A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose-Response Curve

Dual-view jointly learning improves personalized drug synergy prediction

MOTIVATION

Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples.

RESULTS

We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and makes complementary contributes to the final prediction of the drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies.

AVAILABILITY AND IMPLEMENTATION

Source code and data are available at https://github.com/LiHongCSBLab/JointSyn.

FDA-approved disulfiram as a novel treatment for aggressive leukemia

Acute leukemia continues to be a major cause of death from disease worldwide and current chemotherapeutic agents are associated with significant morbidity in survivors. While better and safer treatments for acute leukemia are urgently needed, standard drug development pipelines are lengthy and drug repurposing therefore provides a promising approach. Our previous evaluation of FDA-approved drugs for their antileukemic activity identified disulfiram, used for the treatment of alcoholism, as a candidate hit compound. This study assessed the biological effects of disulfiram on leukemia cells and evaluated its potential as a treatment strategy. We found that disulfiram inhibits the viability of a diverse panel of acute lymphoblastic and myeloid leukemia cell lines (n = 16) and patient-derived xenograft cells from patients with poor outcome and treatment-resistant disease (n = 15). The drug induced oxidative stress and apoptosis in leukemia cells within hours of treatment and was able to potentiate the effects of daunorubicin, etoposide, topotecan, cytarabine, and mitoxantrone chemotherapy. Upon combining disulfiram with auranofin, a drug approved for the treatment of rheumatoid arthritis that was previously shown to exert antileukemic effects, strong and consistent synergy was observed across a diverse panel of acute leukemia cell lines, the mechanism of which was based on enhanced ROS induction. Acute leukemia cells were more sensitive to the cytotoxic activity of disulfiram than solid cancer cell lines and non-malignant cells. While disulfiram is currently under investigation in clinical trials for solid cancers, this study provides evidence for the potential of disulfiram for acute leukemia treatment. KEY MESSAGES: Disulfiram induces rapid apoptosis in leukemia cells by boosting oxidative stress. Disulfiram inhibits leukemia cell growth more potently than solid cancer cell growth. Disulfiram can enhance the antileukemic efficacy of chemotherapies. Disulfiram strongly synergises with auranofin in killing acute leukemia cells by ROS induction. We propose testing of disulfiram in clinical trial for patients with acute leukemia.

Predicting anticancer synergistic drug combinations based on multi-task learning

BACKGROUND

The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks.

METHODS

In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features.

RESULTS

Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies.

CONCLUSION

Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction

Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug–cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug–cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases.

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical-gene-tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure-gene and gene-gene associations and a coarse-granularity transformer encoder to extract chemical-chemical and chemical-cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.

A review of deep learning applications in human genomics using next-generation sequencing data

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

Machine learning in the prediction of cancer therapy

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.

Exploiting the reactive oxygen species imbalance in high-risk paediatric acute lymphoblastic leukaemia through auranofin

BACKGROUND

The prognosis for high-risk childhood acute leukaemias remains dismal and established treatment protocols often cause long-term side effects in survivors. This study aims to identify more effective and safer therapeutics for these patients.

METHODS

A high-throughput phenotypic screen of a library of 3707 approved drugs and pharmacologically active compounds was performed to identify compounds with selective cytotoxicity against leukaemia cells followed by further preclinical evaluation in patient-derived xenograft models.

RESULTS

Auranofin, an FDA-approved agent for the treatment of rheumatoid arthritis, was identified as exerting selective anti-cancer activity against leukaemia cells, including patient-derived xenograft cells from children with high-risk ALL, versus solid tumour and non-cancerous cells. It induced apoptosis in leukaemia cells by increasing reactive oxygen species (ROS) and potentiated the activity of the chemotherapeutic cytarabine against highly aggressive models of infant MLL-rearranged ALL by enhancing DNA damage accumulation. The enhanced sensitivity of leukaemia cells towards auranofin was associated with lower basal levels of the antioxidant glutathione and higher baseline ROS levels compared to solid tumour cells.

CONCLUSIONS

Our study highlights auranofin as a well-tolerated drug candidate for high-risk paediatric leukaemias that warrants further preclinical investigation for application in high-risk paediatric and adult acute leukaemias.

