Modeling Signaling Networks to Advance New Cancer Therapies

Integrins regulate hERG1 dynamics by girdin-dependent Gαi3: signaling and modeling in cancer cells

The hERG1 potassium channel is aberrantly over expressed in tumors and regulates the cancer cell response to integrin-dependent adhesion. We unravel a novel signaling pathway by which integrin engagement by the ECM protein fibronectin promotes hERG1 translocation to the plasma membrane and its association with β1 integrins, by activating girdin-dependent Gαi3 proteins and protein kinase B (Akt). By sequestering hERG1, β1 integrins make it avoid Rab5-mediated endocytosis, where unbound channels are degraded. The cycle of hERG1 expression determines the resting potential (Vrest) oscillations and drives the cortical f-actin dynamics and thus cell motility. To interpret the slow biphasic kinetics of hERG1/β1 integrin interplay, we developed a mathematical model based on a generic balanced inactivation-like module. Integrin-mediated cell adhesion triggers two contrary responses: a rapid stimulation of hERG1/β1 complex formation, followed by a slow inhibition which restores the initial condition. The protracted hERG1/β1 integrin cycle determines the slow time course and cyclic behavior of cell migration in cancer cells.

Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis

The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by in silico knockout analysis. Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system’s dynamics using a discrete, semi-quantitative Petri net model.

It is still a challenge to develop mechanistic models for big molecular systems without the knowledge of enough kinetic parameters of sufficient quality. At the same time, more qualitative and semi-quantitative data have been produced in increasing numbers, e.g., by new high-throughput technologies. This has generated demands for new concepts at appropriate abstraction levels. The Petri net formalism enables the integration of qualitative as well as quantitative data and provides algorithms and methods for model verification and model simulation. Moreover, Petri nets exhibit a clear and coherent visualization. Here, we modeled the molecular switches between cell survival, apoptosis, and necroptosis induced by tumor necrosis factor 1. We were interested not only in an exhaustive exploration of all possible signaling pathways, but also in finding the system’s checkpoints. Our Petri net model comprises 118 biochemical entities, 130 reactions, and 299 edges. We found 279 pathways that describe signal flows from receptor activation to cellular response.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. We applied in silico knockout analyses to the Petri net model and could identify important checkpoints of the tumor necrosis factor 1 signaling pathway.

The TRPC1 Channel Forms a PI3K/CaM Complex and Regulates Pancreatic Ductal Adenocarcinoma Cell Proliferation in a Ca2+-Independent Manner

Dysregulation of the transient receptor canonical ion channel (TRPC1) has been found in several cancer types, yet the underlying molecular mechanisms through which TRPC1 impacts pancreatic ductal adenocarcinoma (PDAC) cell proliferation are incompletely understood. Here, we found that TRPC1 is upregulated in human PDAC tissue compared to adjacent pancreatic tissue and this higher expression correlates with low overall survival. TRPC1 is, as well, upregulated in the aggressive PDAC cell line PANC-1, compared to a duct-like cell line, and its knockdown (KD) reduced cell proliferation along with PANC-1 3D spheroid growth by arresting cells in the G1/S phase whilst decreasing cyclin A, CDK2, CDK6, and increasing p21^CIP1 expression. In addition, the KD of TRPC1 neither affected Ca²⁺ influx nor store-operated Ca²⁺ entry (SOCE) and reduced cell proliferation independently of extracellular calcium. Interestingly, TRPC1 interacted with the PI3K-p85α subunit and calmodulin (CaM); both the CaM protein level and AKT phosphorylation were reduced upon TRPC1 KD. In conclusion, our results show that TRPC1 regulates PDAC cell proliferation and cell cycle progression by interacting with PI3K-p85α and CaM through a Ca²⁺-independent pathway.

Eukaryotic initiation factor-2, gamma subunit, suppresses proliferation and regulates the cell cycle via the MAPK/ERK signaling pathway in acute myeloid leukemia

PURPOSE

The expression of eukaryotic translation initiation factor-2 subunit 3 (EIF2S3) in patients with non-small cell lung and colorectal cancer is lower than that in healthy individuals. However, the functions of EIF2S3 remain unclear, and its study in leukemia has not been reported. The article aims to explore the role of EIF2S3 in AML (acute myeloid leukemia) and its underlying mechanism.

METHODS

Reverse transcription-quantitative PCR was performed to evaluate the expression levels of EIF2S3, and its association with patient prognosis was determined. Inducible HEL-EIF2S3 and HL-60-EIF2S3 cell lines were established by retrovirus infection. Cellular proliferation and the cell cycle were analyzed using Cell Counting Kit-8 and flow cytometric analyses. Tumorigenic ability was evaluated using xenograft nude mouse model. Gene expression profiles were analyzed in HL-60-EIF2S3 cells by next-generation sequencing, and WB analysis was performed to detect the expression of related proteins.

