Network quantification of EGFR signaling unveils potential for targeted combination therapy

PCA-based synthetic sensitivity coefficients for chemical reaction network in cancer

Chemical reaction networks are powerful tools for modeling cell signaling and its disruptions in diseases like cancer. Realistic chemical reaction networks involve hundreds of proteins and reactions, resulting in a model depending on a consistently large number of kinetic parameters. Since finely calibrating all the parameters would require an unrealistic amount of data, proper sensitivity analysis is required to identify a subset of parameters for which fine tuning is needed and thus provide a fundamental tool for the qualitative analysis of the network. We present a multidisciplinary approach for computing a set of synthetic sensitivity indices. These indices rank the kinetic parameters, based on the impact that errors in their values would have on the protein concentration profile at equilibrium. Our tests on a chemical reaction network devised for colorectal cells demonstrate the effectiveness of the considered sensitivity indices in different scenarios including in-silico drug dosage and novel therapeutic target discovery. The Matlab code for computing the synthetic sensitivity indices and the data concerning the network for colorectal cells are available at https://github.com/theMIDAgroup/CRN_sensitivity.

Modeling unveils sex differences of signaling networks in mouse embryonic stem cells

For a short period during early development of mammalian embryos, both X chromosomes in females are active, before dosage compensation is ensured through X-chromosome inactivation. In female mouse embryonic stem cells (mESCs), which carry two active X chromosomes, increased X-dosage affects cell signaling and impairs differentiation. The underlying mechanisms, however, remain poorly understood. To dissect X-dosage effects on the signaling network in mESCs, we combine systematic perturbation experiments with mathematical modeling. We quantify the response to a variety of inhibitors and growth factors for cells with one (XO) or two X chromosomes (XX). We then build models of the signaling networks in XX and XO cells through a semi-quantitative modeling approach based on modular response analysis. We identify a novel negative feedback in the PI3K/AKT pathway through GSK3. Moreover, the presence of a single active X makes mESCs more sensitive to the differentiation-promoting Activin A signal and leads to a stronger RAF1-mediated negative feedback in the FGF-triggered MAPK pathway. The differential response to these differentiation-promoting pathways can explain the impaired differentiation propensity of female mESCs.

Himalayan flora: targeting various molecular pathways in lung cancer

The fatal amplification of lung cancer across the globe and the limitations of current treatment strategies emphasize the necessity for substitute therapeutics. The incorporation of phyto-derived components in chemo treatment holds promise in addressing those challenges. Despite the significant progressions in lung cancer therapeutics, the complexities of molecular mechanism and pathways underlying this disease remain inadequately understood, necessitating novel biomarker targeting. The Himalayas, abundant in diverse plant varieties with established chemotherapeutic potential, presents a promising avenue for investigating potential cures for lung carcinoma. The vast diversity of phytocompounds herein can be explored for targeting the disease. This review delves into the multifaceted targets of lung cancer and explores the established phytochemicals with their specific molecular targets. It emphasizes comprehending the intricate pathways that govern effective therapeutic interventions for lung cancer. Through this exploration of Himalayan flora, this review seeks to illuminate potential breakthroughs in lung cancer management using natural compounds. The amalgamation of Himalayan plant-derived compounds with cautiously designed combined therapeutic approaches such as nanocarrier-mediated drug delivery and synergistic therapy offers an opportunity to redefine the boundaries of lung cancer treatment by reducing the drug resistance and side effects and enabling an effective targeted delivery of drugs. Furthermore, additional studies are obligatory to understand the possible derivation of natural compounds used in current lung cancer treatment from plant species within the Himalayan region.

Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation

Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.

Critical transition and reversion of tumorigenesis

Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.

Design and selection of optimal ErbB-targeting bispecific antibodies in pancreatic cancer

The ErbB family of receptor tyrosine kinases is a primary target for small molecules and antibodies for pancreatic cancer treatment. Nonetheless, the current treatments for this tumor are not optimal due to lack of efficacy, resistance, or toxicity. Here, using the novel BiXAb™ tetravalent format platform, we generated bispecific antibodies against EGFR, HER2, or HER3 by considering rational epitope combinations. We then screened these bispecific antibodies and compared them with the parental single antibodies and antibody pair combinations. The screen readouts included measuring binding to the cognate receptors (mono and bispecificity), intracellular phosphorylation signaling, cell proliferation, apoptosis and receptor expression, and also immune system engagement assays (antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity). Among the 30 BiXAbs™ tested, we selected 3Patri-1Cetu-Fc, 3Patri-1Matu-Fc and 3Patri-2Trastu-Fc as lead candidates. The in vivo testing of these three highly efficient bispecific antibodies against EGFR and HER2 or HER3 in pre-clinical mouse models of pancreatic cancer showed deep antibody penetration in these dense tumors and robust tumor growth reduction. Application of such semi-rational/semi-empirical approach, which includes various immunological assays to compare pre-selected antibodies and their combinations with bispecific antibodies, represents the first attempt to identify potent bispecific antibodies against ErbB family members in pancreatic cancer.