Targeting the T-Cell Lymphoma Epigenome Induces Cell Death, Cancer Testes Antigens, Immune-Modulatory Signaling Pathways

The peripheral T-cell lymphomas (PTCL) could be considered the prototypical epigenetic disease. As a disease, they are uniquely sensitive to histone deacetylase (HDAC) and DNA methyltransferase (DNMT) inhibitors, both alone and in combination, are characterized by a host of mutations in epigenetic genes, and can develop spontaneously in genetically engineered murine models predicated on established recurring mutations in (RHOAG17V) and TET2, an epigenetic gene governing DNA methylation. Given the clinical benefit of HDAC inhibitors (HDACi) and hypomethlyation agents alone and in combination in PTCL, we sought to explore a mechanistic basis for these agents in PTCL. Herein, we reveal profound class synergy between HDAC and DNMT inhibitors in PTCL, and that the combination induces degrees of gene expression that are substantially different and more extensive than that observed for the single agents. A prominent signature of the combination relates to the transcriptional induction of cancer testis antigens and genes involved in the immune response. Interestingly, TBX21 and STAT4, master regulators of TH1 differentiation, were among the genes upregulated by the combination, suggesting the induction of a TH1-like phenotype. Moreover, suppression of genes involved in cholesterol metabolism and the matrisome were also identified. We believe that these data provide a strong rationale for clinical studies, and future combinations leveraging an immunoepigenetic platform.

A multi-herb-combined remedy to overcome hyper-inflammatory response by reprogramming transcription factor profile and shaping monocyte subsets

Traditional Chinese multi-herb-combined prescriptions usually show better performance than a single agent since a group of effective compounds interfere multiple disease-relevant targets simultaneously. Huang-Lian-Jie-Du decoction is a remedy made of four herbs that are widely used to treat oral ulcers, gingivitis, and periodontitis. However, the active ingredients and underlying mechanisms are not clear. To address these questions, we prepared a water extract solution of Huang-Lian-Jie-Du decoction (HLJDD), called it as WEH (Water Extract Solution of HLJDD), and used it to treat LPS-induced systemic inflammation in mice. We observed that WEH attenuated inflammatory responses including reducing production of cytokines, chemokines and interferons (IFNs), further attenuating emergency myelopoiesis, and preventing mice septic lethality. Upon LPS stimulation, mice pretreated with WEH increased circulating Ly6C^- patrolling and splenic Ly6C⁺ inflammatory monocytes. The acute myelopoiesis related transcriptional factor profile was rearranged by WEH. Mechanistically we confirmed that WEH interrupted LPS/TLR4/CD14 signaling-mediated downstream signaling pathways through its nine principal ingredients, which blocked LPS stimulated divergent signaling cascades, such as activation of NF-κB, p38 MAPK, and ERK1/2. We conclude that the old remedy blunts LPS-induced "danger" signal recognition and transduction process at multiple sites. To translate our findings into clinical applications, we refined the crude extract into a pure multicomponent drug by directly mixing these nine chemical entities, which completely reproduced the effect of protecting mice from lethal septic shock. Finally, we reduced a large number of compounds within a multi-herb water extract to seven-chemical combination that exhibited superior therapeutic efficacy compared with WEH.

Using Split Luciferase Assay and anti-HSV Glycoprotein Monoclonal Antibodies to Predict a Functional Binding Site Between gD and gH/gL

Herpes simplex virus (HSV) entry and cell-cell fusion require glycoproteins gD, gH/gL, and gB. HSV entry begins with gD binding its receptor (nectin-1), which then activates gH/gL to enable the conversion of pre-fusion gB to its active form to promote membrane fusion. Virus-neutralizing monoclonal antibodies (Mabs) interfere with one or more of these steps and localization of their epitopes identifies functional sites on each protein. Utilizing this approach, we have identified the gH/gL binding face on gD and the corresponding gD binding site on gH/gL. Here, we used combinations of these Mabs to define the orientation of gD and gH/gL relative to each other. We reasoned that if two Mabs, one directed at gD and the other at gH/gL, block fusion more effectively than when either were used alone (additive), then their epitopes would be spatially distanced and binding of one would not directly interfere with binding of the other during fusion. However, if the two Mabs blocked fusion with equal or lesser efficacy that when either were used alone (indifferent), we propose that their epitopes would be in close proximity in the complex. Using a live cell fusion assay, we found that some Mab pairings blocked the fusion with different mechanisms while other had a similar mechanisms of action. Grouping the different combinations of antibodies into indifferent and additive groups, we present a model for the orientation of gD vis-à-vis gH/gL in the complex.Importance: Virus entry and cell-cell fusion mediated by HSV require four essential glycoproteins, gD, gH/gL, gB and a gD receptor. Virus-neutralizing antibodies directed against any of these proteins bind to residues within key functional sites and interfere with essential steps in the fusion pathway. Thus, the epitopes of these Mabs overlap and point to critical, functional sites on their target proteins. Here, we combined gD and gH/gL antibodies to determine whether they work in an additive or non-additive (indifferent) fashion to block specific events in glycoprotein-driven cell-cell fusion. Identifying combinations of antibodies that have additive effects will help in the rational design of an effective therapeutic "polyclonal antibody" to treat HSV disease. In addition, identification of the exact contact regions between gD and gH/gL can inform the design of small molecules that would interfere with the gD-gH/gL complex formation, thus preventing the virus from entering the host cell.

