Metabolomics of biofluids : from analytical tools to data interpretation

The chapters that comprised this thesis cover a broad range of subjects from analytical method development to clinical application of metabolic profiling. They are united by the facts that all of these studies aimed at analysis of biological fluids and that the presented methods and approaches may ultimately become parts of a robust metabolomics workflow that might be used in a future personalized medicine.Bruker BioSpin GmbH, Germany; Dionex Benelux B.V.; Beckman Coulter (Nederland) B.V.; Bruker Nederland B.V.UBL - phd migration 201

Metabonomics and Intensive Care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901

Androgen receptor profiling predicts prostate cancer outcome

Prostate cancer is the second most prevalent malignancy in men. Biomarkers for outcome prediction are urgently needed, so that high-risk patients could be monitored more closely postoperatively. To identify prognostic markers and to determine causal players in prostate cancer progression, we assessed changes in chromatin state during tumor development and progression. Based on this, we assessed genomewide androgen receptor/chromatin binding and identified a distinct androgen receptor/chromatin binding profile between primary prostate cancers and tumors with an acquired resistance to therapy. These differential androgen receptor/chromatin interactions dictated expression of a distinct gene signature with strong prognostic potential. Further refinement of the signature provided us with a concise list of nine genes that hallmark prostate cancer outcome in multiple independent validation series. In this report, we identified a novel gene expression signature for prostate cancer outcome through generation of multilevel genomic data on chromatin accessibility and transcriptional regulation and integration with publically available transcriptomic and clinical datastreams. By combining existing technologies, we propose a novel pipeline for biomarker discovery that is easily implementable in other fields of oncology

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

Cross-platform analysis of longitudinal data in metabolomics

Proteomic

Genome-wide epigenetic profiling of breast cancer tumors treated with aromatase inhibitors

Aromatase inhibitors (AI) are extensively used in the treatment of estrogen receptor-positive breast cancers, however resistance to AI treatment is commonly observed. Apart from Estrogen receptor (ER?) expression, no predictive biomarkers for response to AI treatment are clinically applied. Yet, since other therapeutic options exist in the clinic, such as tamoxifen, there is an urgent medical need for the development of treatment-selective biomarkers, enabling personalized endocrine treatment selection in breast cancer. In the described dataset, ER? chromatin binding and histone marks H3K4me3 and H3K27me3 were assessed in a genome-wide manner by Chromatin Immunoprecipitation (ChIP) combined with massive parallel sequencing (ChIP-seq). These datasets were used to develop a classifier to stratify breast cancer patients on outcome after AI treatment in the metastatic setting. Here we describe in detail the data and quality control metrics, as well as the clinical information associated with the study, published by Jansen et al.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc