Drug synergy screen and network modeling in dedifferentiated liposarcoma identifies CDK4 and IGF1R as synergistic drug targets

Genetic, Epigenetic and Transcriptome Alterations in Liposarcoma for Target Therapy Selection

Liposarcoma (LPS) is one of the most common adult soft-tissue sarcomas (STS), characterized by a high diversity of histopathological features as well as to a lesser extent by a spectrum of molecular abnormalities. Current targeted therapies for STS do not include a wide range of drugs and surgical resection is the mainstay of treatment for localized disease in all subtypes, while many LPS patients initially present with or ultimately progress to advanced disease that is either unresectable, metastatic or both. The understanding of the molecular characteristics of liposarcoma subtypes is becoming an important option for the detection of new potential targets and development novel, biology-driven therapies for this disease. Innovative therapies have been introduced and they are currently part of preclinical and clinical studies. In this review, we provide an analysis of the molecular genetics of liposarcoma followed by a discussion of the specific epigenetic changes in these malignancies. Then, we summarize the peculiarities of the key signaling cascades involved in the pathogenesis of the disease and possible novel therapeutic approaches based on a better understanding of subtype-specific disease biology. Although heterogeneity in liposarcoma genetics and phenotype as well as the associated development of resistance to therapy make difficult the introduction of novel therapeutic targets into the clinic, recently a number of targeted therapy drugs were proposed for LPS treatment. The most promising results were shown for CDK4/6 and MDM2 inhibitors as well as for the multi-kinase inhibitors anlotinib and sunitinib.

MDM2-p53 in liposarcoma: The need for targeted therapies with novel mechanisms of action

Well-differentiated and dedifferentiated liposarcomas (WDLPS and DDLPS) are rare tumors that arise from lipocytes in soft tissue. There is a high unmet need in patients with these liposarcomas given poor outcomes, particularly for DDLPS. WDLPS and DDLPS share important genetic and histological characteristics - most notably, the amplification of the 2 genes MDM2 and CDK4. Both genes are considered oncogenes because of their ability to shut down tumor suppressor pathways. There are multiple therapeutic approaches that aim to target MDM2 and CDK4 activity for the purpose of restoring intrinsic tumor suppressor cellular response and terminating oncogenesis. However, current understanding of the molecular mechanisms involved in WDLPS and DDLPS pathology is limited. In recent years, significant efforts have been made to refine and implement targeted therapy for this patient population. The use of patient-derived cell and tumor xenograft models has been an important tool for recapitulating WDLPS and DDLPS biology. These models also offer valuable insights for drug development and drug combination studies. Here we offer a review of the current understanding of WDLPS and DDLPS biology and its therapeutic implications.

Computational screen for sex-specific drug effects in a cardiac fibroblast signaling network model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins, which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs demonstrated high validation accuracy compared to in vitro experimental studies in the literature while also elucidating how estrogen signaling can modulate the effect of fibrotic cytokines via multi-pathway interactions. Further, perturbation analysis and drug screening uncovered several drug compounds predicted to generate divergent fibrotic responses in male vs. female conditions, which warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis. Future model development and validation will require more generation of sex-specific data to further enhance modeling capabilities for clinically relevant sex-specific predictions of cardiac fibrosis and treatment.

A comprehensive mechanistic model of adipocyte signaling with layers of confidence

Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70-90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.

Discovery of Ruthenium(II) Metallocompound and Olaparib Synergy for Cancer Combination Therapy

Synergistic drug combinations can extend the use of poly(ADP-ribose) polymerase inhibitors (PARPi) such as Olaparib to BRCA-proficient tumors and overcome acquired or de novo drug resistance. To identify new synergistic combinations for PARPi, we screened a "micro-library" comprising a mix of commercially available drugs and DNA-binding ruthenium(II) polypyridyl complexes (RPCs) for Olaparib synergy in BRCA-proficient triple-negative breast cancer cells. This identified three hits: the natural product Curcumin and two ruthenium(II)-rhenium(I) polypyridyl metallomacrocycles. All combinations identified were effective in BRCA-proficient breast cancer cells, including an Olaparib-resistant cell line, and spheroid models. Mechanistic studies indicated that synergy was achieved via DNA-damage enhancement and resultant apoptosis. Combinations showed low cytotoxicity toward non-malignant breast epithelial cells and low acute and developmental toxicity in zebrafish embryos. This work identifies RPC metallomacrocycles as a novel class of agents for cancer combination therapy and provides a proof of concept for the inclusion of metallocompounds within drug synergy screens.

Computational Screen for Sex-Specific Drug Effects in a Cardiac Fibroblast Network Model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a previously constructed cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs maintained previously high validation when compared to in vitro experimental studies in the literature. A sex-specific perturbation analysis and drug screen uncovered several potential pathways that warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis.

Biochemical reaction network topology defines dose-dependent Drug-Drug interactions

Drug combination therapy is a promising strategy to enhance the desired therapeutic effect, while reducing side effects. High-throughput pairwise drug combination screening is a commonly used method for discovering favorable drug interactions, but is time-consuming and costly. Here, we investigate the use of reaction network topology-guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. We focused on three-node enzymatic networks, with general Michaelis-Menten kinetics. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. The results showed a negative correlation between antagonistic interactions and the dosage of drugs. Overall, the negative feedback loops showed the highest synergistic interactions (the lowest average combination index) and, intriguingly, required the highest drug doses compared to other topologies under the same condition.

