Inactivation of nuclear factor ?B by MIP-based drug combinations augments cell death of breast cancer cells.
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ABSTRACT: Drug combination therapy to treat cancer is a strategic approach to increase successful treatment rate. Optimizing combination regimens is vital to increase therapeutic efficacy with minimal side effects.In the present study, we evaluated the in vitro cytotoxicity of double and triple combinations consisting of 1'S-1'-acetoxychavicol acetate (ACA), Mycobacterium indicus pranii (MIP) and cisplatin (CDDP) against 14 various human cancer cell lines to address the need for more effective therapy. Our data show synergistic effects in MCF-7 cells treated with MIP:ACA, MIP:CDDP and MIP:ACA:CDDP combinations. The type of interaction between MIP, ACA and CDDP was evaluated based on combination index being <0.8 for synergistic effect. Identifying the mechanism of cell death based on previous studies involved intrinsic apoptosis and nuclear factor kappa B (NF-?B) and tested in Western blot analysis. Inactivation of NF-?B was confirmed by p65 and I?B?, while intrinsic apoptosis pathway activation was confirmed by caspase-9 and Apaf-1 expression.All combinations confirmed intrinsic apoptosis activation and NF-?B inactivation.Double and triple combination regimens that target induction of the same death mechanism with reduced dosage of each drug could potentially be clinically beneficial in reducing dose-related toxicities.
<h4>Background</h4>Drug combination therapy to treat cancer is a strategic approach to increase successful treatment rate. Optimizing combination regimens is vital to increase therapeutic efficacy with minimal side effects.<h4>Materials and methods</h4>In the present study, we evaluated the in vitro cytotoxicity of double and triple combinations consisting of 1'S-1'-acetoxychavicol acetate (ACA), <i>Mycobacterium indicus pranii</i> (MIP) and cisplatin (CDDP) against 14 various human cancer cell ...[more]
Project description:An imaged-based profiling and analysis system was developed to predict clinically effective synergistic drug combinations that could accelerate the identification of effective multi-drug therapies for the treatment of triple-negative breast cancer and other challenging malignancies. The identification of effective drug combinations for the treatment of triple-negative breast cancer (TNBC) was achieved by integrating high-content screening, computational analysis, and experimental biology. The approach was based on altered cellular phenotypes induced by 55 FDA-approved drugs and biologically active compounds, acquired using fluorescence microscopy and retained in multivariate compound profiles. Dissimilarities between compound profiles guided the identification of 5 combinations, which were assessed for qualitative interaction on TNBC cell growth. The combination of the microtubule-targeting drug vinblastine with KSP/Eg5 motor protein inhibitors monastrol or ispinesib showed potent synergism in 3 independent TNBC cell lines, which was not substantiated in normal fibroblasts. The synergistic interaction was mediated by an increase in mitotic arrest with cells demonstrating typical ispinesib-induced monopolar mitotic spindles, which translated into enhanced apoptosis induction. The antitumour activity of the combination vinblastine/ispinesib was confirmed in an orthotopic mouse model of TNBC. Compared to single drug treatment, combination treatment significantly reduced tumour growth without causing increased toxicity. Image-based profiling and analysis led to the rapid discovery of a drug combination effective against TNBC in vitro and in vivo, and has the potential to lead to the development of new therapeutic options in other hard-to-treat cancers.
Project description:Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein-protein interactome, we show the existence of six distinct classes of drug-drug-disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.
