Abstract

Simple SummaryRecent methodological developments have enabled studying proteins on a large scale in a quantitative fashion, which helps in building comprehensive views of specific molecular settings. These proteomics approaches, and combining their information to genomics and transcriptomics, have revealed that not all genetic and transcriptomic aberrations in prostate cancer are translated to the proteome. This makes it important to understand which ones are translated. Here, we review recent large-scale proteomics studies on clinical prostate cancer and prostate cancer models which help us understand how prostate cancer develops and evades current drug treatments.Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.

Highlights

  • Prostate cancer is the second most frequent cancer and the fifth cause of cancer-related death among men worldwide [1]

  • Another common alteration in prostate cancer is the inactivation of the tumor-suppressor gene PTEN, which is found in approximately 20% of PCa and up to 50% of advanced tumors and is often caused by gene deletion or mutation [18,19]

  • Quantitative proteomics studies comparing primary tumors to benign tissue (Table 1) describe a number of differentially expressed protein (DEP) which have been identified between PCa and non-malignant tissue from the same patient [59], PCa and benign prostatic hyperplasia (BPH) [50,53,56], low-risk and high-risk prostate cancer groups [46], low- and high-grade prostate cancer [51], different International Society of Urological Pathology (ISUP) grades [50], and even laser-capture microdissection (LCM)-isolated cellular fractions, such as epithelial cells of different Gleason grade [48] or ERG status [60]

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Summary

Introduction

Prostate cancer is the second most frequent cancer and the fifth cause of cancer-related death among men worldwide [1]. Several genome-wide sequencing studies performed on clinical samples of prostate tumors revealed multiple recurrent alterations that involve both coding and non-coding genes that were not previously implicated in prostate cancer tumorigenesis, such as those of NCOA2, FOXA1, SPOP, and IDH1 [23,24,25,26]. These novel targets function as oncogenes or tumor suppressors and allow for a classification of prostate cancers in distinct classes. Proteomic efforts to identify biomarkers from body fluids and vesicles have been reviewed elsewhere [39,40,41,42,43]

Proteomes of Clinical Prostate Cancer Samples
Large-Scale Proteomics of Primary Cancer of the Prostate
Proteomics in Assessing Heterogeneity in Prostate Cancer
Large-Scale Proteomics of Advanced Prostate Cancer
Large-Scale Proteomes of Prostate Cancer Models Provide Mechanistic Insights
Large-Scale Proteomics in Protein Dynamics
Interactomes of AR wt and Mutants
Other Prostate Cancer-Relevant Protein Interactomes
Findings
Conclusions

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