Transcriptome analysis revealed the role of mTOR and MAPK signaling pathways in the white strain of Hypsizygus marmoreus extracts-induced cell death of human hepatoma Hep3B cells

The aim of this study was to investigate the anticancer mechanisms of white genius mushroom (WGM). WGM is a popular edible mushroom in Taiwan and has been demonstrated to mediate potent antiproliferation effects against human Hep3B liver cancer cells in our previous study. According to next generation sequencing technology and KEGG pathway enrichment analysis, mTOR and MAPK signaling pathways were markedly changed during treatment with WGM extracts in Hep3B cells. Therefore, this study examined the effects of WGM extracts on the expression of mTOR and MAPK signaling pathway-related proteins, such as PI3K, Akt, mTOR, Ras, Raf, MEK, ERK, p38 and JNK in Hep3B cells. According to the results of immunoblotting, we demonstrated that the protein expression of the members of PI3K/Akt/mTOR and MAPK signaling pathways were involved in WGM extracts-induced cell death. Furthermore, the inhibitors of PI3K/Akt/mTOR and MAPK signaling pathways such as rapamycin, MK2206, LY3214996 and SB202190, blocked the induction of cell death and vacuoles formation induced by WGM extracts. This study also demonstrated that WGM extracts is able to inhibit Hep3B cell migration and colony formation in a dose-dependent manner. In addition to being a very popular food, WGM should be a pharmacologically safe natural agent for cancer treatment. Therefore, WGM might be designed to develop into a dietary chemopreventive agent for the cancer treatment.

Network inference from perturbation time course data

Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.

Mutations in ALK signaling pathways conferring resistance to ALK inhibitor treatment lead to collateral vulnerabilities in neuroblastoma cells

BACKGROUND

Development of resistance to targeted therapies has tempered initial optimism that precision oncology would improve poor outcomes for cancer patients. Resistance mechanisms, however, can also confer new resistance-specific vulnerabilities, termed collateral sensitivities. Here we investigated anaplastic lymphoma kinase (ALK) inhibitor resistance in neuroblastoma, a childhood cancer frequently affected by activating ALK alterations.

METHODS

Genome-wide forward genetic CRISPR-Cas9 based screens were performed to identify genes associated with ALK inhibitor resistance in neuroblastoma cell lines. Furthermore, the neuroblastoma cell line NBLW-R was rendered resistant by continuous exposure to ALK inhibitors. Genes identified to be associated with ALK inhibitor resistance were further investigated by generating suitable cell line models. In addition, tumor and liquid biopsy samples of four patients with ALK-mutated neuroblastomas before ALK inhibitor treatment and during tumor progression under treatment were genomically profiled.

RESULTS

Both genome-wide CRISPR-Cas9-based screens and preclinical spontaneous ALKi resistance models identified NF1 loss and activating NRASQ61K mutations to confer resistance to chemically diverse ALKi. Moreover, human neuroblastomas recurrently developed de novo loss of NF1 and activating RAS mutations after ALKi treatment, leading to therapy resistance. Pathway-specific perturbations confirmed that NF1 loss and activating RAS mutations lead to RAS-MAPK signaling even in the presence of ALKi. Intriguingly, NF1 loss rendered neuroblastoma cells hypersensitive to MEK inhibition.

CONCLUSIONS

Our results provide a clinically relevant mechanistic model of ALKi resistance in neuroblastoma and highlight new clinically actionable collateral sensitivities in resistant cells.

Application of modular response analysis to medium- to large-size biological systems

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.

A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach

OBJECTIVES

Computational modeling of biological systems is a powerful tool to clarify diverse processes contributing to cancer. The aim is to clarify the complex biochemical and mechanical interactions between cells, the relevance of intracellular signaling pathways in tumor progression and related events to the cancer treatments, which are largely ignored in previous studies.

MATERIALS AND METHODS

A three-dimensional multiscale cell-based model is developed, covering multiple time and spatial scales, including intracellular, cellular, and extracellular processes. The model generates a realistic representation of the processes involved from an implementation of the signaling transduction network.

RESULTS

Considering a benign tumor development, results are in good agreement with the experimental ones, which identify three different phases in tumor growth. Simulating tumor vascular growth, results predict a highly vascularized tumor morphology in a lobulated form, a consequence of cells' motile behavior. A novel systematic study of chemotherapy intervention, in combination with targeted therapy, is presented to address the capability of the model to evaluate typical clinical protocols. The model also performs a dose comparison study in order to optimize treatment efficacy and surveys the effect of chemotherapy initiation delays and different regimens.

