Dataset Information

A Prostate Cancer Proteomics Database for SWATH-MS based Protein Quantification

ABSTRACT: Prostate cancer is the third most frequent cancer in men worldwide, with a notable increase in prevalence over the last two decades. The PSA is the only well-established protein biomarker for prostate cancer diagnosis, staging, and sur-veillance. It frequently leads to inaccurate diagnosis and overtreatment since it is an organ-specific biomarker rather than a tumour-specific biomarker. As a result, one of the primary goals of prostate cancer proteome research is to iden-tify novel biomarkers that can be used with or instead of PSA, particularly in non-invasive blood samples. Thousands of peptides or assays were detected in blood samples from patients with low to high-grade prostate cancer and healthy individuals, allowing data processing of sequential window acquisition of all theoretical mass spectra (SWATH-MS). By assisting in the detection of prostate cancer biomarkers in blood samples, this useful resource will improve our un-derstanding of the role of proteomics in prostate cancer diagnosis and risk assessment.

INSTRUMENT(S): Orbitrap Fusion Lumos, TripleTOF 6600

ORGANISM(S): Homo Sapiens (human)

DISEASE(S): Prostate Adenocarcinoma

SUBMITTER: AMMARA MUAZZAM

LAB HEAD: Paul Townsend

PROVIDER: PXD028651 | Pride | 2021-12-20

REPOSITORIES: Pride

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Publications

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification.

Muazzam Ammara A Chiasserini Davide D Kelsall Janet J Geifman Nophar N Whetton Anthony D AD Townsend Paul A PA

Cancers 20211108 21

Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify a ...[more]

PMID: 34771740

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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.

Dataset Information

A Prostate Cancer Proteomics Database for SWATH-MS based Protein Quantification

Publications

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification.

Similar Datasets

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