Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour 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 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation 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 tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

Findings: 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 the majority replicated where data were available. 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 confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found 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 comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

Interpretation: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology 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 tumours. 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.

Funding: This work was supported by Cancer Research UK (grant no. C8221/A29017).

Keywords: -Omics; Cancer; Genetic epidemiology; Mendelian randomisation; Proteins; Proteomics; Spatial transcriptomics.

Fig. 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 colocalisation 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 colocalisation 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.

Fig. 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 analysed). 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 colocalisation (PP4 > 0.70).

Fig. 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 analysed in main analyses, and with data available to perform replication in an African ancestry and European ancestry population (UKBB = UK Biobank). Odds ratio estimates are scaled per standard deviation increment in genetically predicted circulating protein concentrations.

Fig. 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 visualisation showing MSMB gene expression (top) and histology and tissue status (bottom) from organ-wide spatial transcriptomic data in two tumour 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).

Fig. 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 coloured to demonstrate its central role in the network.