Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment

Antibody dependent cellular cytotoxicity-inducing anti-EGFR antibodies as effective therapeutic option for cutaneous melanoma resistant to BRAF inhibitors

Introduction

About 50% of cutaneous melanoma (CM) patients present activating BRAF mutations that can be effectively targeted by BRAF inhibitors (BRAFi). However, 20% of CM patients exhibit intrinsic drug resistance to BRAFi, while most of the others develop adaptive resistance over time. The mechanisms involved in BRAFi resistance are disparate and globally seem to rewire the cellular signaling profile by up-regulating different receptor tyrosine kinases (RTKs), such as the epidermal growth factor receptor (EGFR). RTKs inhibitors have not clearly demonstrated anti-tumor activity in BRAFi resistant models. To overcome this issue, we wondered whether the shared up-regulated RTK phenotype associated with BRAFi resistance could be exploited by using immune weapons as the antibody-dependent cell cytotoxicity (ADCC)-mediated effect of anti-RTKs antibodies, and kill tumor cells independently from the mechanistic roots.

Methods and results

By using an in vitro model of BRAFi resistance, we detected increased membrane expression of EGFR, both at mRNA and protein level in 4 out of 9 BRAFi-resistant (VR) CM cultures as compared to their parental sensitive cells. Increased EGFR phosphorylation and AKT activation were observed in the VR CM cultures. EGFR signaling appeared dispensable for maintaining resistance, since small molecule-, antibody- and CRISPR-targeting of EGFR did not restore sensitivity of VR cells to BRAFi. Importantly, immune-targeting of EGFR by the anti-EGFR antibody cetuximab efficiently and specifically killed EGFR-expressing VR CM cells, both in vitro and in humanized mouse models in vivo, triggering ADCC by healthy donors' and patients' peripheral blood cells.

Conclusion

Our data demonstrate the efficacy of immune targeting of RTKs expressed by CM relapsing on BRAFi, providing the proof-of-concept supporting the assessment of anti-RTK antibodies in combination therapies in this setting. This strategy might be expected to concomitantly trigger the crosstalk of adaptive immune response leading to a complementing T cell immune rejection of tumors.

GPDRP: a multimodal framework for drug response prediction with graph transformer

BACKGROUND

In the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often characterize drugs as strings, a representation that does not align with the natural description of molecules. Additionally, they ignore gene pathway-specific combinatorial implication.

RESULTS

In this study, we propose drug Graph and gene Pathway based Drug response prediction method (GPDRP), a new multimodal deep learning model for predicting drug responses based on drug molecular graphs and gene pathway activity. In GPDRP, drugs are represented by molecular graphs, while cell lines are described by gene pathway activity scores. The model separately learns these two types of data using Graph Neural Networks (GNN) with Graph Transformers and deep neural networks. Predictions are subsequently made through fully connected layers.

CONCLUSIONS

Our results indicate that Graph Transformer-based model delivers superior performance. We apply GPDRP on hundreds of cancer cell lines' bulk RNA-sequencing data, and it outperforms some recently published models. Furthermore, the generalizability and applicability of GPDRP are demonstrated through its predictions on unknown drug-cell line pairs and xenografts. This underscores the interpretability achieved by incorporating gene pathways.

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.

A landscape of response to drug combinations in non-small cell lung cancer

Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Gene Fusions Create Partner and Collateral Dependencies Essential to Cancer Cell Survival

Gene fusions frequently result from rearrangements in cancer genomes. In many instances, gene fusions play an important role in oncogenesis; in other instances, they are thought to be passenger events. Although regulatory element rearrangements and copy number alterations resulting from these structural variants are known to lead to transcriptional dysregulation across cancers, the extent to which these events result in functional dependencies with an impact on cancer cell survival is variable. Here we used CRISPR-Cas9 dependency screens to evaluate the fitness impact of 3,277 fusions across 645 cell lines from the Cancer Dependency Map. We found that 35% of cell lines harbored either a fusion partner dependency or a collateral dependency on a gene within the same topologically associating domain as a fusion partner. Fusion-associated dependencies revealed numerous novel oncogenic drivers and clinically translatable alterations. Broadly, fusions can result in partner and collateral dependencies that have biological and clinical relevance across cancer types. SIGNIFICANCE: This study provides insights into how fusions contribute to fitness in different cancer contexts beyond partner-gene activation events, identifying partner and collateral dependencies that may have direct implications for clinical care.

SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology

Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients.

PS4DR: a multimodal workflow for identification and prioritization of drugs based on pathway signatures

Background

During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.

Results

Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs.

Conclusion

We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.

A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.

Combination of Anti-Cancer Drugs with Molecular Chaperone Inhibitors

Most molecular chaperones belonging to heat shock protein (HSP) families are known to protect cancer cells from pathologic, environmental and pharmacological stress factors and thereby can hamper anti-cancer therapies. In this review, we present data on inhibitors of the heat shock response (particularly mediated by the chaperones HSP90, HSP70, and HSP27) either as a single treatment or in combination with currently available anti-cancer therapeutic approaches. An overview of the current literature reveals that the co-administration of chaperone inhibitors and targeting drugs results in proteotoxic stress and violates the tumor cell physiology. An optimal drug combination should simultaneously target cytoprotective mechanisms and trigger the imbalance of the tumor cell physiology.

Dual Inhibition of GLUT1 and the ATR/CHK1 Kinase Axis Displays Synergistic Cytotoxicity in KRAS-Mutant Cancer Cells

The advent of molecularly targeted therapeutic agents has opened a new era in cancer therapy. However, many tumors rely on nondruggable cancer-driving lesions. In addition, long-lasting clinical benefits from single-agent therapies rarely occur, as most of the tumors acquire resistance over time. The identification of targeted combination regimens interfering with signaling through oncogenically rewired pathways provides a promising approach to enhance efficacy of single-agent-targeted treatments. Moreover, combination drug therapies might overcome the emergence of drug resistance. Here, we performed a focused flow cytometry-based drug synergy screen and identified a novel synergistic interaction between GLUT1-mediated glucose transport and the cell-cycle checkpoint kinases ATR and CHK1. Combined inhibition of CHK1/GLUT1 or ATR/GLUT1 robustly induced apoptosis, particularly in RAS-mutant cancer cells. Mechanistically, combined inhibition of ATR/CHK1 and GLUT1 arrested sensitive cells in S-phase and led to the accumulation of genotoxic damage, particularly in S-phase. In vivo, simultaneous inhibition of ATR and GLUT1 significantly reduced tumor volume gain in an autochthonous mouse model of Kras^G12D -driven soft tissue sarcoma. Taken together, these findings pave the way for combined inhibition of GLUT1 and ATR/CHK1 as a therapeutic approach for KRAS-driven cancers. SIGNIFICANCE: Dual targeting of the DNA damage response and glucose transport synergistically induces apoptosis in KRAS-mutant cancer, suggesting this combination treatment for clinical validation in KRAS-stratified tumor patients.

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network

Background

Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.

Results

In this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant (R²) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (R_p) respectively, which are significantly better than those of the previous works (Ammad-Ud-Din et al., J Chem Inf Model 54:2347–9, 2014), (Haider et al., PLoS ONE 10:0144490, 2015), (Menden et al., PLoS ONE 8:61318, 2013). However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and R_p and R² drop to barely above 0.

Conclusions

Our approach is able to predict the drug effects on cancer cell lines with high accuracy, and its performance remains stable with less but high-quality data, and with fewer features for the cancer cell lines. tCNNS can also solve the problem of outliers in other feature space. Besides achieving high scores in these statistical metrics, tCNNS also provides some insights into the phenotypic screening. However, the performance of tCNNS drops in the blind test.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2910-6) contains supplementary material, which is available to authorized users.

Optimal control nodes in disease-perturbed networks as targets for combination therapy

Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.

Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.

Targeting the ERK Signaling Pathway in Melanoma

The discovery of the role of the RAS/RAF/MEK/ERK pathway in melanomagenesis and its progression have opened a new era in the treatment of this tumor. Vemurafenib was the first specific kinase inhibitor approved for therapy of advanced melanomas harboring BRAF-activating mutations, followed by dabrafenib and encorafenib. However, despite the excellent results of first-generation kinase inhibitors in terms of response rate, the average duration of the response was short, due to the onset of genetic and epigenetic resistance mechanisms. The combination therapy with MEK inhibitors is an excellent strategy to circumvent drug resistance, with the additional advantage of reducing side effects due to the paradoxical reactivation of the MAPK pathway. The recent development of RAS and extracellular signal-related kinases (ERK) inhibitors promises to add new players for the ultimate suppression of this signaling pathway and the control of pathway-related drug resistance. In this review, we analyze the pharmacological, preclinical, and clinical trial data of the various MAPK pathway inhibitors, with a keen interest for their clinical applicability in the management of advanced melanoma.

