Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis

Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities

HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.

Positive and negative feedback regulation of the TGF-β1 explains two equilibrium states in skin aging

During aging, skin homeostasis is essential for maintaining appearance, as well as biological defense of the human body. In this study, we identified thrombospondin-1 (THBS1) and fibromodulin (FMOD) as positive and negative regulators, respectively, of the TGF-β1-SMAD4 axis in human skin aging, based on in vitro and in vivo omics analyses and mathematical modeling. Using transcriptomic and epigenetic analyses of senescent dermal fibroblasts, TGF-β1 was identified as the key upstream regulator. Bifurcation analysis revealed a binary high-/low-TGF-β1 switch, with THBS1 as the main controller. Computational simulation of the TGF-β1 signaling pathway indicated that THBS1 expression was sensitively regulated, whereas FMOD was regulated robustly. Results of sensitivity analysis and validation showed that inhibition of SMAD4 complex formation was a promising method to control THBS1 production and senescence. Therefore, this study demonstrated the potential of combining data-driven target discovery with mathematical approaches to determine the mechanisms underlying skin aging.

Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction.

Targeted Hyperbranched Nanoparticles for Delivery of Doxorubicin in Breast Cancer Brain Metastasis

Breast cancer brain metastases (BM) are associated with a dismal prognosis and very limited treatment options. Standard chemotherapy is challenging in BM patients because the high dosage required for an effective outcome causes unacceptable systemic toxicities, a consequence of poor brain penetration, and a short physiological half-life. Nanomedicines have the potential to circumvent off-target toxicities and factors limiting the efficacy of conventional chemotherapy. The HER3 receptor is commonly expressed in breast cancer BM. Here, we investigate the use of hyperbranched polymers (HBP) functionalized with a HER3 bispecific-antibody fragment for cancer cell-specific targeting and pH-responsive release of doxorubicin (DOX) to selectively deliver and treat BM. We demonstrated that DOX-release from the HBP carrier was controlled, gradual, and greater in endosomal acidic conditions (pH 5.5) relative to physiologic pH (pH 7.4). We showed that the HER3-targeted HBP with DOX payload was HER3-specific and induced cytotoxicity in BT474 breast cancer cells (IC50: 17.6 μg/mL). Therapeutic testing in a BM mouse model showed that HER3-targeted HBP with DOX payload impacted tumor proliferation, reduced tumor size, and prolonged overall survival. HER3-targeted HBP level detected in ex vivo brain samples was 14-fold more than untargeted-HBP. The HBP treatments were well tolerated, with less cardiac and oocyte toxicity compared to free DOX. Taken together, our HER3-targeted HBP nanomedicine has the potential to deliver chemotherapy to BM while reducing chemotherapy-associated toxicities.

HER3: Toward the Prognostic Significance, Therapeutic Potential, Current Challenges, and Future Therapeutics in Different Types of Cancer

Human epidermal growth factor receptor 3 (HER3) is the only family member of the EGRF/HER family of receptor tyrosine kinases that lacks an active kinase domain (KD), which makes it an obligate binding partner with other receptors for its oncogenic role. When HER3 is activated in a ligand-dependent (NRG1/HRG) or independent manner, it can bind to other receptors (the most potent binding partner is HER2) to regulate many biological functions (growth, survival, nutrient sensing, metabolic regulation, etc.) through the PI3K-AKT-mTOR pathway. HER3 has been found to promote tumorigenesis, tumor growth, and drug resistance in different cancer types, especially breast and non-small cell lung cancer. Given its ubiquitous expression across different solid tumors and role in oncogenesis and drug resistance, there has been a long effort to target HER3. As HER3 cannot be targeted through its KD with small-molecule kinase inhibitors via the conventional method, pharmaceutical companies have used various other approaches, including blocking either the ligand-binding domain or extracellular domain for dimerization with other receptors. The development of treatment options with anti-HER3 monoclonal antibodies, bispecific antibodies, and different combination therapies showed limited clinical efficiency for various reasons. Recent reports showed that the extracellular domain of HER3 is not required for its binding with other receptors, which raises doubt about the efforts and applicability of the development of the HER3-antibodies for treatment. Whereas HER3-directed antibody-drug conjugates showed potentiality for treatment, these drugs are still under clinical trial. The currently understood model for dimerization-induced signaling remains incomplete due to the absence of the crystal structure of HER3 signaling complexes, and many lines of evidence suggest that HER family signaling involves more than the interaction of two members. This review article will significantly expand our knowledge of HER3 signaling and shed light on developing a new generation of drugs that have fewer side effects than the current treatment regimen for these patients.

Cisplatin-induced increase in heregulin 1 and its attenuation by the monoclonal ErbB3 antibody seribantumab in bladder cancer

Cisplatin-based combination chemotherapy is the foundation for treatment of advanced bladder cancer (BlCa), but many patients develop chemoresistance mediated by increased Akt and ERK phosphorylation. However, the mechanism by which cisplatin induces this increase has not been elucidated. Among six patient-derived xenograft (PDX) models of BlCa, we observed that the cisplatin-resistant BL0269 express high epidermal growth factor receptor, ErbB2/HER2 and ErbB3/HER3. Cisplatin treatment transiently increased phospho-ErbB3 (Y1328), phospho-ERK (T202/Y204) and phospho-Akt (S473), and analysis of radical cystectomy tissues from patients with BlCa showed correlation between ErbB3 and ERK phosphorylation, likely due to the activation of ERK via the ErbB3 pathway. In vitro analysis revealed a role for the ErbB3 ligand heregulin1-β1 (HRG1/NRG1), which is higher in chemoresistant lines compared to cisplatin-sensitive cells. Additionally, cisplatin treatment, both in PDX and cell models, increased HRG1 levels. The monoclonal antibody seribantumab, that obstructs ErbB3 ligand-binding, suppressed HRG1-induced ErbB3, Akt and ERK phosphorylation. Seribantumab also prevented tumor growth in both the chemosensitive BL0440 and chemoresistant BL0269 models. Our data demonstrate that cisplatin-associated increases in Akt and ERK phosphorylation is mediated by an elevation in HRG1, suggesting that inhibition of ErbB3 phosphorylation may be a useful therapeutic strategy in BlCa with high phospho-ErbB3 and HRG1 levels.

Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.

HER3 in cancer: from the bench to the bedside

The HER3 protein, that belongs to the ErbB/HER receptor tyrosine kinase (RTK) family, is expressed in several types of tumors. That fact, together with the role of HER3 in promoting cell proliferation, implicate that targeting HER3 may have therapeutic relevance. Furthermore, expression and activation of HER3 has been linked to resistance to drugs that target other HER receptors such as agents that act on EGFR or HER2. In addition, HER3 has been associated to resistance to some chemotherapeutic drugs. Because of those circumstances, efforts to develop and test agents targeting HER3 have been carried out. Two types of agents targeting HER3 have been developed. The most abundant are antibodies or engineered antibody derivatives that specifically recognize the extracellular region of HER3. In addition, the use of aptamers specifically interacting with HER3, vaccines or HER3-targeting siRNAs have also been developed. Here we discuss the state of the art of the preclinical and clinical development of drugs aimed at targeting HER3 with therapeutic purposes.

Evolutionary Trajectories of Primary and Metastatic Pancreatic Neuroendocrine Tumors Based on Genomic Variations

Liver metastases are common in pancreatic neuroendocrine tumors (PanNETs) patients and they are considered a poor prognostic marker. This study aims to analyze the spatiotemporal patterns of genomic variations between primary and metastatic tumors, and to identify the key related biomolecular pathways. We performed next-generation sequencing on paired tissue specimens of primary PanNETs (n = 11) and liver metastases (n = 12). Low genomic heterogeneity between primary PanNETs and liver metastases was observed. Genomic analysis provided evidence that polyclonal seeding is a prevalent event during metastatic progression, and may be associated with the progression-free survival. Besides this, copy number variations of BRCA1/BRCA2 seem to be associated with better prognosis. Pathways analysis showed that pathways in cancer, DNA repair, and cell cycle regulation-related pathways were significantly enriched in primary PanNETs and liver metastases. The study has shown a high concordance of gene mutations between the primary tumor and its metastases and the shared gene mutations may occur during oncogenesis and predates liver metastasis, suggesting an earlier onset of metastasis in patients with PanNETs, providing novel insight into genetic changes in metastatic tumors of PanNETs.

Targeting Tyrosine Kinases in Ovarian Cancer: Small Molecule Inhibitor and Monoclonal Antibody, Where Are We Now?

Ovarian cancer is one of the most lethal gynaecological malignancies worldwide. Despite high success rates following first time treatment, this heterogenous disease is prone to recurrence. Oncogenic activity of receptor tyrosine kinases is believed to drive the progression of ovarian cancer. Here we provide an update on the progress of the therapeutic targeting of receptor tyrosine kinases in ovarian cancer. Broadly, drug classes that inhibit tyrosine kinase/pathways can be classified as small molecule inhibitors, monoclonal antibodies, or immunotherapeutic vaccines. Small molecule inhibitors tested in clinical trials thus far include sorafenib, sunitinib, pazopanib, tivantinib, and erlotinib. Monoclonal antibodies include bevacizumab, cetuximab, pertuzumab, trastuzumab, and seribantumab. While numerous trials have been carried out, the results of monotherapeutic agents have not been satisfactory. For combination with chemotherapy, the monoclonal antibodies appear more effective, though the efficacy is limited by low frequency of target alteration and a lack of useful predictive markers for treatment stratification. There remain critical gaps for the treatment of platinum-resistant ovarian cancers; however, platinum-sensitive tumours may benefit from the combination of tyrosine kinase targeting drugs and PARP inhibitors. Immunotherapeutics such as a peptide B-cell epitope vaccine and plasmid-based DNA vaccine have shown some efficacy both as monotherapeutic agents and in combination therapy, but require further development to validate current findings. In conclusion, the tyrosine kinases remain attractive targets for treating ovarian cancers. Future development will need to consider effective drug combination, frequency of target, and developing predictive biomarker.

An overview on display systems (phage, bacterial, and yeast display) for production of anticancer antibodies; advantages and disadvantages

Antibodies as ideal therapeutic and diagnostic molecules are among the top-selling drugs providing considerable efficacy in disease treatment, especially in cancer therapy. Limitations of the hybridoma technology as routine antibody generation method in conjunction with numerous developments in molecular biology led to the development of alternative approaches for the streamlined identification of most effective antibodies. In this regard, display selection technologies such as phage display, bacterial display, and yeast display have been widely promoted over the past three decades as ideal alternatives to traditional methods. The display of antibodies on phages is probably the most widespread of these methods, although surface display on bacteria or yeast have been employed successfully, as well. These methods using various sizes of combinatorial antibody libraries and different selection strategies possessing benefits in screening potency, generating, and isolation of high affinity antibodies with low risk of immunogenicity. Knowing the basics of each method assists in the design and retrieval process of antibodies suitable for different diseases, including cancer. In this review, we aim to outline the basics of each library construction and its display method, screening and selection steps. The advantages and disadvantages in comparison to alternative methods, and their applications in antibody engineering will be explained. Finally, we will review approved or non-approved therapeutic antibodies developed by employing these methods, which may serve as therapeutic antibodies in cancer therapy.

Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis

Simple Summary

Breast cancer subtypes are characterized by the expression and activity of estrogen-, progesterone- and HER2-receptors and differ by the treatment as well as patient prognosis. Tumors of the HER2-subtype overexpress this receptor and are successfully targeted with anti-HER2 therapies. We wanted to know if the HER2-receptor and the downstream signaling network act similarly also in the other subtypes and if this network could potentially be a therapeutic target beyond the HER2-positive subtype. To this end, we quantitatively assessed the wiring of signaling events in the individual subtypes to unravel the characteristics of HER-signaling. Our data along with a model-based analysis suggest that major parts of the intracellular signal transduction network are unchanged between the different breast cancer subtypes and that the clinical differences mostly come from the different levels at which these receptors are present in tumor cells as well as from the particular mutations that are present in individual tumors.

Abstract

Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand–drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L₁ regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.

From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

With high drug attrition, protein-protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesized that a per-phenotype analysis could improve prediction performance. We compared two global approaches-statistical and distance-based-and our novel per-phenotype approach, 'context-specific interaction' (CSI) analysis, on severe side effect prediction. We used a novel dataset of adverse events (or designated medical events, DMEs) and discovered that CSI had a 50% improvement over global approaches (AUROC 0.77 compared to 0.51), and a 76-95% improvement in average precision (0.499 compared to 0.284, 0.256). Our results provide a quantitative rationale for considering downstream proteins on a per-phenotype basis when using PPI network methods to predict drug phenotypes.