Beyond targeting amplified MDM2 and CDK4 in well differentiated and dedifferentiated liposarcomas: From promise and clinical applications towards identification of progression drivers

Well differentiated and dedifferentiated liposarcomas (WDLPS and DDLPS) are tumors of the adipose tissue poorly responsive to conventional cytotoxic chemotherapy which currently remains the standard-of-care. The dismal prognosis of the DDLPS subtype indicates an urgent need to identify new therapeutic targets to improve the patient outcome. The amplification of the two driver genes MDM2 and CDK4, shared by WDLPD and DDLPS, has provided the rationale to explore targeting the encoded ubiquitin-protein ligase and cell cycle regulating kinase as a therapeutic approach. Investigation of the genomic landscape of WD/DDLPS and preclinical studies have revealed additional potential targets such as receptor tyrosine kinases, the cell cycle kinase Aurora A, and the nuclear exporter XPO1. While the therapeutic significance of these targets is being investigated in clinical trials, insights into the molecular characteristics associated with dedifferentiation and progression from WDLPS to DDLPS highlighted additional genetic alterations including fusion transcripts generated by chromosomal rearrangements potentially providing new druggable targets (e.g. NTRK, MAP2K6). Recent years have witnessed the increasing use of patient-derived cell and tumor xenograft models which offer valuable tools to accelerate drug repurposing and combination studies. Implementation of integrated "multi-omics" investigations applied to models recapitulating WD/DDLPS genetics, histologic differentiation and biology, will hopefully lead to a better understanding of molecular alterations driving liposarcomagenesis and DDLPS progression, as well as to the identification of new therapies tailored on tumor histology and molecular profile.

The Role of ARID1A in the Nonestrogenic Modulation of IGF-1 Signaling

Gaining pharmacologic access to the potential of ARID1A, a tumor suppressor protein, to mediate transcriptional control over cancer gene expression is an unresolved challenge. Retinoid X receptor ligands are pleiotropic, incompletely understood tools that regulate breast epithelial cell proliferation and differentiation. We found that low-dose bexarotene (Bex) combined with the nonselective beta-blocker carvedilol (Carv) reduces proliferation of MCF10DCIS.com cells and markedly suppresses ARID1A levels. Similarly, Carv synergized with Bex in MCF-7 cells to suppress cell growth. Chromatin immunoprecipitation sequencing analysis revealed that under nonestrogenic conditions Bex + Carv alters the concerted genomic distribution of the chromatin remodeler ARID1A and acetylated histone H3K27, at sites related to insulin-like growth factor (IGF) signaling. Several distinct sites of ARID1A enrichment were identified in the IGF-1 receptor and IRS1 genes, associated with a suppression of both proteins. The knock-down of ARID1A increased IGF-1R levels, prevented IGF-1R and IRS1 suppression upon Bex + Carv, and stimulated proliferation. In vitro IGF-1 receptor neutralizing antibody suppressed cell growth, while elevated IGF-1R or IRS1 expression was associated with poor survival of patients with ER-negative breast cancer. Our study demonstrates direct impact of ARID1A redistribution on the expression and growth regulation of IGF-1-related genes, induced by repurposed clinical drugs under nonestrogenic conditions.

IMPLICATIONS

This study underscores the possibility of the pharmacologic modulation of the ARID1A factor to downregulate protumorigenic IGF-1 activity in patients with postmenopausal breast cancer undergoing aromatase inhibitor treatment.

What are the challenges with multi-targeted drug design for complex diseases?

INTRODUCTION

Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account.

AREAS COVERED

In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases.

EXPERT OPINION

Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.

Clinical utility of PDX cohorts to reveal biomarkers of intrinsic resistance and clonal architecture changes underlying acquired resistance to cetuximab in HNSCC

Cetuximab is a widely used drug for treating head and neck squamous cell carcinomas (HNSCCs); however, it provides restricted clinical benefits, and its response duration is limited by drug resistance. Here, we conducted randomized “Phase II-like clinical trials” of 49 HNSCC PDX models and reveal multiple informative biomarkers for intrinsic resistance to cetuximab (e.g., amplification of ANKH, up-regulation of PARP3). After validating these intrinsic resistance biomarkers in another HNSCC PDX cohort (61 PDX models), we generated acquired cetuximab resistance PDX models and analyzed them to uncover resistance mechanisms. Whole exome sequencing and transcriptome sequencing revealed diverse patterns of clonal selection in acquired resistant PDXs, including the emergence of subclones with strongly activated RAS/MAPK. Extending these insights, we show that a combination of a RAC1/RAC3 dual-target inhibitor and cetuximab could overcome acquired cetuximab resistance in vitro and in vivo. Beyond revealing intrinsic resistance biomarkers, our PDX-based study shows how clonal architecture changes underlying acquired resistance can be targeted to expand the therapeutic utility of this important drug to more HNSCC patients.

