1. Jemal, A., et al. Global cancer statistics. CA: a cancer journal for clinicians 61, 69-90 (2011).
2. Bianchi, M., et al. Distribution of metastatic sites in renal cell carcinoma: a population-based analysis. Annals of oncology : official journal of the European Society for Medical Oncology 23, 973-980 (2012).
3. Rini, B.I., et al. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. The New England journal of medicine 380, 1116-1127 (2019).
4. Latif, F., et al. Identification of the von Hippel-Lindau disease tumor suppressor gene. Science 260, 1317-1320 (1993).
5. Maher, E.R., Yates, J.R. & Ferguson-Smith, M.A. Statistical analysis of the two stage mutation model in von Hippel-Lindau disease, and in sporadic cerebellar haemangioblastoma and renal cell carcinoma. Journal of medical genetics 27, 311-314 (1990).
6. Foster, K., et al. Foster K, Prowse A, van den Berg A, Fleming S, Hulsbeek MM, Crossey PA, Richards FM, Cairns P, Affara NA, Ferguson-Smith MA, Buys CHM, Maher ERSomatic mutations of the von Hippel-Lindau disease tumour suppressor gene in non-familial clear cell renal carcinoma. Hum Mol Genet 3: 2169-2173, (1994).
7. Whaley, J.M., et al. Germ-line mutations in the von Hippel-Lindau tumor-suppressor gene are similar to somatic von Hippel-Lindau aberrations in sporadic renal cell carcinoma. American journal of human genetics 55, 1092-1102 (1994).
8. Young, A.C., et al. Analysis of VHL Gene Alterations and their Relationship to Clinical Parameters in Sporadic Conventional Renal Cell Carcinoma. Clinical cancer research : an official journal of the American Association for Cancer Research 15, 7582-7592 (2009).
9. Gossage, L., Eisen, T. & Maher, E.R. VHL, the story of a tumour suppressor gene. Nature reviews. Cancer 15, 55-64 (2015).
10. Sato, Y., et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nature genetics 45, 860-867 (2013).
11. Ivan, M., et al. HIFalpha targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing. Science 292, 464-468 (2001).
12. Jaakkola, P., et al. Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation. Science 292, 468-472 (2001).
13. Semenza, G.L. Hypoxia-inducible factors: mediators of cancer progression and targets for cancer therapy. Trends in pharmacological sciences 33, 207-214 (2012).
14. Bajorin, D.F., Motzer, R.J. & Bosl, G.J. Advances in Urologic Oncology: Results Progress From Successful Interdisciplinary Research. Journal of Clinical Oncology 24, 5479-5481 (2006).
15. 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).
16. Rankin, E.B., Tomaszewski, J.E. & Haase, V.H. Renal Cyst Development in Mice with Conditional Inactivation of the von Hippel-Lindau Tumor Suppressor. Cancer Research 66, 2576 (2006).
17. Frew, I.J., et al. pVHL and PTEN tumour suppressor proteins cooperatively suppress kidney cyst formation. The EMBO journal 27, 1747-1757 (2008).
18. Hsu, T. Complex cellular functions of the von Hippel-Lindau tumor suppressor gene: insights from model organisms. Oncogene 31, 2247-2257 (2012).
19. Albers, J., et al. Combined mutation of Vhl and Trp53 causes renal cysts and tumours in mice. EMBO molecular medicine 5, 949-964 (2013).
20. Hu, C.-J., Wang, L.-Y., Chodosh, L.A., Keith, B. & Simon, M.C. Differential Roles of Hypoxia-Inducible Factor 1α (HIF-1α) and HIF-2α in Hypoxic Gene Regulation. Molecular and Cellular Biology 23, 9361-9374 (2003).
21. 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).
22. Shen, C., et al. Genetic and Functional Studies Implicate <em>HIF1</em>α as a 14q Kidney Cancer Suppressor Gene. Cancer Discovery 1, 222-235 (2011).
23. Fu, L., Wang, G., Shevchuk, M.M., Nanus, D.M. & Gudas, L.J. Generation of a Mouse Model of Von Hippel–Lindau Kidney Disease Leading to Renal Cancers by Expression of a Constitutively Active Mutant of <em>HIF1α</em>. Cancer Research 71, 6848-6856 (2011).