A text-based computational framework for patient -specific modeling for classification of cancers

Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 patients with breast cancer using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.

•

A text file describing biochemical systems is converted into an executable model

•

Patient-specific models incorporate individual gene expression profiles

•

In silico signaling dynamics can be utilized as prognostic biomarkers

•

Personalized kinetic models are capable of predicting potential drug targets

ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer

NTRK gene fusions are drivers of tumorigenesis events that specific Trk-inhibitors can target. Current knowledge of the downstream pathways activated has been previously limited to the pathways of regulator proteins phosphorylated directly by Trk receptors. Here, we aimed to detect genes whose expression is increased in response to the activation of these pathways. We identified and analyzed differentially expressed genes in thyroid cancer samples with NTRK1 or NTRK3 gene fusions, and without any NTRK fusions, versus normal thyroid gland tissues, using data from the Cancer Genome Atlas, the DESeq2 tool, and the Genome Enhancer and geneXplain platforms. Searching for the genes activated only in samples with an NTRK fusion as opposed to those without NTRK fusions, we identified 29 genes involved in nervous system development, including AUTS2, DTNA, ERBB4, FLRT2, FLRT3, RPH3A, and SCN4A. We found that genes regulating the expression of the upregulated genes (i.e., upstream regulators) were enriched in the “signaling by ERBB4” pathway. ERBB4 was also one of three genes encoding master regulators whose expression was increased only in samples with an NTRK fusion. Moreover, the algorithm searching for positive feedback loops for gene promoters and transcription factors (a so-called “walking pathways” algorithm) identified the ErbB4 protein as the key master regulator. ERBB4 upregulation (p-value = 0.004) was confirmed in an independent sample of ETV6-NTRK3-positive FFPE specimens. Thus, ErbB4 is the potential key regulator of the pathways activated by NTRK gene fusions in thyroid cancer. These results are preliminary and require additional biochemical validation.

Efficacy and Safety of Patritumab Deruxtecan (HER3-DXd) in EGFR Inhibitor-Resistant, EGFR-Mutated Non-Small Cell Lung Cancer

Receptor tyrosine-protein kinase ERBB3 (HER3) is expressed in most EGFR-mutated lung cancers but is not a known mechanism of resistance to EGFR inhibitors. HER3-DXd is an antibody-drug conjugate consisting of a HER3 antibody attached to a topoisomerase I inhibitor payload via a tetrapeptide-based cleavable linker. This phase I, dose escalation/expansion study included patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC) with prior EGFR tyrosine kinase inhibitor (TKI) therapy. Among 57 patients receiving HER3-DXd 5.6 mg/kg intravenously once every 3 weeks, the confirmed objective response rate by blinded independent central review (Response Evaluation Criteria in Solid Tumors v1.1) was 39% [95% confidence interval (CI), 26.0-52.4], and median progression-free survival was 8.2 (95% CI, 4.4-8.3) months. Responses were observed in patients with known and unknown EGFR TKI resistance mechanisms. Clinical activity was observed across a broad range of HER3 membrane expression. The most common grade ≥3 treatment-emergent adverse events were hematologic toxicities. HER3-DXd has clinical activity in EGFR TKI-resistant cancers independent of resistance mechanisms, providing an approach to treat a broad range of drug-resistant cancers. SIGNIFICANCE: In metastatic EGFR-mutated NSCLC, after disease progression on EGFR TKI therapy, treatment approaches include genotype-directed therapy targeting a known resistance mechanism or chemotherapy. HER3-DXd demonstrated clinical activity spanning known and unknown EGFR TKI resistance mechanisms. HER3-DXd could present a future treatment option agnostic to the EGFR TKI resistance mechanism.See related commentary by Lim et al., p. 16.This article is highlighted in the In This Issue feature, p. 1.

Evaluation framework for systems models

As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper, we add to this body of work with a framework that focuses on the appropriate use of qualitative and quantitative model evaluation methods. We provide details and references for those wishing to apply these methods, which include sensitivity and identifiability analyses, as well as concepts such as validation and uncertainty quantification. Many of these methods have been used successfully in other fields, but are not as common in QSP modeling. We illustrate how to apply these methods to evaluate QSP models, and propose methods to use in two case studies. We also share examples of misleading results when inappropriate analyses are used.

Epithelial-specific ERBB3 deletion results in a genetic background-dependent increase in intestinal and colon polyps that is mediated by EGFR

ERBB3 has gained attention as a potential therapeutic target to treat colorectal and other types of cancers. To confirm a previous study showing intestinal polyps are dependent upon ERBB3, we generated an intestinal epithelia-specific ERBB3 deletion in C57BL/6-ApcMin/+ mice. Contrary to the previous report showing a significant reduction in intestinal polyps with ablation of ERBB3 on a B6;129 mixed genetic background, we observed a significant increase in polyp number with ablation of ERBB3 on C57BL/6J compared to control littermates. We confirmed the genetic background dependency of ERBB3 by also analyzing polyp development on B6129 hybrid and B6;129 advanced intercross mixed genetic backgrounds, which showed that ERBB3 deficiency only reduced polyp number on the mixed background as previously reported. Increased polyp number with ablation of ERBB3 was also observed in C57BL/6J mice treated with azoxymethane showing the effect is model independent. Polyps forming in absence of ERBB3 were generally smaller than those forming in control mice, albeit the effect was greatest in genetic backgrounds with reduced polyp numbers. The mechanism for differential polyp number in the absence of ERBB3 was through altered proliferation. Backgrounds with increased polyp number with loss of ERBB3 showed an increase in cell proliferation even in non-tumor epithelia, while backgrounds showing reduced polyp number with loss of ERBB3 showed reduced cellular proliferation. Increase polyp number caused by loss of ERBB3 was mediated by increased epidermal growth factor receptor (EGFR) expression, which was confirmed by deletion of Egfr. Taken together, this study raises substantial implications on the use of ERBB3 inhibitors against colorectal cancer. The prediction is that some patients may have increased progression with ERBB3 inhibitor therapy, which is consistent with observations reported for ERBB3 inhibitor clinical trials.

