Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer

QbD-based co-loading of paclitaxel and imatinib mesylate by protamine-coated PLGA nanoparticles effective on breast cancer cells

Aim: Paclitaxel and imatinib mesylate are drugs used in the treatment of breast cancer. Conventional drug-delivery systems have limitations in the effective treatment of breast cancer using the drugs. Materials & methods: Combination index studies were used to identify the optimum ratio of both drugs showing maximum synergistic effect. Using a systematic quality-by-design approach, protamine-coated PLGA nanoparticles co-loaded with paclitaxel and imatinib mesylate were formulated. Further characterization and cell line evaluations were performed. Results: Encapsulation efficiency obtained was 92.54% for paclitaxel and 75.12% for imatinib mesylate. A sustained (24 h) and controlled zero-order drug release was obtained. Conclusion: Formulated nanoparticles had a low IC50 value and enhanced cellular uptake.

In silico approaches for drug repurposing in oncology: a scoping review

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes

At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treatment. High-throughput screening through deep learning can effectively improve the efficiency of discovering synergistic drugs. Nowadays, most of the existing deep learning algorithms for anti-cancer drug synergy prediction use deep neural networks and can only implicitly perform feature interaction. This study proposes a deep learning algorithm, named MolCross, which combines implicit feature interaction with explicit features to improve the accuracy of prediction of the anti-cancer drug synergy score. MolCross uses a deep autoencoder to extract features from high-dimensional input, uses the drug-specific subnetworks and cross-network to perform implicit feature interaction and explicit feature interaction respectively, and finally uses a synergy prediction network to combine the two feature interaction methods to obtain the final prediction results. We adopted a five-fold cross validation and compared MolCross with other four anti-cancer drug synergy prediction models. The results show that MolCross has better prediction performance than other models. MolCross also has good performance in terms of cross-cell line and cross-tissue type. Existing studies have demonstrated that cancer molecular subtypes have different sensitivities to targeted therapy. In this study, the features of cancer molecular subtype were introduced in the model using an embedding layer in MolCross to explore the effect of cancer molecular subtype on anti-cancer drug synergy. We also found that the cancer molecular subtype is one of the main factors affecting the synergy between drugs.

Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.

Epidermal growth factor receptor dual-target inhibitors as a novel therapy for cancer: A review

Overexpression of the epidermal growth factor receptor (EGFR) has been linked to several human cancers, including esophageal cancer, pancreatic cancer, anal cancer, breast cancer, and lung cancer, particularly non-small cell lung cancer (NSCLC). Therefore, EGFR has emerged as a critical target for treating solid tumors. Many 1st-, 2nd-, 3rd-, and 4th-generation EGFR single-target inhibitors with clinical efficacy have been designed and synthesized in recent years. Drug resistance caused by EGFR mutations has posed a significant challenge to the large-scale clinical application of EGFR single-target inhibitors and the discovery of novel EGFR inhibitors. Therapeutic methods for overcoming multipoint EGFR mutations are still needed in medicine. EGFR dual-target inhibitors are more promising than single-target inhibitors as they have a lower risk of drug resistance, higher efficacy, lower dosage, and fewer adverse events. EGFR dual-target inhibitors have been developed sequentially to date, providing new options for remission in patients with previously untreatable malignancies and laying the groundwork for a future generation of compounds. This paper introduces the EGFR family proteins and their synergistic effects with other anticancer targets, and provides a comprehensive review of the development of EGFR dual-target inhibitors in cancer, as well as the opportunities and challenges associated with those fields.

Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks

Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model's root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.

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.

How cells sense and integrate information from different sources

Cell signaling is a fundamental cellular process that enables cells to sense and respond to information in their surroundings. At the molecular level, signaling is primarily carried out by transmembrane protein receptors that can initiate complex downstream signal transduction cascades to alter cellular behavior. In the human body, different cells can be exposed to a wide variety of environmental conditions, and cells express diverse classes of receptors capable of sensing and integrating different signals. Furthermore, different receptors and signaling pathways can crosstalk with each other to calibrate the cellular response. Crosstalk occurs through multiple mechanisms at different levels of signaling pathways. In this review, we discuss how cells sense and integrate different chemical, mechanical, and spatial signals as well as the mechanisms of crosstalk between pathways. To illustrate these concepts, we use a few well-studied signaling pathways, including receptor tyrosine kinases and integrin receptors. Finally, we discuss the implications of dysregulated cellular sensing on driving diseases such as cancer. This article is categorized under: Cancer > Molecular and Cellular Physiology Metabolic Diseases > Molecular and Cellular Physiology.

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

Development of Dual Inhibitors Targeting Epidermal Growth Factor Receptor in Cancer Therapy

Epidermal growth factor receptor (EGFR) is of great significance in mediating cell signaling transduction and tumor behaviors. Currently, third-generation inhibitors of EGFR, especially osimertinib, are at the clinical frontier for the treatment of EGFR-mutant non-small-cell lung cancer (NSCLC). Regrettably, the rapidly developing drug resistance caused by EGFR mutations and the compensatory mechanism have largely limited their clinical efficacy. Given the synergistic effect between EGFR and other compensatory targets during tumorigenesis and tumor development, EGFR dual-target inhibitors are promising for their reduced risk of drug resistance, higher efficacy, lower dosage, and fewer adverse events than those of single-target inhibitors. Hence, we present the synergistic mechanism underlying the role of EGFR dual-target inhibitors against drug resistance, their structure-activity relationships, and their therapeutic potential. Most importantly, we emphasize the optimal target combinations and design strategies for EGFR dual-target inhibitors and provide some perspectives on new challenges and future directions in this field.

Ceruletide and Alpha-1 Antitrypsin as a Novel Combination Therapy for Ischemic Stroke

Ischemic stroke is a primary cause of morbidity and mortality worldwide. Beyond the approved thrombolytic therapies, there is no effective treatment to mitigate its progression. Drug repositioning combinational therapies are becoming promising approaches to identify new uses of existing drugs to synergically target multiple disease-response mechanisms underlying complex pathologies. Here, we used a systems biology-based approach based on artificial intelligence and pattern recognition tools to generate in silico mathematical models mimicking the ischemic stroke pathology. Combinational treatments were acquired by screening these models with more than 5 million two-by-two combinations of drugs. A drug combination (CA) formed by ceruletide and alpha-1 antitrypsin showing a predicted value of neuroprotection of 92% was evaluated for their synergic neuroprotective effects in a mouse pre-clinical stroke model. The administration of both drugs in combination was safe and effective in reducing by 39.42% the infarct volume 24 h after cerebral ischemia. This neuroprotection was not observed when drugs were given individually. Importantly, potential incompatibilities of the drug combination with tPA thrombolysis were discarded in vitro and in vivo by using a mouse thromboembolic stroke model with t-PA-induced reperfusion, revealing an improvement in the forepaw strength 72 h after stroke in CA-treated mice. Finally, we identified the predicted mechanisms of action of ceruletide and alpha-1 antitrypsin and we demonstrated that CA modulates EGFR and ANGPT-1 levels in circulation within the acute phase after stroke. In conclusion, we have identified a promising combinational treatment with neuroprotective effects for the treatment of ischemic stroke.

Machine learning approaches for drug combination therapies

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.

Characterization and comparison of gene-centered human interactomes

The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature. It is therefore unclear to which extent the various interactomes yield similar results when used in the context of interactome-based approaches in network biology. We compared a comprehensive list of human interactomes on the basis of topology, protein complexes, molecular pathways, pathway cross-talk and disease gene prediction. In a general context of relevant heterogeneity, our study provides a series of qualitative and quantitative parameters that describe the state of the art of human interactomes and guidelines for selecting interactomes in future applications.