A Platform of Synthetic Lethal Gene Interaction Networks Reveals that the GNAQ Uveal Melanoma Oncogene Controls the Hippo Pathway through FAK

Activating mutations in GNAQ/GNA11, encoding Gαq G proteins, are initiating oncogenic events in uveal melanoma (UM). However, there are no effective therapies for UM. Using an integrated bioinformatics pipeline, we found that PTK2, encoding focal adhesion kinase (FAK), represents a candidate synthetic lethal gene with GNAQ activation. We show that Gαq activates FAK through TRIO-RhoA non-canonical Gαq-signaling, and genetic ablation or pharmacological inhibition of FAK inhibits UM growth. Analysis of the FAK-regulated transcriptome demonstrated that GNAQ stimulates YAP through FAK. Dissection of the underlying mechanism revealed that FAK regulates YAP by tyrosine phosphorylation of MOB1, inhibiting core Hippo signaling. Our findings establish FAK as a potential therapeutic target for UM and other Gαq-driven pathophysiologies that involve unrestrained YAP function.

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets

BACKGROUND

Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools.

RESULTS

We describe a statistical and visualization framework for the analysis of large-scale drug combination screens. We developed an approach suitable for datasets with large number of drugs pairs even if small number of data points are available per drug pair. We demonstrate our approach using a systematic screen of all possible pairs among 108 cancer drugs applied to melanoma cell lines. In this dataset only two dose-response data points per drug pair and two data points per single drug test were available. We used a Bliss-based linear model, effectively borrowing data from the drug pairs to obtain robust estimations of the singlet viabilities, consequently yielding better estimates of drug synergy. Our method improves data consistency across dosing thus likely reducing the number of false positives. The approach allows to compute p values accounting for standard errors of the modeled singlets and combination viabilities. We further develop a synergy specificity score that distinguishes specific synergies from those arising with promiscuous drugs. Finally, we developed a summarized interactive visualization in a web application, providing efficient access to any of the 439,000 data points in the combination matrix ( http://www.cmtlab.org:3000/combo_app.html ). The code of the analysis and the web application is available at https://github.com/arnaudmgh/synergy-screen .

CONCLUSIONS

We show that statistical modeling of single drug response from drug combination data can help determine significance of synergy and antagonism in drug combination screens with few data point per drug pair. We provide a web application for the rapid exploration of large combinatorial drug screen. All codes are available to the community, as a resource for further analysis of published data and for analysis of other drug screens.

Gefitinib or lapatinib with foretinib synergistically induce a cytotoxic effect in melanoma cell lines

Melanoma is an aggressive cancer type with a high mortality rate and an elevated resistance to conventional treatment. Recently, promising new tools for anti-melanoma targeted therapy have emerged including inhibitors directed against frequently overexpressed receptors of growth factors implicated in the progression of this cancer. The ineffectiveness of single-targeted therapy prompted us to study the efficacy of treatment with a combination of foretinib, a MET (hepatocyte growth factor receptor) inhibitor, and gefitinib or lapatinib, EGFR (epidermal growth factor receptor) inhibitors. We observed a synergistic cytotoxic effect for the combination of foretinib and lapatinib on the viability and proliferation of the examined melanoma cell lines. This combination of inhibitors significantly decreased Akt and Erk phosphorylation, while the drugs used independently were insufficient. Additionally, after treatment with pairs of inhibitors, cells became larger, with more pronounced stress fibers and abnormally shaped nuclei. We also noticed the appearance of polyploid cells and massive enrichment in the G2/M phase. Therefore, combination treatment was much more effective against melanoma cells than a single-targeted approach. Based on our results, we conclude that both EGFR and MET receptors might be effective targets in melanoma therapy. However, variation in their levels in patients should be taken into consideration.