Cluster Gauss-Newton and CellNOpt Parameter Estimation in a Small Protein Signaling Network of Vorinostat and Bortezomib Pharmacodynamics

Ordinary differential equation (ODE)-based models of signal transduction pathways often contain parameters that are unidentifiable or unmeasurable by experimental data, and calibrating such models to data remains challenging. Here, two efficient parameter estimation methods, cluster Gauss-Newton (CGN) and CellNOpt (CNO), were applied to fit a signaling network model of U266 multiple myeloma cells to the activity dynamics of key proteins in response to vorinostat and/or bortezomib. A logic-based network model was constructed and transformed to 17 ODEs with 79 parameters estimated within broad ranges of biologically plausible values. The top 10% best-fit parameters by both methods had high uncertainties with CV > 50% for the majority of parameters. The root mean square and prediction errors were comparable without statistically significant differences between the two methods. Despite uncertain parameter estimation, protein dynamics after the sequential combination of bortezomib and vorinostat was predicted with reasonable accuracy and precision. Global sensitivity analyses of partial rank correlation coefficients and Sobol sensitivity demonstrated that apoptosis induction was most sensitive to parameters governing the activity of the proteasome-JNK-caspase-8 axis. Simulations revealed that the greatest magnitude of pharmacodynamic drug interactions between bortezomib and vorinostat occurred at caspase-9, AKT, and Bcl-2. Two sequential combinations were explored in silico, and the outcome matched qualitatively with an empirical evaluation of the pharmacodynamic interaction based on cell viability. Overall, the CGN and CNO algorithms performed similarly for this ODE-based network model calibration, and the calibrated model provided meaningful insights into cellular signaling mechanisms in response to pharmacological perturbations.

Anti‑HER3 monoclonal antibody exerts antitumor activity in a mouse model of colorectal adenocarcinoma

HER3 belongs to the epidermal growth factor receptor (EGFR) family and is known to form an active heterodimer with other three family members EGFR, HER2, and HER4. HER3 is overexpressed in lung, breast, colon, prostate, and gastric cancers. In the present study, we developed and validated an anti-HER3 monoclonal antibody (mAb), H₃Mab-17 (IgG_2a, kappa), by immunizing mice with HER3-overexpressed CHO-K1 cells (CHO/HER3). H₃Mab-17 was found to react specifically with endogenous HER3 in colorectal carcinoma cell lines, using flow cytometry. The K_D for H₃Mab-17 in CHO/HER3 and Caco-2 (a colon cancer cell line) were determined to be 3.0×10⁻⁹ M and 1.5×10⁻⁹ M via flow cytometry, respectively, suggesting high binding affinity of H₃Mab-17 to HER3. Then, we assessed the H₃Mab-17 antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) against Caco-2, and evaluated its antitumor capacity in a Caco-2 ×enograft model. In vitro experiments revealed H₃Mab-17 had strongly induced both ADCC and CDC against Caco-2 cells. In vivo experiments on Caco-2 ×enografts revealed that H₃Mab-17 treatment significantly reduced tumor growth compared with the control mouse IgG. These data indicated that H₃Mab-17 could be a promising treatment option for HER3-expressing colon cancers.

The Anti-HER3 mAb Seribantumab Effectively Inhibits Growth of Patient-Derived and Isogenic Cell Line and Xenograft Models with Oncogenic NRG1 Fusions

PURPOSE

Oncogenic fusions involving the neuregulin 1 (NRG1) gene are found in approximately 0.2% of cancers of diverse histologies. The resulting chimeric NRG1 proteins bind predominantly to HER3, leading to HER3-HER2 dimerization and activation of downstream growth and survival pathways. HER3 is, therefore, a rational target for therapy in NRG1 fusion-driven cancers.

EXPERIMENTAL DESIGN

We developed novel patient-derived and isogenic models of NRG1-rearranged cancers and examined the effect of the anti-HER3 antibody, seribantumab, on growth and activation of signaling networks in vitro and in vivo.

RESULTS

Seribantumab inhibited NRG1-stimulated growth of MCF-7 cells and growth of patient-derived breast (MDA-MB-175-VII, DOC4-NRG1 fusion) and lung (LUAD-0061AS3, SLC3A2-NRG1 fusion) cancer cells harboring NRG1 fusions or NRG1 amplification (HCC-95). In addition, seribantumab inhibited growth of isogenic HBEC cells expressing a CD74-NRG1 fusion (HBECp53-CD74-NRG1) and induced apoptosis in MDA-MB-175-VII and LUAD-0061AS3 cells. Induction of proapoptotic proteins and reduced expression of the cell-cycle regulator, cyclin D1, were observed in seribantumab-treated cells. Treatment of MDA-MB-175-VII, LUAD-0061AS3, and HBECp53-CD74-NRG1 cells with seribantumab reduced phosphorylation of EGFR, HER2, HER3, HER4, and known downstream signaling molecules, such as AKT and ERK1/2. Significantly, administration of seribantumab to mice bearing LUAD-0061AS3 patient-derived xenograft (PDX) and OV-10-0050 (ovarian cancer with CLU-NRG1 fusion) PDX tumors induced regression of tumors by 50%-100%. Afatinib was much less effective at blocking tumor growth.

CONCLUSIONS

Seribantumab treatment blocked activation of the four ERBB family members and of downstream signaling, leading to inhibition of NRG1 fusion-dependent tumorigenesis in vitro and in vivo in breast, lung, and ovarian patient-derived cancer models.

Building patient-specific models for receptor tyrosine kinase signaling networks

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

Thirty Years of HER3: From Basic Biology to Therapeutic Interventions

HER3 is a pseudokinase member of the EGFR family having a role in both tumor progression and drug resistance. Although HER3 was discovered more than 30 years ago, no therapeutic interventions have reached clinical approval to date. Because the evidence of the importance of HER3 is accumulating, increased amounts of preclinical and clinical trials with HER3-targeting agents are emerging. In this review article, we discuss the most recent HER3 biology in tumorigenic events and drug resistance and provide an overview of the current and emerging strategies to target HER3.

mTOR kinase inhibition disrupts neuregulin 1-ERBB3 autocrine signaling and sensitizes NF2-deficient meningioma cellular models to IGF1R inhibition