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations to become costly and time-consuming. Computational techniques can improve the efficiency of drug combination screening. Despite recent advances in applying machine learning to synergistic drug combination prediction, several challenges remain. First, the performance of existing methods is suboptimal. There is still much space for improvement. Second, biological knowledge has not been fully incorporated into the model. Finally, many models are lack interpretability, limiting their clinical applications. To address these challenges, we have developed a knowledge-enabled and self-attention transformer boosted deep learning model, TranSynergy, which improves the performance and interpretability of synergistic drug combination prediction. TranSynergy is designed so that the cellular effect of drug actions can be explicitly modeled through cell-line gene dependency, gene-gene interaction, and genome-wide drug-target interaction. A novel Shapley Additive Gene Set Enrichment Analysis (SA-GSEA) method has been developed to deconvolute genes that contribute to the synergistic drug combination and improve model interpretability. Extensive benchmark studies demonstrate that TranSynergy outperforms the state-of-the-art method, suggesting the potential of mechanism-driven machine learning. Novel pathways that are associated with the synergistic combinations are revealed and supported by experimental evidences. They may provide new insights into identifying biomarkers for precision medicine and discovering new anti-cancer therapies. Several new synergistic drug combinations have been predicted with high confidence for ovarian cancer which has few treatment options. The code is available at https://github.com/qiaoliuhub/drug_combination.

KLIFS: an overhaul after the first 5 years of supporting kinase research

Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated that keeps on growing. KLIFS-Kinase-Ligand Interaction Fingerprints and Structures-is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.

Multiobjective optimization identifies cancer-selective combination therapies

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.

The Crosstalk between FAK and Wnt Signaling Pathways in Cancer and Its Therapeutic Implication

Focal adhesion kinase (FAK) and Wnt signaling pathways are important contributors to tumorigenesis in several cancers. While most results come from studies investigating these pathways individually, there is increasing evidence of a functional crosstalk between both signaling pathways during development and tumor progression. A number of FAK-Wnt interactions are described, suggesting an intricate, context-specific, and cell type-dependent relationship. During development for instance, FAK acts mainly upstream of Wnt signaling; and although in intestinal homeostasis and mucosal regeneration Wnt seems to function upstream of FAK signaling, FAK activates the Wnt/β-catenin signaling pathway during APC-driven intestinal tumorigenesis. In breast, lung, and pancreatic cancers, FAK is reported to modulate the Wnt signaling pathway, while in prostate cancer, FAK is downstream of Wnt. In malignant mesothelioma, FAK and Wnt show an antagonistic relationship: Inhibiting FAK signaling activates the Wnt pathway and vice versa. As the identification of effective Wnt inhibitors to translate in the clinical setting remains an outstanding challenge, further understanding of the functional interaction between Wnt and FAK could reveal new therapeutic opportunities and approaches greatly needed in clinical oncology. In this review, we summarize some of the most relevant interactions between FAK and Wnt in different cancers, address the current landscape of Wnt- and FAK-targeted therapies in different clinical trials, and discuss the rationale for targeting the FAK-Wnt crosstalk, along with the possible translational implications.

Synergetic Influence of Bismuth Oxide Nanoparticles, Cisplatin and Baicalein-Rich Fraction on Reactive Oxygen Species Generation and Radiosensitization Effects for Clinical Radiotherapy Beams

Purpose

This study aimed to quantify synergetic effects induced by bismuth oxide nanoparticles (BiONPs), cisplatin (Cis) and baicalein-rich fraction (BRF) natural-based agent on the reactive oxygen species (ROS) generation and radiosensitization effects under irradiation of clinical radiotherapy beams of photon, electron and HDR-brachytherapy. The combined therapeutic responses of each compound and clinical radiotherapy beam were evaluated on breast cancer and normal fibroblast cell line.

Methods

In this study, individual BiONPs, Cis, and BRF, as well as combinations of BiONPs-Cis (BC), BiONPs-BRF (BB) and BiONPs-Cis-BRF (BCB) were treated to the cells before irradiation using HDR brachytherapy with 0.38 MeV iridium-192 source, 6 MV photon beam and 6 MeV electron beam. The individual or synergetic effects from the application of the treatment components during the radiotherapy were elucidated by quantifying the ROS generation and radiosensitization effects on MCF-7 and MDA-MB-231 breast cancer cell lines as well as NIH/3T3 normal cell line.