24. Fu, L., Wang, G., Shevchuk, M.M., Nanus, D.M. & Gudas, L.J. Activation of HIF2α in kidney proximal tubule cells causes abnormal glycogen deposition but not tumorigenesis. Cancer research 73, 2916-2925 (2013).
25. Keith, B., Johnson, R.S. & Simon, M.C. HIF1alpha and HIF2alpha: sibling rivalry in hypoxic tumour growth and progression. Nature reviews. Cancer 12, 9-22 (2011).
26. Cowey, C.L. & Rathmell, W.K. VHL gene mutations in renal cell carcinoma: role as a biomarker of disease outcome and drug efficacy. Current oncology reports 11, 94-101 (2009).
27. Thomas, G.V., et al. Hypoxia-inducible factor determines sensitivity to inhibitors of mTOR in kidney cancer. Nature Medicine 12, 122 (2005).
28. Pantuck, A.J., An, J., Liu, H. & Rettig, M.B. NF-κB–Dependent Plasticity of the Epithelial to Mesenchymal Transition Induced by <em>Von Hippel-Lindau</em> Inactivation in Renal Cell Carcinomas. Cancer Research 70, 752-761 (2010).
29. Schokrpur, S., et al. CRISPR-Mediated VHL Knockout Generates an Improved Model for Metastatic Renal Cell Carcinoma. Scientific reports 6, 29032 (2016).
30. Thiery, J.P. Epithelial-mesenchymal transitions in tumour progression. Nature reviews. Cancer 2, 442-454 (2002).
31. Kalluri, R. & Weinberg, R.A. The basics of epithelial-mesenchymal transition. The Journal of clinical investigation 119, 1420-1428 (2009).
32. Fischer, K.R., et al. Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature 527, 472-476 (2015).
33. Zheng, X., et al. Epithelial-to-mesenchymal transition is dispensable for metastasis but induces chemoresistance in pancreatic cancer. Nature 527, 525-530 (2015).
34. Hu, J., Ishihara, M., Chin, A.I. & Wu, L. Establishment of xenografts of urological cancers on chicken chorioallantoic membrane (CAM) to study metastasis. Precis Clin Med 2, 140-151 (2019).
35. Creighton, C.J., et al. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43-49 (2013).
36. Hu, J., et al. A Non-integrating Lentiviral Approach Overcomes Cas9-Induced Immune Rejection to Establish an Immunocompetent Metastatic Renal Cancer Model. Molecular Therapy - Methods & Clinical Development 9, 203-210 (2018).
37. Ribatti, D. The chick embryo chorioallantoic membrane as a model for tumor biology. Experimental cell research 328, 314-324 (2014).
38. Hagedorn, M., et al. Accessing key steps of human tumor progression <em>in vivo</em> by using an avian embryo model. Proceedings of the National Academy of Sciences of the United States of America 102, 1643-1648 (2005).
39. Giard, D.J., et al. In Vitro Cultivation of Human Tumors: Establishment of Cell Lines Derived From a Series of Solid Tumors2. JNCI: Journal of the National Cancer Institute 51, 1417-1423 (1973).
40. Michaylira, C.Z., et al. Periostin, a Cell Adhesion Molecule, Facilitates Invasion in the Tumor Microenvironment and Annotates a Novel Tumor-Invasive Signature in Esophageal Cancer. Cancer Research 70, 5281-5292 (2010).
41. Malanchi, I., et al. Interactions between cancer stem cells and their niche govern metastatic colonization. Nature 481, 85-89 (2011).
42. Dahinden, C., et al. Mining Tissue Microarray Data to Uncover Combinations of Biomarker Expression Patterns that Improve Intermediate Staging and Grading of Clear Cell Renal Cell Cancer. Clinical Cancer Research 16, 88-98 (2010).
43. Field, S., et al. Novel highly specific anti-periostin antibodies uncover the functional importance of the fascilin 1-1 domain and highlight preferential expression of periostin in aggressive breast cancer. 138, 1959-1970 (2016).
