Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines

Abstract

Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a basis for the assessment of gastric cancer signaling and possibly for the development and discovery of predictive biomarkers.

Inhibition of Glutamine Uptake Improves the Efficacy of Cetuximab on Gastric Cancer

“… 23 Amplifications, mutations, or overexpression of MET, HER2, or ERBB3 have been found to help the downstream signaling of EGFR remain activated, thereby reducing the tumor sensitivity to EGFR-targeted therapy. 24 - 26 Several drugs targeting these amplifications or overexpression initially re-sensitize tumors to cetuximab, but generally fail due to drug resistance. 25 - 27 Therefore, it is clinically relevant to explore other potential strategies to improve the efficacy of cetuximab on gastric cancer.…”

Posterior marginalization accelerates Bayesian inference for dynamical systems

“…This approach is often challenging and even infeasible for models with large datasets, since the number of observation parameters can easily exceed the number of model parameters (see e.g. [26, 27]).…”

“…As the marginalization-based approach appeared beneficial for challenging problems, we assessed in a next step whether it enables Bayesian inference for problems for which standard approaches did not provide reproducible results in a reasonable time-frame. Specifically, we considered an ODE model for signal transduction in gastric cancer cells (cell line MKN1) that was developed to unravel response and resistance markers [27]. This model possesses in total 57 unknown parameters, of which 26 are model parameters and 31 are observation parameters (Table 1, M4).…”

Research progress in targeted therapy and immunotherapy for gastric cancer

“…This further inhibits cancer cell proliferation and induces cancer cell apoptosis. [16] As early as >10 years ago, studies reported that tyrosine kinase inhibitors (TKIs) gefitinib and erlotinib were not effective in treating GC, while monoclonal antibodies (mainly cetuximab) were found to be effective in some published experimental studies. [17,18] A randomized, multicenter phase II clinical study (NCT00477711) in China explored the efficacy of cetuximab combined with cisplatin and capecitabine (C + XP) in treating advanced gastric or esophagogastric junction adenocarcinoma.…”

“…Cetuximab can bind to EGFR on the surface of tumor cells, thus inhibiting the interaction with its ligands and blocking the intracellular signal transduction pathway. This further inhibits cancer cell proliferation and induces cancer cell apoptosis [16] …”