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Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing

Abstract

Clear cell renal carcinomas (ccRCCs) can display intratumor heterogeneity (ITH). We applied multiregion exome sequencing (M-seq) to resolve the genetic architecture and evolutionary histories of ten ccRCCs. Ultra-deep sequencing identified ITH in all cases. We found that 73–75% of identified ccRCC driver aberrations were subclonal, confounding estimates of driver mutation prevalence. ITH increased with the number of biopsies analyzed, without evidence of saturation in most tumors. Chromosome 3p loss and VHL aberrations were the only ubiquitous events. The proportion of C>T transitions at CpG sites increased during tumor progression. M-seq permits the temporal resolution of ccRCC evolution and refines mutational signatures occurring during tumor development.

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Figure 1: Regional distribution of nonsynonymous mutations in ten ccRCC tumors.
Figure 2: Variant frequencies for the nonsynonymous somatic mutations in eight ccRCC tumors based on ultra-deep amplicon sequencing.
Figure 3: Phylogenetic trees generated by maximum parsimony from M-seq data for ten ccRCC tumors.
Figure 4: Regional distribution of somatic driver copy number aberrations in ten ccRCC tumors.
Figure 5: Truncal location of driver aberrations and mutation spectrum in ten ccRCC tumors.

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Acknowledgements

We thank the patients, the research nurses at the Royal Marsden Hospital, and Lifetech and Westminster Genomic Services at the University of Westminster, London, for their assistance with validation. C.S. and M. Gerlinger are supported by grants from Cancer Research UK Biomarkers and Imaging Discovery and Development Committee (BIDD), the Medical Research Council and the Seventh European Union Framework Programme, and C.S. is supported by the Breast Cancer Research Foundation and the Rosetrees Trust. We acknowledge the Ramón y Cajal program of the Ministerio de Economía y Competitividad, Spain, and Novartis for funding support for E-PREDICT clinical trials. This study was supported by researchers at the National Institute for Health Research Biomedical Research Centres at University College London Hospitals and at the Royal Marsden Hospital.

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Authors and Affiliations

Authors

Contributions

M. Gerlinger, J.L. and C.S. designed the study. R.F., L.P., M. Gore, D.L.N. and J.L. provided clinical specimens. M. Gerlinger, A.J.R. and R.F. processed the samples. G.S., B.S.-D. and S. Hazell performed histopathological analyses. N. Matthews, B.P., S.B., A.J.R. and A.R. sequenced the samples. S. Horswell, I.V., N. McGranahan, M.P.S., P.M., S.G., P.A.B., A.S. and M. Gerlinger performed bioinformatics analyses. B.S.-D. processed histological samples, which were analyzed by G.S. and S. Horswell. M. Gerlinger, N. McGranahan and C.R.S. analyzed all data. M. Gerlinger, N. McGranahan, C.R.S., P.A.F., J.L. and C.S. interpreted the data. M. Gerlinger, N. McGranahan, C.R.S. and C.S. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Charles Swanton.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 VHL bisulfite sequencing.

DNA extracted from normal kidney (Normal) and Regions 1–9 (R1–R9) from patient EV003 were bisulfite treated and sequenced. Unmethylated DNA is characterized by conversion of guanine to uracil at CpG dinucleotides whereas methylated sites are protected. Arrows indicate the unmethylated CpG in normal tissue and methylated CpG in all tumor regions.

Supplementary Figure 2 Observed number of nonsynonymous variants for N biopsies.

For each patient, the total number of nonsynonymous variants detected in all possible subgroups of biopsies was tallied and represented as a scatter plot displaying the number of biopsies in the subgroup against the total number of variants detected in each subgroup. Mean and s.d. are displayed for each fixed number of biopsies.

Supplementary Figure 3 Density plots of regional VAFs.

Observed VAFs were transformed by dividing all frequencies within a region by the median frequency within that region of the ubiquitous variants for that patient and density plots were constructed from transformed VAFs. Driver mutations for which deep sequencing VAFs were available are displayed as vertical red lines.

Supplementary Figure 4 BAP1 and PBRM1 expression signatures in tumor regions with BAP1 or PBRM1 mutations.

Genes differentially expressed in tumors with BAP1 and PBRM1 mutations were obtained from Kapur et al.3. The expression of these genes was analyzed with the Affymetrix Gene 1.0 Array in all samples where either gene was mutated. Heat-map columns represent samples and rows correspond to probes. The heat map is row-normalized (z-score) and the colors highlight the deviation from the mean expression for each probe. Horizontal bars below the heat map highlight samples with BAP1 (light gray) and PBRM1 (dark gray) mutations and samples from the same patients are colored identically. Columns are ordered by the median expression of all BAP1-specific probes minus the median of all PBRM1-specific probes.

Supplementary Figure 5 Regional Fuhrman grades according to BAP1 and TP53 mutation status.

The distribution of the highest regional Fuhrman grades was compared between tumor regions which harbored mutations in BAP1 or TP53 (TP53/BAP1 mut) and regions which were wild type (WT) for these two genes. The significance was assessed by the Fisher's exact test (P = 0.056).

Supplementary Figure 6 SCNA (LogR) profiles by region.

Exome sequencing–derived LogR values for tumor regions of ten ccRCC tumors. Peaks of ccRCC-specific driver copy number alterations identified by Beroukhim et al.4 are shown as vertical blue (recurrent losses) or red (recurrent gains) bars.

Supplementary Figure 7 Mutational and copy number driver heterogeneity are positively correlated.

The percentage of heterogeneous nonsynonymous mutations (a) and of heterogeneous driver mutations (b) was plotted against the percentage of heterogeneous driver SCNAs and the Pearson correlation coefficient (r) was calculated.

Supplementary Figure 8 Cases with copy-neutral loss of heterozygosity in chromosome 3p.

Exome sequencing–derived LogR values for chromosome 3 with segmentation. Tumor regions in which copy-neutral loss of heterozygosity in chromosome 3p is observed are highlighted in red.

Supplementary Figure 9 Superimposition of driver SCNAs onto phylogenetic trees generated from point mutation data.

Branches for which SCNA data were not available and minority clones were removed from Figure 3 and driver SCNAs from Figure 4 were mapped onto the phylogenetic trees. Sixty-three of 76 driver SCNAs could be mapped to a single trunk or branch. The regional distribution of the remaining 13 SCNAs could not be explained by a single event. For the purpose of illustration we plotted these uniformly as parallel evolution events (highlighted by green boxes). However, the acquisition in a single event and subsequent loss through a second event in some subclones is a further possible explanation.

Supplementary Figure 10 Mutational spectrum of trunk and branch mutations across 96 trinucleotide contexts.

The proportion of each trinucleotide context is shown for all six possible nucleotide substitutions occurring in the trunks or the branches of the phylogenetic trees.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1, 2, 4 and 7, Supplementary Figures 1–10 and Supplementary Note (PDF 3944 kb)

Supplementary Table 3

Details of nonsynonymous somatic mutations by region (XLSX 82 kb)

Supplementary Table 5

Variant allele frequencies by region (XLSX 77 kb)

Supplementary Table 6

Nonsynonymous somatic mutations by inferred subclone (XLSX 79 kb)

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Gerlinger, M., Horswell, S., Larkin, J. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet 46, 225–233 (2014). https://doi.org/10.1038/ng.2891

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