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.

Figures

Figure 1

Regional distribution of nonsynonymous mutations in ten ccRCC tumors. Mutations that failed validation were not included. Heat maps indicate the presence of a mutation (yellow) or its absence (blue) in each region. Category 1 high-confidence driver mutations and category 2 probable driver mutations are highlighted in magenta. The table shows the number of nonsynonymous mutations and the ratio of heterogeneous mutations per tumor. An asterisk indicates where VHL methylation was included in the analysis.

Figure 2

Variant frequencies for the nonsynonymous somatic mutations in eight ccRCC tumors based on ultra-deep amplicon sequencing. Individual mutations are shown on the x axis, and variant frequencies are plotted on the y axis. Category 1 high-confidence driver mutations and category 2 probable driver mutations are labeled on the x axis and are highlighted by dashed boxes. Amplicon generation for deep sequencing failed for VHL mutations in EV005, EV006 and RMH002 and for a BAP1 mutation in RK26, and thus these could not be included. Mutations that define dominant (dom) and minority (min) clones in cases EV005, EV007, RMH008 and RK26 are indicated.

Figure 3

Phylogenetic trees generated by maximum parsimony from M-seq data for ten ccRCC tumors. Trees for EV001 and EV002 are adapted from Gerlinger et al.. Branch and trunk lengths are proportional to the number of nonsynonymous mutations acquired on the corresponding branch or trunk. Driver mutations were acquired by the indicated genes in the branches the arrows indicate. Driver mutations defining parallel evolution events are highlighted by color. Trees are rooted at the germline (GL) DNA sequence, determined by exome sequencing of DNA from peripheral blood.

Figure 4

Regional distribution of somatic driver copy number aberrations in ten ccRCC tumors. The heat map indicates copy number gains (yellow) or losses (blue) for all tumor regions for which copy number profiles could be reconstructed from exome sequencing data. CN, regions displaying copy-neutral LOH of chromosome 3p25.3.

Figure 5

Truncal location of driver aberrations and mutation spectrum in ten ccRCC tumors. (a) Percentage of cases in which a specific driver aberration was located on the trunk of the phylogenetic tree. n indicates the number of cases in which each event was found. Mut, mutation; methyl, methylation. (b,c) Mutation spectrum of nonsynonymous trunk versus branch mutations combined across all cases (b) and per case (c). The number of mutations analyzed is displayed on top of each bar. The difference between the spectra for trunk and branch mutations across all cases was assessed using a χ2 test. For specific mutation types, a Fisher’s exact test was used, and significant P values, corrected for multiple testing using the Benjamini-Hochberg method where appropriate (q values), are shown.