Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients

Abstract

Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasma proteomics, cell line secretomics, tissue specificity, and androgen regulation. A rigorous biomarker development pipeline based on selected reaction monitoring assays was designed to evaluate the most promising candidates. As a result, we qualified 76, and verified 19 proteins in seminal plasma of 67 negative biopsy and 152 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which predicted prostate cancer on biopsy and outperformed age and serum Prostate-Specific Antigen (PSA). A machine-learning approach for data analysis provided improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house immunoassay, TGM4 protein was upregulated 3.7-fold (p = 0.006) and revealed AUC = 0.66 for detecting prostate cancer on biopsy for patients with serum PSA ≥4 ng/ml and age ≥50. Very low levels of TGM4 (120 pg/ml) were detected in blood serum. Collectively, our study demonstrated rigorous evaluation of one of the remaining and not well-explored prostate-specific proteins within the medium-abundance proteome of seminal plasma. Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.

Keywords: Prostate cancer biomarkers; TGM4; XGBoost; absolute quantification; assay development; machine learning; protein-glutamine gamma-glutamyltransferase 4; selected reaction monitoring; seminal plasma; serum/plasma.

Graphical abstract

Fig. 1.

Selection of candidate proteins. A, The most promising PCa biomarker candidates were selected with five independent experimental and data mining approaches. B, The combined 147 candidates were subjected to the SRM method development, followed by qualification and verification phases in SP and blood serum. A single peptide per protein was measured in the qualification and verification phases. C, A bottom-up proteomic approach and two-dimensional liquid chromatography followed by shotgun mass spectrometry and label-free quantification were used to identify differentially expressed proteins in pools of SP samples from patients with negative biopsy (NBx, serum PSA >4 ng/ml, n = 5), low-grade PCa (LG, GS = 6, PSA >4 ng/ml, n = 5) and high-grade PCa (GS≥8, PSA >4 ng/ml, n = 5) patients. Log2 differences and the t test with Benjamini-Hochberg false-discovery rate-adjusted p values were calculated with Perseus software, and 1% FDR was used as a cut-off to select differentially expressed proteins for each comparison.

Fig. 2.

Performance of a multiplex SRM assay in the verification phase. A, Endogenous peptides and heavy peptide internal standards were multiplexed in a single SRM assay. B, Representative calibration curves used to quantify TGM4 protein in 67 negative biopsy and 152 PCa SP digests distributed between six 96-well plates. Similar curves were obtained for the rest of proteins (supplemental Fig. S8). Light-to-heavy ratios for TGM4 in each sample were plotted against the corresponding calibration curve, to derive TGM4 concentrations. C, Representative SRM transitions for the light endogenous and heavy internal standard peptides for TGM4 protein.

Fig. 3.

The most promising candidates measured in the verification phase. Using the stable-isotope dilution multiplex SRM assay, 19 candidates and 6 control proteins (KLK3, MUC6, MUC5B, SG2A1, SACA3 and VTNC) were quantified in the negative biopsy (NBx, n = 67) and PCa (n = 152) SP samples. Horizontal lines represent median values in SP.

Fig. 4.

Machine-learning analysis to identify multi-variable combinations of markers. A, Twenty two variables (19 candidate SP proteins, seminal KLK3, serum PSA and age) were used to generate all possible 1- to 5- marker combinations. XGBoost algorithm was applied to identify combinations with the highest F₀₅-measure scores and calculate AUCs, sensitivities, specificities, PPVs and NPVs. Stringent 10 × 10 cross-validation was applied to reduce over-fitting. Top combinations were verified on the whole dataset of patients to ensure that each potential marker had feature scores higher than a randomly generated feature. Finally, 100-fold bootstrapping was used to estimate mean values for performance metrics and calculate 95% confidence intervals. B, XGBoost importance of individual markers to differentiate between PCa and negative biopsy, as compared with random features. C, Diagnostic performance of top combinations, with 95% confidence intervals estimated using 100-fold bootstrapping. Combination of TGM4 with PAEP protein improved AUC and sensitivity to differentiate between negative biopsy and PCa, whereas additional markers did not further increase AUCs.

Fig. 5.

TGM4 diagnostic performance. Performance of TGM4 protein as measured by an in-house ELISA in all 228 SP samples (A) and 80 blood serum samples (B). For comparison, age and serum PSA provided AUCs 0.60 ([95% CI 0.53–0.68]; MWU p = 0.0105) and 0.56 ([0.48–0.63]; p = 0.18) in the same cohorts of patients. C, TGM4 performance in three phases (qualification in SP by SRM, independent verification in SP by SRM, and independent verification in SP by ELISA) for the most clinically relevant patient groups (serum PSA ≥4 ng/ml and age ≥50 years old). As measured by ELISA, TGM4 provided AUC = 0.66 to predict PCa on biopsy and outperformed age and serum PSA. Concentration cut-off >1.74 μg/ml revealed 92% specificity at 31% sensitivity, and substantially increased specificity of serum PSA ≥4 ng/ml to detect PCa.