Circulatory prostate cancer proteome landscapes and prognostic biomarkers in metastatic castrate resistant prostate cancer

Abstract

Background: Plasma-based high-plex proteomic profiling were performed in prostate cancer (PC) patients using the Olink® Explore Proximity Extension Assay to identify plasma proteins associated in different PC states and to explore potential prognostic biomarkers. The progressive PC states include local, organ-confined PC (local PC), metastatic hormone-sensitive PC (mHSPC) and metastatic castrate-resistant PC (mCRPC).

Methods: Plasma samples were uniformly processed from 84 PC patients (10 patients with local PC; 29 patients with mHSPC; 45 patients with mCRPC). A proteome-wide association study was performed to identify proteins differentially overexpressed in progressive cancer states. Specifically, a sequential screening approach was employed where proteins overexpressed from one disease state were assessed for overexpression in the progressive disease state. Linear regression, analysis of variance, and t-tests were used for this approach. Differentially expressed proteins (DEPs) in mCRPC were then used to construct a prognostic model for overall survival (OS) in mCRPC patients using the Cox Proportional Hazard Model. The predictive performance of this model was assessed using time-dependent area under the receiver operating characteristic curves (tAUC) in an independent sample of mCRPC patients. The tAUC of the prognostic model was then compared to that of a model excluding DEPs to evaluate the added value of circulatory proteins in predicting survival.

Results: Of 736 tumor-associated proteins, 26 were differentially expressed across local PC, mHSPC, and mCRPC states. Among these, 20 were overexpressed in metastatic states compared to local, and in mCRPC compared to mHSPC states. Of these 20 proteins, Ribonucleoside-diphosphate reductase subunit M2 (RRM2) was identified as a prognostic biomarker for OS in mCRPC, with a hazard ratio of 2.30 (95% confidence interval (CI) 1.17-4.51) per normalized expression unit increase. The tAUC of the model including previously identified clinical prognostic factors was 0.62 (95% CI 0.29-0.91), whereas the model that includes RRM2 with clinical prognostic factors was 0.87 (95% CI 0.51-0.98).

Conclusions: Plasma proteome profiling can identify novel circulatory DEPs associated with mCRPC state survivals. Overexpression of RRM2 is linked to poor mCRPC survival and its inclusion alongside conventional prognostic factors enhances the predictive performance of the prognostic model.

Keywords: Landscape; Prognostic model; Prostate cancer; Proteomes.

Fig. 1

Study approach and objectives: Cohort A consists of plasma samples from patients with local, organ-confined stage prostate cancer. Cohort B included plasma from metastatic hormone-sensitive prostate cancer patients, and Cohort C included plasma from patients with metastatic castrate-resistant prostate cancer who experienced biochemical relapse (defined as serially rising Serum Prostate Specific Antigen (PSA) on continuous metastatic Hormone-Sensitive Prostate Cancer (mHSPC) treatments) without new clinical evidence of progression. Cohort D included patients with metastatic castration-resistant prostate cancer who progressed with clinical/imaging-based criteria in addition to biochemical relapses. (DEPs: Differentially Expressed Proteins)

Fig. 2

Heatmap of the normalized protein expression (NPX) of 736 oncology proteins grouped by cohorts: The top 72 rows represent proteins that have been previously reported to be associated with prostate cancer biology and are also listed in the Olink® panel. The names of these 72 proteins can be found in the Supplementary Table SI. This is followed by a blank row, below which the NPX expression patterns of the remaining proteins are listed in the panel. Each column represents a patient with protein expression in red representing overexpression and low expression in blue using NPX units. The NPX values were individually standardized by subtracting the sample mean and dividing by the sample standard deviation to facilitate comparability across proteins. To address the issue of extreme values dominating the color scale in heatmap visualizations—thereby obscuring patterns in the data—the standardized values were truncated at -2 and 2. This truncation ensured that the coloring scale remained interpretable and effectively highlighted meaningful variations

Fig. 3

Heatmap of the normalized protein expression (NPX) of 26 differentially expressed proteins grouped by three cancer progressive states: Heatmap of NPX values for 26 differentially expressed proteins identified in Objective 1. The rows represent proteins and columns represent patient sample. Proteins are clustered using Euclidean distance. Patient samples are grouped by the three cancer progressive states as done in Objective 1. The NPX values are individually standardized by subtracting the sample mean and dividing by the sample standard deviation to facilitate comparability across proteins