CONCLUSIONS

Results not only provide detailed insights into tumor progression, but also support suggestions for clinical implementation. This is a major step toward the goal of predicting the effects of not only traditional chemotherapy but also tumor-targeted therapies.

Boolean modelling as a logic-based dynamic approach in systems medicine

Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive, one for present or active. Because of this approximation, Boolean modelling is applicable to large diagrams, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.

Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects

Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is infeasible to experimentally screen the enormous search space of all possible drug combinations. Therefore, developing computational models to efficiently and accurately identify potential anti-cancer synergistic drug combinations has attracted a lot of attention from the scientific community. Hypothesis-driven explicit mathematical methods or network pharmacology models have been popular in the last decade and have been comprehensively reviewed in previous surveys. With the surge of artificial intelligence and greater availability of large-scale datasets, machine learning especially deep learning methods are gaining popularity in the field of computational models for anti-cancer drug synergy prediction. Machine learning-based methods can be derived without strong assumptions about underlying mechanisms and have achieved state-of-the-art prediction performances, promoting much greater growth of the field. Here, we present a structured overview of available large-scale databases and machine learning especially deep learning methods in computational predictive models for anti-cancer drug synergy prediction. We provide a unified framework for machine learning models and detail existing model architectures as well as their contributions and limitations, shedding light into the future design of computational models. Besides, unbiased experiments are conducted to provide in-depth comparisons between reviewed papers in terms of their prediction performance.

Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and via the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospho-proteomic profiling confirmed the cell-specific feedback effects and synergy of MEK and IGFR targeted treatment. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies. Our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.

Combinatorial treatment with Gefitinib and Bay11-7085 sensitizes primary Gefitinib-resistant OSCC cells by influencing the EGFR- NFκB signaling axis

EGFR-targeted therapies are reported to yield modest effect in OSCC. Activation of NFκB signaling is considered as molecular driver of EGFR inhibitor resistance in various cancers. In this scenario, present study focused on the molecular crosstalk between EGFR and NFκB signaling pathways and its therapeutic importance in OSCC. The EGFR- NFκB p65 co-expressed human OSCC cell lines UPCI:SCC066, UPCI:SCC040 and UM-SCC083B were used for in vitro studies. Recombinant human EGF, siRNAs, Western blot and qRT-PCR were used to dissect the molecular crosstalk between EGFR-NFκB signaling pathways in OSCCs. The effect of NFκB p65 knockdown on cancer hallmarks was studied by respective functional assays and RNA-Seq analysis was performed to identify the differentially expressed genes upon NFκB p65 knockdown. Gefitinib and Bay 11-7085 combination treatment was done to study the chemotherapeutic potential of EGFR- NFκB axis. Significant positive correlation between EGFR and NFκB p65 expression was observed in Head and Neck TCGA data set. EGFR induction or knockdown respectively stimulate or impair the NFκB signaling in EGFR- NFκB p65 co-expressed OSCC cell lines. NFκB p65 knockdown causes apoptosis and suppresses the viability, colony formation, migration, invasion, and spheroid formation. Using RNA-seq analysis, we identified PIK3CD as the NFκB target gene, which is commonly involved in these functions. Gefitinib and Bay 11-7085 combination treatment was found to be useful in chemosensitizing the Gefitinib-resistant OSCC cells by capitulating the EGFR- NFκB signaling axis. Combination treatment using Gefitinib and Bay 11-7085 enhanced the apoptosis and reduced cell viability and colony formation in a synergistic way. Our data demonstrated that EGFR-NFκB signaling axis plays a key role in the pathogenesis of OSCCs. Therefore, simultaneous therapeutic intervention of these pathways may be a good alternative approach for the management of OSCCs.

Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

Background

Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.

Methods

Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies.

Results

Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS.

Conclusions

Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00925-8.

Deciphering the signaling network of breast cancer improves drug sensitivity prediction

One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.

Cytokine combinations for human blood stem cell expansion induce cell type- and cytokine-specific signaling dynamics

How hematopoietic stem cells (HSCs) integrate signals from their environment to make fate decisions remains incompletely understood. Current knowledge is based on either averages of heterogeneous populations or snapshot analyses, both missing important information about the dynamics of intracellular signaling activity. By combining fluorescent biosensors with time-lapse imaging and microfluidics, we measured the activity of the extracellular signal-regulated kinase (ERK) pathway over time (i.e. dynamics) in live single human umbilical cord blood HSCs and multipotent progenitor cells (MPPs). In single cells, ERK signaling dynamics were highly heterogeneous and depended on the cytokines, their combinations, and cell types. ERK signaling was activated by SCF and FLT3L in HSCs, but by SCF, IL3 and GCSF in MPPs. Different cytokines and their combinations led to distinct ERK signaling dynamics frequencies, and ERK dynamics in HSCs were more transient than those in MPPs. A combination of 5 cytokines recently shown to maintain HSCs in long-term culture, had a more-than-additive effect in eliciting sustained ERK dynamics in HSCs. ERK signaling dynamics also predicted future cell fates. E.g. CD45RA expression increased more in HSC daughters with intermediate than with transient or sustained ERK signaling. We demonstrate heterogeneous, cytokine- and cell type- specific ERK signaling dynamics, illustrating their relevance in regulating HSPC fates.

