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[Preprint]. 2023 Dec 6:rs.3.rs-3675752.
doi: 10.21203/rs.3.rs-3675752/v1.

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

Richard Houlston  1 , Richard Culliford  1 , Sam Lawrence  1 , Charlie Mills  1 , Zayd Tippu  2 , Daniel Chubb  3 , Alex Cornish  1 , Lisa Browining  4 , Ben Kinnersley  5 , Robert Bentham  1 , Amit Sud  1 , Husayn Pallikonda  2 , Anna Frangou  4 , Andreas Gruber  6 , Kevin Litchfield  7 , David Wedge  8 , James Larkin  9 , Samra Turajlic  10
Affiliations

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

Richard Houlston et al. Res Sq. .

Update in

Abstract

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 the most detailed somatic mutational landscape to date. We identify new driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for drug repurposing. 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 twin observations that higher T-cell infiltration is associated with better outcome 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.

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Conflict of interest statement

DECLARATION OF INTERESTS S.T. has received speaking fees from Roche, AstraZeneca, Novartis and Ipsen. ST has the following patents filed: Indel mutations as a therapeutic target and predictive biomarker PCTGB2018/051892 and PCTGB2018/051893 and Clear Cell Renal Cell Carcinoma Biomarkers P113326GB. None of the other authors have a conflict of interest.

Figures

Figure 1
Figure 1. Overview of the Gel cohort of ccRCC patients.
(a) The location of the 13 Genomic Medicine Centers (GMCs) across England from which patients were recruited; (b) The breakdown of the cohort by tumour grade and stage. Figure created using BioRender.
Figure 2
Figure 2. Frequency of nonsynonymous mutations in driver genes.
The colour scheme indicates whether the mutational frequency of a driver gene is reported as being above (blue) or below (red) 1% in other ccRCC cohorts.
Figure 3
Figure 3. Biological pathways in ccRCC.
(a) The SWI/SNF pathway; (b) The MAPK signalling pathway; (c)The TP53 pathway; (d)The RAS/ERK and hypoxia pathway; (e)The VHL/HIF1A pathway. Driver genes identified shown in blue, non-ccRCC driver genes in green and other pathway genes in grey. The number in the bottom left is the nonsynonymous mutational frequency and the number in the bottom right the copy number alteration (CNA) frequency. RTK, Receptor Tyrosine Kinase. Figure created using BioRender.
Figure 4
Figure 4. Copy number alterations and structural variants.
(a) Frequency of copy number alterations across the ccRCC cohort. Copy number losses are coded in blue shades and copy number gains shown in red. The focal copy number alterations, as identified by GISTIC, are annotated along with predicted target gene; (b) The distribution of structural variants across the ccRCC cohort. Structural variants are classified as deletions, tandem duplications or unclassified structural variants. The black ticks on the y axis correspond to the chromosome start, centromere and end position while the orange ticks represent identified structural variant hotspot regions.
Figure 5
Figure 5. Mutational signatures.
The mutational burden of single base substitution signatures.
Figure 6
Figure 6. Mutational timing.
(a) The proportion of clonal and subclonal nonsynonymous mutations in driver genes; (b)Odds ratio (OR) with 95% confidence intervals that a mutation in a driver gene is clonal; OR >1.0 indicates a mutation is more likely to be clonal. The genes in blue are significantly more likely to be clonal or subclonal. Genes in red have no subclonal mutations; (c) The relative ordering of mutations in driver genes; (d) Estimates of the real time at which copy number gains occur during tumour evolution.
Figure 7
Figure 7. Immune landscape of ccRCC.
(a) Neoantigen burden and immune escape mutations. Lower bars show antigen processing genes (APG) and HLA alterations present in each cancer; (b) Somatic mutations in each of the antigen presentation pathway genes. The number in the bottom left is the truncating mutation count and the number in the bottom right the number of biallelic nonsynonymous mutations. APGs in purple, IFN-γ pathway genes in blue, epigenetic modifier genes in brown, CD274 comprises the PD-L1 receptor and CD58 receptor is encoded by CD58. Figure created using BioRender.
Figure 8
Figure 8. Kaplan Meier survival curves of overall survival.
Relationship between OS and (a) VHL mutation status; (b) PBRM1 mutation status; (c) Structural variant (SV) count; (d) TCRA T-cell fraction.
See this image and copyright information in PMC

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