Patients
Patients were selected for EV isolation followed by EV proteome profiling based on below criteria. Information was collected prospectively in a single time for each patient before any treatment (treatment naïve). Patient enrollments started in April 2020 and ended in May 2022. All enrollments were from a single center, Centro Hospitalar e Universitário Lisboa Central, Lisbon, Portugal. Enrollment was continued untill the number samples were in accordance with the study protocol. The final cohort were composed of healthy donors (BPD, N=24), patients without Crib and IDC histological prostate patterns (non-IDC/non-Crib, N=21), and patients with Crib and/or IDC histological prostate patterns (IDC/Crib, N=55) at biopsy.
Inclusion Criteria:
1. Men over 18 years old.
2. No previous history of PCa treatments.
Exclusion Criteria
1. History of other forms of focal treatment of PCa
2. Radical surgery performed in the context of “salvage” strategy, due to recurrence or local persistence.
3. Neoadjuvant and/or adjuvant treatment (includes any type of hormonotherapy as LHRH agonists/antagonists)
4. History of urothelial cancer (bladder or upper urinary tract)
Mid-stream urine (30 – 120 mL) from PCa suspects were collected, immediately frozen at -80° C and stored upon collection until EV isolation. The experimental protocols were approved by the medical agencies and ethics committees of NOVA Medical School (82/2020/CEFCM). All patients signed informed consent before trial participation.
Clinical data collected were prePSA (ELISA), histological type, Gleason grade/score (World Health Organization (WHO) and the College of American Pathologists (CAP)), number of positive cores, histological patterns Crib and IDC-p at biopsy. Samples were organized into two experimental batches for urinary EV isolation followed by LC-MS analysis with random batch allocation of the sample groups BPD and IDC/Crib. Subsequent analysis adjusted for batch effects in the limma regression models to minimize confounding batch effects. All clinical measurements were obtained in a blinded manner without knowledge of the final clinical outcome. This approach ensured that the assessors performing the measurements remained unbiased and uninfluenced by the eventual results, minimizing potential researcher bias. The numbers of samples to collect were estimated based on standard error obtained on LC-MS from previous studies performed in our group and estimated with the R function pwr assuming paired testing. The number of samples collected and the single center collection were considered appropriate for a pilot study. Strobe check list is presented in supplementary data.
Isolation of Extracellular Vesicles and particles from urine
Frozen urine specimens were thawed and centrifuged at 3000× g for 20 min at 4 ◦C and then at 12,000× g for 60 min at 4 ◦C. Clarified urine was ultracentrifuged in an Optima TM L-80XP ultracentrifuge (Beckman Coulter, Brea, CA, USA) at 170,000× g at 4 ◦C for 120 min with a Type 32 Ti rotor to pellet EVs. The supernatant was carefully removed, and crude EV-containing pellets were resuspended in ice-cold PBS.
Protein Measurements
Following manufacturer's instructions, a bicinchoninic acid (BCA) protein assay kit (Pierce Biotechnology, Rockford, IL, USA) was used to measure the protein concentrations in isolated exosome fractions.
Western blotting
For western blotting (WB) assay, 5 microgram sEV protein were mixed with Laemmli sample buffer (BioRad) boiled for 10 min at 100°C. Then, the samples were resolved by SDS-PAGE followed by transfer onto nitrocellulose membranes (Cytiva). Blocking was performed during 1h with 5% skim milk in TBST 0,1% or PBST 0,1 % Tween or 5% BSA in TBST 0,1% Tween. Primary antibodies (CD63, SICGEN (AB0047); Alix, SICGEN (AB0327), TOM20, BD Biosciences (612278), GRP75, Cell Signaling Technology (2816S)) were incubated overnight at 4°C and secondary antibodies (HRP-AffiniPure Donkey Anti-Goat IgG (H+L), HRP-AffiniPure Goat Anti-Mouse IgG (H+L), HRP- Affini Pure Goa Anti-Rabbit IgG (H+L), Jackson Immuno-Research) during 1h at room temperature (RT). Development was performed using ECL™ prime Western blotting detection reagent (Cytiva) and the Chemidoc Touch Imager (BioRad).