Precision medicine and phosphoproteomics for the identification of novel targeted therapeutic avenues in sarcomas

Rapid advances in genomic technologies have enabled in-depth interrogation of cancer genomes, revealing novel and unexpected therapeutic targets in many cancer types. Identifying actionable dependencies in the diverse and heterogeneous group of sarcomas, particularly those that occur in children or adolescents and young adults (AYAs), remains especially challenging. These patients rarely harbor actionable genomic aberrations, no targeted agent is approved, and outcomes have remained poor for the past decades. This underlines a clear need to refine our methods for target identification. Phosphoproteomics studies in sarcoma showed the power of such analyses to capture novel actionable drivers that are not accompanied by mutational events or gene amplifications. This Review makes the case that incorporating phosphoproteomic molecular profiling alongside (functional) genomics technologies can significantly expand therapeutic target identification, and pinpoint drug mechanisms of action, in pediatric and AYA sarcoma patients. We explore the utility and prospects of phosphoproteomics in personalized medicine.

The Art and Science of Building a Computational Model to Understand Hemostasis

Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a “good” model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A.

PERK inhibition attenuates vascular remodeling in pulmonary arterial hypertension caused by BMPR2 mutation

Pulmonary arterial hypertension (PAH) is a fatal disease characterized by excessive pulmonary vascular remodeling. However, despite advances in therapeutic strategies, patients with PAH bearing mutations in the bone morphogenetic protein receptor type 2 (BMPR2)-encoding gene present severe phenotypes and outcomes. We sought to investigate the effect of PER-like kinase (PERK), which participates in one of three major pathways associated with the unfolded protein response (UPR), on PAH pathophysiology in BMPR2 heterozygous mice. BMPR2 heterozygosity in pulmonary artery smooth muscle cells (PASMCs) decreased the abundance of the antiapoptotic microRNA miR124-3p through the arm of the UPR mediated by PERK. Hypoxia promoted the accumulation of unfolded proteins in BMPR2 heterozygous PASMCs, resulting in increased PERK signaling, cell viability, cellular proliferation, and glycolysis. Proteomic analyses revealed that PERK ablation suppressed PDGFRβ-STAT1 signaling and glycolysis in hypoxic BMPR2 heterozygous PASMCs. Furthermore, PERK ablation or PERK inhibition ameliorated pulmonary vascular remodeling in the Sugen/chronic hypoxia model of PAH, irrespective of BMPR2 status. Hence, these findings suggest that PERK inhibition is a promising therapeutic strategy for patients with PAH with or without BMPR2 mutation.

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

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

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.

Predicting drug synergy for precision medicine using network biology and machine learning

Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.

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.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Direct targeting of oncogenic RAS mutants with a tumor-specific cytosol-penetrating antibody inhibits RAS mutant-driven tumor growth

Oncogenic RAS mutant (RASMUT) proteins have been considered undruggable via conventional antibody regimens owing to the intracellular location restricting conventional-antibody accessibility. Here, we report a pan-RAS-targeting IgG antibody, inRas37, which directly targets the intracellularly activated form of various RASMUT subtypes after tumor cell-specific internalization into the cytosol to block the interactions with effector proteins, thereby suppressing the downstream signaling. Systemic administration of inRas37 exerted a potent antitumor activity in a subset of RASMUT tumor xenografts in mice, but little efficacy in RASMUT tumors with concurrent downstream PI3K mutations, which were overcome by combination with a PI3K inhibitor. The YAP1 protein was up-regulated as an adaptive resistance-inducing response to inRas37 in RASMUT-dependent colorectal tumors; accordingly, a combination of inRas37 with a YAP1 inhibitor manifested synergistic antitumor effects in vitro and in vivo. Our study offers a promising pan-RAS-targeting antibody and the corresponding therapeutic strategy against RASMUT tumors.

Targeted Gene Expression Profile Reveals CDK4 as Therapeutic Target for Selected Patients With Adrenocortical Carcinoma

Adrenocortical carcinomas (ACC) are aggressive tumors with a heterogeneous prognosis and limited therapeutic options for advanced stages. This study aims to identify novel drug targets for a personalized treatment in ACC. RNA was isolated from 40 formalin-fixed paraffin-embedded ACC samples. We evaluated gene expression of 84 known cancer drug targets by reverse transcriptase quantitative real time-PCR and calculated fold change using 5 normal adrenal glands as reference (overexpression by fold change >2.0). The most promising candidate cyclin-dependent kinase 4 (CDK4) was investigated at protein level in 104 ACC samples and tested by in vitro experiments in two ACC cell lines (NCI-H295R and MUC1). The most frequently overexpressed genes were TOP2A (100% of cases, median fold change = 16.5), IGF2 (95%, fold change = 52.9), CDK1 (80%, fold change = 6.7), CDK4 (62%, fold change = 2.6), PLK4 (60%, fold change = 2.8), and PLK1 (52%, fold change = 2.3). CDK4 was chosen for functional validation, as it is actionable by approved CDK4/6-inhibitors (e.g., palbociclib). Nuclear immunostaining of CDK4 significantly correlated with mRNA expression (R = 0.52, P < 0.005). We exposed both NCI-H295R and MUC1 cell lines to palbociclib and found a concentration- and time-dependent reduction of cell viability, which was more pronounced in the NCI-H295R cells in line with higher CDK4 expression. Furthermore, we tested palbociclib in combination with insulin-like growth factor 1/insulin receptor inhibitor linsitinib showing an additive effect. In conclusion, we demonstrate that RNA profiling is useful to discover potential drug targets and that CDK4/6 inhibitors are promising candidates for treatment of selected patients with ACC.