Results

The ROS generated in the presence of Cis stimulated the most substantial amount of ROS compared to the BiONPs and BRF. Meanwhile, the combination of the components had induced the higher ROS levels for photon beam than the brachytherapy and electron beam. The highest ROS enhancement relative to the control is attributable to the presence of BC combination in MDA-MB-231 cells, in comparison to the BB and BCB combinations. The radiosensitization effects which were quantified using the sensitization enhancement ratio (SER) indicate the highest value by BC in MCF-7 cells, followed by BCB and BB treatment. The radiosensitization effects are found to be more prominent for brachytherapy in comparison to photon and electron beam.

Conclusion

The BiONPs, Cis and BRF are the potential radiosensitizers that could improve the efficiency of radiotherapy to eradicate the cancer cells. The combination of these potent radiosensitizers might produce multiple effects when applied in radiotherapy. The BC combination is found to have the highest SER, followed by the BCB combination. This study is also the first to investigate the effect of BRF in combination with BiONPs (BB) and BC (BCB) treatments.

Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling

Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted "neutral" (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington's epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach.

Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy

In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.

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.

Quantitative analysis of yeast MAPK signaling networks and crosstalk using a microfluidic device

Eukaryotic cells developed complex mitogen-activated protein kinase (MAPK) signaling networks to sense their intra- and extracellular environment and respond to various stress conditions. For example, S. cerevisiae uses five distinct MAP kinase pathways to orchestrate meiosis or respond to mating pheromones, osmolarity changes and cell wall stress. Although each MAPK module has been studied individually, the mechanisms underlying crosstalk between signaling pathways remain poorly understood, in part because suitable experimental systems to monitor cellular outputs when applying different signals are lacking. Here, we investigate the yeast MAPK signaling pathways and their crosstalk, taking advantage of a new microfluidic device coupled to quantitative microscopy. We designed specific micropads to trap yeast cells in a single focal plane, and modulate the magnitude of a given stress signal by microfluidic serial dilution while keeping other signaling inputs constant. This approach enabled us to quantify in single cells nuclear relocation of effectors responding to MAPK activation, like Yap1 for oxidative stress, and expression of stress-specific reporter expression, like pSTL1-qV and pFIG1-qV for high-osmolarity or mating pheromone signaling, respectively. Using this quantitative single-cell analysis, we confirmed bimodal behavior of gene expression in response to Hog1 activation, and quantified crosstalk between the pheromone- and cell wall integrity (CWI) signaling pathways. Importantly, we further observed that oxidative stress inhibits pheromone signaling. Mechanistically, this crosstalk is mediated by Pkc1-dependent phosphorylation of the scaffold protein Ste5 on serine 185, which prevents Ste5 recruitment to the plasma membrane.

Drug antagonism and single-agent dominance result from differences in death kinetics

Cancer treatment generally involves drugs used in combinations. Most previous work has focused on identifying and understanding synergistic drug-drug interactions; however, understanding antagonistic interactions remains an important and understudied issue. To enrich for antagonism and reveal common features of these combinations, we screened all pairwise combinations of drugs characterized as activators of regulated cell death. This network is strongly enriched for antagonism, particularly a form of antagonism that we call 'single-agent dominance'. Single-agent dominance refers to antagonisms in which a two-drug combination phenocopies one of the two agents. Dominance results from differences in cell death onset time, with dominant drugs acting earlier than their suppressed counterparts. We explored mechanisms by which parthanatotic agents dominate apoptotic agents, finding that dominance in this scenario is caused by mutually exclusive and conflicting use of Poly(ADP-ribose) polymerase 1 (PARP1). Taken together, our study reveals death kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between cell death pathways.

Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Metastases are the main cause of death in advanced breast cancer (BC) patients. Although chemotherapy and hormone therapy are current treatment strategies, drug resistance is frequent and still not completely understood. In this study, a bioinformatics analysis was performed on BC patients to explore the molecular mechanisms associated with BC metastasis. Microarray gene expression profiles of metastatic and non metastatic BC patients were downloaded from Gene Expression Omnibus (GEO) dataset. Raw data were normalized and merged using the Combat tool. Pathways enriched with differently expressed genes were identified and a pathway co-expression network was generated using Pearson's correlation. We identified from this network, which includes 17 pathways and 128 interactions, the pathways that most influence the network efficiency. Moreover, protein interaction network was investigated to identify hub genes of the pathway network. The prognostic role of the network was evaluated with a survival analysis using an independent dataset. In conclusion, the pathway co-expression network could contribute to understanding the mechanism and development of BC metastases.

Stratification and prediction of drug synergy based on target functional similarity

Drug combinations can expand therapeutic options and address cancer’s resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to prioritise drug combinations in high-throughput screens and to stratify synergistic responses. At the core of our approach is the observation that the likelihood of synergy increases when targeting proteins with either strong functional similarity or dissimilarity. We estimate the similarity applying a multitask machine learning approach to basal gene expression and response to single drugs. We tested 7 protein target pairs (representing 29 combinations) and predicted their synergies in 33 breast cancer cell lines. In addition, we experimentally validated predicted synergy of the BRAF/insulin receptor combination (Dabrafenib/BMS-754807) in 48 colorectal cancer cell lines. We anticipate that our approaches can be used for prioritization of drug combinations in large scale screenings, and to maximize the efficacy of drugs already known to induce synergy, ultimately enabling patient stratification.

Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.

A Combinatorial Strategy for Targeting BRAF V600E-Mutant Cancers with BRAFV600E Inhibitor (PLX4720) and Tyrosine Kinase Inhibitor (Ponatinib)

PURPOSE

Most aggressive thyroid cancers are commonly associated with a BRAF ^V600E mutation. Preclinical and clinical data in BRAF ^V600E cancers suggest that combined BRAF and MEK inhibitor treatment results in a response, but resistance is common. One mechanism of acquired resistance is through persistent activation of tyrosine kinase (TK) signaling by alternate pathways. We hypothesized that combination therapy with BRAF and multitargeting TK inhibitors (MTKI) might be more effective in BRAF ^V600E thyroid cancer than in single-agent or BRAF and MEK inhibitors.

EXPERIMENTAL DESIGN

The combined drug activity was analyzed to predict any synergistic effect using high-throughput screening (HTS) of active drugs. We performed follow-up in vitro and in vivo studies to validate and determine the mechanism of action of synergistic drugs.

RESULTS

The MTKI ponatinib and the BRAF inhibitor PLX4720 showed synergistic activity by HTS. This combination significantly inhibited proliferation, colony formation, invasion, and migration in BRAF ^V600E thyroid cancer cell lines and downregulated pERK/MEK and c-JUN signaling pathways, and increased apoptosis. PLX4720-resistant BRAF ^V600E cells became sensitized to the combination treatment, with decreased proliferation at lower PLX4720 concentrations. In an orthotopic thyroid cancer mouse model, combination therapy significantly reduced tumor growth (P < 0.05), decreased the number of metastases (P < 0.05), and increased survival (P < 0.05) compared with monotherapy and vehicle control.

CONCLUSIONS

Combination treatment with ponatinib and PLX4720 exhibited significant synergistic anticancer activity in preclinical models of BRAF ^V600E thyroid cancer, in addition to overcoming PLX4720 resistance. Our results suggest this combination should be tested in clinical trials.