Integrating functional genomics to accelerate mechanistic personalized medicine

The advent of deep sequencing technologies has resulted in the deciphering of tremendous amounts of genetic information. These data have led to major discoveries, and many anecdotes now exist of individual patients whose clinical outcomes have benefited from novel, genetically guided therapeutic strategies. However, the majority of genetic events in cancer are currently undrugged, leading to a biological gap between understanding of tumor genetic etiology and translation to improved clinical approaches. Functional screening has made tremendous strides in recent years with the development of new experimental approaches to studying ex vivo and in vivo drug sensitivity. Numerous discoveries and anecdotes also exist for translation of functional screening into novel clinical strategies; however, the current clinical application of functional screening remains largely confined to small clinical trials at specific academic centers. The intersection between genomic and functional approaches represents an ideal modality to accelerate our understanding of drug sensitivities as they relate to specific genetic events and further understand the full mechanisms underlying drug sensitivity patterns.

Anti-Melanoma Activities of Haspin Inhibitor CHR-6494 Deployed as a Single Agent or in a Synergistic Combination with MEK Inhibitor

Background: Melanoma is a heterogeneous malignancy that presents an immense challenge in therapeutic development. Recent approaches targeting the oncogenic MAP kinase pathways have shown tremendous improvement in the overall survival of patients with advanced melanoma. However, there is still an urgent need for identification of new strategies to overcome drug resistances and to improve therapeutic efficacy. Haspin (Haploid Germ Cell-Specific Nuclear Protein Kinase) belongs to a selected group of mitotic kinases and is required for normal mitosis progression. In contrast to inhibitors of other mitotic kinases, anti-tumor potential of haspin inhibitors has not been well explored. Herein, we aim to examine effects of CHR-6494, a small molecule inhibitor of haspin, in melanoma cells.

Methods: Anti-tumor activities of the haspin inhibitor CHR-6494 were tested in a number of melanoma cell lines either as a single agent or in combination with the MEK inhibitor Trametinib (GSK1120212). Experiments are based on: 1) Cell viability determined by the crystal violet staining assay; 2) apoptotic responses measured by the caspase 3/7 activity assay and western blot analysis for the level of cleaved PARP (Poly ADP-Ribose Polymerase); 3) cell cycle analysis conducted using flow cytometry; and 4) cell migratory ability assessed by the scratch assay and the transwell migration assay.

Results: We have found that CHR-6494 alone elicits a dose dependent inhibitory effect on the viability of several melanoma cell lines. This growth inhibition is accompanied by an increase in apoptotic responses. More importantly, CHR-6494 appears to synergize with the MEK inhibitor Trametinib in suppressing cell growth and enhancing apoptosis in both wild type and BRAFV600E mutant melanoma cell lines. Administering of these two small molecules as a combination is also capable of suppressing cell migration to a greater extent than the individual agent.

Conclusion: These results suggest that haspin can be considered as a viable anti-melanoma target, and that concomitant inhibition of haspin and MEK activities with small molecules could represent a novel therapeutic strategy with improved efficacy for treatment of melanoma.

Jak Stat signaling and cancer: Opportunities, benefits and side effects of targeted inhibition

The effects of Jak Stat signaling and the persistent activation of Stat3 and Stat5 on tumor cell survival, proliferation and invasion have made the Jak Stat pathway a favorite target for drug development and cancer therapy. This notion was strengthened when additional biological functions of Stat signaling in cancer and their roles in the regulation of cytokine dependent inflammation and immunity in the tumor microenvironment were discovered. Stats act not only as transcriptional inducers, but affect gene expression via epigenetic modifications, induce epithelial mesenchymal transition, generate a pro-tumorigenic microenvironment, promote cancer stem cell self-renewal and differentiation, and help to establish the pre-metastatic niche formation. The effects of Jak Stat inhibition on the suppression of pro-inflammatory responses appears most promising and could become a strategy in the prevention of tumor progression. The direct and mediated mechanisms of Jak Stat signaling in and on tumors cells, the interactions with other signaling pathways and transcription factors and the targeting of the functionally crucial secondary modifications of Stat molecules suggest novel approaches to the future development of Jak Stat based cancer therapeutics.

Feasibility of Ultra-High-Throughput Functional Screening of Melanoma Biopsies for Discovery of Novel Cancer Drug Combinations

