TY - JOUR A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Annika M. T. U. A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Maier, Julia A1 - Buchholz, Malte A1 - Gress, Thomas M. A1 - Kestler, Angelika M. R. A1 - Kestler, Hans A. T1 - A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective JF - npj Systems Biology and Applications N2 - AbstractPancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes. UR - https://doi.org/10.1038/s41540-023-00283-8 Y1 - 2023 UR - https://doi.org/10.1038/s41540-023-00283-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38071 SN - 2056-7189 VL - 9 IS - 1 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Lausser, Ludwig A1 - Schäfer, Lisa M. A1 - Werle, Silke D. A1 - Kestler, Angelika M. R. A1 - Kestler, Hans A. T1 - Detecting Ordinal Subcascades JF - Neural Processing Letters N2 - Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations. UR - https://doi.org/10.1007/s11063-020-10362-0 KW - ordinal classification KW - classifier cascades KW - error bounds KW - subsets KW - supersets Y1 - 2020 UR - https://doi.org/10.1007/s11063-020-10362-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27555 SN - 1573-773X SN - 1370-4621 VL - 52 IS - 3 SP - 2583 EP - 2605 PB - Springer Science+Business Media CY - Dordrecht ER - TY - JOUR A1 - Seufferlein, Thomas A1 - Lausser, Ludwig A1 - Stein, Alexander A1 - Arnold, Dirk A1 - Prager, Gerald A1 - Kasper-Virchow, Stefan A1 - Niedermeier, Michael A1 - Müller, Lothar A1 - Kubicka, Stefan A1 - König, Alexander A1 - Büchner-Steudel, Petra A1 - Wille, Kai A1 - Berger, Andreas W. A1 - Kestler, Angelika M. R. A1 - Kraus, Johann M. A1 - Werle, Silke D. A1 - Perkhofer, Lukas A1 - Ettrich, Thomas J. A1 - Kestler, Hans A. T1 - Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial JF - PLOS ONE N2 - Background Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab. Patients and methods 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM). Results Using random forests, we developed a predictive machine learning model that discriminated between the situations of “no progress within 100 days before radiological progress” and “progress within 100 days before radiological progress”. We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity. Conclusions We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress. UR - https://doi.org/10.1371/journal.pone.0304324 Y1 - 2024 UR - https://doi.org/10.1371/journal.pone.0304324 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49003 SN - 1932-6203 VL - 19 IS - 6 PB - PLOS CY - San Francisco ER -