Dataset Information

Identifying Determinants of EGFR-Targeted Therapeutic Biochemical Efficacy Using Computational Modeling.

ABSTRACT: We modeled cellular epidermal growth factor receptor (EGFR) tyrosine phosphorylation dynamics in the presence of receptor-targeting kinase inhibitors (e.g., gefitinib) or antibodies (e.g., cetuximab) to identify systematically the factors that contribute most to the ability of the therapeutics to antagonize EGFR phosphorylation, an effect we define here as biochemical efficacy. Our model identifies distinct processes as controlling gefitinib or cetuximab biochemical efficacy, suggests biochemical efficacy is favored in the presence of certain EGFR ligands, and suggests new drug design principles. For example, the model predicts that gefitinib biochemical efficacy is preferentially sensitive to perturbations in the activity of tyrosine phosphatases regulating EGFR, but that cetuximab biochemical efficacy is preferentially sensitive to perturbations in ligand binding. Our results highlight numerous other considerations that determine biochemical efficacy beyond those reflected by equilibrium affinities. By integrating these considerations, our model also predicts minimum therapeutic combination concentrations to maximally reduce receptor phosphorylation.

SUBMITTER: Monast CS

PROVIDER: S-EPMC4474171 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying Determinants of EGFR-Targeted Therapeutic Biochemical Efficacy Using Computational Modeling.

Monast C S CS Lazzara M J MJ

CPT: pharmacometrics & systems pharmacology 20141015

We modeled cellular epidermal growth factor receptor (EGFR) tyrosine phosphorylation dynamics in the presence of receptor-targeting kinase inhibitors (e.g., gefitinib) or antibodies (e.g., cetuximab) to identify systematically the factors that contribute most to the ability of the therapeutics to antagonize EGFR phosphorylation, an effect we define here as biochemical efficacy. Our model identifies distinct processes as controlling gefitinib or cetuximab biochemical efficacy, suggests biochemica ...[more]

PMID: 25317724

Similar Datasets

Project description:The treatment of multi-drug resistant (MDR) cancer is a clinical challenge. Many MDR cells over-express epidermal growth factor receptor (EGFR). We exploit this expression through the development of EGFR-targeted, polymer blend nanocarriers for the treatment of MDR cancer using paclitaxel (a common chemotherapeutic agent) and lonidamine (an experimental drug; mitochondrial hexokinase 2 inhibitor). An orthotopic model of MDR human breast cancer was developed in nude mice and used to evaluate the safety and efficacy of nanoparticle treatment. The efficacy parameters included tumor volume measurements from day 0 through 28 days post-treatment, terminal tumor weight measurements, tumor density and morphology assessment through hematoxylin and eosin staining of excised tumors, and immunohistochemistry of tumor sections for MDR protein markers (P-glycoprotein, Hypoxia Inducible Factor, EGFR, Hexokinase 2, and Stem Cell Factor). Toxicity was assessed by tracking changes in animal body weight from day 0 through 28 days post-treatment, by measuring plasma levels of the liver enzymes ALT (Alanine Aminotransferase) and LDH (lactate dehydrogenase), and by white blood cell and platelet counts. In these studies, this nanocarrier system demonstrated superior efficacy relative to combination (paclitaxel/lonidamine) drug solution and single agent treatments in nanoparticle and solution form. The combination nanoparticles were the only treatment group that decreased tumor volume, sustaining this decrease until the 28 day time point. In addition, treatment with the EGFR-targeted lonidamine/paclitaxel nanoparticles decreased tumor density and altered the MDR phenotype of the tumor xenografts. These EGFR-targeted combination nanoparticles were considerably less toxic than solution treatments. Due to the flexible design and simple conjugation chemistry, this nanocarrier system could be used as a platform for the development of other MDR cancer therapies; the use of this system for EGFR-targeted, combination paclitaxel/lonidamine therapy is an advance in personalized medicine.

