Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics

Abstract

Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

Methods: We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

Results: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions.

Conclusion: Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

Keywords: Prostate Cancer; Proteomics; cis-pQTL; plasma; protein.

Figure 1.

Association of genetically predicted protein concentrations with prostate cancer risk presented as a Manhattan plot where position is given by cis-pQTL coordinate (chromosome and base-pair position) labelled with their association with cancer risk and the highest colocalization probability from single or conditional iterative methods (PP4). Points highlighted as filled-in are those with evidence of a shared causal locus (PP4 > 0.7) with point size reflecting PP4 magnitude, which can vary between 0 and 1. Risk associations with MR p > Bonferroni correction threshold were not subject to colocalization analyses. The strongest protein-cancer association per chromosome is labelled and a zoomed-in plot for MSMB (rs10993994) on chromosome 10 is shown in the upper right-hand corner.

Figure 2.

Odds ratios (95% confidence intervals) for genetically predicted protein levels and prostate cancer risk (for proteins with p< Bonferroni threshold based on 0.05/number of proteins analyzed). Odds ratio estimates are scaled per standard deviation increment in genetically predicted relative circulating protein concentrations. Filled circles represent Bonferroni-significant associations and asterisks indicate evidence for colocalization (PP4 > 0.70).

Figure 3.

Odds ratios (95% confidence intervals) for genetically predicted protein levels and overall prostate cancer risk for proteins with p< Bonferroni threshold based on 0.05/number of proteins analyzed in main analyses, and with data available to perform replication in an African ancestry and European ancestry population. Odds ratio estimates are scaled per standard deviation increment in genetically predicted circulating protein concentrations.

Figure 4.

A) MSMB association with overall, early onset, and aggressive prostate cancer risk with replication in the FinnGen and UK Biobank populations and in an African ancestry population. Odds ratio (95% confidence interval) estimates are scaled per standard deviation increment in genetically predicted circulating MSMB concentrations B) Spatial visualization showing MSMB gene expression (top) and histology and tissue status (bottom) from organ-wide spatial transcriptomic data in two tumor sections (GG: Gleason grade group: GG1, Gleason score of 6 or lower; GG2, Gleason score of 3+4 = 7; GG4, Gleason score of 8). C) Violin plots representing gene expression in each spatial transcriptomics spot according to histological status. Statistical differences are indicated: **** p < 0.0001 (Kruskal–Wallis; post-test: Dunn’s test).

Figure 5.

Gene network from iterative random forests of the difference in gene expression between benign and GG4 prostate histology (Gleason Score = 8). Arrows indicate direction of influence and shape of the network. MSMB is colored to demonstrate its central role in the network.