Spatial Distribution of Private Gene Mutations in Clear Cell Renal Cell Carcinoma

Abstract

Intra-tumour heterogeneity is the molecular hallmark of renal cancer, and the molecular tumour composition determines the treatment outcome of renal cancer patients. In renal cancer tumourigenesis, in general, different tumour clones evolve over time. We analysed intra-tumour heterogeneity and subclonal mutation patterns in 178 tumour samples obtained from 89 clear cell renal cell carcinoma patients. In an initial discovery phase, whole-exome and transcriptome sequencing data from paired tumour biopsies from 16 ccRCC patients were used to design a gene panel for follow-up analysis. In this second phase, 826 selected genes were targeted at deep coverage in an extended cohort of 89 patients for a detailed analysis of tumour heterogeneity. On average, we found 22 mutations per patient. Pairwise comparison of the two biopsies from the same tumour revealed that on average, 62% of the mutations in a patient were detected in one of the two samples. In addition to commonly mutated genes (VHL, PBRM1, SETD2 and BAP1),&nbsp;frequent subclonal mutations with low variant allele frequency (<10%) were observed in TP53 and in mucin coding genes MUC6, MUC16, and MUC3A. Of the 89 ccRCC tumours, 87 (~98%) harboured private mutations, occurring in only one of the paired tumour samples. Clonally exclusive pathway pairs were identified using the WES data set from 16 ccRCC patients. Our findings imply that shared and private mutations significantly contribute to the complexity of differential gene expression and pathway interaction and might explain the clonal evolution of different molecular renal cancer subgroups. Multi-regional sequencing is central for the identification of subclones within ccRCC.

Keywords: clonal exclusivity; intra-tumour heterogeneity; private mutations.

Figure 1

Experimental design. The first phase includes 16 clear cell renal cell carcinoma (ccRCC) patients of which two spatially separated biopsies from the primary tumour and a matched normal sample were collected. Whole-exome sequencing and transcriptome sequencing was performed and the detected mutations informed the selection of genes for the panel of the second phase. The second phase includes an extended cohort of patients and the selected genes were targeted with higher coverage. From a total of 89 patients, we analysed two spatially separated tumour biopsies and a matched normal sample per patient. Fourteen of the patients in this panel data set were also among the 16 from the first phase.

Figure 2

Genetic and transcriptomic diversity in 16 patients. (A) Number of shared (orange) and private (yellow, blue) mutations in the WES data set. A shared alteration was detected in both samples of a patient, whereas a private alteration was only found in one of the two samples. The two biopsies of the same tumour are labelled “TU1” and “TU2”. (B) Number of differentially expressed genes in the RNA-seq data set. (C) The most overrepresented Reactome pathways among the differentially expressed genes. The colour indicates the alteration status.

Figure 3

Genetic diversity in the panel seq data of 89 ccRCC patients. Top: numbers of shared and private mutations in the data set. Bottom: the heatmap highlights the mutations that were detected in the most frequently mutated genes. The four most frequently altered genes in ccRCC are VHL, PBRM1, BAP1, and SETD2 [17] and are highlighted in bold. If a gene was hit by multiple mutations that all have the same status (shared, private TU1, or private TU2), the following ordering is applied to prioritise which colour is shown in the heatmap, starting with the highest priority: stop gained, start gained, frameshift indel, inframe indel, missense, splice site, five prime UTR, three prime UTR, synonymous. That means, if a gene has, e.g., a missense and a synonymous mutation, the missense mutation will be displayed in the heatmap. The variants were annotated with SnpEff [22] (Supplementary Table S2). Mutations in non-coding regions are omitted from the heatmap.

Figure 4

The two most striking clonally exclusive pathway pairs from the clonal exclusivity test when performed on the pathway level of the WES data set from 16 ccRCC patients. The pathway pairs are affected in patients 8 and 14 and in each patient, a different subset of genes is mutated. Hence, this clonal exclusivity pattern is only detectable on the pathway level. (A) The table displays the genes that are mutated in these pathways. (B) The heatmaps illustrate in which clones the genes in these pathways are mutated. (C) The proportion of cells from the clones in each of the two samples from the two patients. The label “N” represents the fraction of normal cells in the biopsy. (D) The data set also includes RNA-seq data from each sample. Among the differentially expressed genes in each sample, the pathways are significantly overrepresented in some of the samples.