Project description:DNA sequencing studies have identified specific recurrent somatic mutations that drive the aggressiveness of localized prostate cancers. Surprisingly, though, it is poorly understood how the prostate cancer proteome is shaped by genomic, epigenomic and transcriptomic dysregulation. To fill this gap, we profiled the whole genomes, methylomes, epigenomes, transcriptomes and proteomes of 55 localized, intermediate-risk, prostate cancers. This multi-modal dataset revealed that the genomic subtypes of prostate cancer converge on four proteomic subtypes, which are associated with distinct clinical trajectories. ETS fusion genes, the most common mutation in prostate tumours, perturb the proteome and transcriptome in divergent ways – with different genes and pathways affected at each level. Indeed, mRNA abundance changes explain only ~10% of variability in protein levels. Perhaps as a direct result, prognostic biomarkers that combine genomic or epigenomic features with proteomic ones significantly outperform those comprised of either molecular feature alone. These data suggest that the proteome of prostate cancer is shaped by a complex interplay of genomic, epigenomic, transcriptomic and post-transcriptional dysregulation.
Project description:DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.