@article{SeufferleinLausserSteinetal.2024, author = {Seufferlein, Thomas and Lausser, Ludwig and Stein, Alexander and Arnold, Dirk and Prager, Gerald and Kasper-Virchow, Stefan and Niedermeier, Michael and M{\"u}ller, Lothar and Kubicka, Stefan and K{\"o}nig, Alexander and B{\"u}chner-Steudel, Petra and Wille, Kai and Berger, Andreas W. and Kestler, Angelika M. R. and Kraus, Johann M. and Werle, Silke D. and Perkhofer, Lukas and Ettrich, Thomas J. and Kestler, Hans A.}, title = {Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial}, volume = {19}, pages = {e0304324}, journal = {PLOS ONE}, number = {6}, publisher = {PLOS}, address = {San Francisco}, issn = {1932-6203}, doi = {https://doi.org/10.1371/journal.pone.0304324}, year = {2024}, abstract = {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.}, language = {en} }