Synergistic Cytotoxicity of Renieramycin M and Doxorubicin in MCF-7 Breast Cancer Cells

Renieramycin M (RM) is a KCN-stabilized tetrahydroisoquinoline purified from the blue sponge Xestospongia sp., with nanomolar IC₅₀s against several cancer cell lines. Our goal is to evaluate its combination effects with doxorubicin (DOX) in estrogen receptor positive MCF-7 breast cancer cells. MCF-7 cells were treated simultaneously or sequentially with various combination ratios of RM and DOX for 72 h. Cell viability was determined using the MTT assay. Synergism or antagonism was determined using curve-shift analysis, combination index method and isobologram analysis. Synergism was observed with pharmacologically achievable concentrations of DOX when administered simultaneously, but not sequentially. The IC₉₅ values of RM and DOX after combination were reduced by up to four-fold and eight-fold, respectively. To gain insights on the mechanism of synergy, real-time profiling, cell cycle analysis, apoptosis assays, and transcriptome analysis were conducted. The combination treatment displayed a similar profile with DNA-damaging agents and induced a greater and faster cell killing. The combination treatment also showed an increase in apoptosis. DOX induced S and G2/M arrest while RM did not induce significant changes in the cell cycle. DNA replication and repair genes were downregulated commonly by RM and DOX. p53 signaling and cell cycle checkpoints were regulated by DOX while ErbB/PI3K-Akt, integrin and focal adhesion signaling were regulated by RM upon combination. Genes involved in cytochrome C release and interferon gamma signaling were regulated specifically in the combination treatment. This study serves as a basis for in vivo studies and provides a rationale for using RM in combination with other anticancer 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.

Rational Design of Multitarget-Directed Ligands: Strategies and Emerging Paradigms

Due to the complexity of multifactorial diseases, single-target drugs do not always exhibit satisfactory efficacy. Recently, increasing evidence indicates that simultaneous modulation of multiple targets may improve both therapeutic safety and efficacy, compared with single-target drugs. However, few multitarget drugs are on market or in clinical trials, despite the best efforts of medicinal chemists. This article discusses the systematic establishment of target combination, lead generation, and optimization of multitarget-directed ligands (MTDLs). Moreover, we analyze some MTDLs research cases for several complex diseases in recent years and the physicochemical properties of 117 clinical multitarget drugs, with the aim to reveal the trends and insights of the potential use of MTDLs.

Dynamic Targeting in Cancer Treatment

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

Targeted treatment of anaerobic cancer. Patent evaluation of US2016279084 and US2017056350

INTRODUCTION

Based on the initial studies of J. Folkman in 1970s, which led to the proposal of the antiangiogenic therapy, many drugs targeting VEGF or its receptors have been developed with some of them approved for cancer treatment. However, these molecules so far have shown only a limited effect on survival benefits in patients. Thus, new approaches are needed to treat this disease. Considering that cancer utilizes both aerobic and anaerobic glycolytic pathway, authors of patents US2016279084 and US2017056350 propose a method to eradicate the disease, able to affect both metabolic pathways. Areas covered: Patent US2016279084 describes a method consisting of the utilization of either a pharmaceutical cocktail containing antiglycolytic agents (a lactate transporter inhibitor and a NKCC inhibitor) and an angiogenesis inhibitor or a pharmaceutical cocktail containing a lactate transporter inhibitor and an angiogenesis inhibitor in combination with blood vessel occlusion. Patent US2017056350 is strictly related to US2016279084; indeed, it proposes a method consisting of blood vessel occlusion and treatment with a pharmaceutical cocktail, containing the carbonic anhydrase inhibitor bumetanide in presence or absence of an angiogenesis inhibitor. Expert opinion: Although the proposed methodology is very interesting and promising, further studies are necessary to assess the clinical applicability of the inventions.

The Role of the Anti-Aging Protein Klotho in IGF-1 Signaling and Reticular Calcium Leak: Impact on the Chemosensitivity of Dedifferentiated Liposarcomas

By inhibiting Insulin-Like Growth Factor-1-Receptor (IGF-1R) signaling, Klotho (KL) acts like an aging- and tumor-suppressor. We investigated whether KL impacts the aggressiveness of liposarcomas, in which IGF-1R signaling is frequently upregulated. Indeed, we observed that a higher KL expression in liposarcomas is associated with a better outcome for patients. Moreover, KL is downregulated in dedifferentiated liposarcomas (DDLPS) compared to well-differentiated tumors and adipose tissue. Because DDLPS are high-grade tumors associated with poor prognosis, we examined the potential of KL as a tool for overcoming therapy resistance. First, we confirmed the attenuation of IGF-1-induced calcium (Ca²⁺)-response and Extracellular signal-Regulated Kinase 1/2 (ERK1/2) phosphorylation in KL-overexpressing human DDLPS cells. KL overexpression also reduced cell proliferation, clonogenicity, and increased apoptosis induced by gemcitabine, thapsigargin, and ABT-737, all of which are counteracted by IGF-1R-dependent signaling and activate Ca²⁺-dependent endoplasmic reticulum (ER) stress. Then, we monitored cell death and cytosolic Ca²⁺-responses and demonstrated that KL increases the reticular Ca²⁺-leakage by maintaining TRPC6 at the ER and opening the translocon. Only the latter is necessary for sensitizing DDLPS cells to reticular stressors. This was associated with ERK1/2 inhibition and could be mimicked with IGF-1R or MEK inhibitors. These observations provide a new therapeutic strategy in the management of DDLPS.