Purpose: Successful development of targeted therapy combinations for cancer patients depends on first discovering such combinations in predictive preclinical models. Stable cell lines and mouse xenograft models can have genetic and phenotypic drift and may take too long to generate to be useful as a personalized medicine tool.Experimental Design: To overcome these limitations, we have used a platform of ultra-high-throughput functional screening of primary biopsies preserving both cancer and stroma cell populations from melanoma patients to nominate such novel combinations from a library of thousands of drug combinations in a patient-specific manner within days of biopsy. In parallel, patient-derived xenograft (PDX) mouse models were created and novel combinations tested for their ability to shrink matched PDXs.Results: The screening method identifies specific drug combinations in tumor cells with patterns that are distinct from those obtained from stable cell lines. Screening results were highly specific to individual patients. For patients with matched PDX models, we confirmed that individualized novel targeted therapy combinations could inhibit tumor growth. In particular, a combination of multi-kinase and PI3K/Akt inhibitors was effective in some BRAF-wild-type melanomas, and the addition of cediranib to the BRAF inhibitor PLX4720 was effective in a PDX model with BRAF mutation.Conclusions: This proof-of-concept study demonstrates the feasibility of using primary biopsies directly for combinatorial drug discovery, complementing stable cell lines and xenografts, but with much greater speed and efficiency. This process could potentially be used in a clinical setting to rapidly identify therapeutic strategies for individual patients. Clin Cancer Res; 23(16); 4680-92. ©2017 AACR.

A Computational Approach for Identifying Synergistic Drug Combinations

A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.

While targeted therapies have achieved remarkable antitumor responses, resistance to targeted agents frequently develops and renders the targeted drug ineffective. Combination therapies have been successful in delaying resistance and overcoming resistance. Additional goals of combination therapy are to achieve enhanced effectiveness through drug synergy and to reduce the frequency and severity of adverse events through lower individual drug dosage levels. However the identification of synergistic drug combinations is often a labor and resource intensive process. Therefore a systematic method for identifying optimal combinations would be highly impactful. Here we present a computational method for predicting synergistic and effective drug combinations using only single drug efficacy information. We have applied and validated our method on a high-throughput drug screen of 780 combinations involving 40 individual molecules in the context of mutant BRAF melanoma. Additionally we have made predictions and validated 11 previously untested drug combinations with a diverse set of outcomes.

Patient-derived tumour xenografts for breast cancer drug discovery

Despite remarkable advances in our understanding of the drivers of human malignancies, new targeted therapies often fail to show sufficient efficacy in clinical trials. Indeed, the cost of bringing a new agent to market has risen substantially in the last several decades, in part fuelled by extensive reliance on preclinical models that fail to accurately reflect tumour heterogeneity. To halt unsustainable rates of attrition in the drug discovery process, we must develop a new generation of preclinical models capable of reflecting the heterogeneity of varying degrees of complexity found in human cancers. Patient-derived tumour xenograft (PDTX) models prevail as arguably the most powerful in this regard because they capture cancer's heterogeneous nature. Herein, we review current breast cancer models and their use in the drug discovery process, before discussing best practices for developing a highly annotated cohort of PDTX models. We describe the importance of extensive multidimensional molecular and functional characterisation of models and combination drug-drug screens to identify complex biomarkers of drug resistance and response. We reflect on our own experiences and propose the use of a cost-effective intermediate pharmacogenomic platform (the PDTX-PDTC platform) for breast cancer drug and biomarker discovery. We discuss the limitations and unanswered questions of PDTX models; yet, still strongly envision that their use in basic and translational research will dramatically change our understanding of breast cancer biology and how to more effectively treat it.

miR-194 is a negative regulator of GEF-H1 pathway in melanoma

The incidence and associated mortality of melanoma continues to increase worldwide. At present, there is no curative therapy for advanced stage of melanoma. It is necessary to find new indicators of prognosis and therapeutic targets. Increasing evidence shows that miRNA can provide potential candidate biomarkers for melanoma and therapeutic targets. GEF-H1, a regulator of RhoA, as oncogenic driver in melanoma, promotes the growth and invasion of melanoma. miR-194 is a tumor-suppressor gene in multiple tumors, such as bladder and non-small cell lung cancer, and clear cell renal cell carcinoma. In the present study, we demonstrated that GEF-H1 serves as target of miR-194. Overexpression of miR-194 downregulates the GEF-H1/RhoA pathway, inhibits melanoma cancer cell proliferation and metastasis. Furthermore, miR-194 expression is negatively associated with tumor-node-metastasis (TNM) stages. Briefly, our findings provided new theoretical basis for melanoma treatment.

Discovery of vascular endothelial growth factor receptor tyrosine kinase inhibitors by quantitative structure–activity relationships, molecular dynamics simulation and free energy calculation

Employing the combined strategy to understand the features of KDR–ligands complexes, and provide a basis for rational design of inhibitors.