RESULTS

The expression of EIF2S3 in patients with AML was lower than that experiencing CR (P = 0.02). Furthermore, EIF2S3 overexpression inhibited cellular proliferation, halted G0/1 to S phase cell cycle progression, and inhibited tumorigenicity (P = 0.015). 479 differentially expressed genes were identified between HL60-EIF2S3 DOX (-) and HL60-EIF2S3 DOX ( +) cells via NGS and several of them involved in MAPK/ERK signaling pathway. The phosphorylation levels of ERK decreased when EIF2S3 was overexpressed (P < 0.050).

CONCLUSION

EIF2S3 overexpression may result in a decrease in cellular proliferation, cell cycle arrest, and tumorigenic inhibition via the MAPK/ERK signaling pathway in AML cells.

Network Approaches for Dissecting the Immune System

The immune system is a complex biological network composed of hierarchically organized genes, proteins, and cellular components that combat external pathogens and monitor the onset of internal disease. To meet and ultimately defeat these challenges, the immune system orchestrates an exquisitely complex interplay of numerous cells, often with highly specialized functions, in a tissue-specific manner. One of the major methodologies of systems immunology is to measure quantitatively the components and interaction levels in the immunologic networks to construct a computational network and predict the response of the components to perturbations. The recent advances in high-throughput sequencing techniques have provided us with a powerful approach to dissecting the complexity of the immune system. Here we summarize the latest progress in integrating omics data and network approaches to construct networks and to infer the underlying signaling and transcriptional landscape, as well as cell-cell communication, in the immune system, with a focus on hematopoiesis, adaptive immunity, and tumor immunology. Understanding the network regulation of immune cells has provided new insights into immune homeostasis and disease, with important therapeutic implications for inflammation, cancer, and other immune-mediated disorders.

High-Complexity shRNA Libraries and PI3 Kinase Inhibition in Cancer: High-Fidelity Synthetic Lethality Predictions

Deregulated signal transduction is a cancer hallmark, and its complexity and interconnectivity imply that combination therapy should be considered, but large data volumes that cover the complexity are required in user-friendly ways. Here, we present a searchable database resource of synthetic lethality with a PI3 kinase signal transduction inhibitor by performing a saturation screen with an ultra-complex shRNA library containing 30 independent shRNAs per gene target. We focus on Ras-PI3 kinase signaling with T cell leukemia as a screening platform for multiple clinical and experimental reasons. Our resource predicts multiple combination-based therapies with high fidelity, ten of which we confirmed with small molecule inhibitors. Included are biochemical assays, as well as the IPI145 (duvelisib) inhibitor. We uncover the mechanism of synergy between the PI3 kinase inhibitor GDC0941 (pictilisib) and the tubulin inhibitor vincristine and demonstrate broad synergy in 28 cell lines of 5 cancer types and efficacy in preclinical leukemia mouse trials.

Computational models of melanoma

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research.

Cell Membrane-Camouflaged Nanocarriers for Cancer Diagnostic and Therapeutic

Cell membrane (CM)-camouflaged nanocarriers (CMNPs) are the tools of a biomimetic strategy that has attracted significant attention. With a wide range of nanoparticle cores and CMs available, various creative CMNP designs have been studied for cancer diagnosis and therapy. The various functional CM molecules available allow CMNPs to demonstrate excellent properties such as prolonged circulation time, immune escape ability, reduced systemic toxicity, and homologous targeting ability when camouflaged with CMs derived from various types of natural cells including red and white blood cells, platelets, stem cells, and cancer cells. In this review, we summarize various CMNPs employed for cancer chemotherapy, immunotherapy, phototherapy, and in vivo imaging. We also predict future challenges and opportunities for fundamental and clinical studies.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Cracking the context-specific PI3K signaling code

Specificity in signal transduction is determined by the ability of cells to "encode" and subsequently "decode" different environmental signals. Akin to computer software, this "signaling code" governs context-dependent execution of cellular programs through modulation of signaling dynamics and can be corrupted by disease-causing mutations. Class IA phosphoinositide 3-kinase (PI3K) signaling is critical for normal growth and development and is dysregulated in human disorders such as benign overgrowth syndromes, cancer, primary immune deficiency, and metabolic syndrome. Despite decades of PI3K research, understanding of context-dependent regulation of the PI3K pathway and of the underlying signaling code remains rudimentary. Here, we review current knowledge on context-specific PI3K signaling and how technological advances now make it possible to move from a qualitative to quantitative understanding of this pathway. Insight into how cellular PI3K signaling is encoded or decoded may open new avenues for rational pharmacological targeting of PI3K-associated diseases. The principles of PI3K context-dependent signal encoding and decoding described here are likely applicable to most, if not all, major cell signaling pathways.

