Synergistic Drug Combinations Research Articles

Treatment Strategies of Colistin Resistance Acinetobacter baumannii Infections

Treatment Strategies of Colistin Resistance Acinetobacter baumannii Infections

The role of the master cancer regulator Pin1 in the development and treatment of cancer.

This review examines the complex role of Pin1 in the development and treatment of cancer. Pin1 is the only peptidyl-prolyl isomerase (PPIase) that can recognize and isomerize phosphorylated Ser/Thr-Pro peptide bonds. Pin1 catalyzes a structural change in phosphorylated Ser/Thr-Pro motifs that can modulate protein function and thereby impact cell cycle regulation and tumorigenesis. The molecular mechanisms by which Pin1 contributes to oncogenesis are reviewed, including Pin1 overexpression and its correlation with poor cancer prognosis, and the contribution of Pin1 to aggressive tumor phenotypes involved in therapeutic resistance is discussed, with an emphasis on cancer stem cells, the epithelial-to-mesenchymal transition (EMT), and immunosuppression. The therapeutic potential of Pin1 inhibition in cancer is discussed, along with the promise and the difficulties in identifying potent, drug-like, small-molecule Pin1 inhibitors. The available evidence supports the efficacy of targeting Pin1 as a novel cancer therapeutic by analyzing the role of Pin1 in a complex network of cancer-driving pathways and illustrating the potential of synergistic drug combinations with Pin1 inhibitors for treating aggressive and drug-resistant tumors.

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations.

Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.

PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction

MotivationDrug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines.ResultsIn this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS.Scientific contributionFirst, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations. Second, the permutable fusion mechanism combine the drug and cell line features by integrating information of different channels, enabling the utilization of complex relationships between drugs and cell lines. Third, comparative and ablation experiments provide evidence of the efficacy of PermuteDDS in predicting drug-drug synergy.

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction

Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP.

Abstract 947: A nanodroplet cell processing platform for identifying synergistic drug combinations for neuroblastoma treatment in TH-MYCN transgenic mice

Abstract Neuroblastoma is the most common pediatric extracranial solid tumor and contributes to more than 15% of child mortality. It is characterized by extreme heterogeneity and high-risk tumors often resulting in poor prognosis. Currently, chemotherapy is a prevailing standard treatment for neuroblastoma. However, it is plagued by multiple challenges such as drug resistance and debilitating side effects. Synergistic drug combination is proposed as an innovative approach to potentially improve clinical outcomes of single-agent chemotherapy. We propose a novel approach to screen for effective chemo-synergistic drug combinations using Bioinspired nanodroplet processing (BioNDP) screening platform. This platform overcomes the limitations of conventional drug screening approaches, including the requirement for large cell numbers and the low translation rate from bench to bedside. In this study, we utilize TH-MYCN transgenic mice, a well-known genetically engineered mouse model of neuroblastoma that recapitulates critical genetic and clinical aspects of high-risk MYCN-amplified neuroblastoma. With its genetically homogeneous background and spontaneous tumor development, TH-MYCN mice offer a valuable comparison to traditional xenograft mouse models. We have also developed an optimal procedure of primary murine tumor isolation using both negative and positive selection approaches. The purity of the isolated cells was verified with GD2 expression, one of well-known marker of neuroblastoma. In addition, we have successfully identified an effective drug combination of cyclophosphamide, doxorubicin, and vincristine, using primary neuroblastoma tumor samples derived from TH-MYCN transgenic mice as a target. The in vitro synergistic effect of primary mouse tumor cell cytotoxicity was confirmed by using luminescent cell viability assay. Further in vivo experiments have demonstrated the therapeutic efficacy of this selected drug combination in suppressing tumor growth and improving survival rate in all TH-MYCN transgenic mice treated. To sum up, we aim to establish a future patient-centric paradigm by which minimal primary cell samples from a personalized biopsy of patients may undergo drug screening, leading to the identification of a tailored drug combination. The BioNDP platform we developed may become a pivotal resource for personalized therapy to ensure a faster and more efficient treatment strategy for patients. Citation Format: Yen-Tzu Liao, Zhi-Kai Yu, Ching-Te Kuo, Wen-Ming Hsu, Yi-Xun Huang, Hsinyu Lee. A nanodroplet cell processing platform for identifying synergistic drug combinations for neuroblastoma treatment in TH-MYCN transgenic mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 947.

