Recurrent chromosomal gains and heterogeneous driver mutations characterise papillary renal cancer evolution

Abstract

Papillary renal cell carcinoma (pRCC) is an important subtype of kidney cancer with a problematic pathological classification and highly variable clinical behaviour. Here we sequence the genomes or exomes of 31 pRCCs, and in four tumours, multi-region sequencing is undertaken. We identify BAP1, SETD2, ARID2 and Nrf2 pathway genes (KEAP1, NHE2L2 and CUL3) as probable drivers, together with at least eight other possible drivers. However, only ~10% of tumours harbour detectable pathogenic changes in any one driver gene, and where present, the mutations are often predicted to be present within cancer sub-clones. We specifically detect parallel evolution of multiple SETD2 mutations within different sub-regions of the same tumour. By contrast, large copy number gains of chromosomes 7, 12, 16 and 17 are usually early, monoclonal changes in pRCC evolution. The predominance of large copy number variants as the major drivers for pRCC highlights an unusual mode of tumorigenesis that may challenge precision medicine approaches.

Figure 1. Somatic SNV spectra.

Data are derived from the exome of each cancer. For the multi-region cancers, only the region with the highest mutation burden is displayed to provide a comparison with the other tumours. Note that cancers with very few somatic SNVs are shown for the sake of completeness.

Figure 2. Distribution of selected somatic SNVs with predicted pathogenic effects and indels across cancers.

The germline FH mutation, the somatic CDKN2A deletion and the large deletion with break point within ARID1A are also shown for completeness. Note that copy number and LOH data are not shown for cancers GK101, GK102, GK116_1, GK116_2, GK116_3, RK133, RK30 and RK36 since these lack SNP array data.

Figure 3. Regional distributions of non-synonymous somatic mutations in four pRCCs.

For these M-seq cancers, the heat maps indicate the presence of a mutation (yellow) or its absence (blue) in each region. The non-M-seq sample GK116_2 is shown alongside GK116_1 for comparative purposes. Note that the SETD2 mutation p.Glu1667X in RK36 LN was identified in the combined call of all regions of this tumour, but not called by the M-seq pipeline; subsequent inspection showed the M-seq call to be a false negative (Supplementary Fig. 11). Each picture shows the regions of core biopsies and regions harvested at nephrectomy. Phylogenetic trees were generated by UPGMA from Ion Torrent M-seq SNV data. Branch and trunk lengths are proportional to the number of non-synonymous mutations acquired. No cancer showed a significant difference between the spectra of SNVs present on the trunk or branches (P>0.05, details not shown). Putative driver SCNAs and SNVs are shown on their respective branch. For clarity, sub-clonal SCNA gains are not shown for the highly branched tumour RK36; these involve chromosomes 7, 16 and 17, and are present in regions 5, 9, 1, 10, 3, LN and (apart from chr17) 8. The apparent discordancy between the SNV-based trees and SCNAs may reflect chance, genomic instability, recurrent mutations or reversion mutations.