A Combination CDK4/6 and IGF1R Inhibitor Strategy for Ewing Sarcoma

PURPOSE

Novel targeted therapeutics have transformed the care of subsets of patients with cancer. In pediatric malignancies, however, with simple tumor genomes and infrequent targetable mutations, there have been few new FDA-approved targeted drugs. The cyclin-dependent kinase (CDK)4/6 pathway recently emerged as a dependency in Ewing sarcoma. Given the heightened efficacy of this class with targeted drug combinations in other cancers, as well as the propensity of resistance to emerge with single agents, we aimed to identify genes mediating resistance to CDK4/6 inhibitors and biologically relevant combinations for use with CDK4/6 inhibitors in Ewing.

EXPERIMENTAL DESIGN

We performed a genome-scale open reading frame (ORF) screen in 2 Ewing cell lines sensitive to CDK4/6 inhibitors to identify genes conferring resistance. Concurrently, we established resistance to a CDK4/6 inhibitor in a Ewing cell line.

RESULTS

The ORF screen revealed IGF1R as a gene whose overexpression promoted drug escape. We also found elevated levels of phospho-IGF1R in our resistant Ewing cell line, supporting the relevance of IGF1R signaling to acquired resistance. In a small-molecule screen, an IGF1R inhibitor scored as synergistic with CDK4/6 inhibitor treatment. The combination of CDK4/6 inhibitors and IGF1R inhibitors was synergistic in vitro and active in mouse models. Mechanistically, this combination more profoundly repressed cell cycle and PI3K/mTOR signaling than either single drug perturbation.

CONCLUSIONS

Taken together, these results suggest that IGF1R inhibitors activation is an escape mechanism to CDK4/6 inhibitors in Ewing sarcoma and that dual targeting of CDK4/6 inhibitors and IGF1R inhibitors provides a candidate synergistic combination for clinical application in this disease.

Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures

The parasite Plasmodium falciparum is the most lethal species of Plasmodium to cause serious malaria infection in humans, and with resistance developing rapidly novel treatment modalities are currently being sought, one of which being combinations of existing compounds. The discovery of combinations of antimalarial drugs that act synergistically with one another is hence of great importance; however an exhaustive experimental screen of large drug space in a pairwise manner is not an option. In this study we apply our machine learning approach, Combination Synergy Estimation (CoSynE), which can predict novel synergistic drug interactions using only prior experimental combination screening data and knowledge of compound molecular structures, to a dataset of 1,540 antimalarial drug combinations in which 22.2% were synergistic. Cross validation of our model showed that synergistic CoSynE predictions are enriched 2.74 × compared to random selection when both compounds in a predicted combination are known from other combinations among the training data, 2.36 × when only one compound is known from the training data, and 1.5 × for entirely novel combinations. We prospectively validated our model by making predictions for 185 combinations of 23 entirely novel compounds. CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for nine of them (45%), corresponding to an enrichment of 1.70 × compared to random selection from this prospective data set. Such enrichment corresponds to a 41% reduction in experimental effort. Interestingly, we found that pairwise screening of the compounds CoSynE individually predicted to be synergistic would result in an enrichment of 1.36 × compared to random selection, indicating that synergy among compound combinations is not a random event. The nine novel and correctly predicted synergistic compound combinations mainly (where sufficient bioactivity information is available) consist of efflux or transporter inhibitors (such as hydroxyzine), combined with compounds exhibiting antimalarial activity alone (such as sorafenib, apicidin, or dihydroergotamine). However, not all compound synergies could be rationalized easily in this way. Overall, this study highlights the potential for predictive modeling to expedite the discovery of novel drug combinations in fight against antimalarial resistance, while the underlying approach is also generally applicable.

Palbociclib resistance confers dependence on an FGFR-MAP kinase-mTOR-driven pathway in KRAS-mutant non-small cell lung cancer

CDK4 is emerging as a target in KRAS-mutant non-small cell lung cancer (NSCLC). We demonstrate that KRAS-mutant NSCLC cell lines are initially sensitive to the CDK4/6 inhibitor palbociclib, but readily acquire resistance associated with increased expression of CDK6, D-type cyclins and cyclin E. Resistant cells also demonstrated increased ERK1/2 activity and sensitivity to MEK and ERK inhibitors. Moreover, MEK inhibition reduced the expression and activity of cell cycle proteins mediating palbociclib resistance. In resistant cells, ERK activated mTOR, driven in part by upstream FGFR1 signaling resulting from the extracellular secretion of FGF ligands. A genetically-engineered mouse model of KRAS-mutant NSCLC initially sensitive to palbociclib similarly developed acquired resistance with increased expression of cell cycle mediators, ERK1/2 and FGFR1. In this model, resistance was delayed with combined palbociclib and MEK inhibitor treatment. These findings implicate an FGFR1–MAP kinase–mTOR pathway resulting in increased expression of D-cyclins and CDK6 that confers palbociclib resistance and indicate that CDK4/6 inhibition acts to promote MAP kinase dependence.

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.

An integrated framework for identification of effective and synergistic anti-cancer drug combinations

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.

Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer

Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types.