Abstract 7126: Synergistic combinations and tissue type-specificity of compounds targeting components of the DNA damage response pathway in 2D culture and 3D spheroid cell viability assays

Abstract Poly (ADP-ribose) polymerase (PARP) inhibitors have changed the landscape of treatment for homologous recombination (HR)-deficient cancers. PARP inhibitors are synthetically lethal with deficiencies in genes involved in the HR pathway, such as BRCA2, but resistance against PARP inhibitors is a major problem in the clinic. The use of new synergistic drug combinations targeting different components of the DNA damage response (DDR) pathway may overcome PARP inhibitor resistance. Sixteen small molecule inhibitors acting on different targets in the DDR pathway (including PARP, Polθ and ATR) were profiled in 2D culture and 3D spheroid cell viability assays. Intracellular ATP content was used as an indirect readout of cell number. Compound synergies were determined by profiling combinations of compounds in a 6-by-6 matrix, and quantifying synergistic effects using the Excess over Bliss score [1]. A pair of isogenic BRCA2-deficient and -proficient DLD-1 colon adenocarcinoma cell lines was used as a model of BRCA2-deficient tumors. In 2D cell viability assays, PARP inhibitors and the Polθ inhibitor ART558 were more active in BRCA2-deficient compared to BRCA2-proficient DLD-1 cells, whereas an ATR inhibitor (ceralasertib) was equally potent in both cell lines. Combining olaparib with ART558 in 2D culture and 3D spheroid studies showed synergistic effects in BRCA2-deficient DLD-1 cells. Moreover, olaparib synergized with ceralasertib in both BRCA2-deficient and -proficient DLD-1 cells. Different DDR-targeting inhibitors were profiled in over a hundred cancer cell lines from diverse tumor tissue origin in 2D assays. This showed a relatively high potency for PARP inhibitors in ovarian cancer cell lines compared to cell lines derived from other tissue types, which is consistent with the clinical application of PARP inhibitors for treatment of ovarian cancers. For ATR inhibitors and ART558, kidney and myeloid cancer cell lines were found to be most sensitive, respectively, suggesting potential therapeutic utility of ATR and Polθ inhibitors for treatment of these cancer types. The genomic determinants of the different sensitivities were investigated by comparing compound potencies in the cell viability assays with the mutation status of more than 500 genes involved in cancer or the DDR pathway in the cell lines. Additionally, cancer cell line sensitivity was correlated with the expression levels of more than 19,000 genes in these cell lines to identify novel RNA signatures capable of predicting response to DDR-targeting inhibitors. This study uncovers new synergistic drug combinations that increase the efficiency of PARP inhibitors, and provides new insights into the determinants of cancer cell line sensitivity to compounds targeting different components of the DDR pathway. [1] Liu et al. Stat. Biopharm. Res. 10, 112-22; 2018 Citation Format: Janneke J. Melis, Daphne J. Kluitmans, Jeffrey J. Kooijman, Jelle Dylus, Sabine Wetemans, Suzan Geerdink-Datema, Jeroen A. de Roos, Yvonne Grobben, Guido J. Zaman. Synergistic combinations and tissue type-specificity of compounds targeting components of the DNA damage response pathway in 2D culture and 3D spheroid cell viability assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7126.

