Exome sequencing of osteosarcoma reveals mutation signatures reminiscent of BRCA deficiency

Abstract

Osteosarcomas are aggressive bone tumours with a high degree of genetic heterogeneity, which has historically complicated driver gene discovery. Here we sequence exomes of 31 tumours and decipher their evolutionary landscape by inferring clonality of the individual mutation events. Exome findings are interpreted in the context of mutation and SNP array data from a replication set of 92 tumours. We identify 14 genes as the main drivers, of which some were formerly unknown in the context of osteosarcoma. None of the drivers is clearly responsible for the majority of tumours and even TP53 mutations are frequently mapped into subclones. However, >80% of osteosarcomas exhibit a specific combination of single-base substitutions, LOH, or large-scale genome instability signatures characteristic of BRCA1/2-deficient tumours. Our findings imply that multiple oncogenic pathways drive chromosomal instability during osteosarcoma evolution and result in the acquisition of BRCA-like traits, which could be therapeutically exploited.

Figure 1. Somatic SNV spectra and mutation signatures.

(a,b) Data are derived from exomes of each tumour. Note that OS with very few somatic SNVs are included for sake of completeness.

Figure 2. Copy-number variation analysis.

(a) Copy-number profile of 123 OS genomes. (b) Significantly over-represented copy-number alterations. The most important over-represented SCNA loci are highlighted. Blue, gains; red, losses. (c) The number of copy-number alterations fulfilling specific criteria of a BRCA-like phenotype shown for each tumour (threshold line in red). (d) Homologous recombination deficiency (HRD) score shown for each tumour. The threshold for considering a tumour to be BRCA deficient was set to 15 (red dashed line).

Figure 3. Mutations burden of OS driver genes.

(a) Mutation profiles of selected OS driver genes, split by copy-number alterations and single-nucleotide changes/indels. The median variant allelic frequency (VAF) is plotted for each gene. (b) Distribution of selected somatic SNVs with predicted pathogenic effects and indels across cancers. Note that only clonal copy-number losses and disruptive gains are shown here. Blue, disruptive gains (that is, one breakpoint within a gene); red, losses.

Figure 4. Copy-number alterations in BRCA1/2 and binding partners.

(a,b) Frequencies of clonal and subclonal SCNA mutations. (c) SCNA mutational profiles of selected OS cell lines.

Figure 5. Cell viability assays.

MNNG, SAOS2 and SJSA-1 OS cells were treated for 72 h with indicated talazoparib, temozolomide and SN-38 concentrations. Cell viability was assessed by a MTT assay and is expressed as percentage of untreated cells. Data are shown as mean±s.d. of three independent experiments, each performed in triplicates. MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide.

Figure 6. Comprehensive analysis of two tumours.

(a) A genome-wide plot depicting mutational burden, SCNAs and intra- and inter-chromosomal structural variants. Large structural rearrangements have been detected from whole-genome mate-pair sequencing data as described in Supplementary Note 1 and Supplementary Methods. (b) Copy-number profile of selected chromosomes targeted by chromoplexy. Note that chromoplexy likely overlaps with additional structural events including chromothripsis-like events and breakage–bridge fusions. (c) Posterior cellular frequencies of somatic exome mutations. Main driver genes are in blue.