Project description:The model is based on publication:
Mathematical analysis of gefitinib resistance of lung adenocarcinoma caused by MET amplification
Abstract:
Gefitinib, one of the tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR), is effective for treating lung adenocarcinoma harboring EGFR mutation; but later, most cases acquire a resistance to gefitinib. One of the mechanisms conferring gefitinib resistance to lung adenocarcinoma is the amplification of the MET gene, which is observed in 5–22% of gefitinib-resistant tumors. A previous study suggested that MET amplification could cause gefitinib resistance by driving ErbB3-dependent activation of the PI3K pathway. In this study, we built a mathematical model of gefitinib resistance caused by MET amplification using lung adenocarcinoma HCC827-GR (gefitinib resistant) cells. The molecular reactions involved in gefitinib resistance consisted of dimerization and phosphorylation of three molecules, EGFR, ErbB3, and MET were described by a series of ordinary differential equations. To perform a computer simulation, we quantified each molecule on the cell surface using flow cytometry and estimated unknown parameters by dimensional analysis. Our simulation showed that the number of active ErbB3 molecules is around a hundred-fold smaller than that of active MET molecules. Limited contribution of ErbB3 in gefitinib resistance by MET amplification is also demonstrated using HCC827-GR cells in culture experiments. Our mathematical model provides a quantitative understanding of the molecular reactions underlying drug resistance.
Project description:Modified Recon2.2 where the host biomass objective function has been modified to reflect the average composition of the proteome of human lung cells (based on expression data from the human proteome atlas) and where a new additional biomass objective function has been added to represent the production of SARS-CoV-2 viral particles.
2021-03-02 | MODEL2010280002 | BioModels
Project description:lung microbiome of lung cancer patients
Project description:<p>SARS-CoV-2 infection causes severe pulmonary manifestations, with poorly understood mechanisms and limited treatment options. Hyperferritinemia and disrupted lung iron homeostasis in COVID-19 patients imply that ferroptosis, an iron-dependent cell death, may occur. Immunostaining and lipidomic analysis in COVID-19 lung autopsies reveal increases in ferroptosis markers, including transferrin receptor 1 and malondialdehyde accumulation in fatal cases. COVID-19 lungs display dysregulation of lipids involved in metabolism and ferroptosis. We find increased ferritin light chain associated with severe COVID-19 lung pathology. Iron overload promotes ferroptosis in both primary cells and cancerous lung epithelial cells. In addition, ferroptosis markers strongly correlate with lung injury severity in a COVID-19 lung disease model using male Syrian hamsters. These results reveal a role for ferroptosis in COVID-19 pulmonary disease; pharmacological ferroptosis inhibition may serve as an adjuvant therapy to prevent lung damage during SARS-CoV-2 infection.</p>
Project description:Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis.