@article{ElMeseryRosenthalRauertWunderlichetal.2019,
  author    = {El-Mesery, Mohamed and Rosenthal, Tina and Rauert-Wunderlich, Hilka and Schreder, Martin and St{\"u}hmer, Thorsten and Leich, Ellen and Schlosser, Andreas and Ehrenschwender, Martin and Wajant, Harald and Siegmund, Daniela},
  title     = {The NEDD8-activating enzyme inhibitor MLN4924 sensitizes a TNFR1+ subgroup of multiple myeloma cells for TNF-induced cell death},
  series = {Cell Death \& Disease},
  volume    = {10},
  journal   = {Cell Death \& Disease},
  doi       = {10.1038/s41419-019-1860-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-226666},
  year      = {2019},
  abstract  = {The NEDD8-activating enzyme (NAE) inhibitor MLN4924 inhibits cullin-RING ubiquitin ligase complexes including the SKP1-cullin-F-box E3 ligase βTrCP. MLN4924 therefore inhibits also the βTrCP-dependent activation of the classical and the alternative NFĸB pathway. In this work, we found that a subgroup of multiple myeloma cell lines (e.g., RPMI-8226, MM.1S, KMS-12BM) and about half of the primary myeloma samples tested are sensitized to TNF-induced cell death by MLN4924. This correlated with MLN4924-mediated inhibition of TNF-induced activation of the classical NFκB pathway and reduced the efficacy of TNF-induced TNFR1 signaling complex formation. Interestingly, binding studies revealed a straightforward correlation between cell surface TNFR1 expression in multiple myeloma cell lines and their sensitivity for MLN4924/TNF-induced cell death. The cell surface expression levels of TNFR1 in the investigated MM cell lines largely correlated with TNFR1 mRNA expression. This suggests that the variable levels of cell surface expression of TNFR1 in myeloma cell lines are decisive for TNF/MLN4924 sensitivity. Indeed, introduction of TNFR1 into TNFR1-negative TNF/MLN4924-resistant KMS-11BM cells, was sufficient to sensitize this cell line for TNF/MLN4924-induced cell death. Thus, MLN4924 might be especially effective in myeloma patients with TNFR1+ myeloma cells and a TNFhigh tumor microenvironment.},
  language  = {en}
}
@article{LodaKrebsDanhofetal.2019,
  author    = {Loda, Sophia and Krebs, Jonathan and Danhof, Sophia and Schreder, Martin and Solimando, Antonio G. and Strifler, Susanne and Rasche, Leo and Kort{\"u}m, Martin and Kerscher, Alexander and Knop, Stefan and Puppe, Frank and Einsele, Hermann and Bittrich, Max},
  title     = {Exploration of artificial intelligence use with ARIES in multiple myeloma research},
  series = {Journal of Clinical Medicine},
  volume    = {8},
  journal   = {Journal of Clinical Medicine},
  number    = {7},
  issn      = {2077-0383},
  doi       = {10.3390/jcm8070999},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197231},
  pages     = {999},
  year      = {2019},
  abstract  = {Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented "A Rule-based Information Extraction System" (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.},
  language  = {en}
}