A systematic analysis of signaling reactivation and drug resistance

Increasing evidence suggests that the reactivation of initially inhibited signaling pathways causes drug resistance. Here, we analyze how network topologies affect signaling responses to drug treatment. Network-dependent drug resistance is commonly attributed to negative and positive feedback loops. However, feedback loops by themselves cannot completely reactivate steady-state signaling. Newly synthesized negative feedback regulators can induce a transient overshoot but cannot fully restore output signaling. Complete signaling reactivation can only occur when at least two routes, an activating and inhibitory, connect an inhibited upstream protein to a downstream output. Irrespective of the network topology, drug-induced overexpression or increase in target dimerization can restore or even paradoxically increase downstream pathway activity. Kinase dimerization cooperates with inhibitor-mediated alleviation of negative feedback. Our findings inform drug development by considering network context and optimizing the design drug combinations. As an example, we predict and experimentally confirm specific combinations of RAF inhibitors that block mutant NRAS signaling.

Kholodenko et al. uncover signaling network circuitries and molecular mechanisms necessary and sufficient for complete reactivation or overshoot of steady-state signaling after kinase inhibitor treatment. The two means to revive signaling output fully are through network topology or reactivation of the kinase activity of the primary drug target. Blocking RAF dimer activity by a combination of type I½ and type II RAF inhibitors efficiently blocks mutant NRAS-driven ERK signaling.

Disentangling Pro-mitotic Signaling during Cell Cycle Progression using Time-Resolved Single-Cell Imaging

Cells rely on input from extracellular growth factors to control their proliferation during development and adult homeostasis. Such mitogenic inputs are transmitted through multiple signaling pathways that synergize to precisely regulate cell cycle entry and progression. Although the architecture of these signaling networks has been characterized in molecular detail, their relative contribution, especially at later cell cycle stages, remains largely unexplored. By combining quantitative time-resolved measurements of fluorescent reporters in untransformed human cells with targeted pharmacological inhibitors and statistical analysis, we quantify epidermal growth factor (EGF)-induced signal processing in individual cells over time and dissect the dynamic contribution of downstream pathways. We define signaling features that encode information about extracellular ligand concentrations and critical time windows for inducing cell cycle transitions. We show that both extracellular signal-regulated kinase (ERK) and phosphatidylinositol 3-kinase (PI3K) activity are necessary for initial cell cycle entry, whereas only PI3K affects the duration of S phase at later stages of mitogenic signaling.

An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk

Modular response analysis (MRA) is a widely used inference technique developed to uncover directions and strengths of connections in molecular networks under a steady-state condition by means of perturbation experiments. We devised several extensions of this methodology to search genomic data for new associations with a biological network inferred by MRA, to improve the predictive accuracy of MRA-inferred networks, and to estimate confidence intervals of MRA parameters from datasets with low numbers of replicates. The classical MRA computations and their extensions were implemented in a freely available R package called aiMeRA (https://github.com/bioinfo-ircm/aiMeRA/). We illustrated the application of our package by assessing the crosstalk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers, such as breast cancer. Based on new data generated for this study, our analysis revealed potential cross-inhibition mediated by the shared corepressors NRIP1 and LCoR. We designed aiMeRA for non-specialists and to allow biologists to perform their own analyses.

Capitalizing on Synthetic Lethality of MYC to Treat Cancer in the Digital Age

Deregulation of MYC is among the most frequent oncogenic drivers of cancer. Developing targeted therapies against MYC is, therefore, one of the most critical unmet needs of cancer therapy. Unfortunately, MYC has been labelled as undruggable due to the lack of success in developing clinically relevant MYC-targeted therapies. Synthetic lethality is a promising approach that targets MYC-dependent vulnerabilities in cancer. However, translating the synthetic lethality targets to the clinics is still challenging due to the complex nature of cancers. This review highlights the most promising mechanisms of MYC synthetic lethality and how these discoveries are currently translated into the clinic. Finally, we discuss how in silico computational platforms can improve clinical success of synthetic lethality-based therapy.

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles. We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model's ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets. This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.

Mutation-specific effects of NRAS oncogenes in colorectal cancer cells

In colorectal cancer (CRC), the prevalence of NRAS mutations (5-9%) is inferior to that of KRAS mutations (40-50%). NRAS mutations feature lately during tumour progression and drive resistance to anti-EGFR therapy in KRAS wild-type tumours. To elucidate specific functions of NRAS mutations in CRC, we expressed doxycycline-inducible G12D and Q61K mutations in the CRC cell line Caco-2. A focused phospho-proteome analysis based on the Bio-Plex platform, which interrogated the activity of MAPK, PI3K, mTOR, STAT, p38, JNK and ATF2, did not reveal significant differences between Caco-2 cells expressing NRAS^G12D, NRAS^Q61K and KRAS^G12V. However, phenotypic read-outs were different. The NRAS Q61K mutation promoted anchorage-independent proliferation and tumorigenicity, similar to features driven by canonical KRAS mutations. In contrast, expression of NRAS^G12D resulted in reduced proliferation and apoptosis. At the transcriptome level, we saw upregulation of cytokines and chemokines. IL1A, IL11, CXCL8 (IL-8) and CCL20 exhibited enhanced secretion into the culture medium. In addition, RNA sequencing results indicated activation of the IL1-, JAK/STAT-, NFκB- and TNFα signalling pathways. These results form the basis for an NRAS^G12D-driven inflammatory phenotype in CRC.

CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.

Evaluation of the contribution of randomised cancer clinical trials evaluating agents without documented single-agent activity

BACKGROUND

With the development of targeted agents, the approach to combination cancer therapy has evolved to focus on identifying ways in which pathway inhibition by one agent may enhance the activity of other agents. In theory, this implies that under this new paradigm, agents are no longer required to show single-agent activity, as the pathway inhibited by the targeted agent may only have a therapeutic effect when given with other agents. This raises the question of the extent to which anticancer agents without single-agent activity can contribute to effective combination regimens.

PATIENTS AND METHODS

We reviewed outcomes of randomised phase 2 combination trials sponsored by the National Cancer Institute Cancer Therapy Evaluation Program that were activated in 2008 to 2017 and noted the single-agent activity of the experimental agents.

RESULTS

Fifty-three trials were identified, and 50 had available results: 7 (14%), 15 (30%) and 28 (56%) had experimental agents with single-agent activity classified as active, inactive and indeterminate, respectively. Thirteen per cent (95% CI=1.7% to 40.5%) of trials evaluating inactive agents and 11.6% (95% CI=3.9% to 25.1%) of trials evaluating agents without known single-agent activity (pooled inactive and indeterminate) were positive, compared with 42.9% (95% CI=9.9% to 81.6%) for agents with single-agent activity.

CONCLUSIONS

Incorporating agents without documented single-agent activity into treatment regimens is unlikely to produce meaningful improvements in activity unless there is compelling biological rationale. This finding has important implications for the prioritisation of anticancer agents for combination testing, and for the allocation of clinical trial resources.

Combining kinase inhibitors for optimally co-targeting cancer and drug escape by exploitation of drug target promiscuities

Cancers resist targeted therapeutics by drug-escape signaling. Multitarget drugs co-targeting cancer and drug-escape mediators (DEMs) are clinically advantageous. DEM coverage may be expanded by drug combinations. This work evaluated to what extent the kinase DEMs (KDEMs) can be optimally co-targeted by drug combinations based on target promiscuities of individual drugs. We focused on 41 approved and 28 clinical trial small molecule kinase inhibitor drugs with available experimental kinome and clinical pharmacokinetic data. From the kinome inhibitory profiles of these drugs, drug combinations were assembled for optimally co-targeting an established cancer target (EGFR, HER2, ABL1, or MEK1) and 9-16 target-associated KDEMs at comparable potency levels as that against the cancer target. Each set of two-, three-, and four-drug combinations co-target 36-71%, 44-89%, 50-88%, and 27-55% KDEMs of EGFR, HER2, ABL1, and MEK1, respectively, compared with the 36, 33, 38, and 18% KDEMs maximally co-targeted by an existing drug or drug combination approved or clinically tested for the respective cancer. Some co-targeted KDEMs are not covered by any existing drug or drug combination. Our work suggested that novel drug combinations may be constructed for optimally co-targeting cancer and drug escape by the exploitation of drug target promiscuities.

Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction

Discrete dynamical modeling shows promise in prioritizing drug combinations for screening efforts by reducing the experimental workload inherent to the vast numbers of possible drug combinations. We have investigated approaches to predict combination responses across different cancer cell lines using logic models generated from one generic prior-knowledge network representing 144 nodes covering major cancer signaling pathways. Cell-line specific models were configured to agree with baseline activity data from each unperturbed cell line. Testing against experimental data demonstrated a high number of true positive and true negative predictions, including also cell-specific responses. We demonstrate the possible enhancement of predictive capability of models by curation of literature knowledge further detailing subtle biologically founded signaling mechanisms in the model topology. In silico model analysis pinpointed a subset of network nodes highly influencing model predictions. Our results indicate that the performance of logic models can be improved by focusing on high-influence node protein activity data for model configuration and that these nodes accommodate high information flow in the regulatory network.

Identifiability and experimental design in perturbation studies

MOTIVATION

A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network's response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimizing the experimental design is essential to achieve a meaningful characterization of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters.

RESULTS

We show analytically that the identifiability of network parameters can be determined by an intuitive maximum-flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved, on average, with less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments.

AVAILABILITY AND IMPLEMENTATION

IdentiFlow is available at github.com/GrossTor/IdentiFlow.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reduced replication origin licensing selectively kills KRAS-mutant colorectal cancer cells via mitotic catastrophe

To unravel vulnerabilities of KRAS-mutant CRC cells, a shRNA-based screen specifically inhibiting MAPK pathway components and targets was performed in CaCo2 cells harboring conditional oncogenic KRAS^G12V. The custom-designed shRNA library comprised 121 selected genes, which were previously identified to be strongly regulated in response to MEK inhibition. The screen showed that CaCo2 cells expressing KRAS^G12V were sensitive to the suppression of the DNA replication licensing factor minichromosome maintenance complex component 7 (MCM7), whereas KRAS^wt CaCo2 cells were largely resistant to MCM7 suppression. Similar results were obtained in an isogenic DLD-1 cell culture model. Knockdown of MCM7 in a KRAS-mutant background led to replication stress as indicated by increased nuclear RPA focalization. Further investigation showed a significant increase in mitotic cells after simultaneous MCM7 knockdown and KRAS^G12V expression. The increased percentage of mitotic cells coincided with strongly increased DNA damage in mitosis. Taken together, the accumulation of DNA damage in mitotic cells is due to replication stress that remained unresolved, which results in mitotic catastrophe and cell death. In summary, the data show a vulnerability of KRAS-mutant cells towards suppression of MCM7 and suggest that inhibiting DNA replication licensing might be a viable strategy to target KRAS-mutant cancers.

Robust network inference using response logic

Motivation

A major challenge in molecular and cellular biology is to map out the regulatory networks of cells. As regulatory interactions can typically not be directly observed experimentally, various computational methods have been proposed to disentangling direct and indirect effects. Most of these rely on assumptions that are rarely met or cannot be adapted to a given context.

Results

We present a network inference method that is based on a simple response logic with minimal presumptions. It requires that we can experimentally observe whether or not some of the system’s components respond to perturbations of some other components, and then identifies the directed networks that most accurately account for the observed propagation of the signal. To cope with the intractable number of possible networks, we developed a logic programming approach that can infer networks of hundreds of nodes, while being robust to noisy, heterogeneous or missing data. This allows to directly integrate prior network knowledge and additional constraints such as sparsity. We systematically benchmark our method on KEGG pathways, and show that it outperforms existing approaches in DREAM3 and DREAM4 challenges. Applied to a novel perturbation dataset on PI3K and MAPK pathways in isogenic models of a colon cancer cell line, it generates plausible network hypotheses that explain distinct sensitivities toward various targeted inhibitors due to different PI3K mutants.

Availability and implementation

A Python/Answer Set Programming implementation can be accessed at github.com/GrossTor/response-logic. Data and analysis scripts are available at github.com/GrossTor/response-logic-projects.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exact solving and sensitivity analysis of stochastic continuous time Boolean models

Background

Solutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all attractors. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways, raising the necessity of parameter sensitivity analysis. We address these two issues by an exact calculation method for this class of models.

Results

We show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses graph theoretical and matrix calculation methods previously applied in the context of chemical kinetics. In this version of the asynchronous updating framework the states of a logical model define a continuous time Markov chain and for a given initial condition the stationary solution is fully defined by the right and left nullspace of the master equation’s kinetic matrix. We use topological sorting of the state transition graph and the dependencies between the nullspaces and the kinetic matrix to derive the stationary solution without simulations. We apply this calculation to several published Boolean models to analyze the under-explored question of the effect of transition rates on the stationary solutions and show they can be sensitive to parameter changes. The analysis distinguishes processes robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights.

Conclusion

Up to an intermediate size (the biggest model analyzed is 23 nodes) stochastic Boolean models can be efficiently solved by an exact matrix method, without using Monte Carlo simulations. Sensitivity analysis with respect to the model’s timescale parameters often reveals a small subset of all parameters that primarily determine the stationary probability of attractor states.

Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy

Targeted drugs aim to treat cancer by directly inhibiting oncogene activity or oncogenic pathways, but drug resistance frequently emerges. Due to the intricate dynamics of cancer signaling networks, which contain complex feedback regulations, cancer cells can rewire these networks to adapt to and counter the cytotoxic effects of a drug, thereby limiting the efficacy of targeted therapies. To identify a combinatorial drug target that can overcome such a limitation, we developed a Boolean network simulation and analysis framework and applied this approach to a large-scale signaling network of colorectal cancer with integrated genomic information. We discovered Src as a critical combination drug target that can overcome the adaptive resistance to the targeted inhibition of mitogen-activated protein kinase pathway by blocking the essential feedback regulation responsible for resistance. The proposed framework is generic and can be widely used to identify drug targets that can overcome adaptive resistance to targeted therapies.

