Neratinib, a pan ERBB/HER inhibitor, restores sensitivity of PTEN-null, BRAFV600E melanoma to BRAF/MEK inhibition.

Introduction: Approximately 50% of melanomas harbor an activating BRAFV600E mutation. Standard of care involves a combination of inhibitors targeting mutant BRAF and MEK1/2, the substrate for BRAF in the MAPK pathway. PTEN loss-of-function mutations occur in ~40% of BRAFV600E melanomas, resulting in increased PI3K/AKT activity that enhances resistance to BRAF/MEK combination inhibitor therapy. Methods: To compare the response of PTEN null to PTEN wild-type cells in an isogenic background, CRISPR/Cas9 was used to knock out PTEN in a melanoma cell line that harbors a BRAFV600E mutation. RNA sequencing, functional kinome analysis, and drug synergy screening were employed in the context of BRAF/MEK inhibition. Results: RNA sequencing and functional kinome analysis revealed that the loss of PTEN led to an induction of FOXD3 and an increase in expression of the FOXD3 target gene, ERBB3/HER3. Inhibition of BRAF and MEK1/2 in PTEN null, BRAFV600E cells dramatically induced the expression of ERBB3/HER3 relative to wild-type cells. A synergy screen of epigenetic modifiers and kinase inhibitors in combination with BRAFi/MEKi revealed that the pan ERBB/HER inhibitor, neratinib, could reverse the resistance observed in PTEN null, BRAFV600E cells. Conclusions: The findings indicate that PTEN null BRAFV600E melanoma exhibits increased reliance on ERBB/HER signaling when treated with clinically approved BRAFi/MEKi combinations. Future studies are warranted to test neratinib reversal of BRAFi/MEKi resistance in patient melanomas expressing ERBB3/HER3 in combination with its dimerization partner ERBB2/HER2.

Bisphosphonates synergistically enhance the antifungal activity of azoles in dermatophytes and other pathogenic molds.

Superficial infections of the skin, hair, and nails by fungal dermatophytes are the most prevalent of human mycoses, and many infections are refractory to treatment. As current treatment options are limited, recent research has explored drug synergy with azoles for dermatophytoses. Bisphosphonates, which are approved to treat osteoporosis, can synergistically enhance the activity of azoles in diverse yeast pathogens but their activity has not been explored in dermatophytes or other molds. Market bisphosphonates risedronate, alendronate, and zoledronate (ZOL) were evaluated for antifungal efficacy and synergy with three azole antifungals: fluconazole (FLC), itraconazole (ITR), and ketoconazole (KET). ZOL was the most active bisphosphonate tested, displaying moderate activity against nine dermatophyte species (MIC range 64-256 µg/mL), and was synergistic with KET in eight of these species. ZOL was also able to synergistically improve the anti-biofilm activity of KET and combining KET and ZOL prevented the development of antifungal resistance. Rescue assays in Trichophyton rubrum revealed that the inhibitory effects of ZOL alone and in combination with KET were due to the inhibition of squalene synthesis. Fluorescence microscopy using membrane- and ROS-sensitive probes demonstrated that ZOL and KET:ZOL compromised membrane structure and induced oxidative stress. Antifungal activity and synergy between bisphosphonates and azoles were also observed in other clinically relevant molds, including species of Aspergillus and Mucor. These findings indicate that repurposing bisphosphonates as antifungals is a promising strategy for revitalising certain azoles as topical antifungals, and that this combination could be fast-tracked for investigation in clinical trials. IMPORTANCE: Fungal infections of the skin, hair, and nails, generally grouped together as "tineas" are the most prevalent infectious diseases globally. These infections, caused by fungal species known as dermatophytes, are generally superficial, but can in some cases become aggressive. They are also notoriously difficult to resolve, with few effective treatments and rising levels of drug resistance. Here, we report a potential new treatment that combines azole antifungals with bisphosphonates. Bisphosphonates are approved for the treatment of low bone density diseases, and in fungi they inhibit the biosynthesis of the cell membrane, which is also the target of azoles. Combinations were synergistic across the dermatophyte species and prevented the development of resistance. We extended the study to molds that cause invasive disease, finding synergy in some problematic species. We suggest bisphosphonates could be repurposed as synergents for tinea treatment, and that this combination could be fast-tracked for use in clinical therapy.

Spectrum of activity and mechanisms of azole-bisphosphonate synergy in pathogenic Candida.