Colorectal Cancer Growth Retardation through Induction of Apoptosis, Using an Optimized Synergistic Cocktail of Axitinib, Erlotinib, and Dasatinib

Patients with advanced colorectal cancer (CRC) still depend on chemotherapy regimens that are associated with significant limitations, including resistance and toxicity. The contribution of tyrosine kinase inhibitors (TKIs) to the prolongation of survival in these patients is limited, hampering clinical implementation. It is suggested that an optimal combination of appropriate TKIs can outperform treatment strategies that contain chemotherapy. We have previously identified a strongly synergistic drug combination (SDC), consisting of axitinib, erlotinib, and dasatinib that is active in renal cell carcinoma cells. In this study, we investigated the activity of this SDC in different CRC cell lines (SW620, HT29, and DLD-1) in more detail. SDC treatment significantly and synergistically decreased cell metabolic activity and induced apoptosis. The translation of the in-vitro-based results to in vivo conditions revealed significant CRC tumor growth inhibition, as evaluated in the chicken chorioallantoic membrane (CAM) model. Phosphoproteomics analysis of the tested cell lines revealed expression profiles that explained the observed activity. In conclusion, we demonstrate promising activity of an optimized mixture of axitinib, erlotinib, and dasatinib in CRC cells, and suggest further translational development of this drug mixture.

The Landscape of Atypical and Eukaryotic Protein Kinases

Kinases are attractive anticancer targets due to their central role in the growth, survival, and therapy resistance of tumor cells. This review explores the two primary kinase classes, the eukaryotic protein kinases (ePKs) and the atypical protein kinases (aPKs), and provides a structure-centered comparison of their sequences, structures, hydrophobic spines, mutation and SNP hotspots, and inhibitor interaction patterns. Despite the limited sequence similarity between these two classes, atypical kinases commonly share the archetypical kinase fold but lack conserved eukaryotic kinase motifs and possess altered hydrophobic spines. Furthermore, atypical kinase inhibitors explore only a limited number of binding modes both inside and outside the orthosteric binding site. The distribution of genetic variations in both classes shows multiple ways they can interfere with kinase inhibitor binding. This multilayered review provides a research framework bridging the eukaryotic and atypical kinase classes.

Quantifying Risk Pathway Crosstalk Mediated by miRNA to Screen Precision drugs for Breast Cancer Patients

Breast cancer has become the most common cancer that leads to women's death. Breast cancer is a complex, highly heterogeneous disease classified into various subtypes based on histological features, which determines the therapeutic options. System identification of effective drugs for each subtype remains challenging. In this work, we present a computational network biology approach to screen precision drugs for different breast cancer subtypes by considering the impact intensity of candidate drugs on the pathway crosstalk mediated by miRNAs. Firstly, we constructed and analyzed the subtype-specific risk pathway crosstalk networks mediated by miRNAs. Then, we evaluated 36 Food and Drug Administration (FDA)-approved anticancer drugs by quantifying their effects on these subtype-specific pathway crosstalk networks and combining with survival analysis. Finally, some first-line treatments of breast cancer, such as Paclitaxel and Vincristine, were optimized for each subtype. In particular, we performed precision screening of subtype-specific therapeutic drugs and also confirmed some novel drugs suitable for breast cancer treatment. For example, Sorafenib was applicable for the basal subtype treatment, Irinotecan was optimum for Her2 subtype treatment, Vemurafenib was suitable for the LumA subtype treatment, and Vorinostat could apply to LumB subtype treatment. In addition, the mechanism of these optimal therapeutic drugs in each subtype of breast cancer was further dissected. In summary, our study offers an effective way to screen precision drugs for various breast cancer subtype treatments. We also dissected the mechanism of optimal therapeutic drugs, which may provide novel insight into the precise treatment of cancer and promote researches on the mechanisms of action of drugs.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

Natural Product Target Network Reveals Potential for Cancer Combination Therapies

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

Cascade-amplification of therapeutic efficacy: An emerging opportunity in cancer treatment

Increasing research evidence reveals that cancer is complex disease involving many biological factors, processes and systems, which may severely limit the actual efficacy of conventional monotonic anticancer approaches. To overcome these obstacles in cancer treatment, a new strategy has been proposed by combining multiple synergistic therapeutic modalities accessing different but inherently related targets and acting sequentially. A major benefit of this strategy is that the multi-target mechanism could result in a cascade-amplification effect leading to enhanced anticancer activity. In this review, we provide a critical discussion on the application of cascade-amplification strategy in the treatment of various cancer indications, focusing on the rational combination of therapeutic agents and their mechanisms of action. A concise yet comprehensive analysis on the potential therapeutic benefit of this strategy was also included. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