Anticancer drug synergy prediction in understudied tissues using transfer learning

OBJECTIVE

Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems.

MATERIALS AND METHODS

We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues.

RESULTS

We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues.

CONCLUSIONS

Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.

Pharmacological Activation of Non-canonical NF-κB Signaling Activates Latent HIV-1 Reservoirs In Vivo

"Shock and kill" strategies focus on purging the latent HIV-1 reservoir by treating infected individuals with therapeutics that activate the latent virus and subsequently eliminating infected cells. We have previously reported that induction of non-canonical nuclear factor κB (NF-κB) signaling through a class of small-molecule antagonists known as Smac mimetics can reverse HIV-1 latency. Here, we describe the development of Ciapavir (SBI-0953294), a molecule specifically optimized for HIV-1 latency reversal that was found to be more efficacious as a latency-reversing agent than other Smac mimetics under clinical development for cancer. Critically, this molecule induced activation of HIV-1 reservoirs in vivo in a bone marrow, liver, thymus (BLT) humanized mouse model without mediating systemic T cell activation. This study provides proof of concept for the in vivo efficacy and safety of Ciapavir and indicates that Smac mimetics can constitute a critical component of a safe and efficacious treatment strategy to eliminate the latent HIV-1 reservoir.

DIGREM: an integrated web-based platform for detecting effective multi-drug combinations

MOTIVATION

Synergistic drug combinations are a promising approach to achieve a desirable therapeutic effect in complex diseases through the multi-target mechanism. However, in vivo screening of all possible multi-drug combinations remains cost-prohibitive. An effective and robust computational model to predict drug synergy in silico will greatly facilitate this process.

RESULTS

We developed DIGREM (Drug-Induced Genomic Response models for identification of Effective Multi-drug combinations), an online tool kit that can effectively predict drug synergy. DIGREM integrates DIGRE, IUPUI_CCBB, gene set-based and correlation-based models for users to predict synergistic drug combinations with dose-response information and drug-treated gene expression profiles.

AVAILABILITY AND IMPLEMENTATION

http://lce.biohpc.swmed.edu/drugcombination.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

Motivation

While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.

Results

DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations.

Availability and implementation

DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy.

Contact

klambauer@bioinf.jku.at.

Supplementary information

Supplementary data are available at Bioinformatics online.

A novel isoflavone, ME-344, targets the cytoskeleton in acute myeloid leukemia

The isoflavone ME-344 is a potent anti-cancer agent with preclinical and clinical efficacy in solid tumors. Yet, the mechanism of action of ME-344 has not been fully defined and the preclinical efficacy in leukemia has not been established. Therefore, we investigated the anti-leukemic properties and mechanism of action of ME-344. In a panel of 7 leukemia cell lines, ME-344 was cytotoxic with an IC₅₀ in the range of 70–260 nM. In addition, ME-344 was cytotoxic to primary AML patient samples over normal hematopoietic cells. In an OCI-AML2 xenograft model, ME-344 reduced tumor growth by up to 95% of control without evidence of toxicity. Mechanistically, ME-344 increased mitochondrial ROS generation in leukemic cells. However, antioxidant treatment did not rescue cell death, suggesting that ME-344 had additional targets beyond the mitochondria. We demonstrated that ME-344 inhibited tubulin polymerization by interacting with tubulin near the colchicine-binding site. Furthermore, inhibition of tubulin polymerization was functionally important for ME-344 induced death. Finally, we showed that ME-344 synergizes with vinblastine in leukemia cells. Thus, our study demonstrates that ME-344 displays preclinical efficacy in leukemia through a mechanism at least partly related to targeting tubulin polymerization.

PIK3CA hotspot mutations differentially impact responses to MET targeting in MET-driven and non-driven preclinical cancer models

Background

The MET receptor tyrosine kinase represents a promising target in cancer. PIK3CA activating mutations are common in several tumor types and can potentially confer resistance to anti-receptor tyrosine kinase therapy.