Abstract 5835: Genome-Wide CRISPR gene knockout screens combined with selpercatinib identifies potent combination therapies for RET driven medullary thyroid carcinoma

Abstract Background: Medullary thyroid carcinoma (MTC) is a rare neuroendocrine tumor driven primarily by activating mutations in the RET proto-oncogene. It accounts for approximately 13% of thyroid cancer-related deaths. Total thyroidectomy is the main treatment option for MTC patients; however, its efficacy is limited due to the high prevalence of metastatic disease at diagnosis. Vandetanib, cabozantinib, and selpercatinib are RET signaling inhibitors (RETi), that have been approved for treating advanced MTC. Despite their effectiveness, many patients relapse or have refractory disease due to eventual resistance to these RETis. Methods: In our study, we employed genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) gene knockout (KO) in MTC cell lines treated with selpercatinib or vehicle to investigate the genetic mechanisms of resistance or sensitivity to RETi. Our overarching goal was to identify potential synergistic or additive drug combinations that may prevent or treat resistant disease. Results: Our research identified key genes and pathways confirming the sensitivity or resistance of MTC cells to RETi. Notably, apoptotic genes along with RAS and mTOR signaling pathways were found to play a significant role in resistance development. Knocking out negative regulators of these pathways (such as NF1 and TSC2) conferred growth advantages to cells treated with RETi. Alternatively, knocking out BCL2, an antiapoptotic protein, led to increased lethality. These findings suggested that targeting RAS, mTOR, or antiapoptotic pathways in conjunction with RETi may be synergistic or have additive activity. To validate the CRISPR screening, we selectively knocked down NF1 in MTC cells and found it led to a nearly two-fold increase in IC50 for selpercatinib. Furthermore, in-vitro and in-vivo testing confirmed significant synergistic activity when using the combination of trametinib, an MEK inhibitor (MEKi; downstream of the RAS pathway), with selpercatinib. Conclusions and Future Directions: We have identified a potent synergistic combination of MEKi with RETi as a novel synergistic drug combination to treat MTC, which may also prevent resistance to RETi. These results will be translated to patients through clinical trials conduced at the NCI. Citation Format: Abdelrahman Rahmy, Arwa Fallatah, David Milewski, Meijie Tian, Young Song, Yong Kim, Hsien-Chao Chou, Xinyu Wen, Chaoyu Wang, Jun Wei, Robert Hawley, John Glod, Javed Khan. Genome-Wide CRISPR gene knockout screens combined with selpercatinib identifies potent combination therapies for RET driven medullary thyroid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5835.

Abstract 4310: Impact of drug combination schedules on the evolution of tumor resistance

Abstract The emergence of drug resistance during treatment is a critical limiting factor for the long-term effectiveness of anticancer therapies. The use of drug combinations limits the emergence of resistance, thereby prolonging their therapeutic utility. The effect of a drug combination is dependent both on the concentration of the individual drugs and the extent of their interaction (synergistic, additive, or antagonistic). Thus, understanding the scheduling implications for efficacy can be valuable for optimizing tumor response and time to resistance for the combination. To examine this more closely, we used a pharmacokinetic/pharmacodynamic (PK/PD) modeling approach, coupling one-compartment PK models of two hypothetical drugs based on set synchronous or asynchronous (offset) schedules with an evolutionary model of tumor clone growth. We simulated four tumor subpopulations representing binary resistance to one or both drugs, using an exponential growth model with growth penalties based on drug concentrations. We used the model to simulate the effects of pairs of antagonistic, synergistic, and additive drugs on the growth of sensitive and resistant populations in a tumor. Final tumor volume and resistant population were used to compare the efficacy of various drug schedules. As expected, the effect of a drug interaction—either synergistic or antagonistic—is greatest when dosing is synchronous, as this results in the greatest simultaneous drug concentration. The effect of a drug interaction is proportional to the sensitive population size, so the effect of drug interactions decreases over time as the sensitive population is eliminated. Intuitively, the final tumor volume decreases as the strength of a synergistic interaction increases until the sensitive population is eliminated. The resistant populations are unaffected by the strength of the interaction for both antagonistic and synergistic combinations. Resistant populations are not affected by interactions between drugs to which they are resistant, so the growth of resistant populations over time is independent of drug interactions. Crucially, for effective drugs—that is, drugs that eliminate the sensitive population—synergy and antagonism converge to additivity as the sensitive population dies out because the remaining resistant populations are not affected by drug interactions. As synergistic and additive drug combinations are equivalent in the long-term, synergy and asynchronous dosing could be used to limit toxicity without sacrificing efficacy. The evolution of resistance also explains why in vitro synergy or antagonism may not translate in vivo. Taken together, our work provides several unintuitive insights about combination drug scheduling with practical implications for therapeutic design. Citation Format: Madison Stoddard, Andrew Chen, Lin Yuan, Debra Van Egeren, Dean Bottino, Arijit Chakravarty. Impact of drug combination schedules on the evolution of tumor resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4310.

Abstract 4563: Ferroptotic mechanism of drug induced toxicity of PARPi and AurKi in prostate cancer

Abstract BACKGROUND: Cancer is typically treated with a combination of drugs rather than monotherapy stratagems, but identification of synergistic drug combinations for the treatment of cancers is challenging. Here we present the combination of PARP and Aurora Kinase inhibitors synergize through induction of the ferroptosis pathway of cell death. METHODS: We identified these drugs through an unbiased screen of enriched drug class response correlations. We then validated a synergistic cell killing effect of multiple drugs of each class by in vitro drug screening. Next, we performed reversal experiments using a ferroptosis inhibitor and lipid peroxide staining to confirm ferroptosis is a relevant cell death pathway. We performed Western immunoblot analysis in cells overexpressing putative Aurora Kinase phospho-substrates in the relevant pathway of interest, and performed site directed mutagenesis experiments to show the relevant amino acid altered by kinase activity. We also performed a series of qPCR and co-IP experiments to show PARPi effect on the antioxidant signaling pathway. RESULTS: We validated and identified synergy in a prostate cancer specific context. Next, we found Talazoparib and Alisertib induced ferroptosis after synergy was reduced with a ferroptosis inhibitor and lipid peroxides increased accumulation. We found a novel mechanism action for Alisertib, wherein Aurora Kinase inhibitors can alter the phospho-status and activity of the iron reduction enzyme Steap3.We also found that PARP inhibitors can affect the Nrf2-driven antioxidant signaling pathway on a transcriptional level. CONCLUSION: Aurora Kinase inhibitors and PARP inhibitors for the treatment of prostate cancer was identified and was validated as synergistic, which revealed new functional roles of both drugs in the context of cell killing. These findings represent a new clinical combination of drugs in prostate cancer and reveal a novel role for this drug class in ferroptosis. Citation Format: August John, Kuntian Luo, Zhenkun Lou, Liewei Wang. Ferroptotic mechanism of drug induced toxicity of PARPi and AurKi in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4563.

Abstract 943: Identification and validation of unusual drug combinations identified from combination databases

Abstract There is a noted trend in the identification and subsequent approval of rational drug combinations to treat various diseases, particularly cancer. When applied in a clinical setting, synergistic combinations can have significant benefits including improved patient response and avoiding or overcoming drug resistance. Although multiple strategies have been successfully implemented to identify rational drug combinations, extensive validation is initially required to confirm a combination as synergistic. The existence and growing use of web-based repositories containing comprehensive drug combination data is a key resource and can be a valuable starting point for identifying synergistic combinations. However, are synergistic combinations from these datasets expected or rational and can they be validated experimentally? By mining available drug combinations from the DrugCombo database, we wanted to identify unusual drug combinations reported as synergistic and experimentally validate these with dose-response assays. In addition, we set out to establish if cytokines could be used as an inhibition biomarker to easily assess synergy. We found multiple unexpected synergies including a predicted synergy between the JAK1/2 inhibitor Ruxolitinib with Maraviroc, a CCR5 antagonist, or Levetiracetam, an SV2A binding anti-epileptic reported in the lymphoma cell line, L-1236. Experimentally, these predicted synergies could not be validated. In the context of synergistic drug combinations, this work shows that experimental validation is crucial, particularly for combinations with uncertain biological mechanisms. Citation Format: Rainer Grant, Marco Pinheiro, Rachel Weller Roska, Robert Felix. Identification and validation of unusual drug combinations identified from combination databases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 943.

Abstract 694: Evaluation of a synergistic drug combination with 177Lu-rhPSMA-10.1 for prostate cancer: Results of an in vitro screen and in vivo proof of concept study

Abstract Purpose: Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) has been shown to extend survival in men with advanced prostate cancer (PCa). Novel radiohybrid (rh) PSMA-targeted 177Lu-rhPSMA-10.1 has shown promising preclinical efficacy and advantageous radiation dosimetry in humans. We conducted an in vitro screen to identify known anticancer drugs with potential for synergistic interaction with 177Lu-rhPSMA-10.1. Here we present key screening data and a subsequent in vivo efficacy analysis of the lead novel drug combination in PSMA-expressing 22Rv1 PCa xenografts. Methods: Over 150 FDA-approved anticancer drugs were screened in a clonogenic survival assay of 22Rv1 cells using the test drug alone, at a range of concentrations <20 µM to determine the IC50, and results compared to incubations of the drug + 15 MBq/mL 177Lu-rhPSMA-10.1 after 10 d. A focused screen of 5 lead candidates was then conducted to determine the impact of 177Lu-rhPSMA-10.1 (0-25 MBq/mL) on the drug IC50. A synergy score was determined using the zero interaction potency (ZIP) reference model and the multi-dimensional synergy of combinations (MuSyc) platform. Subsequently, to evaluate the efficacy of the lead combination, 177Lu-rhPSMA-10.1 (single 30 MBq iv dose) and Cobimetinib (0.25 mg orally per day for 21 d) alone and in combination were administered to 22Rv1 tumor-bearing NMRI nude mice (n = 8 per group plus untreated controls). Tumor volume was measured 2x week for 69 d. Two-way ANOVA and Tukey’s multiple comparisons test (data analyzed until n = 3 remained per group) and Kaplan-Meier Log-rank survival analyses were performed. Results: The in vitro screen identified MEK inhibitor Cobimetinib as a lead candidate for synergistic combination with 177Lu-rhPSMA-10.1 across a wide concentration range, with a ZIP synergy score of 13.25% (95% CI ± 2.17) and promising results on MuSyc analysis. The 177Lu-rhPSMA-10.1 + Cobimetinib combination significantly suppressed tumor growth in vivo vs untreated controls (from day 13-30; p<0.01) and 177Lu-rhPSMA-10.1 alone (from day 17-30; p<0.001). The median survival in the combination group (49 d) was significantly longer vs the untreated group (23 d; p=0.001) and the group treated with 177Lu-rhPSMA-10.1 alone (36 d; p=0.002). Conclusions: Through an extensive in vitro screen, we identified Cobimetinib to have potential for a synergistic anti-tumor effect in combination with 177Lu-rhPSMA-10.1. This may be due to inhibition of the MEK-MAPK pathway by Cobimetinib during DNA damage response, resulting in radiosensitization of cancer cells to 177Lu-labeled RLT agents such as 177Lu-rhPSMA-10.1. This novel combination showed an enhanced therapeutic efficacy vs the single agents in 22Rv1 xenografts and the lack of overlapping monotherapy toxicity reported in the clinic supports clinical investigation in men with PCa. Citation Format: Caroline Foxton, Bart Cornelissen, Edward O'Neill, Bradley Waldron, Freja Pretzmann, Rikke Veggerby Grønlund, Mathias Wikke Hallund, Daniel J. Stevens. Evaluation of a synergistic drug combination with 177Lu-rhPSMA-10.1 for prostate cancer: Results of an in vitro screen and in vivo proof of concept study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 694.

Abstract 564: Therapeutic targeting of JAK/STAT signaling and histone acetyltransferase activity in post-myeloproliferative neoplasm secondary AML

Abstract Post-myeloproliferative neoplasm secondary Acute Myeloid Leukemia (Post-MPN sAML) is an aggressive and lethal hematologic malignancy arising from myeloproliferative neoplasms (MPN). Hyperactivation of JAK/STAT signaling was identified in 50-90% of MPNs - underscoring its role in driving MPN pathogenesis and transformation to post-MPN sAML. Ruxolitinib, a Type-I JAK2 inhibitor approved for the treatment of myelofibrosis, was tested in a Phase II clinical trial in post-MPN sAML. The trial revealed that ruxolitinib improved the quality of life in patients but was not disease-modifying. Thus, there is an urgent need to find synergistic drug combinations capable of complementing the therapeutic activity of JAK2 inhibitors in post-MPN sAML. Here, we demonstrate that combination treatment of ruxolitinib and CBP30, a bromodomain inhibitor of histone acetyltransferases (HAT) CBP and p300, enhances the therapeutic activity of ruxolitinib in post-MPN sAML. Genome-wide CRISPR loss-of-function screens in JAK2-mutated HEL cells determined that knockout of CBP sensitizes leukemic cells to JAK2 inhibitors. Consistently, functional validation through CRISPR/Cas9 demonstrated that genetic deletion of CBP but not of p300 renders HEL cells sensitive to ruxolitinib treatment. Pharmacologic inhibition of JAK/STAT signaling and HAT activity through ruxolitinib and CBP30 treatment in post-MPN sAML lines HEL and SET2 decreases proliferation and induces apoptosis and cell cycle arrest. Mechanistically, the synergism between ruxolitinib and CBP30 elicits a unique transcriptome profile in HEL with significant downregulation of E2F and MYC targets. In addition, epigenetic profiling revealed that the synergism induces global changes in H3K27ac signals with downregulation and upregulation of H3K27ac signals in predicted STAT5/BCL3 and GATA-bounded regions, respectively. Remarkably, combination treatment of ruxolitinib and CBP30 in HEL luciferase NSG mouse xenografts showed significant leukemia regression and decrease in spleen size compared to mice treated with vehicle or single agents. Overall, these results substantiate the potential of targeting JAK/STAT signaling and histone acetyltransferase activity in the treatment of post-MPN sAML. Citation Format: Kalay Bertulfo, Koen Debackere, Hannah Miller, Cindy Ma, Ryan Najac, Adolfo Ferrando, Teresa Palomero. Therapeutic targeting of JAK/STAT signaling and histone acetyltransferase activity in post-myeloproliferative neoplasm secondary AML [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 564.

High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line

BackgroundThe development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations.MethodsTo address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses.ResultsBy examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails.ConclusionsIn conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

A review on graph neural networks for predicting synergistic drug combinations

Combinational therapies with synergistic effects provide a powerful treatment strategy for tackling complex diseases, particularly malignancies. Discovering these synergistic combinations, often involving various compounds and structures, necessitates exploring a vast array of compound pairings. However, practical constraints such as cost, feasibility, and complexity hinder exhaustive in vivo and in vitro experimentation. In recent years, machine learning methods have made significant inroads in pharmacology. Among these, Graph Neural Networks (GNNs) have gained increasing attention in drug discovery due to their ability to represent complex molecular structures as networks, capture vital structural information, and seamlessly handle diverse data types. This review aims to provide a comprehensive overview of various GNN models developed for predicting effective drug combinations, examining the limitations and strengths of different models, and comparing their predictive performance. Additionally, we discuss the datasets used for drug synergism prediction and the extraction of drug-related information as predictive features. By summarizing the state-of-the-art GNN-driven drug combination prediction, this review aims to offer valuable insights into the promising field of computational pharmacotherapy.

The Promise of Combination Therapies with FOXM1 Inhibitors for Cancer Treatment.

Forkhead box M1 (FOXM1) is a transcription factor in the forkhead (FOX) family, which is required for cellular proliferation in normal and neoplastic cells. FOXM1 is highly expressed in many different cancers, and its expression is associated with a higher tumor stage and worse patient-related outcomes. Abnormally high expression of FOXM1 in cancers compared to normal tissue makes FOXM1 an attractive target for pharmacological inhibition. FOXM1-inhibiting agents and specific FOXM1-targeted small-molecule inhibitors have been developed in the lab and some of them have shown promising efficacy and safety profiles in mouse models. While the future goal is to translate FOXM1 inhibitors to clinical trials, potential synergistic drug combinations can maximize anti-tumor efficacy while minimizing off-target side effects. Hence, we discuss the rationale and efficacy of all previously studied drug combinations with FOXM1 inhibitors for cancer therapies.

Synergistic Drug Combinations Promote the Development of Resistance in Acute Myeloid Leukemia.

Preclinical strategies to identify combinations for cancer treatment often focus on identifying synergistic combinations. This study shows that in AML cells combinations that rely on synergy can increase the likelihood of developing resistance, suggesting that combination screening strategies may benefit from a more holistic approach rather than focusing on drug synergy. See related commentary by Bhola and Letai, p. 81. This article is featured in Selected Articles from This Issue, p. 80.

Integrated network pharmacology, metabolomics, and transcriptomics of Huanglian-Hongqu herb pair in non-alcoholic fatty liver disease

Ethnopharmacological relevanceThe Huanglian-Hongqu herb pair (HH) is a synergistic drug combination used to treat non-alcoholic fatty liver disease (NAFLD). However, the molecular mechanism underlying the therapeuticeffects of HH requires further elucidation. Aim of the studyThe present study explored the potential mechanism of HH in treating NAFLD. Materials and methodsUPLC-Q-TOF-MS was employed to identify the drug constituents in HH. A NAFLD rat model was induced by a high-fat diet (HFD) and treated with different doses of HH. The functional mechanism of HH in NAFLD rats was predicted using network pharmacology, metabolomics and transcriptomics. Immunohistochemistry, real-time PCR, and Western blot were performed to validate the key mechanisms. ResultsPharmacodynamic assessment demonstrated that HH exhibited improvements in lipid deposition and reduced hepatic oxidative stress in NAFLD rats. Hepatic wide-target metabolomics revealed that HH primarily modulated amino acids and their metabolites, fatty acids, organic acids and their derivatives, bile acids, and other liver metabolites. The enriched pathways included metabolic pathways, primary bile acid biosynthesis, and bile secretion. Network pharmacology analysis indicated that HH regulated the key pathways in NAFLD, notably PPAR, AMPK, NF-κB and other signaling pathways. Furthermore, hepatic transcriptomics, based on Illumina RNA-Seq sequencing analyses, suggested that HH improved NAFLD through metabolic pathways, the PPAR signaling pathway, primary bile acid biosynthesis, and fatty acid metabolism. Further mechanistic studies indicated that HH could regulate the genes and proteins associated with the PPAR signaling pathway. ConclusionOur findings demonstrated that the potential therapeutic benefits of HH in ameliorating NAFLD by targeting the PPAR signaling pathway, thereby facilitating a more extensive use of HH in NAFLD.