We applied a systems modelling approach combining mechanistic modelling and biological experimentation to identify effective drug combinations for triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer with no approved targeted treatment. The model predicted and prioritized the synergistic combinations as confirmed by experimental data, demonstrating the power of this approach. Moreover, analysis of clinical data of TNBC patients and patient-specific modelling simulation enabled us to stratify the patients into subgroups with distinct susceptibility to specific drug combinations, and thus defined a subset of patient that could benefit from the combined treatments.

CImbinator: a web-based tool for drug synergy analysis in small- and large-scale datasets

Motivation

Drug synergies are sought to identify combinations of drugs particularly beneficial. User-friendly software solutions that can assist analysis of large-scale datasets are required.

Results

CImbinator is a web-service that can aid in batch-wise and in-depth analyzes of data from small-scale and large-scale drug combination screens. CImbinator offers to quantify drug combination effects, using both the commonly employed median effect equation, as well as advanced experimental mathematical models describing dose response relationships.

Availability and Implementation

CImbinator is written in Ruby and R. It uses the R package drc for advanced drug response modeling. CImbinator is available at http://cimbinator.bioinfo.cnio.es , the source-code is open and available at https://github.com/Rbbt-Workflows/combination_index . A Docker image is also available at https://hub.docker.com/r/mikisvaz/rbbt-ci_mbinator/ .

Contact

asmund.flobak@ntnu.no or miguel.vazquez@cnio.es.

Supplementary information

Supplementary data are available at Bioinformatics online.

Combinatorial Drug Testing in 3D Microtumors Derived from GBM Patient-Derived Xenografts Reveals Cytotoxic Synergy in Pharmacokinomics-informed Pathway Interactions

Glioblastoma multiforme (GBM), the most common form of primary malignant brain cancer in adults, is a devastating disease for which effective treatment has remained elusive for over 75 years. One reason for the minimal progress during this time is the lack of accurate preclinical models to represent the patient's tumor's in vivo environment, causing a disconnect in drug therapy effectiveness between the laboratory and clinic. While patient-derived xenografts (PDX's or xenolines) are excellent human tumor representations, they are not amenable to high throughput testing. Therefore, we developed a miniaturized xenoline system (microtumors) for drug testing. Nineteen GBM xenolines were profiled for global kinase (kinomic) activity revealing actionable kinase targets associated with intracranial tumor growth rate. Kinase inhibitors for these targets (WP1066, selumetinib, crizotinib, and cediranib) were selected for single and combination therapy using a fully human-derived three-dimensional (3D) microtumor model of GBM xenoline cells embedded in HuBiogel for subsequent molecular and phenotype assays. GBM microtumors closely resembled orthotopically-implanted tumors based on immunohistochemical analysis and displayed kinomic and morphological diversity. Drug response testing could be reproducibly performed in a 96-well format identifying several synergistic combinations. Our findings indicate that 3D microtumors can provide a suitable high-throughput model for combination drug testing.

MLN-8237: A dual inhibitor of aurora A and B in soft tissue sarcomas

Aurora kinases have become an attractive target in cancer therapy due to their deregulated expression in human tumors. Liposarcoma, a type of soft tissue sarcoma in adults, account for approximately 20% of all adult soft tissue sarcomas. There are no effective chemotherapies for majority of these tumors. Efforts made to define the molecular basis of liposarcomas lead to the finding that besides the amplifications of CDK4 and MDM2, Aurora Kinase A, also was shown to be overexpressed. Based on these as well as mathematic modeling, we have carried out a successful preclinical study using CDK4 and IGF1R inhibitors in liposarcoma. MLN8237 has been shown to be a potent and selective inhibitor of Aurora A. MLN-8237, as per our results, induces a differential inhibition of Aurora A and B in a dose dependent manner. At a low nanomolar dose, cellular effects such as induction of phospho-Histone H3 (Ser10) mimicked as that of the inhibition of Aurora kinase A followed by apoptosis. However, micromolar dose of MLN-8237 induced polyploidy, a hallmark effect of Aurora B inhibition. The dose dependent selectivity of inhibition was further confirmed by using siRNA specific inhibition of Aurora A and B. This was further tested by time lapse microscopy of GFP-H2B labelled cells treated with MLN-8237. LS141 xenograft model at a dose of 30 mg/kg also showed efficient growth suppression by selective inhibition of Aurora Kinase A. Based on our data, a dose that can target only Aurora A will be more beneficial in tumor suppression.

A phosphokinome-based screen uncovers new drug synergies for cancer driven by liver-specific gain of nononcogenic receptor tyrosine kinases

Genetic mutations leading to oncogenic variants of receptor tyrosine kinases (RTKs) are frequent events during tumorigenesis; however, the cellular vulnerability to nononcogenic RTK fluctuations has not been characterized. Here, we demonstrated genetically that in the liver subtle increases in wild-type Met RTK levels are sufficient for spontaneous tumors in mice (Alb-R26^Met ), conceptually illustrating how the shift from physiological to pathological conditions results from slight perturbations in signaling dosage. By analyzing 96 different genes in a panel of tumor samples, we demonstrated that liver tumorigenesis modeled by Alb-R26^Met mice corresponds to a subset of hepatocellular carcinoma (HCC) patients, thus establishing the clinical relevance of this HCC mouse model. We elucidated the regulatory networks underlying tumorigenesis by combining a phosphokinome screen with bioinformatics analysis. We then used the signaling diversity results obtained from Alb-R26^Met HCC versus control livers to design an "educated guess" drug screen, which led to the identification of new, deleterious synthetic lethal interactions. In particular, we report synergistic effects of mitogen-activated protein kinase kinase, ribosomal S6 kinase, and cyclin-dependent kinase 1/2 in combination with Bcl-XL inhibition on a panel of liver cancer cells. Focusing on mitogen-activated protein kinase kinase and Bcl-XL targeting, we mechanistically demonstrated concomitant down-regulation of phosphorylated extracellular signal-regulated kinase and myeloid cell leukemia 1 levels. Of note, a phosphorylated extracellular signal-regulated kinase+/BCL-XL⁺ /myeloid cell leukemia 1+ signature, deregulated in Alb-R26^Met tumors, characterizes a subgroup of HCC patients with poor prognosis.

