Dataset Information

Parallel reaction monitoring assay development for multiple sclerosis biomarkers in cerebrospinal fluid.

ABSTRACT: We have developed and tested parallel reaction monitoring (PRM) assays for 25 proteins highly relevant as cerebrospinal fluid (CSF) biomarkers for multiple sclerosis, representing various biological processes affected in the disease. The proteins were represented by 72 peptides selected according to relevant guidelines and available literature. Stability testing revealed 64 peptides with low intra- and inter-day variations, with 44 also being stably digested after 16 hours of trypsin digestion, and 37 furthermore showing a significant difference between multiple sclerosis and controls, thereby confirming literature findings. Calibration curves were developed using spike-in of synthetic light and heavy peptides in rat plasma.

ORGANISM(S): Homo Sapiens Rattus Norvegicus

SUBMITTER: Astrid Guldbrandsen

PROVIDER: PXD017281 | panorama | Thu May 20 00:00:00 BST 2021

REPOSITORIES: PanoramaPublic

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Publications

Development of robust targeted proteomics assays for cerebrospinal fluid biomarkers in multiple sclerosis.

Guldbrandsen Astrid A Lereim Ragnhild Reehorst RR Jacobsen Mari M Garberg Hilde H Kroksveen Ann Cathrine AC Barsnes Harald H Berven Frode S FS

Clinical proteomics 20200918

<h4>Background</h4>Verification of cerebrospinal fluid (CSF) biomarkers for multiple sclerosis and other neurological diseases is a major challenge due to a large number of candidates, limited sample material availability, disease and biological heterogeneity, and the lack of standardized assays. Furthermore, verification studies are often based on a low number of proteins from a single discovery experiment in medium-sized cohorts, where antibodies and surrogate peptides may differ, thus only pr ...[more]

PMID: 32963504

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Project description:Background: Validated markers to predict outcome in rectal cancer patients treated with multimodal therapy remain elusive. Identifying molecular profiles for disease prognosis would be required for the design of clinical trials aimed at optimizing risk-adapted therapies. We have therefore used whole genome expression profiling of tumors of a large cohort of patients enrolled in the multicenter trials of the German Rectal Cancer Study Group (GRCSG) to identify molecular profiles for individualized therapy. Methods: We prospectively collected pretherapeutic biopsies from patients (n=300) treated according to the GRCSG trial guidelines from seven different German surgical departments using rigid quality controls. These samples were profiled by global gene expression analysis and a classifier developed to predict postoperative lymph node status and Disease Free Survival (DFS). The performance of the classifier was validated with an independent, prospectively collected set of samples. Findings: The final training and test set included 198 patients. Analyzes for postoperative nodal status and DFS revealed 69 and 674 differentially regulated genes, respectively. Depending on the classifier the accuracy to predict lymph node status ranged from 64% to 69% with negative predictive values between 72% and 74%. Stratification according to DFS resulted in a good (n=99), bad (n=96) and a small very bad prognosis group (n=3). Based on linear discriminant analysis the classifier for positive lymph node status was validated in 47 independent patients and revealed an accuracy of 72% and a positive predictive value of 100%. Thereby, prediction based on molecular profiling is superior to prediction based on conventional clinical markers. Interpretation: Whole genome expression analysis of pretherapeutical biopsies resulted in a molecular classifier of disease prognosis. This classifier was successfully validated. The high positive predictive value for post therapeutic lymph node status allows identification of patients requiring alternative or intensified treatment protocols. These data are currently validated for their clinical applicability and should be taken as basis for future molecular driven clinical trials to develop risk-adapted treatments.

Dataset Information

Parallel reaction monitoring assay development for multiple sclerosis biomarkers in cerebrospinal fluid.

Publications

Development of robust targeted proteomics assays for cerebrospinal fluid biomarkers in multiple sclerosis.

Similar Datasets

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