunitinib (left) and from tumours following a switch to PT2399 (right). Scale bars, 100 μm.

Extended Data Figure 3 RNA-seq analyses of vehicle and PT2399-treated tumourgrafts.

a, Unsupervised clustering analyses of all tumourgraft samples (sensitive (S) and resistant (R), both vehicle (V)- and PT2399 (P)-treated) showing clustering by tumourgraft line. b, RNA sequencing in sensitive tumourgrafts evaluating the effects of PT2399 on selected HIF-2 target genes. All tests completed using mixed model analysis with compound symmetrical covariance structure for mice in the same tumourgraft line. Values were log2-transformed for analysis; raw values depicted in all graphs as individual bars.

Extended Data Figure 4 HIF-2α and HIF-1α levels in sensitive and resistant tumourgrafts.

a, HIF-2α and HIF-1α IHC. 786-O cells, which express high levels of HIF-2α, shown as controls. Scale bars, 100 μm. b, Western blot analyses showing heterogeneity within tumours but with overall similar results (compare to Fig. 3c). Green, sensitive; red, resistant. Asterisks, underloaded samples. c, Heatmap from RNA-seq analysis showing differentially expressed genes in sensitive (S) versus resistant (R) tumourgrafts based on uniform cutoff (see Extended Data Table 3). See Supplementary Fig. 1 for gel source images.

Extended Data Figure 5 Evaluation of imaging characteristics of tumours in patients corresponding to sensitive, intermediate, and resistant tumourgrafts.

CT scan images from patient tumours that gave rise to tumourgrafts according to tumourgraft sensitivity to PT2399. Tumours were classified into masses with peripheral hypervascularity and a central non-enhancing area (blue outline), focally infiltrating (brown outline) and diffuse infiltrating (yellow outline). Three of the seven resistant tumours presented as non-mass-like, infiltrative neoplasms (red arrows) whereas another tumour presented with both a largely necrotic renal mass and retroperitoneal lymph nodes (black outline; white arrows).

Extended Data Figure 6 Prolonged disease control in heavily pretreated patient with metastatic ccRCC with sensitive (XP165) tumourgraft.

CT images of selected lesions in patient treated with highly related HIF-2 inhibitor (PT2385) in phase 1 clinical trial showing overall stability in the size of lesions over time. Start of treatment, day 0.

Extended Data Table 1 Tumourgraft features

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Extended Data Table 2 RNA sequencing read data

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Extended Data Table 3 Number of differentially regulated RNAs across tumourgraft groups by RNA-seq analysis

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Extended Data Table 4 Top 15 downregulated and top 15 upregulated pathways in sensitive tumours treated with PT2399

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Supplementary information

Supplementary Information

This file contains Supplementary Figure 1(gel source data) and Supplementary Figure 2 (Tumor dimensions). (PDF 2694 kb)

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Chen, W., Hill, H., Christie, A. et al. Targeting renal cell carcinoma with a HIF-2 antagonist.Nature 539, 112–117 (2016). https://doi.org/10.1038/nature19796

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• Received: 23 September 2015
• Accepted: 26 August 2016
• Published: 05 September 2016
• Issue Date: 03 November 2016
• DOI: https://doi.org/10.1038/nature19796