Candidiasis places a significant burden on human health and can range from common superficial vulvovaginal and oral infections to invasive diseases with high mortality. The most common Candida species implicated in human disease is Candida albicans, but other species like Candida glabrata are emerging. The use of azole antifungals for treatment is limited by increasing rates of resistance. This study explores repositioning bisphosphonates, which are traditionally used for osteoporosis, as antifungal synergists that can improve and revitalize the use of azoles. Risedronate, alendronate, and zoledronate (ZOL) were tested against isolates from six different species of Candida, and ZOL produced moderate antifungal activity and strong synergy with azoles like fluconazole (FLC), particularly in C. glabrata. FLC:ZOL combinations had increased fungicidal and antibiofilm activity compared to either drug alone, and the combination prevented the development of antifungal resistance. Mechanistic investigations demonstrated that the synergy was mediated by the depletion of squalene, resulting in the inhibition of ergosterol biosynthesis and a compromised membrane structure. In C. glabrata, synergy compromised the function of membrane-bound multidrug transporters and caused an accumulation of reactive oxygen species, which may account for its acute sensitivity to FLC:ZOL. The efficacy of FLC:ZOL in vivo was confirmed in a Galleria mellonella infection model, where combinations improved the survival of larvae infected with C. albicans and C. glabrata to a greater extent than monotherapy with FLC or ZOL, and at reduced dosages. These findings demonstrate that bisphosphonates and azoles are a promising new combination therapy for the treatment of topical candidiasis. IMPORTANCE: Candida is a common and often very serious opportunistic fungal pathogen. Invasive candidiasis is a prevalent cause of nosocomial infections with a high mortality rate, and mucocutaneous infections significantly impact the quality of life of millions of patients a year. These infections pose substantial clinical challenges, particularly as the currently available antifungal treatment options are limited in efficacy and often toxic. Azoles are a mainstay of antifungal therapy and work by targeting the biosynthesis of ergosterol. However, there are rising rates of acquired azole resistance in various Candida species, and some species are considered intrinsically resistant to most azoles. Our research demonstrates the promising therapeutic potential of synergistically enhancing azoles with non-toxic, FDA-approved bisphosphonates. Repurposing bisphosphonates as antifungal synergists can bypass much of the drug development pipeline and accelerate the translation of azole-bisphosphonate combination therapy.

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

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

Proof-of-concept studies with a computationally designed Mpro inhibitor as a synergistic combination regimen alternative to Paxlovid.

As the SARS-CoV-2 virus continues to spread and mutate, it remains important to focus not only on preventing spread through vaccination but also on treating infection with direct-acting antivirals (DAA). The approval of Paxlovid, a SARS-CoV-2 main protease (Mpro) DAA, has been significant for treatment of patients. A limitation of this DAA, however, is that the antiviral component, nirmatrelvir, is rapidly metabolized and requires inclusion of a CYP450 3A4 metabolic inhibitor, ritonavir, to boost levels of the active drug. Serious drug-drug interactions can occur with Paxlovid for patients who are also taking other medications metabolized by CYP4503A4, particularly transplant or otherwise immunocompromised patients who are most at risk for SARS-CoV-2 infection and the development of severe symptoms. Developing an alternative antiviral with improved pharmacological properties is critical for treatment of these patients. By using a computational and structure-guided approach, we were able to optimize a 100 to 250 µM screening hit to a potent nanomolar inhibitor and lead compound, Mpro61. In this study, we further evaluate Mpro61 as a lead compound, starting with examination of its mode of binding to SARS-CoV-2 Mpro. In vitro pharmacological profiling established a lack of off-target effects, particularly CYP450 3A4 inhibition, as well as potential for synergy with the currently approved alternate antiviral, molnupiravir. Development and subsequent testing of a capsule formulation for oral dosing of Mpro61 in B6-K18-hACE2 mice demonstrated favorable pharmacological properties, efficacy, and synergy with molnupiravir, and complete recovery from subsequent challenge by SARS-CoV-2, establishing Mpro61 as a promising potential preclinical candidate.

DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile.

Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.

Antineoplastic Drug Synergy of Artesunate with Navitoclax in Models of High-Grade Serous Ovarian Cancer.

Artesunate belongs to a class of medications derived from the sweet wormwood plant (Artemisia annua) known as artemisinins. Artesunate has traditionally been used as a frontline treatment for severe malaria but has also demonstrated antineoplastic activity against various malignancies, including ovarian cancer. Data suggest that artesunate exacerbates cellular oxidative stress, triggering apoptosis. In the current study, we investigated the ability of navitoclax, an inhibitor of the antiapoptotic Bcl-2 protein family, to enhance artesunate efficacy in ovarian cancer cells. Artesunate and navitoclax both demonstrated antiproliferative effects on 2D and 3D ovarian cancer cell models as single agents. Upon combination of navitoclax with artesunate, antineoplastic drug synergy was also observed in each of the 2D cell lines and ovarian tumor organoid models tested. Further investigation of this drug combination using intraperitoneal CAOV3 xenograft models in BALB/scid mice showed that the artesunate/navitoclax doublet was superior to single-agent artesunate and vehicle control treatment. However, it did not outperform single-agent navitoclax. With optimization, this drug combination could provide a new therapeutic option for ovarian cancer and warrants further preclinical investigation.

Auranofin repurposing for lung and pancreatic cancer: low CA12 expression as a marker of sensitivity in patient-derived organoids, with potentiated efficacy by AKT inhibition.

BACKGROUND: This study explores the repurposing of Auranofin (AF), an anti-rheumatic drug, for treating non-small cell lung cancer (NSCLC) adenocarcinoma and pancreatic ductal adenocarcinoma (PDAC). Drug repurposing in oncology offers a cost-effective and time-efficient approach to developing new cancer therapies. Our research focuses on evaluating AF's selective cytotoxicity against cancer cells, identifying RNAseq-based biomarkers to predict AF response, and finding the most effective co-therapeutic agents for combination with AF. METHODS: Our investigation employed a comprehensive drug screening of AF in combination with eleven anticancer agents in cancerous PDAC and NSCLC patient-derived organoids (n = 7), and non-cancerous pulmonary organoids (n = 2). Additionally, we conducted RNA sequencing to identify potential biomarkers for AF sensitivity and experimented with various drug combinations to optimize AF's therapeutic efficacy. RESULTS: The results revealed that AF demonstrates a preferential cytotoxic effect on NSCLC and PDAC cancer cells at clinically relevant concentrations below 1 µM, sparing normal epithelial cells. We identified Carbonic Anhydrase 12 (CA12) as a significant RNAseq-based biomarker, closely associated with the NF-κB survival signaling pathway, which is crucial in cancer cell response to oxidative stress. Our findings suggest that cancer cells with low CA12 expression are more susceptible to AF treatment. Furthermore, the combination of AF with the AKT inhibitor MK2206 was found to be particularly effective, exhibiting potent and selective cytotoxic synergy, especially in tumor organoid models classified as intermediate responders to AF, without adverse effects on healthy organoids. CONCLUSION: Our research offers valuable insights into the use of AF for treating NSCLC and PDAC. It highlights AF's cancer cell selectivity, establishes CA12 as a predictive biomarker for AF sensitivity, and underscores the enhanced efficacy of AF when combined with MK2206 and other therapeutics. These findings pave the way for further exploration of AF in cancer treatment, particularly in identifying patient populations most likely to benefit from its use and in optimizing combination therapies for improved patient outcomes.

PRMT1 promotes pancreatic cancer development and resistance to chemotherapy.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal types of cancer, and novel treatment regimens are direly needed. Epigenetic regulation contributes to the development of various cancer types, but its role in the development of and potential as a therapeutic target for PDAC remains underexplored. Here, we show that PRMT1 is highly expressed in murine and human pancreatic cancer and is essential for cancer cell proliferation and tumorigenesis. Deletion of PRMT1 delays pancreatic cancer development in a KRAS-dependent mouse model, and multi-omics analyses reveal that PRMT1 depletion leads to global changes in chromatin accessibility and transcription, resulting in reduced glycolysis and a decrease in tumorigenic capacity. Pharmacological inhibition of PRMT1 in combination with gemcitabine has a synergistic effect on pancreatic tumor growth in vitro and in vivo. Collectively, our findings implicate PRMT1 as a key regulator of pancreatic cancer development and a promising target for combination therapy.

Optimizing combination therapy in prostate cancer: mechanistic insights into the synergistic effects of Paclitaxel and Sulforaphane-induced apoptosis.

