Dataset Information

QUEST-MS for plasma biomarkers complementing the specificity of PSA test

ABSTRACT: 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.

INSTRUMENT(S): instrument model

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Koji Ueda

PROVIDER: PXD000383 | Pride | 2014-01-28

REPOSITORIES: Pride

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Publications

Plasma low-molecular-weight proteome profiling identified neuropeptide-Y as a prostate cancer biomarker polypeptide.

Ueda Koji K Tatsuguchi Ayako A Saichi Naomi N Toyama Atsuhiko A Tamura Kenji K Furihata Mutsuo M Takata Ryo R Akamatsu Shusuke S Igarashi Masahiro M Nakayama Masato M Sato Taka-Aki TA Ogawa Osamu O Fujioka Tomoaki T Shuin Taro T Nakamura Yusuke Y Nakagawa Hidewaki H

Journal of proteome research 20130911 10

In prostate cancer diagnosis, PSA test has greatly contributed to the early detection of prostate cancer; however, expanding overdiagnosis and unnecessary biopsies have emerged as serious issues. 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 ...[more]

PMID: 23991666

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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

QUEST-MS for plasma biomarkers complementing the specificity of PSA test

Publications

Plasma low-molecular-weight proteome profiling identified neuropeptide-Y as a prostate cancer biomarker polypeptide.

Similar Datasets

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