Amyloidosis causes downregulation of SorLA, SorCS1 and SorCS3 expression in mice

Accumulation of β-amyloid peptide (Aβ) is regarded as a primary cause of Alzheimer's disease (AD). Aβ is derived by sequential cleavage of the amyloid precursor protein (APP). Alterations in the subcellular targeting of APP are thought to affect the degree of Aβ production. Sorting receptors, such as SorLA, convey subcellular targeting of APP. Dysfunction of SorLA, and likely of the related receptors SorCS1 and SorCS3, cause AD. Nevertheless, disease progression could also provoke altered expression of the receptors. Here, we assessed if Aβ plaque formation promotes altered expression of SorLA, SorCS1 and SorCS3. We analyzed transcript levels during aging and after amyloidosis in brain areas characterized by early amyloid plaque formation in an AD mouse model (APPPS1) and wild types. We observed stable expression levels during aging (1-12 months). After plaque formation, SorCS1 and SorLA expression were markedly reduced in the frontal cerebral cortex and to a minor extent in the hippocampus, whereas SorCS3 expression was solely reduced in the frontal cerebral cortex. Our results indicate that disease progression, associated with Aβ accumulation, can negatively regulate expression of the receptors.

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.

Personalized signaling models for personalized treatments

Dynamic mechanistic models, that is, those that can simulate behavior over time courses, are a cornerstone of molecular systems biology. They are being used to model molecular mechanisms with varying degrees of granularity-from elementary reactions to causal links-and to describe these systems by various dynamic mathematical frameworks, such as Boolean networks or systems of differential equations. The models can be based exclusively on experimental data, or on prior knowledge of the underlying biological processes. The latter are typically generic, but can be adapted to a certain context, such as a particular cell type, after training with context-specific data. Dynamic mechanistic models that are based on biological knowledge have great potential for modeling specific systems, because they require less data for training to provide biological insight in particular into causal mechanisms, and to extrapolate to scenarios that are outside the conditions they have been trained on.

Mechanistic Understanding of Curcumin's Therapeutic Effects in Lung Cancer

Lung cancer is among the most common cancers with a high mortality rate worldwide. Despite the significant advances in diagnostic and therapeutic approaches, lung cancer prognoses and survival rates remain poor due to late diagnosis, drug resistance, and adverse effects. Therefore, new intervention therapies, such as the use of natural compounds with decreased toxicities, have been considered in lung cancer therapy. Curcumin, a natural occurring polyphenol derived from turmeric (Curcuma longa) has been studied extensively in recent years for its therapeutic effects. It has been shown that curcumin demonstrates anti-cancer effects in lung cancer through various mechanisms, including inhibition of cell proliferation, invasion, and metastasis, induction of apoptosis, epigenetic alterations, and regulation of microRNA expression. Several in vitro and in vivo studies have shown that these mechanisms are modulated by multiple molecular targets such as STAT3, EGFR, FOXO3a, TGF-β, eIF2α, COX-2, Bcl-2, PI3KAkt/mTOR, ROS, Fas/FasL, Cdc42, E-cadherin, MMPs, and adiponectin. In addition, limitations, strategies to overcome curcumin bioavailability, and potential side effects as well as clinical trials were also reviewed.

Comparative Network Reconstruction using mixed integer programming

Motivation

Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem.

Results

Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on possibly incomplete perturbation data, and to identify which edges differ quantitatively between two or more signaling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant, PTPN11 KO cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wild-type MAPK signaling, possibly through an alternative RAF-isoform.

Availability and implementation

CNR is available as a python module at https://github.com/NKI-CCB/cnr. Additionally, code to reproduce all figures is available at https://github.com/NKI-CCB/CNR-analyses.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cell type-dependent differential activation of ERK by oncogenic KRAS in colon cancer and intestinal epithelium

Oncogenic mutations in KRAS or BRAF are frequent in colorectal cancer and activate the ERK kinase. Here, we find graded ERK phosphorylation correlating with cell differentiation in patient-derived colorectal cancer organoids with and without KRAS mutations. Using reporters, single cell transcriptomics and mass cytometry, we observe cell type-specific phosphorylation of ERK in response to transgenic KRAS^G12V in mouse intestinal organoids, while transgenic BRAF^V600E activates ERK in all cells. Quantitative network modelling from perturbation data reveals that activation of ERK is shaped by cell type-specific MEK to ERK feed forward and negative feedback signalling. We identify dual-specificity phosphatases as candidate modulators of ERK in the intestine. Furthermore, we find that oncogenic KRAS, together with β-Catenin, favours expansion of crypt cells with high ERK activity. Our experiments highlight key differences between oncogenic BRAF and KRAS in colorectal cancer and find unexpected heterogeneity in a signalling pathway with fundamental relevance for cancer therapy.

