Oral Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

The proteogenomic landscape of curable prostate cancer (#131)

Ankit Sinha 1 , Vincent Huang 2 , Julie Livingstone 2 , Thomas Kislinger 1 3 , Paul C Boutros 1 2 4 5 6 7
  1. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
  2. Ontario Institute for Cancer Research, Toronto, Ontario, Canada
  3. Princess Margaret Cancer Centre, Toronto, Ontario, Canada
  4. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
  5. Department of Human Genetics, University of California, Los Angeles, California, USA
  6. Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, California, USA
  7. Department of Urology, University of California, Los Angeles, California, USA

Contributions from large international consortia have extensively characterized the mutational landscape and signaling alterations that drive tumour initiation and progression. For example, DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumour proteome remains poorly understood. Hence, integration along the central dogma may provide more accurate multi-omic biomarkers. To test this hypothesis, we systematically profiled and integrated whole-genome, epigenome, transcriptome and proteome profiles of 76 clinically annotated, localized intermediate-risk prostate cancer tumours [1]. As a result, we discovered that genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. Although ETS fusions are the most common alteration in prostate tumours; however, they influence different genes and pathways at the proteome and transcriptome. Additionally, changes in mRNA abundance explained approximately 10% of the variation in protein abundance. Hence, in our study, prognostic biomarkers that combine genomic or epigenomic features with proteomic features significantly outperform biomarkers comprised of a single data type.

  1. Sinha et al., 2019, Cancer Cell 35, 414–427