Methods

MET and/or PI3K pathway inhibition was assessed in NIH3T3 cells harboring MET-activating point mutation with or without ectopic expression of PIK3CA^E545K and PIK3CA^H1047R, as well as in MET-expressing head and neck cancer cells with endogenous PIK3CA mutations. Endpoints included PI3K pathway activation, cell proliferation, colony-forming ability, cell death, wound-healing, and an in vivo model.

Results

PIK3CA^E545K and PIK3CA^H1047R confer resistance to MET inhibition in MET-driven models. PIK3CA^H1047R was more potent than PIK3CA^E545K at inducing resistance in PI3K pathway activation, cell proliferation, colony-forming ability, induction of cell death and wound-healing upon MET inhibition. Resistance to MET inhibition could be synergistically overcome by co-targeting PI3K. Furthermore, combined MET/PI3K inhibition led to enhanced anti-tumor activity in vivo in tumors harboring PIK3CA^H1047R. In head and neck cancer cells the combination of MET/PI3K inhibitors led to more-than-additive effects.

Conclusions

PIK3CA mutations can lead to resistance to MET inhibition, supporting future clinical evaluation of combinations of PI3K and MET inhibitors in common scenarios of malignant neoplasms featuring aberrant MET expression and PIK3CA mutations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0660-5) contains supplementary material, which is available to authorized users.

Biomolecular Network-Based Synergistic Drug Combination Discovery

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

A simple gene set-based method accurately predicts the synergy of drug pairs

Background

The advance in targeted therapy has greatly increased the effectiveness of clinical cancer therapy and reduced the cytotoxicity of treatments to normal cells. However, patients still suffer from cancer relapse due to the occurrence of drug resistance. It is of great need to explore potential combinatorial drug therapy since individual drug alone may not be sufficient to inhibit continuous activation of cancer-addicted genes or pathways. The DREAM challenge has confirmed the potentiality of computational methods for predicting synergistic drug combinations, while the prediction accuracy can be further improved.

Methods

Based on previous reports, we hypothesized the similarity in biological functions or genes perturbed by two drugs can determine their synergistic effects. To test the feasibility of the hypothesis, we proposed three scoring systems: co-gene score, co-GS score, and co-gene/GS score, measuring the similarities in genes with significant expressional changes, enriched gene sets, and significantly changed genes within an enriched gene sets between a pair of drugs, respectively. Performances of these scoring systems were evaluated by the probabilistic c-index (PC-index) devised by the DREAM consortium. We also applied the proposed method to the Connectivity Map dataset to explore more potential synergistic drug combinations.

Results

Using a gold standard derived by the DREAM consortium, we confirmed the prediction power of the three scoring systems (all P-values < 0.05). The co-gene/GS score achieved the best prediction of drug synergy (PC-index = 0.663, P-value < 0.0001), outperforming all methods proposed during DREAM challenge. Furthermore, a binary classification test showed that co-gene/GS scoring was highly accurate and specific. Since our method is constructed on a gene set-based analysis, in addition to synergy prediction, it provides insights into the functional relevance of drug combinations and the underlying mechanisms by which drugs achieve synergy.

Conclusions

Here we proposed a novel and simple method to predict and investigate drug synergy, and validated its efficacy to accurately predict synergistic drug combinations and to comprehensively explore their underlying mechanisms. The method is widely applicable to expression profiles of other drug treatments and is expected to accelerate the realization of precision cancer treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0310-3) contains supplementary material, which is available to authorized users.

Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes

MOTIVATION

Combinatorial therapies have been under intensive research for cancer treatment. However, due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Hence, it is important to develop computational tools that can predict compound combination effects, prioritize combinations and limit the search space to facilitate and accelerate the development of combinatorial therapies.

RESULTS

In this manuscript we consider the NCI-DREAM Drug Synergy Prediction Challenge dataset to identify features informative about combination effects. Through systematic exploration of differential expression profiles after single compound treatments and comparison of molecular structures of compounds, we found that synergistic levels of combinations are statistically significantly associated with compounds' dissimilarity in structure and similarity in induced gene expression changes. These two types of features offer complementary information in predicting experimentally measured combination effects of compound pairs. Our findings offer insights on the mechanisms underlying different combination effects and may help prioritize promising combinations in the very large search space.

AVAILABILITY AND IMPLEMENTATION

The R code for the analysis is available on https://github.com/YiyiLiu1/DrugCombination CONTACT: hongyu.zhao@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.