Project description:There is a discrepancy in the efficacy of epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) treatment for advanced lung adenocarcinoma (LUAD) patients with EGFR sensitizing mutations (mEGFR). Molecular markers other than mEGFR remain to be investigated to better predict EGFR-TKI efficacy. Here, 49 LUAD patients with mEGFR (19 deletions or 21 L858R mutations) who received the first-generation EGFR-TKI icotinib therapy were included and stratified into 25 good-responders with a progression-free survival (PFS) longer than 11 months and 24 poor-responders with a PFS shorter than 11 months. We conducted targeted metabolomic detection and next-generation sequencing on serum and tissue samples, respectively. Subsequently, two metabolomic profiling-based discriminant models were constructed for icotinib efficacy prediction, 10 metabolites overlapped in both models ensured high credibility for distinguishing good- and poor-responders. Seven of the 10 metabolites displayed significant differences between the two groups, which belong to lipids including ceramides (Cers), lysophosphatidylcholines (LPCs), lysophosphatidylethanolamines (LPEs), sphingomyelins (SMs), and free fatty acids (FAs). Briefly, LPC 16:1, LPC 22:5-1, and LPE 18:2 decreased in poor-responders, while Cer 36:1-3, Cer 38:1-3, SM 36:1-2 and SM 42:2 increased in poor-responders. In parallel, we identified 6 co-mutated genes (ARID1A, ARID1B, BCR, FANCD2, PTCH1, and RBM10) which were significantly correlated with a shorter PFS. Additionally, 4 efficacy-related metabolites (Cer 36:1-3, Cer 38:1-3, SM 36:1-2, and LPC 16:1) showed significant differences between the mutant and wild-type of 4 efficacy-related genes (ARID1A, ARID1B, BCR, and RBM10). SM 36:1-2 elevated while LPC 16:1 decreased in ARID1A, BCR, and RBM10 mutant groups compared to the wild-type groups. Cer 36:1-3 increased in the ARID1A and BCR mutant groups, and Cer 38:1-3 only rose in the ARID1A mutant group. Furthermore, we observed a causal-mediator-network-based interrelation between the 4 concurrently mutated genes and the 4 metabolites related metabolic genes in glycerophospholipid metabolism and sphingolipid metabolism pathways. This study demonstrated that lipids metabolism and concurrently mutated genes with mEGFR were associated with the icotinib efficacy, which provides novel perspectives in classifying clinical responses of mEGFR LUAD patients and reveals the potential of non-invasive pretreatment serum metabolites in predicting EGFR-TKI efficacy.

Project description:Oncogenic protein tyrosine phosphatases have long been viewed as drug targets of interest, and recently developed allosteric inhibitors of SH2 domain-containing phosphatase-2 (SHP2) have entered clinical trials. However, the ability of phosphatases to regulate many targets directly or indirectly and to both promote and antagonize oncogenic signaling may make the efficacy of phosphatase inhibition challenging to predict. Here we explore the consequences of antagonizing SHP2 in glioblastoma, a recalcitrant cancer where SHP2 has been proposed as a useful drug target. Measuring protein phosphorylation and expression in glioblastoma cells across 40 signaling pathway nodes in response to different drugs and for different oxygen tensions revealed that SHP2 antagonism has network-level, context-dependent signaling consequences that affect cell phenotypes (e.g., cell death) in unanticipated ways. To map specific signaling consequences of SHP2 antagonism to phenotypes of interest, a data-driven computational model was constructed based on the paired signaling and phenotype data. Model predictions aided in identifying three signaling processes with implications for treating glioblastoma with SHP2 inhibitors. These included PTEN-dependent DNA damage repair in response to SHP2 inhibition, AKT-mediated bypass resistance in response to chronic SHP2 inhibition, and SHP2 control of hypoxia-inducible factor expression through multiple MAPKs. Model-generated hypotheses were validated in multiple glioblastoma cell lines, in mouse tumor xenografts, and through analysis of The Cancer Genome Atlas data. Collectively, these results suggest that in glioblastoma, SHP2 inhibitors antagonize some signaling processes more effectively than existing kinase inhibitors but can also limit the efficacy of other drugs when used in combination. SIGNIFICANCE: These findings demonstrate that allosteric SHP2 inhibitors have multivariate and context-dependent effects in glioblastoma that may make them useful components of some combination therapies, but not others.

Dataset Information

Identifying Determinants of EGFR-Targeted Therapeutic Biochemical Efficacy Using Computational Modeling.

Publications

Identifying Determinants of EGFR-Targeted Therapeutic Biochemical Efficacy Using Computational Modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets