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R. Culliford, Sam Lawrence, Charlie Mills, Z. Tippu, D. Chubb, A. Cornish, Lisa Browning, B. Kinnersley, R. Bentham, Amit Sud, H. Pallikonda, Mehran Oyeyemi Janet Guy Kate Victoria Styliani Megan Cha Afshar Akala Brown Faust Fife Foy Germanou Giles G, Mehran Afshar, Oyeyemi Akala, Janet Brown, Guy Faust, K. Fife, Victoria Foy, Styliani Germanou, Megan Giles, Charlotte Grieco, Simon Grummet, Ankit Jain, Anuradha Kanwar, A. Protheroe, Iwan Raza, Ahmed Rehan, S. Rudman, J. Santiapillai, N. Sarwar, P. Seeva, Amy Strong, Maria Toki, M. Tran, Rippie K Tutika, T. Waddell, M. Wheater, A. Frangou, Andreas J. Gruber, K. Litchfield, D. Wedge, J. Larkin, S. Turajlic, R. Houlston
8 15. 7. 2024.

Whole genome sequencing refines stratification and therapy of patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing for a detailed description of the somatic mutational landscape of ccRCC. We identify candidate driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for therapeutic interventions. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The observations that higher T-cell infiltration is associated with better overall survival and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients. The genomic landscape of clear cell renal cell carcinoma (ccRCC) remains to be comprehensively characterised. Here, whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project was used to identify potential drivers and clinical correlations to inform the development of therapies.


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