Meningiomas (MNs), arising from the arachnoid/meningeal layer, are nonresponsive to chemotherapies, with ∼50% showing loss of the Neurofibromatosis 2 (NF2) tumor suppressor gene. Previously, we established NF2 loss activates mechanistic target of rapamycin complex 1 (mTORC1) and mechanistic target of rapamycin complex 2 (mTORC2) signaling, leading to clinical trials for NF2 and MN. Recently our omics studies identified activated ephrin (EPH) receptor and Src family kinases upon NF2 loss. Here, we report increased expression of several ligands in NF2-null human arachnoidal cells (ACs) and the MN cell line Ben-Men-1, particularly neuregulin-1/heregulin (NRG1), and confirm increased NRG1 secretion and activation of V-ERB-B avian erythroblastic leukemia viral oncogene homolog 3 (ERBB3) receptor kinase. Conditioned-medium from NF2-null ACs or exogenous NRG1 stimulated ERBB3, EPHA2, and mTORC1/2 signaling, suggesting pathway crosstalk. NF2-null cells treated with an ERBB3-neutralizing antibody partially downregulated mTOR pathway activation but showed no effect on viability. mTORC1/2 inhibitor treatment decreased NRG1 expression and downregulated ERBB3 while re-activating pAkt T308, suggesting a mechanism independent of NRG1-ERBB3 but likely involving activation of another upstream receptor kinase. Transcriptomics after mTORC1/2 inhibition confirmed decreased ERBB3/ERBB4 while revealing increased expression of insulin-like growth factor receptor 1 (IGF1R). Drug treatment co-targeting mTORC1/2 and IGF1R/insulin receptor attenuated pAkt T308 and showed synergistic effects on viability. Our findings indicate potential autocrine signaling where NF2 loss leads to secretion/activation of NRG1-ERBB3 signaling. mTORC1/2 inhibition downregulates NRG1-ERBB3, while upregulating pAkt T308 through an adaptive response involving IGF1R/insulin receptor and co-targeting these pathways may prove effective for treatment of NF2-deficient MN.

The Role of Age, Neutrophil Infiltration and Antibiotics Timing in the Severity of Streptococcus pneumoniae Pneumonia. Insights from a Multi-Level Mathematical Model Approach

Bacterial pneumonia is one of the most prevalent infectious diseases and has high mortality in sensitive patients (children, elderly and immunocompromised). Although an infection, the disease alters the alveolar epithelium homeostasis and hinders normal breathing, often with fatal consequences. A special case is hospitalized aged patients, which present a high risk of infection and death because of the community acquired version of the Streptococcus pneumoniae pneumonia. There is evidence that early antibiotics treatment decreases the inflammatory response during pneumonia. Here, we investigate mechanistically this strategy using a multi-level mathematical model, which describes the 24 first hours after infection of a single alveolus from the key signaling networks behind activation of the epithelium to the dynamics of the local immune response. With the model, we simulated pneumonia in aged and young patients subjected to different antibiotics timing. The results show that providing antibiotics to elderly patients 8 h in advance compared to young patients restores in aged individuals the effective response seen in young ones. This result suggests the use of early, probably prophylactic, antibiotics treatment in aged hospitalized people with high risk of pneumonia.

A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway

Simple Summary

Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine.

Abstract

A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.

The tissue proteome in the multi-omic landscape of kidney disease

Kidney research is entering an era of 'big data' and molecular omics data can provide comprehensive insights into the molecular footprints of cells. In contrast to transcriptomics, proteomics and metabolomics generate data that relate more directly to the pathological symptoms and clinical parameters observed in patients. Owing to its complexity, the proteome still holds many secrets, but has great potential for the identification of drug targets. Proteomics can provide information about protein synthesis, modification and degradation, as well as insight into the physical interactions between proteins, and between proteins and other biomolecules. Thus far, proteomics in nephrology has largely focused on the discovery and validation of biomarkers, but the systematic analysis of the nephroproteome can offer substantial additional insights, including the discovery of mechanisms that trigger and propagate kidney disease. Moreover, proteome acquisition might provide a diagnostic tool that complements the assessment of a kidney biopsy sample by a pathologist. Such applications are becoming increasingly feasible with the development of high-throughput and high-coverage technologies, such as versatile mass spectrometry-based techniques and protein arrays, and encourage further proteomics research in nephrology.

Phage Display Derived Monoclonal Antibodies: From Bench to Bedside

Monoclonal antibodies (mAbs) have become one of the most important classes of biopharmaceutical products, and they continue to dominate the universe of biopharmaceutical markets in terms of approval and sales. They are the most profitable single product class, where they represent six of the top ten selling drugs. At the beginning of the 1990s, an in vitro antibody selection technology known as antibody phage display was developed by John McCafferty and Sir. Gregory Winter that enabled the discovery of human antibodies for diverse applications, particularly antibody-based drugs. They created combinatorial antibody libraries on filamentous phage to be utilized for generating antigen specific antibodies in a matter of weeks. Since then, more than 70 phage–derived antibodies entered clinical studies and 14 of them have been approved. These antibodies are indicated for cancer, and non-cancer medical conditions, such as inflammatory, optical, infectious, or immunological diseases. This review will illustrate the utility of phage display as a powerful platform for therapeutic antibodies discovery and describe in detail all the approved mAbs derived from phage display.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.

ERK and Akt exhibit distinct signaling responses following stimulation by pro-angiogenic factors

Background

Angiogenesis plays an important role in the survival of tissues, as blood vessels provide oxygen and nutrients required by the resident cells. Thus, targeting angiogenesis is a prominent strategy in many different settings, including both tissue engineering and cancer treatment. However, not all of the approaches that modulate angiogenesis lead to successful outcomes. Angiogenesis-based therapies primarily target pro-angiogenic factors such as vascular endothelial growth factor-A (VEGF) or fibroblast growth factor (FGF) in isolation, and there is a limited understanding of how these promoters combine together to stimulate angiogenesis. Targeting one pathway could be insufficient, as alternative pathways may compensate, diminishing the overall effect of the treatment strategy.

Methods

To gain mechanistic insight and identify novel therapeutic strategies, we have developed a detailed mathematical model to quantitatively characterize the crosstalk of FGF and VEGF intracellular signaling. The model focuses on FGF- and VEGF-induced mitogen-activated protein kinase (MAPK) signaling to promote cell proliferation and the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) pathway, which promotes cell survival and migration. We fit the model to published experimental datasets that measure phosphorylated extracellular regulated kinase (pERK) and Akt (pAkt) upon FGF or VEGF stimulation. We validate the model with separate sets of data.

Results

We apply the trained and validated mathematical model to characterize the dynamics of pERK and pAkt in response to the mono- and co-stimulation by FGF and VEGF. The model predicts that for certain ranges of ligand concentrations, the maximum pERK level is more responsive to changes in ligand concentration compared to the maximum pAkt level. Also, the combination of FGF and VEGF indicates a greater effect in increasing the maximum pERK compared to the summation of individual effects, which is not seen for maximum pAkt levels. In addition, our model identifies the influential species and kinetic parameters that specifically modulate the pERK and pAkt responses, which represent potential targets for angiogenesis-based therapies.

