QUEST-MS for plasma biomarkers complementing the specificity of PSA test
Ontology highlight
ABSTRACT:
INSTRUMENT(S): LTQ Orbitrap Velos
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Koji Ueda
PROVIDER: MSV000098420 | MassIVE | Wed Jul 02 14:19:00 BST 2025
SECONDARY ACCESSION(S): PXD000383
REPOSITORIES: MassIVE
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Project description:To explore plasma biomarkers complementing the specificity of PSA test, we developed a unique proteomic technology QUEST-MS (Quick Enrichment of Small Targets for Mass Spectrometry). The QUEST-MS method based on 96-well formatted sequential reversed phase chromatography allowing efficient enrichment of < 20 kDa proteins quickly and reproducibly. The LC/MS/MS data from 24 healthy controls, 19 benign prostate hypertrophy (BPH) patients, and 73 prostate cancer patients plasma were subjected to label-free quantification analysis on Expressionist proteome server. Data processing: Mascot Server 2.3.1 was used with the database SwissProt 2011_08 (531473 sequences,188463640 residues). Enzyme: semiTrypsin. Search parameters: Peptide Mass Tolerance : +- 3 ppm, Fragment Mass Tolerance:+-0.8 Da. Fixed modifications:Carbamidomethyl (C). Variable modifications:Oxidation (M),Deamidated (NQ). Max Missed Cleavages:2.
2014-01-28 | PXD000383 | Pride
Project description:Clinical management of prostate cancer remains a significant challenge due to the lack of available tests for guiding treatment decisions. The blood Prostate-Specific Antigen (PSA) test has facilitated early detection and intervention of prostate cancer. However, blood PSA levels are less effective in distinguishing aggressive from indolent prostate cancers and other benign prostatic diseases. Thus, the development of novel approaches specific for prostate cancer that can differentiate aggressive from indolent disease remains an urgent medical need. In the current study, we evaluated urine specimens from prostate cancer patients instead of serum using liquid chromatography-tandem mass spectrometry (LC-MS/MS), with the aim of identifying effective prostate cancer biomarkers. Glycoproteins from urine samples of prostate cancer patients with different Gleason scores were characterized via solid phase extraction of N-linked glycosite-containing peptides and LC-MS/MS. In total, 2923 unique glycosite-containing peptides were identified. Comparison of urine-based glycoproteins with those identified from aggressive and non-aggressive prostate cancer tissues as well as sera from prostate cancer patients revealed that the majority of aggressive prostate cancer-associated glycoproteins were more readily detected in patient urine than serum samples. Our data collectively indicate that urine provides a highly reliable source for biomarker testing in patients with aggressive prostate cancer.
2022-02-28 | PXD004495 | Pride
Project description:Prostate-specific antigen, a blood serum biomarker of prostate cancer, lacks specificity and prognostic significance, so considerable efforts are devoted to developing novel biomarkers. Seminal plasma, due to its proximity to prostate, is a promising fluid for biomarker discovery and non-invasive diagnostics. In this study, we investigated if seminal plasma proteins could increase specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 148 most promising biomarker candidates, we combined proteins identified through five independent data mining or experimental approaches: tissue transcriptomics, seminal plasma proteomics, cell secretomics, tissue specificity and androgen regulation. A rigorous biomarker development pipeline based on targeted proteomics assays was designed to evaluate the most promising candidates. We qualified 77 and verified 19 proteins in seminal plasma of 67 negative biopsy and 155 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which could predict prostate cancer on biopsy and outperformed age and serum PSA. Machine-learning approaches also revealed improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house ELISA, TGM4 was up-regulated 3.7-fold (P=0.006) and revealed AUC 0.66 for detecting prostate cancer on biopsy for patients serum PSA≥4 ng/mL and age≥50. Low levels of TGM4 (120 pg/mL) were detected in blood serum, but could not differentiate between negative biopsy, prostate cancer or prostate inflammation. To conclude, performance of TGM4 warrants its further investigation within the distinct genomic subtypes of prostate cancer and evaluation for the inclusion into emerging multi-biomarker panels.
