Oral Presentation 25th Annual Lorne Proteomics Symposium 2020

Identification of aggressive prostate cancer through in-depth proteomics of tissues and urines (#43)

Thomas Kislinger 1
  1. Princess Margaret Cancer Center, Toronto, ONTARIO, Canada

For the 135,000 American men diagnosed with low- or intermediate-risk prostate cancer (PC) each year, clinical outcomes are heterogeneous: 50-80% will be disease-free 10 years following curative-intent therapy while 10-20% will experience recurrence within 18 months, portending lethality. Conversely, 25% diagnosed with low-risk PC elect to enter Active Surveillance, where their disease is monitored by repeat PSA tests and ultrasound guided biopsy to rule out the presence of aggressive PC. Current clinical prognostic factors do not accurately predict disease aggression and clinical outcome for individual men resulting in undertreatment of occult aggressive disease and overtreatment of indolent disease. We have applied proteomics analyses of direct expressed prostatic secretions (dEPS), post-DRE-urines and tissues to identify proteomics signatures of aggressive disease. Combining comprehensive proteomics profiling of dEPS fluids with targeted proteomics and computational biology we discovered robust signatures for extracapsular prostate cancer (Kim et al. Nat Commun. 2016). We are extending on this discovery by developing novel approaches for proteomics profiling of prostate fluids stratified into low, intermediate and high-risk prostate cancer. Our goals are to develop biomarkers to follow patients on active surveillance. In parallel, we are performing proteomics analyses of prostate tissues that have already been extensively profiled by the Canadian Prostate Cancer Genome Network. We have integrated genomic, epigenomic, transcriptomic, and proteomic data generated from 76 intermediate-risk prostate cancer patients. We discovered that the prostate cancer proteome yields four subgroups that differ from previously published DNA-based subgroups and are associated with differential biochemical recurrence. Our data indicated that integration of complementary biomolecules led to the best predictive accuracy (Sinha et al. Cancer Cell 2019). Our results show that proteomics complements other -omics data in stratifying prostate cancer patients and is an underutilized approach for precision medicine. All data has been parsed into a relational database that currently contains quantitative data for over 10,000 proteins. Integration of these data with the rich clinical annotation will enable objective data mining and selection of candidate biomarkers for validation by targeted proteomics.