@article{MaerzKurlbaumRocheLancasteretal.2021,
  author    = {M{\"a}rz, Juliane and Kurlbaum, Max and Roche-Lancaster, Oisin and Deutschbein, Timo and Peitzsch, Mirko and Prehn, Cornelia and Weismann, Dirk and Robledo, Mercedes and Adamski, Jerzy and Fassnacht, Martin and Kunz, Meik and Kroiss, Matthias},
  title     = {Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors},
  series = {Frontiers in Endocrinology},
  volume    = {12},
  journal   = {Frontiers in Endocrinology},
  issn      = {1664-2392},
  doi       = {10.3389/fendo.2021.722656},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245710},
  year      = {2021},
  abstract  = {Context Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet. Objective Evaluation of quantitative metabolomics as a diagnostic tool for PPGL. Design Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study. Patients Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded. Results Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines. By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5\%, accuracy 67.3\%) was obtained by using Gradient Boosting Machine Modelling. Conclusions The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.},
  language  = {en}
}
@article{VeyKapsnerFuchsetal.2019,
  author    = {Vey, Johannes and Kapsner, Lorenz A. and Fuchs, Maximilian and Unberath, Philipp and Veronesi, Giulia and Kunz, Meik},
  title     = {A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning},
  series = {Cancers},
  volume    = {11},
  journal   = {Cancers},
  number    = {10},
  issn      = {2072-6694},
  doi       = {10.3390/cancers11101606},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193240},
  year      = {2019},
  abstract  = {The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.},
  language  = {en}
}