Clonal architectures and driver mutations in metastatic melanomas

Abstract

To reveal the clonal architecture of melanoma and associated driver mutations, whole genome sequencing (WGS) and targeted extension sequencing were used to characterize 124 melanoma cases. Significantly mutated gene analysis using 13 WGS cases and 15 additional paired extension cases identified known melanoma genes such as BRAF, NRAS, and CDKN2A, as well as a novel gene EPHA3, previously implicated in other cancer types. Extension studies using tumors from another 96 patients discovered a large number of truncation mutations in tumor suppressors (TP53 and RB1), protein phosphatases (e.g., PTEN, PTPRB, PTPRD, and PTPRT), as well as chromatin remodeling genes (e.g., ASXL3, MLL2, and ARID2). Deep sequencing of mutations revealed subclones in the majority of metastatic tumors from 13 WGS cases. Validated mutations from 12 out of 13 WGS patients exhibited a predominant UV signature characterized by a high frequency of C->T transitions occurring at the 3' base of dipyrimidine sequences while one patient (MEL9) with a hypermutator phenotype lacked this signature. Strikingly, a subclonal mutation signature analysis revealed that the founding clone in MEL9 exhibited UV signature but the secondary clone did not, suggesting different mutational mechanisms for two clonal populations from the same tumor. Further analysis of four metastases from different geographic locations in 2 melanoma cases revealed phylogenetic relationships and highlighted the genetic alterations responsible for differential drug resistance among metastatic tumors. Our study suggests that clonal evaluation is crucial for understanding tumor etiology and drug resistance in melanoma.

Figure 1. Mutation pattern, spectrum, and clinical features in 15 metastases from 13 WGS melanoma cases.

Mutations found in genes from MAP kinase, PI3K-AKT, RB/TP53 pathways and glutamate receptors are shown. Copy number alterations and structural variants found in BRAF, NRAS, TP53, CDKN2A/2B, and CCND1 are also displayed. The numbers and frequencies of tier 1 transition and transversion events identified in all 15 tumors are shown.

Figure 2. Mutation distribution in BRAF, NRAS, CDKN2A, EPHA3, GRIN2A, GRIN2B, PTPRT, and ASXL3.

The locations of conserved protein domains are highlighted. Each nonsynonymous substitution, splice site mutation, or indel is designated with a circle at the representative protein position with color to indicate the translational effects of the mutation.

Figure 3. Overview of subclonal landscape in melanoma (MEL1, 8, 9, and 10) and their associated driver mutations.

Two plots are shown for each case: kernel density (top), followed by the plot of tumor variant allele frequency by sequence depth for sites from selected copy number neutral regions (see Methods). Data shown are from chromosomes 1, 3, 4, 6, 10, and 13 for MEL1, from chromosomes 1, 2, 5, 12, and 20 for MEL8, from chromosomes 1, 3, 4, 6, 7, 12, and 13 for MEL9, and from chromosomes 6, 7, 10, 13, and 15 for MEL10. The data show evidence of two clusters in MEL1, MEL8, MEL9 and MEL10 with the majority of mutations from the lower allele frequency clusters. Mutations detected in significantly mutated genes in this study and genes implicated in Hodis et al. were labeled.

Figure 4. Dissecting mutational mechanisms using subclonal mutations.

(a) Overall sequence context surrounding C->T transitions in MEL9 and MEL10. (b) Density plots showing the founding clone and subclone in MEL9 and MEL10. (c) Mutation context analysis of the founding clones detected a UV mutation signature in both MEL9 and MEL10. (d) Mutation context analysis of secondary clones detected a UV signature in MEL10 but not MEL9.

Figure 5. Comparison of Circos plots of the metastatic samples from two tissue sites of the same individuals (MEL5 and MEL13).

In MEL5, pancreas tumor specific structural variants (inversions and deletions) are clustered on chromosomes 2 and 5, and pancreas or lung enriched rearrangements are drawn in yellow. In MEL13, highly similar copy number and structural variant patterns between lung and chest wall metastases are shown. No purity-based copy number corrections were used for plotting copy number.

Figure 6. Comparison of clonality patterns of metastatic samples from two tissue sites of the same individuals (MEL5 and MEL13).

Kernel density and variant allele frequency by sequence depth plots for each metastasis in MEL5 and MEL13. Data shown are from chromosomes 17, 18, and 21 for MEL5, and from chromosomes 3, 7, and 14 for MEL13. The plots indicate multiple clones in MEL5 with enrichment from lung to pancreas in MEL5, and nearly identical clonal pattern in both metastases in MEL13.

Figure 7. Phylogenetic and mutational relationships among four metastatic samples from different sites of the same individuals (MEL 167).

Geographic locations and CT scans of metastasis samples in MEL167 with three tumor samples from small bowel (mass 1, mass 2, and mass 3) and one from mensenteric lymph node. Phylogenetic relationships, mutation patterns, and copy number landscape in all four tumors were shown. Purity based VAF corrections were applied prior to phylogenetic analysis.

Figure 8. Phylogenetic and mutational relationships among four metastatic samples from different sites of the same individuals (MEL 174).

Geographic locations and CT scans of metastasis samples in MEL 174 with 2 samples from liver (liver tumor 1, liver tumor 2), one from omentum, and one from portal nodule. Phylogenetic relationships among 4 metastatic tumors were shown. Purity based VAF corrections were applied prior to phylogenetic analysis. Increased variant allele frequency of BRAF (V600E) in liver 2 and portal module, both tumors showed resistance to vemurafinib treatment. BRAF amplifications were also shown in liver 2 and portal module tumors.