Extended Data Fig. 3: Discovery of prostate cancer drivers.

a, The number and types of PCAWG driver genes and elements studied in our cohort. b, Recurrent copy number alterations among 183 prostate tumours identified with a 99% confidence level using GISTIC v2 (Supplementary Methods). The figure shows GISTIC peaks of significant regions of recurrent amplification (red) or deletion (blue) supported by FDR < 0.01. c, Genome-wide scan for significantly recurrent breakpoints in our study. The quantile-quantile plot shows two-sided P-values for mutational densities across 183 prostate cancer patients. Multiple hypothesis corrections using the false discovery rate (FDR; Benjamini–Hochberg method) are shown in Supplementary Table 4. Generalized linear modelling (GLM) of somatic mutation densities along the genome with significant background mutational processes adjusted in the model is also shown. d, Bionano Genomics optical genome mapping at the HLA complex. Examples of HLA translocations from a European patient (ID 12543) and an African patient (ID UP2360) studied in this cohort are characterized by pairs of optical maps, each carrying a fusion junction with flanking fragments aligning to one side of the two reference breakpoints. Using the recurrent HLA breakpoints identified in this study, the genome map of the African specimen is found to have a low-end fusion function matched with chromosome 6 through a manual inspection of unfiltered consensus maps using Bionano Access v1.5.2. Note that the HLA alternate contig fused in the European tumour is different from one suggested by short-read sequencing (chr6_GL000252v2_alt). The reference genome map is an in silico digest of the human reference hg38 with the DLE-1 enzyme. Genome map sizes are indicated on the horizontal axis, in megabase (Mb) units. Matching fluorescent labels between sample and reference genome map are connected by grey lines.