Conclusions

Overall, the model predicts the combination effects of FGF and VEGF stimulation on ERK and Akt quantitatively and provides a framework to mechanistically explain experimental results and guide experimental design. Thus, this model can be utilized to study the effects of pro- and anti-angiogenic therapies that particularly target ERK and/or Akt activation upon stimulation with FGF and VEGF.

Therapeutic Monoclonal Antibodies in Clinical Practice against Cancer

The importance of monoclonal antibodies in oncology has increased drastically following the discovery of Milstein and Kohler. Since the first approval of the monoclonal antibody, i.e. Rituximab in 1997 by the FDA, there was a decline in further applications but this number has significantly increased over the last three decades for various therapeutic applications due to the lesser side effects in comparison to the traditional chemotherapy methods. Presently, numerous monoclonal antibodies have been approved and many are in queue for approval as a strong therapeutic agent for treating hematologic malignancies and solid tumors. The main target checkpoints for the monoclonal antibodies against cancer cells include EGFR, VEGF, CD and tyrosine kinase which are overexpressed in malignant cells. Other immune checkpoints like CTLA-4, PD-1 and PD-1 receptors targeted by the recently developed antibodies increase the capability of the immune system in destroying the cancerous cells. Here, in this review, the mechanism of action, uses and target points of the approved mAbs against cancer have been summarized.

Mathematical Model Predicts Effective Strategies to Inhibit VEGF-eNOS Signaling

The endothelial nitric oxide synthase (eNOS) signaling pathway in endothelial cells has multiple physiological significances. It produces nitric oxide (NO), an important vasodilator, and enables a long-term proliferative response, contributing to angiogenesis. This signaling pathway is mediated by vascular endothelial growth factor (VEGF), a pro-angiogenic species that is often targeted to inhibit tumor angiogenesis. However, inhibiting VEGF-mediated eNOS signaling can lead to complications such as hypertension. Therefore, it is important to understand the dynamics of eNOS signaling in the context of angiogenesis inhibitors. Thrombospondin-1 (TSP1) is an important angiogenic inhibitor that, through interaction with its receptor CD47, has been shown to redundantly inhibit eNOS signaling. However, the exact mechanisms of TSP1's inhibitory effects on this pathway remain unclear. To address this knowledge gap, we established a molecular-detailed mechanistic model to describe VEGF-mediated eNOS signaling, and we used the model to identify the potential intracellular targets of TSP1. In addition, we applied the predictive model to investigate the effects of several approaches to selectively target eNOS signaling in cells experiencing high VEGF levels present in the tumor microenvironment. This work generates insights for pharmacologic targets and therapeutic strategies to inhibit tumor angiogenesis signaling while avoiding potential side effects in normal vasoregulation.

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

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.

Unraveling the causal differences between drug responders and non-responders is an important challenge. The information can help to understand molecular mechanisms and to guide the selection and design of targeted therapies. Here, we approach this problem for cetuximab treatment for gastric cancer using mechanistic mathematical modeling. The proposed model describes responder and non-responder gastric cancer cell lines and can predict the response in several validation experiments. Our analysis provides a differentiated view on mutations and explains, for instance, the relevance of MET mutations and the insignificance of PIK3CA mutation in the considered cell lines. The model might potentially provide the basis for understanding the recent failure of several clinical studies.

gQSPSim: A SimBiology-Based GUI for Standardized QSP Model Development and Application

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface–based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology‐built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation‐ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim’s capabilities with a standard target‐mediated drug disposition model and a published model of anti‐proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.

Inhibition of the PI3K-AKT-mTOR pathway suppresses the adipocyte-mediated proliferation and migration of breast cancer cells

Objective: Although it is well known that adipocyte significantly affects breast cancer progression, its mechanism has not been fully understood. Here, we analyzed the effect of adipocytes on breast cancer progression including cell proliferation and migration. Materials and Methods: We treated the conditioned media obtained from mouse 3T3-L1-derived or human adipose tissue-derived mesenchymal stem cells (hAMSC)-derived adipocytes to breast cancer cells, MCF-7 and MDA-MB-231. And then, cells viability and proliferation were analyzed using MTT assays and colony forming assays, respectively. Also mRNA expression of inflammatory cytokines and proteins expression in main signal pathway were analyzed by RT-qPCR and immunoblotting, respectively. Results: Adipocyte-derived conditioned media increased the proliferation and migration of MCF-7 and MDA-MB-231 cells while little effects in a human normal immortalized mammary epithelial cell line MCF10A. In addition, adipocyte-derived conditioned media induced phosphorylation of AKT and mTOR and upregulated the expression of target genes of the PI3K-AKT-mTOR pathway including IL6, IL1β, IL1α and TNFα in breast cancer cells. Furthermore, BEZ235 a dual inhibitor of PI3K and mTOR significantly decreased the adipocyte-mediated the proliferation and migration of breast cancer cells. Conclusion: Adipocyte-derived conditioned media enhance the proliferation and migration of breast cancer cells through the PI3K-AKT-mTOR pathway, supporting the importance of heterotypic interactions between breast cancer cells and adipocytes in the tumor microenvironment.

Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

BACKGROUND

Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications.

METHODS

We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation.

RESULTS

The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context.

CONCLUSIONS

The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.

10D1F, an Anti-HER3 Antibody that Uniquely Blocks the Receptor Heterodimerization Interface, Potently Inhibits Tumor Growth Across a Broad Panel of Tumor Models

In recent years, HER3 has increasingly been implicated in the progression of a variety of tumor types and in acquired resistance to EGFR and HER2 therapies. Whereas EGFR and HER2 primarily signal through the MAPK pathway, HER3, as a heterodimer with EGFR or HER2, potently activates the PI3K pathway. Despite its critical role, previous attempts to target HER3 with neutralizing antibodies have shown disappointing efficacy in the clinic, most likely due to suboptimal and indirect mechanisms of action that fail to completely block heterodimerization; for example, tumors can escape inhibition of ligand binding by upregulating ligand-independent mechanisms of HER3 activation. We therefore developed 10D1F, a picomolar affinity, highly specific anti-HER3 neutralizing antibody that binds the HER3 heterodimerization interface, a region that was hitherto challenging to raise antibodies against. We demonstrate that 10D1F potently inhibits both EGFR:HER3 and HER2:HER3 heterodimerization to durably suppress activation of the PI3K pathway in a broad panel of tumor models. Even as a monotherapy, 10D1F shows superior inhibition of tumor growth in the same cell lines both in vitro and in mouse xenograft experiments, when compared with other classes of anti-HER3 antibodies. This includes models demonstrating ligand-independent activation of heterodimerization as well as constitutively activating mutations in the MAPK pathway. Possessing favorable pharmacokinetic and toxicologic profiles, 10D1F uniquely represents a new class of anti-HER3 neutralizing antibodies with a novel mechanism of action that offers significant potential for broad clinical benefit.10D1F is a novel anti-HER3 antibody that uniquely binds the receptor dimerization interface to block ligand-dependent and independent heterodimerization with EGFR/HER2 and thus more potently inhibits tumor growth than existing anti-HER3 antibodies.