44. Klatte, T., et al. Hypoxia-inducible factor 1 alpha in clear cell renal cell carcinoma. Clinical cancer research : an official journal of the American Association for Cancer Research 13, 7388-7393 (2007).
45. Strilic, B., et al. Tumour-cell-induced endothelial cell necroptosis via death receptor 6 promotes metastasis. Nature 536, 215-218 (2016).
46. Nowell, P.C. The clonal evolution of tumor cell populations. Science (New York, N.Y.) 194, 23-28 (1976).
47. Kang, Y., et al. A multigenic program mediating breast cancer metastasis to bone. Cancer cell 3, 537-549 (2003).
48. Minn, A.J., et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518-524 (2005).
49. Takeshita, S., Kikuno, R., Tezuka, K. & Amann, E. Osteoblast-specific factor 2: cloning of a putative bone adhesion protein with homology with the insect protein fasciclin I. The Biochemical journal 294 ( Pt 1), 271-278 (1993).
50. Morra, L. & Moch, H. Periostin expression and epithelial-mesenchymal transition in cancer: a review and an update. Virchows Archiv : an international journal of pathology 459, 465-475 (2011).
51. Nakazawa, Y., et al. Periostin blockade overcomes chemoresistance via restricting the expansion of mesenchymal tumor subpopulations in breast cancer. Scientific reports 8, 4013-4013 (2018).
52. Zhu, M., et al. Neutralizing monoclonal antibody to periostin inhibits ovarian tumor growth and metastasis. Molecular cancer therapeutics 10, 1500-1508 (2011).
53. Neelakantan, D., et al. EMT cells increase breast cancer metastasis via paracrine GLI activation in neighbouring tumour cells. Nature communications 8, 15773 (2017).
54. Gerlinger, M., et al. Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing. New England Journal of Medicine 366, 883-892 (2012).
55. Turajlic, S., et al. Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal. Cell 173, 581-594.e512 (2018).
56. Ishihara, M., et al. Comparing Metastatic Clear Cell Renal Cell Carcinoma Model Established in Mouse Kidney and on Chicken Chorioallantoic Membrane. JoVE, e60314 (2020).
57. Sharrow, A.C., Ishihara, M., Hu, J., Kim, I.H. & Wu, L. Using the Chicken Chorioallantoic Membrane In Vivo Model to Study Gynecological and Urological Cancers. JoVE, e60651 (2020).
58. DePristo, M.A., et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature genetics 43, 491-498 (2011).
59. Cibulskis, K., et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology 31, 213-219 (2013).
60. McLaren, W., et al. The Ensembl Variant Effect Predictor. Genome biology 17, 122 (2016).
61. Talevich, E., Shain, A.H., Botton, T. & Bastian, B.C. CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol 12, e1004873 (2016).
62. Zhang, J. CNTools: Convert segment data into a region by sample matrix to allow for other high level computational analyses. R package (Version 1.38.0) (2016).
63. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43-49 (2013).
64. Aran, D., Sirota, M. & Butte, A.J. Systematic pan-cancer analysis of tumour purity. Nature communications 6, 8971 (2015).
65. Hoadley, K.A., et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929-944 (2014).
66. Zheng, G.X., et al. Massively parallel digital transcriptional profiling of single cells. 8, 14049 (2017).
67. Li, W.V. & Li, J.J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nature communications 9, 997 (2018).
68. Hao, Y., et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587.e3529 (2021).
69. Leland McInnes, J.H., James Melville. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint arXiv 1802.03426(2018).
70. Finak, G., et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome biology 16, 278 (2015).
71. Korotkevich, G., Vladimir Sukhov, Nikolay Budin, Boris Shpak, Maxim N. Artyomov, and Alexey Sergushichev. Fast gene set enrichment analysis. BioRxiv 060012(2021).
72. Dolgalev, I. msigdbr: MSigDB gene sets for multiple organisms in a tidy data format. (2019).
73. Patro, R., Duggal, G., Love, M.I., Irizarry, R.A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14, 417-419 (2017).
74. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology 15, 550 (2014).
75. Kolde, R., (Maintainer) Raivo Kolde. Package 'pheatmap'. R package 1 no. 7(2015).