Abstract B079: Identification of synergistic drug combinations including MEK inhibitor and agents modulating the tumor microenvironment in an electroporation-induced pancreatic cancer model

Abstract Pancreatic ductal adenocarcinoma (PDAC) is refractory to cytostatic and immuno-oncology therapeutic strategies, leaving surgical resection of the tumor as the only treatment that can significantly prolong survival. We are therefore exploring optimized treatment strategies targeting cancer cells as well as infiltrating immune cells and the tumor microenvironment (TME) in an autochthonous mouse model for PDAC. Single focal tumors are induced through in vivo electroporation of gene constructs into the pancreas which encode mutated Kras and facilitate knock out the tumor suppressor genes P53, Cdkn2a and/or Smad4 via CRISPR/Cas9. The resulting tumors recapitulate the human disease with respect to genetics, histology and TME composition. Furthermore, this model allows the generation of tumors reflecting different PDAC subtypes, as well as the resection of primary tumors in combination with the (neo-)adjuvant regimens. Like the human disease, these tumors are refractory to chemotherapy and immune checkpoint blockade, whereas targeted therapy by means of MEK inhibitors merely leads to temporary delay of tumor outgrowth. We therefore tested the combination of MEK inhibitor treatment with different, potentially complementary modalities. We found the combination with agonist anti-CD40 antibodies to be highly effective. Prominent regressions are observed, and in combination with primary tumor resection long-term complete responses can be achieved even in a metastatic variant of the model. Apart from the direct cytostatic impact of MEKi on the Kras-transformed tumor cells, the mechanism of action of this synergistic drug combination involves enhancement of the anti-tumor T-cell response, suppression of CD4+ Tregs, and reduction of MDSCs and M2-type macrophages in favor of M1-type pro-inflammatory macrophages. A second highly effective regimen as identified in our experiments involves the combination of MEK inhibitors with the multi-kinase inhibitor regorafenib. In contrast to the aforementioned regimen, the complementary action of these drugs involves modulation of the TME by regorafenib. While the clinical translation of the MEKi/CD40 Ab regimen is hampered by toxicity issues encountered with agonist anti-CD40 Ab, the synergy as observed in the MEKi/regorafenib regiment provides a tangible path towards clinical testing. Citation Format: Aline S. Konrad, Tanja Hägele, Stefan Zens, Caroline Vent, Moritz Hamberger, Sarah Baiker, Aron Maes, Carl-Stephan Leonhardt, Oliver Strobel, Oliver Politz, Dieter Zopf, Daniel Baumann, Julian Mochayedi, Rienk Offringa. Identification of synergistic drug combinations including MEK inhibitor and agents modulating the tumor microenvironment in an electroporation-induced pancreatic cancer model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B079.

Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks

Drug combinations are frequently used to treat cancer to reduce side effects and increase efficacy. The experimental discovery of drug combination synergy is time-consuming and expensive for large datasets. Therefore, an efficient and reliable computational approach is required to investigate these drug combinations. Advancements in deep learning can handle large datasets with various biological problems. In this study, we developed a SynergyGTN model based on the Graph Transformer Network to predict the synergistic drug combinations against an untreated cancer cell line expression profile. We represent the drug via a graph, with each node and edge of the graph containing nine types of atomic feature vectors and four bonds features, respectively. The cell lines represent based on their gene expression profiles. The drug graph was passed through the GTN layers to extract a generalized feature map for each drug pairs. The drug pair extracted features and cell-line gene expression profiles were concatenated and subsequently subjected to processing through multiple densely connected layers. SynergyGTN outperformed the state-of-the-art methods, with a receiver operating characteristic area under the curve improvement of 5% on the 5-fold cross-validation. The accuracy of SynergyGTN was further verified through three types of cross-validation tests strategies namely leave-drug-out, leave-combination-out, and leave-tissue-out, resulting in improvement in accuracy of 8%, 1%, and 2%, respectively. The Astrazeneca Dream dataset was utilized as an independent dataset to validate and assess the generalizability of the proposed method, resulting in an improvement in balanced accuracy of 13%. In conclusion, SynergyGTN is a reliable and efficient computational approach for predicting drug combination synergy in cancer treatment. Finally, we developed a web server tool to facilitate the pharmaceutical industry and researchers, as available at: http://nsclbio.jbnu.ac.kr/tools/SynergyGTN/.