Nanoparticle tracking EV measurements
A NanoSight NS300 instrument (Malvern Panalytical, Malvern, UK) was used to determine the concentrations and sizes of the EVs in the samples. Samples were diluted in PBS to a final volume of 1 ml to reach the ideal particle concentration of 1 × 108 – 2 × 109 particles/mL. The samples were loaded to the sample chamber in a continuous flow by a syringe pump. The instrument was equipped with a 488 nm laser and a sCMOS camera. The focus for each sample was manually adjusted to achieve optimal visualization of particles and for each measurement five videos of 60 seconds were captured. For all experiments the following settings were used: temperature: 25°C; Syringe speed: 20; Viscosity: 0.9 cP; camera level setting ranged from 13–14 in light scatter mode (LSM). After capture, the videos have been analysed by the in-build NanoSight Software NTA 3.4 Build 3.4.4 with a detection threshold of 5. To minimize variability, all camera and detection threshold settings were kept the same and all particles over 300 nm of diameter were excluded from the analysis.
Electron Microscopy
5 μL of each sample was incubated on glow-discharged (0.5 min) formvar-carbon coated copper mesh grids (Electron Microscopy Sciences) for 2 min, before washing 10 times with dH2O. Samples were negatively stained with 2% uranyl acetate in dH2O for 2 minutes, before blotting dry and imaging with a Hitachi H-7650 TEM equipped with an AMT XR41 M digital camera.
Peptide Sample Preparation
Samples containing a minimum of 20 μg of total EV proteins were further processed by the filter-aided sample preparation (FASP) method. In short, protein solutions containing SDS and DTT were loaded onto filtering columns (Millipore, Billerica, MA, USA) and washed exhaustively with 8M urea (GE, Healthcare, Marlborough, MA, USA) in HEPES buffer (Sigma-Aldrich, Saint Louis, MO, USA) as previously described45,46. Proteins were equilibrated with ammonium bicarbonate solution prior to trypsin digestion overnight at 37°C (Sigma-Aldrich, Saint Louis, MO, USA). Overnight cleavage of proteins was carried out using sequencing-grade trypsin (Promega, Madison, WI, USA).
Mass Spectrometry Analysis
As previously described13, samples were analyzed by mass spectrometry-based proteomics using nano-LC-MSMS equipment (Dionex RSLCnano 3000) coupled to an Exploris 480 Orbitrap mass spectrometer (Thermo Scientific, Hemel Hempstead, UK). In brief, samples were loaded onto a custom-made fused capillary pre-column (2 cm length, 360 μm OD, 75 μm ID, flowrate 5 μL per minute for 6 min) packed with ReproSil Pur C18 5.0 μm resin (Dr. Maisch, Ammerbuch-Entringen, Germany), and separated using a capillary column (25 cm length, 360 μm outer diameter, 75 μm inner diameter) packed with ReproSil Pur C18 1.9-μm resin (Dr. Maisch, Ammerbuch-Entringen, Germany) at a flow of 250 nL per minute. A 56 min linear gradient from 89% A (0.1% formic acid) to 32% B (0.1% formic acid in 80% acetonitrile) was applied. Mass spectra were acquired in positive ion mode in a data-dependent manner by switching between one Orbitrap survey MS scan (mass range m/z 350 to m/z 1200) followed by the sequential isolation and higher-energy collision dissociation (HCD) fragmentation and Orbitrap detection of fragment ions of the most intense ions with a cycle time of 2 s between each MS scan. MS and MSMS
settings: maximum injection times were set to “Auto”, normalized collision energy was 30%, ion selection threshold for MSMS analysis was 10,000 counts, and dynamic exclusion of sequenced ions was set to 30 s.