Dissecting RAF Inhibitor Resistance by Structure-based Modeling Reveals Ways to Overcome Oncogenic RAS Signaling

Clinically used RAF inhibitors are ineffective in RAS mutant tumors because they enhance homo- and heterodimerization of RAF kinases, leading to paradoxical activation of ERK signaling. Overcoming enhanced RAF dimerization and the resulting resistance is a challenge for drug design. Combining multiple inhibitors could be more effective, but it is unclear how the best combinations can be chosen. We built a next-generation mechanistic dynamic model to analyze combinations of structurally different RAF inhibitors, which can efficiently suppress MEK/ERK signaling. This rule-based model of the RAS/ERK pathway integrates thermodynamics and kinetics of drug-protein interactions, structural elements, posttranslational modifications, and cell mutational status as model rules to predict RAF inhibitor combinations for inhibiting ERK activity in oncogenic RAS and/or BRAFV600E backgrounds. Predicted synergistic inhibition of ERK signaling was corroborated by experiments in mutant NRAS, HRAS, and BRAFV600E cells, and inhibition of oncogenic RAS signaling was associated with reduced cell proliferation and colony formation.

A selective cyclin-dependent kinase 4, 6 dual inhibitor, Ribociclib (LEE011) inhibits cell proliferation and induces apoptosis in aggressive thyroid cancer

The RB-E2F1 pathway is an important mechanism of cell-cycle control, and deregulation of this pathway is one of the key factors contributing to tumorigenesis. Cyclin-dependent kinases (CDKs) and Cyclin D have been known to increase in aggressive thyroid cancer. However, there has been no study to investigate effects of a selective CDK 4/6 inhibitor, Ribociclib (LEE011), in thyroid cancer. Performing Western blotting, we found that RB phosphorylation and the expression of Cyclin D are significantly higher in papillary thyroid cancer (PTC) cell lines as well as anaplastic thyroid cancer (ATC) cell lines, compared with normal thyroid cell line and follicular thyroid cancer cell line. LEE011 dose-dependently inhibited RB phosphorylation and also decreased the expressions of its target genes such as FOXM1, Cyclin A1, and Myc in ATC. Furthermore, LEE011 induced cell cycle arrest in G0-G1 phase and cell apoptosis, and inhibited cell proliferation in ATC. Consistently, oral administration of LEE011 to ATC xenograft models strongly inhibited tumor growth with decreased expressions of pRB, pAKT and Ki-67, and also significantly increased tumor cell apoptosis. Taken together, our data support the rationale for clinical development of the CDK4/6 inhibitor as a therapy for patients with aggressive thyroid cancer.

Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines

In individuals, heterogeneous drug-response phenotypes result from a complex interplay of dose, drug specificity, genetic background, and environmental factors, thus challenging our understanding of the underlying processes and optimal use of drugs in the clinical setting. Here, we use mass-spectrometry-based quantification of molecular response phenotypes and logic modeling to explain drug-response differences in a panel of cell lines. We apply this approach to cellular cholesterol regulation, a biological process with high clinical relevance. From the quantified molecular phenotypes elicited by various targeted pharmacologic or genetic treatments, we generated cell-line-specific models that quantified the processes beneath the idiotypic intracellular drug responses. The models revealed that, in addition to drug uptake and metabolism, further cellular processes displayed significant pharmacodynamic response variability between the cell lines, resulting in cell-line-specific drug-response phenotypes. This study demonstrates the importance of integrating different types of quantitative systems-level molecular measurements with modeling to understand the effect of pharmacological perturbations on complex biological processes.

Logic Modeling in Quantitative Systems Pharmacology

Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

A compendium of ERK targets

The RAF-MEK-ERK cascade is one of the most studied signaling pathways as it controls many vital cellular programs. There has been an immense amount of effort to determine ERK target proteins involved in regulating these programs. Classical biochemical and genetic approaches have elicited hundreds of direct ERK substrates, and with the advent of phospho-proteomic technologies, numerous studies have expanded the number of ERK target proteins. Here, we compile a comprehensive ERK target phospho-site archive, in which we gathered information from various research studies, and we provide this archive as an online database to form a searchable compendium of ERK targets.

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM's ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge.

Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation

Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro‐proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type‐specific proliferation. First, cell type‐specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate‐limiting for faster cycling cells while slower cell cycles are controlled at the G1‐S progression. The integrated mathematical model of Epo‐driven proliferation explains cell type‐specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti‐proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.

Efficient randomization of biological networks while preserving functional characterization of individual nodes

BACKGROUND

Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results' significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i.e. their degrees and connection signs), hence including potential biases in the assessment.

RESULTS

Building on our previous work about rewiring bipartite networks, we propose a method for rewiring any type of unweighted networks. In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data.

CONCLUSIONS

BiRewire3 it is freely available at https://www.bioconductor.org/packages/BiRewire/ , and it should have a broad application as it allows an efficient and analytically derived statistical assessment of results from any network biology tool.

Looking beyond the cancer cell for effective drug combinations

Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.