Phage Display Derived Monoclonal Antibodies: From Bench to Bedside

Monoclonal antibodies (mAbs) have become one of the most important classes of biopharmaceutical products, and they continue to dominate the universe of biopharmaceutical markets in terms of approval and sales. They are the most profitable single product class, where they represent six of the top ten selling drugs. At the beginning of the 1990s, an in vitro antibody selection technology known as antibody phage display was developed by John McCafferty and Sir. Gregory Winter that enabled the discovery of human antibodies for diverse applications, particularly antibody-based drugs. They created combinatorial antibody libraries on filamentous phage to be utilized for generating antigen specific antibodies in a matter of weeks. Since then, more than 70 phage-derived antibodies entered clinical studies and 14 of them have been approved. These antibodies are indicated for cancer, and non-cancer medical conditions, such as inflammatory, optical, infectious, or immunological diseases. This review will illustrate the utility of phage display as a powerful platform for therapeutic antibodies discovery and describe in detail all the approved mAbs derived from phage display.

Neuropeptide bombesin receptor activation stimulates growth of lung cancer cells through HER3 with a MAPK-dependent mechanism

Despite recent advances in treatment of non-small cell lung cancer (NSCLC), prognosis still remains poor and new therapeutic approaches are needed. Studies demonstrate the importance of the EGFR/HER-receptor family in NSCLC growth, as well as that of other tumors. Recently, HER3 is receiving increased attention because of its role in drug resistance and aggressive growth. Activation of overexpressed G-protein-coupled receptors (GPCR) can also initiate growth by transactivating EGFR/HER-family members. GPCR transactivation of EGFR has been extensively studied, but little is known of its ability to transactivate other EGFR/HER-members, especially HER3. To address this, we studied the ability of bombesin receptor (BnR) activation to transactivate all EGFR/HER-family members and their principal downstream signaling cascades, the PI3K/Akt- and MAPK/ERK-pathways, in human NSCLC cell-lines. In all three cell-lines studied, which possessed EGFR, HER2 and HER3, Bn rapidly transactivated EGFR, HER2 and HER3, as well as Akt and ERK. Immunoprecipitation studies revealed Bn-induced formation of both HER3/EGFR- and HER3/HER2-heterodimers. Specific EGFR/HER3 antibodies or siRNA-knockdown of EGFR and HER3, demonstrated Bn-stimulated activation of EGFR/HER members is initially through HER3, not EGFR. In addition, specific inhibition of HER3, HER2 or MAPK, abolished Bn-stimulated cell-growth, while neither EGFR nor Akt inhibition had an effect. These results show HER3 transactivation mediates all growth effects of BnR activation through MAPK. These results raise the possibility that targeting HER3 alone or with GPCR activation and its signal cascades, may be a novel therapeutic approach in NSCLC. This is especially relevant with the recent development of HER3-blocking antibodies.

Dual targeting of IGF-1R and ErbB3 as a potential therapeutic regimen for ovarian cancer

Therapeutically targeting receptor tyrosine kinases has proven to be paramount to overcoming chemotherapy resistance in several cancer indications, improving patient outcomes. Insulin-Like Growth Factor Receptor 1 (IGF-1R) and Epidermal Growth Factor Receptor 3 (ErbB3) have been implicated as two such drivers of resistance, however their simultaneous role in ovarian cancer chemotherapy resistance remains poorly elucidated. The aim of this work is to determine the effects of dual IGF-1R/ErbB3 inhibition on ovarian cancer cell signaling, growth, and in vivo efficacy. Assessment of in vitro chemotherapy response across a panel of ovarian cancer cell lines revealed that increased IGF-1R cell surface expression correlates with decreased sensitivity to chemotherapy, and that growth induced by IGF-1R and ErbB3 ligands is blocked by the tetravalent bispecific antibody targeting IGF-1R and ErbB3, istiratumab. In vitro chemotherapy treatment increased ovarian cancer cell line capacity to activate prosurvival PI3K signaling in response to ligand, which could be prevented with istiratumab treatment. Furthermore, in vivo efficacy of standard of care chemotherapies using a xenograft model of ovarian cancer was potentiated with istiratumab. Our results suggest a role for IGF-1R and ErbB3 in driving chemotherapy resistance of ovarian cancer.

Identification of a five-mRNA signature as a novel potential prognostic biomarker in pediatric Wilms tumor

Background

The aim of this study was to generate a prognostic model to predict survival outcome in pediatric Wilms tumor (WT).

Methods

The data including mRNA expression and clinical information of pediatric WT patients were downloaded from the Therapeutically Available Research to Generate Effective Treatments (TARGET) database. The differentially expressed genes were identified and a prognostic signature of pediatric WT was generated according to the results of univariate and multivariate Cox analysis. Receiver operating characteristic (ROC) curve was used to evaluate the five‐mRNA signature in pediatric Wilms tumor patients. Bootstrap test with 500 times was used to perform the internal validation.

Results

We identified 6,964 differentially expressed mRNAs associated with pediatric WT, including 3,190 downregulated mRNAs and 3,774 up‐regulated mRNAs. Univariate and multivariate Cox analysis identified five mRNAs (SPRY1, SPIN4, MAP7D3, C10orf71, and SPAG11A) to establish a predictive model. The risk score formula is as follows: Risk score = 0.3036*SPIN4 + 0.8576*MAP7D3 −0.1548*C10orf71 −0.7335*SPRY1 −0.2654*SPAG11A. The pediatric WT patients were divided into low‐risk group and high‐risk group based on the median risk score (value = 1.1503). The receiver operating characteristic (ROC) curve analysis revealed good performance of the 5‐mRNA prognostic model (the area under the curve [AUC] was 0.821). Bootstrap test (Bootstrap resampling times = 500) was used to perform the internal validation and revealed that the AUC was 0.822. REACTOME, KEGG, and BIOCARTA pathway analyses demonstrated that these survival‐related genes were mainly enriched in ErbB2 and ErbB3 signaling pathways, and calcium signaling pathway.