Database Search
The data obtained from the 200 LC-MS runs of urine EV samples from 24 controls and 76 PCa cases, characterized following radical prostatectomy (55 with and 21 without Cribriform pattern and/or IDC) each run as technical duplicates were analyzed. The LC-MS data were searched using VEMS47 and MaxQuant48 (Version 2.1.0.0). The MSMS spectra were searched against a standard human proteome database from UniProt (3AUP000005640).
Permuted protein sequences, where arginine and lysine were not permuted, were included in the database for VEMS and FDR in MaxQuant version 2.1.0.0 were based on reversed sequences. 1% FDR threshold was applied for peptide and protein identifications. Trypsin cleavage allowing a maximum of four missed cleavages was used. Carbamidomethyl cysteine was included as fixed modification. Methionine oxidation, lysine and N-terminal protein acetylation, were included as variable modifications. No restriction was applied for minimal peptide length
for VEMS search. All other search parameters were default values. The downstream analysis presented is based on the MaxQuant results.
Estimation of analytical variability
In our comprehensive proteomic investigation, we meticulously evaluated the precision of our experimental raw measurements (prior to quality filtering or normalization), as evidenced by a calculated average coefficient of variation (CV) of 34.1%. The mean CV was estimated to 13.1% after normalization. This average CV is based on all measurements on all proteins in the technical replicas. This indicative measure underscores the reliability and consistency of protein abundance quantification across technical replicates, affirming the robustness of our proteomic profiling methodology.
Statistical Analysis
Statistical analysis of identified proteins was performed in R statistical programming language. Quantitative data from MaxQuant and VEMS were analyzed in R statistical programming language version 4.04 (The R Foundation, Vienna, Austria). Protein label free quantitation (iBAQ) and protein spectral counts from the two programs were preprocessed by removing common MS contaminants, followed by a log2(x + 1) transformation and removing common MS contaminants. iBAQ values from the duplicated measurements were averaged. No imputation of missing or zero value protein quantitation values were performed in the analysis. Information on sample grouping based on histological patterns which were used for pairwise comparisons were complete for all samples. Protein iBAQ values were subjected to statistical analysis utilizing the R package limma18, where the contrast for different pairwise comparisons was specified for the main clinical groups BPD, non-IDC/non-Crib and IDC/Crib (Tables S1-3). Samples were processed in two large batches to minimize experimental bias. For sensitivity analysis, various linear regression models including terms to correct for batch effect and PSA were tested and these models displayed minimal effect on the number significantly regulated proteins called after correction for multiple testing. For example, batch effect had no effect for the comparison IDC/Crib versus BPD and cancer versus BPD. For non-IDC/non-Crib versus BPD only a difference of two more significantly regulated proteins were observed.
Correction for multiple testing was applied using the method of Benjamini & Hochberg49. Volcano plots were constructed with ggplot software (The R Foundation, Vienna, Austria). To test for increasing trend in iBAQ values as Gleason grade increase, the Jonckheere's test were calculated using the R package clinfun50. It examined whether there is a significant trend in iBAQ values across the increasing levels of Gleason grade. A low p-value indicates strong evidence against the null hypothesis of no trend, suggesting a significant increasing pattern. For correlation analysis a few missing values were present for same patients and cases with missing values for correlation analysis were excluded. Sensitivity of the analysis was assessed by comparing protein markers obtained by correlating to clinical parameters that are known to correlate with sample grouping.
Functional Enrichment Analysis
Functional enrichment based on the hypergeometric probability test was performed as described previously in R51,52. Functional enrichment was based on extracting all functional categories for which at least one of the samples showed a significant enrichment based on the hypergeometric probability test51,52. For these functional categories, the matching proteins’ gene names and numbers of proteins matching the functional categories were extracted, and the estimated p values were –log10 transformed and plotted as heatmaps. Functional enrichment was performed for all identified proteins in each sample group and for deregulated proteins when comparing sample groups. Cellular component (CC), biological process (BP), molecular function (MF), KEGG and cancer hallmark functional annotations were considered in the analysis.