CONCLUSION

Our genetic studies highlight the heightened vulnerability of liver cells to subtle changes in nononcogenic RTK levels, allowing them to acquire a molecular profile that facilitates the full tumorigenic program; furthermore, our outcomes uncover new synthetic lethal interactions as potential therapies for a cluster of HCC patients. (Hepatology 2017;66:1644-1661).

Frequent amplification of receptor tyrosine kinase genes in welldifferentiated/ dedifferentiated liposarcoma

Well-differentiated liposarcoma (WDLPS) and dedifferentiated liposarcoma (DDLPS) are closely related tumors commonly characterized by MDM2/CDK4 gene amplification, and lack clinically effective treatment options when inoperable. To identify novel therapeutic targets, we performed targeted genomic sequencing analysis of 19 WDLPS and 37 DDLPS tumor samples using a panel of 104 cancer-related genes (NCC oncopanel v3) developed specifically for genomic testing to select suitable molecular targeted therapies. The results of this analysis indicated that these sarcomas had very few gene mutations and a high frequency of amplifications of not only MDM2 and CDK4 but also other genes. Potential driver mutations were found in only six (11%) samples; however, gene amplification events (other than MDM2 and CDK4 amplification) were identified in 30 (54%) samples. Receptor tyrosine kinase (RTK) genes in particular were amplified in 18 (32%) samples. In addition, growth of a WDLPS cell line with IGF1R amplification was suppressed by simultaneous inhibition of CDK4 and IGF1R, using palbociclib and NVP-AEW541, respectively. Combination therapy with CDK4 and RTK inhibitors may be an effective therapeutic option for WDLPS/DDLPS patients with RTK gene amplification.

Insulin-Like Growth Factor (IGF) Pathway Targeting in Cancer: Role of the IGF Axis and Opportunities for Future Combination Studies

Despite a strong preclinical rationale for targeting the insulin-like growth factor (IGF) axis in cancer, clinical studies of IGF-1 receptor (IGF-1R)-targeted monotherapies have been largely disappointing, and any potential success has been limited by the lack of validated predictive biomarkers for patient enrichment. A large body of preclinical evidence suggests that the key role of the IGF axis in cancer is in driving treatment resistance, via general proliferative/survival mechanisms, interactions with other mitogenic signaling networks, and class-specific mechanisms such as DNA damage repair. Consequently, combining IGF-targeted agents with standard cytotoxic agents, other targeted agents, endocrine therapies, or immunotherapies represents an attractive therapeutic approach. Anti-IGF-1R monoclonal antibodies (mAbs) do not inhibit IGF ligand 2 (IGF-2) activation of the insulin receptor isoform-A (INSR-A), which may limit their anti-proliferative activity. In addition, due to their lack of specificity, IGF-1R tyrosine kinase inhibitors are associated with hyperglycemia as a result of interference with signaling through the classical metabolic INSR-B isoform; this may preclude their use at clinically effective doses. Conversely, IGF-1/IGF-2 ligand-neutralizing mAbs inhibit proliferative/anti-apoptotic signaling via IGF-1R and INSR-A, without compromising the metabolic function of INSR-B. Therefore, combination regimens that include these agents may be more efficacious and tolerable versus IGF-1R-targeted combinations. Herein, we review the preclinical and clinical experience with IGF-targeted therapies to-date, and discuss the rationale for future combination approaches as a means to overcome treatment resistance.

The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity

To date, over 100 small-molecule oncology drugs have been approved by the FDA. Because of the inherent heterogeneity of tumors, these small molecules are often administered in combination to prevent emergence of resistant cell subpopulations. Therefore, new combination strategies to overcome drug resistance in patients with advanced cancer are needed. In this study, we performed a systematic evaluation of the therapeutic activity of over 5,000 pairs of FDA-approved cancer drugs against a panel of 60 well-characterized human tumor cell lines (NCI-60) to uncover combinations with greater than additive growth-inhibitory activity. Screening results were compiled into a database, termed the NCI-ALMANAC (A Large Matrix of Anti-Neoplastic Agent Combinations), publicly available at https://dtp.cancer.gov/ncialmanac Subsequent in vivo experiments in mouse xenograft models of human cancer confirmed combinations with greater than single-agent efficacy. Concomitant detection of mechanistic biomarkers for these combinations in vivo supported the initiation of two phase I clinical trials at the NCI to evaluate clofarabine with bortezomib and nilotinib with paclitaxel in patients with advanced cancer. Consequently, the hypothesis-generating NCI-ALMANAC web-based resource has demonstrated value in identifying promising combinations of approved drugs with potent anticancer activity for further mechanistic study and translation to clinical trials. Cancer Res; 77(13); 3564-76. ©2017 AACR.