2019-08-02 | PXD007657 | Pride
Project description:Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models
Natalie Kronik1¤, Yuri Kogan1, Moran Elishmereni1, Karin Halevi-Tobias1, Stanimir Vuk-Pavlovic ́2.,Zvia Agur1*.1Institute for Medical BioMathematics, Bene Ataroth, Israel,2College of Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
Abstract
Background:Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients.We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simplemathematical models.Methodology/Principal Findings:We developed a general mathematical model encompassing the basic interactions of avaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cellvaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes inprostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were dividedinto each patient’s training set and his validation set. The training set, used for model personalization, contained thepatient’s initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized modelswere simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set.The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients(the coefficient of determination between the predicted and observed PSA values wasR2= 0.972). The model could notaccount for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validatedpersonalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccinationregimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Conclusions/Significance:Using a few initial measurements, we constructed robust patient-specific models of PCaimmunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value andfeasibility of individualized model-suggested immunotherapy protocols.
2020-01-13 | MODEL2001130003 | BioModels
Project description:Background: Early detection of prostate cancer (PCa) is limited by the lack of accurate non-invasive biomarkers easily evaluable in men within a screening context. Prostate-specific antigene (PSA) measurement leads to high percentages of both false positives and false negatives. In the era of liquid biopsies, we propose to combine PSA dosage with the evaluation of circulating microRNAs (miRs), to discriminate between men harbouring or not PCa. Methods: miR profiling of plasma samples from 60 men with PCa, 51 with benign prostatic hyperplasia (BPH) and 27 healthy donors (HD) was performed using microarrays. Univariate analysis (linear models with an empirical Bayes approach) and multivariate penalized logistic regression models were applied to highlight miRs and clinical variables associated with the presence of malignant disease, and were combined in a classification score. Finally, the developed score was tested on an independent dataset of 242 plasma samples (68 PCa, 8 premalignant lesions, 93 BPH, 73 HD), where miR expression was evaluated by RT-qPCR. Results: Out of 2006 miRs analyzed, a linear combination of miR-103a-3p and let-7a-5p with PSA defined our score. A classification rule based on this score allowed reclassification of samples with accuracy (0.61), sensitivity (0.87), specificity (0.35) and AUC (0.68) higher than those obtained by PSA alone. On the validation set, the same classification rule still performed better than PSA alone in terms of specificity (0.57 versus 0.55) and AUC (0.76 versus 0.74), allowing correct reclassification of all but one tumors not detected by PSA and 33% of BPH with PSA in the 4-16 ng/ml range. Conclusion: The combination of two circulating miRs with PSA could be an interesting biomarker to detect the presence of PCa (even when PSA levels are below the standard cut-off of 4 ng/ml) and the absence of PCa in an important fraction of men with high PSA, who may avoid unnecessary biopsies.
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Project description:Purpose: Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population. Method:A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 235 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. Patients treated with RP between 2000 and 2006 were identified from the Mayo Clinic tumor registry for a case-cohort study design. This involved identification of all patients with metastasis and a representative of the full cohort .Thus, men at high risk of recurrence post-RP (open/robotic) with no prior neoadjuvant/prostate cancer treatment were selected based on any of preoperative PSA >20 ng/mL, pathological Gleason score (GS) â¥8, SVI, or GPSM (GS; preoperative PSA; SVI; margins) score â¥10.The cohort of 1,010 men included 73 cases with metastasis as evidenced by CT or bone scan. A 20% random sample (n=202) was drawn from the cohort. This included 19 of 73 metastatic cases. To increase sampling of metastasis, the remaining 54 metastatic cases were added, resulting in a final study set of 256 patients. 21 samples did not pass QC and were eliminated from this study. Patients not experiencing metastasis regardless of BCR (defined as follow-up PSA â¥0.4 ng/mL >30 days post-RP) were censored at last follow-up. The study was approved by Mayo Clinic Institutional Review Board.
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Project description:Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3,000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan – PROteomics of MIcroparticles with Super-SILAC Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5,374 plasma-microparticle proteins, and revealed a predictive signature of 3 proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMIS-Quan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation and quantification in both the biomedical research and in the clinical worlds.
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