BACKGROUND: Combination therapies in cancer treatment have demonstrated synergistic or additive outcomes while also reducing the development of drug resistance compared to monotherapy. This study explores the potential of combining the chemotherapeutic agent Paclitaxel (PTX) with Sulforaphane (SFN), a natural compound primarily found in cruciferous vegetables, to enhance treatment efficacy in prostate cancer. METHODS: Two prostate cancer cell lines, PC-3 and LNCaP, were treated with varying concentrations of PTX, SFN, and their combination. Cell viability was assessed using the thiazolyl blue tetrazolium bromide (MTT) assay to determine the EC50 values. Western blot analysis was conducted to evaluate the expression of Bax, Bcl2, and Caspase-3 activation proteins in response to individual and combined treatments of PTX and SFN. Fluorescent microscopy was employed to observe morphological changes indicative of apoptotic stress in cell nuclei. Flow cytometry analysis was utilized to assess alterations in cell cycle phases, such as redistribution and arrest. Statistical analyses, including Student's t-tests and one-way analysis of variance with Tukey's correction, were performed to determine significant differences between mono- and combination treatments. RESULTS: The impact of PTX, SFN, and their combination on cell viability reduction was evaluated in a dose-dependent manner. The combined treatment enhanced PTX's effects and decreased the EC50 values of both drugs compared to individual treatments. PTX and SFN treatments differentially regulated the expression of Bax and Bcl2 proteins in PC-3 and LNCaP cell lines, favoring apoptosis over cell survival. Our data indicated that combination therapy significantly increased Bax protein expression and the Bax/Bcl2 ratio compared to PTX or SFN alone. Flow cytometry analysis revealed alterations in cell cycle phases, including S-phase arrest and an increased population of apoptotic cells. Notably, the combination treatments did not have a discernible impact on necrotic cells. Signs of apoptotic cell death were confirmed through Caspase-3 cleavage, and morphological changes in cell nuclei were assessed via western blot and fluorescent microscopy. CONCLUSION: This combination therapy of PTX and SFN has the potential to improve prostate cancer treatment by minimizing side effects while maintaining efficacy. Mechanistic investigations revealed that SFN enhances PTX efficacy by promoting apoptosis, activating caspase-3, inducing nuclear morphology changes, modulating the cell cycle, and altering Bax and Bcl2 protein expression. These findings offer valuable insights into the synergistic effects of PTX and SFN, supporting the optimization of combination therapy and providing efficient therapeutic strategies in preclinical research.

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.

Synergistic Effects of Temozolomide and Doxorubicin in the Treatment of Glioblastoma Multiforme: Enhancing Efficacy through Combination Therapy.

Glioblastoma multiforme (GBM), a grade IV (WHO classification) malignant brain tumor, poses significant challenges in treatment. The current standard treatment involves surgical tumor removal followed by radiation and chemotherapeutic interventions. However, despite these efforts, the median survival for GBM patients remains low. Temozolomide, an alkylating agent capable of crossing the blood-brain barrier, is currently the primary drug for GBM treatment. Its efficacy, however, is limited, leading to the exploration of combination treatments. In this study, we have investigated the synergistic effects of combining temozolomide with doxorubicin, a chemotherapeutic agent widely used against various cancers. Our experiments, conducted on both temozolomide-sensitive (U87) and -resistant cells (GBM43 and GBM6), have demonstrated a synergistic inhibition of brain cancer cells with this combination treatment. Notably, the combination enhanced doxorubicin uptake and induced higher apoptosis in temozolomide-resistant GBM43 cells. The significance of our findings lies in the potential application of this combination treatment, even in cases of temozolomide resistance. Despite doxorubicin's inability to cross the blood-brain barrier, our results open avenues for alternative delivery methods, such as conjugation with carriers like albumin or local administration at the surgical site through a hydrogel application system. Our study suggests that the synergistic interaction between temozolomide and doxorubicin holds promise for enhancing the efficacy of glioblastoma treatment. The positive outcomes observed in our experiments provide confidence in considering this strategy for the benefit of patients with glioblastoma.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations. METHODS: The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs' biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2). RESULTS: The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool. CONCLUSION:  The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.

Neosetophomone B induces apoptosis in multiple myeloma cells via targeting of AKT/SKP2 signaling pathway.

Multiple myeloma (MM) is a hematologic malignancy associated with malignant plasma cell proliferation in the bone marrow. Despite the available treatments, drug resistance and adverse side effects pose significant challenges, underscoring the need for alternative therapeutic strategies. Natural products, like the fungal metabolite neosetophomone B (NSP-B), have emerged as potential therapeutic agents due to their bioactive properties. Our study investigated NSP-B's antitumor effects on MM cell lines (U266 and RPMI8226) and the involved molecular mechanisms. NSP-B demonstrated significant growth inhibition and apoptotic induction, triggered by reduced AKT activation and downregulation of the inhibitors of apoptotic proteins and S-phase kinase protein. This was accompanied by an upregulation of p21Kip1 and p27Cip1 and an elevated Bax/BCL2 ratio, culminating in caspase-dependent apoptosis. Interestingly, NSP-B also enhanced the cytotoxicity of bortezomib (BTZ), an existing MM treatment. Overall, our findings demonstrated that NSP-B induces caspase-dependent apoptosis, increases cell damage, and suppresses MM cell proliferation while improving the cytotoxic impact of BTZ. These findings suggest that NSP-B can be used alone or in combination with other medicines to treat MM, highlighting its importance as a promising phytoconstituent in cancer therapy.