Molecular mechanism and therapeutic implications of selinexor (KPT-330) in liposarcoma

Exportin-1 mediates nuclear export of multiple tumor suppressor and growth regulatory proteins. Aberrant expression of exportin-1 is noted in human malignancies, resulting in cytoplasmic mislocalization of its target proteins. We investigated the efficacy of selinexor against liposarcoma cells both in vitro and in vivo. Exportin-1 was highly expressed in liposarcoma samples and cell lines as determined by immunohistochemistry, western blot, and immunofluorescence assay. Knockdown of endogenous exportin-1 inhibited proliferation of liposarcoma cells. Selinexor also significantly decreased cell proliferation as well as induced cell cycle arrest and apoptosis of liposarcoma cells. The drug also significantly decreased tumor volumes and weights of liposarcoma xenografts. Importantly, selinexor inhibited insulin-like growth factor 1 (IGF1) activation of IGF-1R/AKT pathway through upregulation of insulin-like growth factor binding protein 5 (IGFBP5). Further, overexpression and knockdown experiments showed that IGFBP5 acts as a tumor suppressor and its expression was restored upon selinexor treatment of liposarcoma cells. Selinexor decreased aurora kinase A and B levels in these cells and inhibitors of these kinases suppressed the growth of the liposarcoma cells. Overall, our study showed that selinexor treatment restored tumor suppressive function of IGFBP5 and inhibited aurora kinase A and B in liposarcoma cells supporting the usefulness of selinexor as a potential therapeutic strategy for the treatment of this cancer.

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

Phosphoproteomics in translational research: a sarcoma perspective

Phosphoproteomics has been extensively used as a preclinical research tool to characterize the phosphorylated components of the cancer proteome. Advances in the field have yielded insights into new drug targets, mechanisms of disease progression and drug resistance, and biomarker discovery. However, application of this technology to clinical research has been challenging because of practical issues relating to specimen integrity and tumour heterogeneity. Beyond these limitations, phosphoproteomics has the potential to play a pivotal role in translational studies and contribute to advances in different tumour groups, including rare disease sites like sarcoma. In this review, we propose that deploying phosphoproteomic technologies in translational research may facilitate the identification of better defined predictive biomarkers for patient stratification, inform drug selection in umbrella trials and identify new combinations to overcome drug resistance. We provide an overview of current phosphoproteomic technologies, such as affinity-based assays and mass spectrometry-based approaches, and discuss their advantages and limitations. We use sarcoma as an example to illustrate the current challenges in evaluating targeted kinase therapies in clinical trials. We then highlight useful lessons from preclinical studies in sarcoma biology to demonstrate how phosphoproteomics may address some of these challenges. Finally, we conclude by offering a perspective and list the key measures required to translate and benchmark a largely preclinical technology into a useful tool for translational research.

Targeting CDK4 and CDK6: From Discovery to Therapy

Biochemical and genetic characterization of D-type cyclins, their cyclin D-dependent kinases (CDK4 and CDK6), and the polypeptide CDK4/6 inhibitor p16(INK4)over two decades ago revealed how mammalian cells regulate entry into the DNA synthetic (S) phase of the cell-division cycle in a retinoblastoma protein-dependent manner. These investigations provided proof-of-principle that CDK4/6 inhibitors, particularly when combined with coinhibition of allied mitogen-dependent signal transduction pathways, might prove valuable in cancer therapy. FDA approval of the CDK4/6 inhibitor palbociclib used with the aromatase inhibitor letrozole for breast cancer treatment highlights long-sought success. The newest findings herald clinical trials targeting other cancers.

SIGNIFICANCE

Rapidly emerging data with selective inhibitors of CDK4/6 have validated these cell-cycle kinases as anticancer drug targets, corroborating longstanding preclinical predictions. This review addresses the discovery of these CDKs and their regulators, as well as translation of CDK4/6 biology to positive clinical outcomes and development of rational combinatorial therapies.

CDK4 is an essential insulin effector in adipocytes

Insulin resistance is a fundamental pathogenic factor that characterizes various metabolic disorders, including obesity and type 2 diabetes. Adipose tissue contributes to the development of obesity-related insulin resistance through increased release of fatty acids, altered adipokine secretion, and/or macrophage infiltration and cytokine release. Here, we aimed to analyze the participation of the cyclin-dependent kinase 4 (CDK4) in adipose tissue biology. We determined that white adipose tissue (WAT) from CDK4-deficient mice exhibits impaired lipogenesis and increased lipolysis. Conversely, lipolysis was decreased and lipogenesis was increased in mice expressing a mutant hyperactive form of CDK4 (CDK4(R24C)). A global kinome analysis of CDK4-deficient mice following insulin stimulation revealed that insulin signaling is impaired in these animals. We determined that insulin activates the CCND3-CDK4 complex, which in turn phosphorylates insulin receptor substrate 2 (IRS2) at serine 388, thereby creating a positive feedback loop that maintains adipocyte insulin signaling. Furthermore, we found that CCND3 expression and IRS2 serine 388 phosphorylation are increased in human obese subjects. Together, our results demonstrate that CDK4 is a major regulator of insulin signaling in WAT.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.