In silico perturbation of drug targets in pan-cancer analysis combining multiple networks and pathways

The knowledge of cancer cell response to conventional therapies is crucial in order to choose the correct therapy of patients affected by cancer. The major problem is generally attributed to the lack of specific biological processes able to predict the therapy efficacy. Here, we optimized a computational method for the analysis of gene networks able to detect and quantify the effects of a drug in a pan-cancer study. Overall, our method, using several network topological measures has identified a cancer gene network with a key role in biological processes. The gene network, able to classify with a good performance cancer vs normal samples, was modulated in silico to evaluate the effects of new or approved drugs. This computational model could offer an interesting hint to decipher molecular mechanisms contributing to resistance or inefficacy of drugs.

A multifunctional toolkit for target-directed cancer therapy

Here we present 2shRNA, a shRNA-based nanobinder, which can simultaneously attack two therapeutic targets involved in drug resistance pathways and can additionally bind accessory molecules such as cell targeting peptides or fluorophores. We create 2shRNAs designed to specifically kill HER2+ breast cancer cells in the absence of a transfecting agent.

A combination of curcumin and oligomeric proanthocyanidins offer superior anti-tumorigenic properties in colorectal cancer

Combining anti-cancer agents in cancer therapies is becoming increasingly popular due to improved efficacy, reduced toxicity and decreased emergence of resistance. Here, we test the hypothesis that dietary agents such as oligomeric proanthocyanidins (OPCs) and curcumin cooperatively modulate cancer-associated cellular mechanisms to inhibit carcinogenesis. By a series of in vitro assays in colorectal cancer cell lines, we showed that the anti-tumorigenic properties of the OPCs-curcumin combination were superior to the effects of individual compounds. By RNA-sequencing based gene-expression profiling in six colorectal cancer cell lines, we identified the cooperative modulation of key cancer-associated pathways such as DNA replication and cell cycle pathways. Moreover, several pathways, including protein export, glutathione metabolism and porphyrin metabolism were more effectively modulated by the combination of OPCs and curcumin. We validated genes belonging to these pathways, such as HSPA5, SEC61B, G6PD, HMOX1 and PDE3B to be cooperatively modulated by the OPCs-curcumin combination. We further confirmed that the OPCs-curcumin combination more potently suppresses colorectal carcinogenesis and modulated expression of genes identified by RNA-sequencing in mice xenografts and in colorectal cancer patient-derived organoids. Overall, by delineating the cooperative mechanisms of action of OPCs and curcumin, we make a case for the clinical co-administration of curcumin and OPCs as a treatment therapy for patients with colorectal cancer.

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

Proximal Pathway Enrichment Analysis for Targeting Comorbid Diseases via Network Endopharmacology

The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients.

In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition

Background

Despite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC). Multiple alternative mechanisms upon drug treatment are involved to offset therapeutic effects, eventually causing drug resistance or treatment failure.

Methods

Here, we optimized a computational method to discover novel drug target pathways in cancer subtypes using pathway cross-talk inhibition (PCI). The in silico method is based on the detection and quantification of the pathway cross-talk for distinct cancer subtypes. From a BC data set of The Cancer Genome Atlas, we have identified different networks of cross-talking pathways for different BC subtypes, validated using an independent BC dataset from Gene Expression Omnibus. Then, we predicted in silico the effects of new or approved drugs on different BC subtypes by silencing individual or combined subtype-derived pathways with the aim to find new potential drugs or more effective synergistic combinations of drugs.

Results

Overall, we identified a set of new potential drug target pathways for distinct BC subtypes on which therapeutic agents could synergically act showing antitumour effects and impacting on cross-talk inhibition.

Conclusions

We believe that in silico methods based on PCI could offer valuable approaches to identifying more tailored and effective treatments in particular in heterogeneous cancer diseases.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1535-2) contains supplementary material, which is available to authorized users.

How the evolution of multicellularity set the stage for cancer

Neoplastic growth and many of the hallmark properties of cancer are driven by the disruption of molecular networks established during the emergence of multicellularity. Regulatory pathways and molecules that evolved to impose regulatory constraints upon networks established in earlier unicellular organisms enabled greater communication and coordination between the diverse cell types required for multicellularity, but also created liabilities in the form of points of vulnerability in the network that when mutated or dysregulated facilitate the development of cancer. These factors are usually overlooked in genomic analyses of cancer, but understanding where vulnerabilities to cancer lie in the networks of multicellular species would provide important new insights into how core molecular processes and gene regulation change during tumourigenesis. We describe how the evolutionary origins of genes influence their roles in cancer, and how connections formed between unicellular and multicellular genes that act as key regulatory hubs for normal tissue homeostasis can also contribute to malignant transformation when disrupted. Tumours in general are characterised by increased dependence on unicellular processes for survival, and major dysregulation of the control structures imposed on these processes during the evolution of multicellularity. Mounting molecular evidence suggests altered interactions at the interface between unicellular and multicellular genes play key roles in the initiation and progression of cancer. Furthermore, unicellular network regions activated in cancer show high degrees of robustness and plasticity, conferring increased adaptability to tumour cells by supporting effective responses to environmental pressures such as drug exposure. Examining how the links between multicellular and unicellular regions get disrupted in tumours has great potential to identify novel drivers of cancer, and to guide improvements to cancer treatment by identifying more effective therapeutic strategies. Recent successes in targeting unicellular processes by novel compounds underscore the logic of such approaches. Further gains could come from identifying genes at the interface between unicellular and multicellular processes and manipulating the communication between network regions of different evolutionary ages.

Network science in clinical trials: A patient-centered approach

There has been a paradigm shift in translational oncology with the advent of novel molecular diagnostic tools in the clinic. However, several challenges are associated with the integration of these sophisticated tools into clinical oncology and daily practice. High-throughput profiling at the DNA, RNA and protein levels (omics) generate a massive amount of data. The analysis and interpretation of these is non-trivial but will allow a more thorough understanding of cancer. Linear modelling of the data as it is often used today is likely to limit our understanding of cancer as a complex disease, and at times under-performs to capture a phenotype of interest. Network science and systems biology-based approaches, using machine learning and network science principles, that integrate multiple data sources, can uncover complex changes in a biological system. This approach will integrate a large number of potential biomarkers in preclinical studies to better inform therapeutic decisions and ultimately make substantial progress towards precision medicine. It will however require development of a new generation of clinical trials. Beyond discussing the challenges of high-throughput technologies, this review will develop a framework on how to implement a network science approach in new clinical trial designs in order to advance cancer care.

In silico-based screen synergistic drug combinations from herb medicines: a case using Cistanche tubulosa

Neuroinflammation is characterized by the elaborated inflammatory response repertoire of central nervous system tissue. The limitations of the current treatments for neuroinflammation are well-known side effects in the clinical trials of monotherapy. Drug combination therapies are promising strategies to overcome the compensatory mechanisms and off-target effects. However, discovery of synergistic drug combinations from herb medicines is rare. Encouraged by the successfully applied cases we move on to investigate the effective drug combinations based on system pharmacology among compounds from Cistanche tubulosa (SCHENK) R. WIGHT. Firstly, 63 potential bioactive compounds, the related 133 direct and indirect targets are screened out by Drug-likeness evaluation combined with drug targeting process. Secondly, Compound-Target network is built to acquire the data set for predicting drug combinations. We list the top 10 drug combinations which are employed by the algorithm Probability Ensemble Approach (PEA), and Compound-Target-Pathway network is then constructed by the 12 compounds of the combinations, targets, and pathways to unearth the corresponding pharmacological actions. Finally, an integrating pathway approach is developed to elucidate the therapeutic effects of the herb in different pathological features-relevant biological processes. Overall, the method may provide a productive avenue for developing drug combination therapeutics.