KRAS^G12V and BRAF^V600E are oncogenic mutations that activate ERK signalling. Here, the authors use single cell analysis in intestinal organoids and show that BRAF^V600E activates ERK in all intestinal cell types, while KRAS^G12V induces ERK activation in only a subset of cells, depending on cell differentiation state.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Mapping connections in signaling networks with ambiguous modularity

Modular Response Analysis (MRA) is a suite of methods that under certain assumptions permits the precise reconstruction of both the directions and strengths of connections between network modules from network responses to perturbations. Standard MRA assumes that modules are insulated, thereby neglecting the existence of inter-modular protein complexes. Such complexes sequester proteins from different modules and propagate perturbations to the protein abundance of a downstream module retroactively to an upstream module. MRA-based network reconstruction detects retroactive, sequestration-induced connections when an enzyme from one module is substantially sequestered by its substrate that belongs to a different module. Moreover, inferred networks may surprisingly depend on the choice of protein abundances that are experimentally perturbed, and also some inferred connections might be false. Here, we extend MRA by introducing a combined computational and experimental approach, which allows for a computational restoration of modular insulation, unmistakable network reconstruction and discrimination between solely regulatory and sequestration-induced connections for a range of signaling pathways. Although not universal, our approach extends MRA methods to signaling networks with retroactive interactions between modules arising from enzyme sequestration effects.

The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods

A large body of data have accumulated that characterize the gene regulatory network of stem cells. Yet, a comprehensive and integrative understanding of this complex network is lacking. Network reverse engineering methods that use transcriptome data to derive these networks may help to uncover the topology in an unbiased way. Many methods exist that use co-expression to reconstruct networks. However, it remains unclear how these methods perform in the context of stem cell differentiation, as most systematic assessments have been made for regulatory networks of unicellular organisms. Here, we report a systematic benchmark of different reverse engineering methods against functional data. We show that network pruning is critical for reconstruction performance. We also find that performance is similar for algorithms that use different co-expression measures, i.e. mutual information or correlation. In addition, different methods yield very different network topologies, highlighting the challenge of interpreting these resulting networks as a whole.This article is part of the theme issue 'Designer human tissue: coming to a lab near you'.

Modelling of Protein Kinase Signaling Pathways in Melanoma and Other Cancers

Melanoma is a highly aggressive tumor with a strong dependence on intracellular signaling pathways. Almost half of all melanomas are driven by mutations in the v-Raf murine sarcoma viral oncogene homolog B (BRAF) with BRAFV600E being the most prevalent mutation. Recently developed targeted treatment directed against mutant BRAF and downstream mitogen-activated protein kinase (MAPK) MAP2K1 (also termed MEK1) have improved overall survival of melanoma patients. However, the MAPK signaling pathway is far more complex than a single chain of consecutively activated MAPK enzymes and it contains nested-, inherent feedback mechanisms, crosstalk with other signaling pathways, epigenetic regulatory mechanisms, and interacting small non-coding RNAs. A more complete understanding of this pathway is needed to better understand melanoma development and mechanisms of treatment resistance. Network reconstruction, analysis, and modelling under the systems biology paradigm have been used recently in different malignant tumors including melanoma to analyze and integrate 'omics' data, formulate mechanistic hypotheses on tumorigenesis, assess and personalize anticancer therapy, and propose new drug targets. Here we review the current knowledge of network modelling approaches in cancer with a special emphasis on melanoma.

Isoform-specific Ras signaling is growth factor dependent

HRAS, NRAS, and KRAS isoforms are almost identical proteins that are ubiquitously expressed and activate a common set of effectors. In vivo studies have revealed that they are not biologically redundant; however, the isoform specificity of Ras signaling remains poorly understood. Using a novel panel of isogenic SW48 cell lines endogenously expressing wild-type or G12V-mutated activated Ras isoforms, we have performed a detailed characterization of endogenous isoform-specific mutant Ras signaling. We find that despite displaying significant Ras activation, the downstream outputs of oncogenic Ras mutants are minimal in the absence of growth factor inputs. The lack of mutant KRAS-induced effector activation observed in SW48 cells appears to be representative of a broad panel of colon cancer cell lines harboring mutant KRAS. For MAP kinase pathway activation in KRAS-mutant cells, the requirement for coincident growth factor stimulation occurs at an early point in the Raf activation cycle. Finally, we find that Ras isoform-specific signaling was highly context dependent and did not conform to the dogma derived from ectopic expression studies.

Dynamic Targeting in Cancer Treatment

With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data

Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.