Current advances in comprehensive omics data mining for oncology and cancer research.

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Extending the boundaries of cancer therapeutic complexity with literature text mining.

Drug combination therapy is a main pillar of cancer therapy. As the number of possible drug candidates for combinations grows, the development of optimal high complexity combination therapies (involving 4 or more drugs per treatment) such as RCHOP-I and FOLFIRINOX becomes increasingly challenging due to combinatorial explosion. In this paper, we propose a text mining (TM) based tool and workflow for rapid generation of high complexity combination treatments (HCCT) in order to extend the boundaries of complexity in cancer treatments. Our primary objectives were: (1) Characterize the existing limitations in combination therapy; (2) Develop and introduce the Plan Builder (PB) to utilize existing literature for drug combination effectively; (3) Evaluate PB's potential in accelerating the development of HCCT plans. Our results demonstrate that researchers and experts using PB are able to create HCCT plans at much greater speed and quality compared to conventional methods. By releasing PB, we hope to enable more researchers to engage with HCCT planning and demonstrate its clinical efficacy.

Inhibition of EIF2α Dephosphorylation Decreases Cell Viability and Synergizes with Standard-of-Care Chemotherapeutics in Head and Neck Squamous Cell Carcinoma.

Drug resistance is a common cause of therapy failure in head and neck squamous cell carcinoma (HNSCC). One approach to tackling it is by targeting fundamental cellular processes, such as translation. The eukaryotic translation initiation factor 2α (EIF2α) is a key player in canonical translation initiation and integrates diverse stress signals; when phosphorylated, it curbs global protein synthesis. This study evaluates EIF2α expression and phosphorylation in HNSCC. A small-molecule inhibitor of EIF2α dephosphorylation, salubrinal, was tested in vitro, followed by viability assays, flow cytometry, and immunoblot analyses. Patient-derived 3D tumor spheres (PD3DS) were cultured with salubrinal and their viability assessed. Lastly, salubrinal was evaluated with standard-of-care chemotherapeutics. Our analysis of RNA and proteomics data shows elevated EIF2α expression in HNSCC. Immunohistochemical staining reveals increasing EIF2α abundance from premalignant lesions to invasive and metastatic carcinoma. In immunoblots from intraoperative samples, EIF2α expression and steady-state phosphorylation are higher in HNSCC than in neighboring normal tissue. Inhibition of EIF2α dephosphorylation decreases HNSCC cell viability and clonogenic survival and impairs the G1/S transition. Salubrinal also decreases the viability of PD3DS and acts synergistically with cisplatin, 5-fluorouracil, bleomycin, and proteasome inhibitors. Our results indicate that pharmacological inhibition of EIF2α dephosphorylation is a potential therapeutic strategy for HNSCC.

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.

Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8,798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that Random Forest and Gradient Boosting Trees models trained on the augmented data achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.

Allosteric SHP2 inhibition increases apoptotic dependency on BCL2 and synergizes with venetoclax in FLT3- and KIT-mutant AML.

Mutations in the receptor tyrosine kinases (RTKs) FLT3 and KIT are frequent and associated with poor outcomes in acute myeloid leukemia (AML). Although selective FLT3 inhibitors (FLT3i) are clinically effective, remissions are short-lived due to secondary resistance characterized by acquired mutations constitutively activating the RAS/MAPK pathway. Hereby, we report the pre-clinical efficacy of co-targeting SHP2, a critical node in MAPK signaling, and BCL2 in RTK-driven AML. The allosteric SHP2 inhibitor RMC-4550 suppresses proliferation of AML cell lines with FLT3 and KIT mutations, including cell lines with acquired resistance to FLT3i. We demonstrate that pharmacologic SHP2 inhibition unveils an Achilles' heel of RTK-driven AML, increasing apoptotic dependency on BCL2 via MAPK-dependent mechanisms, including upregulation of BMF and downregulation of MCL1. Consequently, RMC-4550 and venetoclax are synergistically lethal in AML cell lines and in clinically relevant xenograft models. Our results provide mechanistic rationale and pre-clinical evidence for co-targeting SHP2 and BCL2 in RTK-driven AML.