Conclusion

The five‐mRNA signature can predict the prognosis of patients with pediatric WT. It has significant implication in the understanding of therapeutic targets for pediatric WT patients. However, further study is needed to validate this five‐mRNA signature and uncover more novel diagnostic or prognostic mRNAs candidates in pediatric WT patients.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Hierarchical optimization for the efficient parametrization of ODE models

Motivation

Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems.

Results

In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches.

Availability and implementation

The code is included in the MATLAB toolbox PESTO which is available at http://github.com/ICB-DCM/PESTO.

Supplementary information

Supplementary data are available at Bioinformatics online.

Time-dependent antagonist-agonist switching in receptor tyrosine kinase-mediated signaling

BACKGROUND

ErbB4/HER4 is a unique member of the ErbB family of receptor tyrosine kinases concerning its activation of anti-proliferative JAK2-STAT5 pathway when stimulated by ligand Neuregulin (NRG). Activation of this pathway leads to expression of genes like β-casein which promote cell differentiation. Recent experimental studies on mouse HC11 mammary epithelial cells stimulated by ligand Neuregulin (NRG) showed a time-dependent switching behavior in the β-casein expression. This behavior cannot be explained using currently available mechanistic models of the JAK-STAT pathway. We constructed an improved mechanistic model which introduces two crucial modifications to the canonical HER4-JAK2-STAT5 pathway based on literature findings. These modifications include competitive HER4 heterodimerization with other members of the ErbB family and a slower JAK2 independent activation STAT5 through HER4. We also performed global sensitivity analysis on the model to test the robustness of the predictions and parameter combinations that are sensitive to the outcome.

RESULTS

Our model was able to reproduce the time-dependent switching behavior of β-casein and also establish that the modifications mentioned above to the canonical JAK-STAT pathway are necessary to reproduce this behavior. The sensitivity studies show that the competitive HER4 heterodimerization reactions have a profound impact on the sensitivity of the pathway to NRG stimulation, while the slower JAK2-independent pathway is necessary for the late stage promotion of β-casein mRNA transcription. The difference in the time scales of the JAK-dependent and JAK-independent pathways was found to be the main contributing factor to the time-dependent switch. The transport rates controlling activated STAT5 dimer nuclear import and β-casein mRNA export to cytoplasm affected the time delay between NRG stimulation and peak β-casein mRNA activity.

CONCLUSION

This study highlights the effect of competitive and parallel reaction pathways on both short and long-term dynamics of receptor-mediated signaling. It provides robust and testable predictions of the dynamical behavior of the HER4 mediated JAK-STAT pathway which could be useful in designing treatments for various cancers where this pathway is activated/altered.

Development of Effective Therapeutics Targeting HER3 for Cancer Treatment

HER3 is the third member of the human epidermal growth factor receptor (HER/EGFR) family, and unlike its other family members, is unique due to its minimal intrinsic kinase activity. As a result, HER3 has to interact with another receptor tyrosine kinase (RTK), such as EGFR or HER2, in order to activate the PI-3 K/Akt, MEK/MAPK, Jak/Stat pathways, as well as Src kinase. Over-expression of HER3 in various human cancers promotes tumor progression by increasing metastatic potential and acting as a major cause of treatment failure. Effective inhibition of HER3, and/or the key downstream mediators of HER3 signaling, is thought to be required to overcome resistance and enhance therapeutic efficacy. To date, there is no known HER3-targeted therapy that is approved for breast cancer, with a number of anti-HER3 antibodies current in various stages of development and clinical testing. Recent data suggests that the epigenetic strategy of using a histone deacetylase (HDAC) inhibitor, or functional cooperative miRNAs, may be an effective way to abrogate HER3 signaling. Here, we summarize the latest advances in our understanding of the mechanism of HER3 signaling in tumor progression, with continuing research towards the identification of therapeutic anti-HER3 antibodies. We will also examine the potential to develop novel epigenetic approaches that specifically target the HER3 receptor, along with important key downstream mediators that are involved in cancer treatment.

Context-dependent regulation of receptor tyrosine kinases: Insights from systems biology approaches

Receptor tyrosine kinases (RTKs) are cell membrane proteins that provide cells with the ability to sense proteins in their environments. Many RTKs are essential to development and organ growth. Derangement of RTKs-by mutation or by overexpression-is central to several developmental and adult disorders including cancer, short stature, and vascular pathologies. The mechanism of action of RTKs is complex and is regulated by contextual components, including the existence of multiple competing ligands and receptors in many families, the intracellular location of the RTK, the dynamic and cell-specific coexpression of other RTKs, and the commonality of downstream signaling pathways. This means that both the state of the cell and the microenvironment outside the cell play a role, which makes sense given the pivotal location of RTKs as the nexus linking the extracellular milieu to intracellular signaling and modification of cell behavior. In this review, we describe these different contextual components through the lens of systems biology, in which both computational modeling and experimental "omics" approaches have been used to better understand RTK networks. The complexity of these networks is such that using these systems biology approaches is necessary to get a handle on the mechanisms of pathology and the design of therapeutics targeting RTKs. In particular, we describe in detail three concrete examples (involving ErbB3, VEGFR2, and AXL) that illustrate how systems approaches can reveal key mechanistic and therapeutic insights. This article is categorized under: Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Therapeutic Methods.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Network spectra for drug-target identification in complex diseases: new guns against old foes

The fundamental understanding of altered complex molecular interactions in a diseased condition is the key to its cure. The overall functioning of these molecules is kind of jugglers play in the cell orchestra and to anticipate these relationships among the molecules is one of the greatest challenges in modern biology and medicine. Network science turned out to be providing a successful and simple platform to understand complex interactions among healthy and diseased tissues. Furthermore, much information about the structure and dynamics of a network is concealed in the eigenvalues of its adjacency matrix. In this review, we illustrate rapid advancements in the field of network science in combination with spectral graph theory that enables us to uncover the complexities of various diseases. Interpretations laid by network science approach have solicited insights into molecular relationships and have reported novel drug targets and biomarkers in various complex diseases.