Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

Abstract

Prostate cancer (PCa) is annually the most frequently diagnosed cancer in the male population. To date, the diagnostic path for PCa detection includes the dosage of serum prostate-specific antigen (PSA) and the digital rectal exam (DRE). However, PSA-based screening has insufficient specificity and sensitivity; besides, it cannot discriminate between the aggressive and indolent types of PCa. For this reason, the improvement of new clinical approaches and the discovery of new biomarkers are necessary. In this work, expressed prostatic secretion (EPS)-urine samples from PCa patients and benign prostatic hyperplasia (BPH) patients were analyzed with the aim of detecting differentially expressed proteins between the two analyzed groups. To map the urinary proteome, EPS-urine samples were analyzed by data-independent acquisition (DIA), a high-sensitivity method particularly suitable for detecting proteins at low abundance. Overall, in our analysis, 2615 proteins were identified in 133 EPS-urine specimens obtaining the highest proteomic coverage for this type of sample; of these 2615 proteins, 1670 were consistently identified across the entire data set. The matrix containing the quantified proteins in each patient was integrated with clinical parameters such as the PSA level and gland size, and the complete matrix was analyzed by machine learning algorithms (by exploiting 90% of samples for training/testing using a 10-fold cross-validation approach, and 10% of samples for validation). The best predictive model was based on the following components: semaphorin-7A (sema7A), secreted protein acidic and rich in cysteine (SPARC), FT ratio, and prostate gland size. The classifier could predict disease conditions (BPH, PCa) correctly in 83% of samples in the validation set. Data are available via ProteomeXchange with the identifier PXD035942.

Machine Learning Pipeline to analyze Clinical and Proteomics Data: experiences on a prostate cancer case

“…Features are then ranked according to the total number of methods selecting them as relevant. See also [19] for biological considerations. laboratory at Magna Graecia University, were analysed with the goal of relating data and peptides as possible biomarkers in prostate cancer diagnosis.…”

“…The proposed pipeline allows to include, in the data analysis, clinical information used in the ML methods. By applying the pipeline on a real prosta cancer use case, we prove that by integrating sample clinical information (such as gland prostate sice) in the analyzed biological sample the results are enriched for better clinical interpretation [19], allowing to achieve disease early predictions performance or guiding clinical procedures (such as biopsy).…”

Combination of prostate volume and apparent diffusion coefficient can stratify patients with a PI‐RADS score of 3 to reduce unnecessary prostate biopsies

“…So, selecting suitable patients will reduce unnecessary prostate biopsies. In addition, liquid biopsy is an alternative method of biopsy using primarily bioliquid such as urine, blood, and expressed prostatic secretions (EPS) 26,27 . Drago et al 28 constructed a model based on metabolites in EPS‐Urine and this model had a predictive accuracy of 87.50% for PI‐RADS score 3 patients (28/32).…”

“…In addition, liquid biopsy is an alternative method of biopsy using primarily bioliquid such as urine, blood, and expressed prostatic secretions (EPS). 26,27 Drago et al 28 constructed a model based on metabolites in EPS-Urine and this model had a predictive accuracy of 87.50% for PI-RADS score 3 patients (28/32). Considering that with the improvement of the quality of life, the number of obese patients undergoing prostate biopsies is also increasing.…”

Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis

“…To build the DIA library, 20 µg of total peptides (PmixA and PmixB) was loaded on a high-capacity StageTip containing a greater amount of C18 stationary phase (threefold) to perform basic reversed-phase fractionation, as previously reported [31]. In detail, the peptide mixture was acidified with 0.1% of trifluoroacetic acid (TFA) in order to achieve a pH lower than 3.…”