A combined tissue-engineered/in silico signature tool patient stratification in lung cancer.

Patient-tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix-based three-dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR-activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho-arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho-kinase array, proliferation, and apoptosis), we generated cell line-specific in silico topologies and condition-specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS-mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.

Inhibition of Cytosolic Phospholipase A2α (cPLA2α) by Medicinal Plants in Relation to Their Phenolic Content.

The cytosolic phospholipase A2α(cPLA2α) is one of the potential targets for anti-inflammatory drugs, since this enzyme plays a key role in the inflammation processes seen in health disorders, like asthma, allergic reactions, arthritis and neuronal diseases. In this study, cPLA2α inhibition by 43 methanol extracts from medicinal plants rich in polyphenols was determined. The eight most active extracts were derived from Ribes nigrum (IC50 of 27.7 µg/mL), Ononis spinosa (IC50 of 39.4 µg/mL), Urtica dioica (IC50 of 44.32 µg/mL), Betula sp. (IC50 of 58.02 µg/mL), Sanguisorba officinalis (IC50 of 76.25 µg/mL), Orthosiphon stamineus (IC50 of 78.83 µg/mL), Petasites hybridus (IC50 of 81.02 µg/mL) and Tussilago farfara (IC50 of 123.28 µg/mL). Additionally, the antioxidant activities of these extracts were determined with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and their phenolic content with the Folin-Ciocalteu reagent. Antioxidant activity showed a non-linear, positive correlation to the phenolic content, but no correlation of PLA2 inhibition with phenolic content could be established. This study provides evidence that cPLA2α may be a relevant target for anti-inflammatory agents.