Project description:Burkitt lymphoma (BL) is a highly aggressive B-cell lymphoma associated with MYC translocation. Here, we describe drug response profiling of 42 blood cancer cell lines including 17 BL to 32 drugs targeting key cancer pathways and provide a systematic study of drug combinations in BL cell lines. Based on drug response, we identified cell line specific sensitivities, i.e. to venetoclax driven by BCL2 overexpression and partitioned subsets of BL driven by response to kinase inhibitors. In the combination screen, including BET, BTK and PI3K inhibitors, we identified synergistic combinations of PI3K and BTK inhibition with drugs targeting Akt, mTOR, BET and doxorubicin. A detailed comparison of PI3K and BTKi combinations identified subtle differences, in line with convergent pathway activity. Most synergistic combinations were identified for the BET inhibitor OTX015, which showed synergistic effects for 41% of combinations including inhibitors of PI3K/AKT/mTOR signalling. The strongest synergy was observed for the combination of the CDK 2/7/9 inhibitor SNS032 and OTX015. Our data provide a landscape of drug combination effects in BL and suggest that targeting CDK and BET could provide a novel vulnerability of BL.
Project description:We examined the mode-of-action of synergistic drug combinations by microarray analysis. We focused on the drug combination of capsaicin and mitoxantrone, which were the top predicted drug pair identified by SyndrumNET. Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses. The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML), and it outperformed the previous methods in terms of high accuracy. We performed in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibited synergistic anticancer effects. Our mode-of-action analysis also revealed that the drug synergy of the top predicted combination of capsaicin and mitoxantrone was due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway. The proposed method is expected to be useful for various complex diseases.
Project description:CCCTC-binding factor (CTCF) is an important epigenetic regulator implicated in multiple cellular processes, including growth, proliferation, differentiation, and apoptosis. Although CTCF deletion or mutation has been associated with human breast cancer, the role of CTCF in breast cancer is questionable. We investigated the biological functions of CTCF in breast cancer and the underlying mechanism. The results showed that CTCF expression in human breast cancer cells and tissues was significantly lower than that in normal breast cells and tissues. In addition, CTCF expression correlated significantly with cancer stage (P = 0.043) and pathological differentiation (P = 0.029). Furthermore, CTCF overexpression resulted in the inhibition of proliferation, migration, and invasion, while CTCF knockdown induced these processes in breast cancer cells. Transcriptome analysis and further experimental confirmation in MDA-MD-231 cells revealed that forced overexpression of CTCF might attenuate the DNA-binding ability of nuclear factor-kappaB (NF-κB) p65 subunit and inhibit activation of NF-κB and its target pro-oncogenes (tumor necrosis factor alpha-induced protein 3 [TNFAIP3]) and genes for growth-related proteins (early growth response protein 1 [EGR1] and growth arrest and DNA-damage-inducible alpha [GADD45a]). The present study provides a new insight into the tumor suppressor roles of CTCF in breast cancer development and suggests that the CTCF/NF-κB pathway is a potential target for breast cancer therapy.
Project description:Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) is associated with relatively poor outcomes due to its metastatic propensity, frequent failure to respond to chemotherapy, and lack of alternative, targeted treatment options, despite decades of major research efforts. Our studies sought to identify promising targeted therapeutic candidates for TNBC through in vitro screening of 1,363 drugs in patient-derived xenograft (PDX) models. Using this approach, we generated a dataset that can be used to assess and compare responses of various breast cancer PDXs to many different drugs. Through a series of further drug screening assays and two-drug combination testing, we identified that the combination of afatinib (epidermal growth factor receptor (EGFR) inhibitor) and YM155 (inhibitor of baculoviral inhibitor of apoptosis repeat-containing 5 (BIRC5; survivin) expression) is synergistically cytotoxic across multiple models of basal-like TNBC and reduces PDX mammary tumor growth in vivo. We found that YM155 reduces EGFR expression in TNBC cells, shedding light on its potential mechanism of synergism with afatinib. Both EGFR and BIRC5 are highly expressed in basal-like PDXs, cell lines, and patients, and high expression of both genes reduces metastasis-free survival, suggesting that co-targeting of these proteins holds promise for potential clinical success in TNBC.
Project description:Cancer is a disease that affects and kills millions of people worldwide. Breast cancer, especially, has a high incidence and mortality, and is challenging to treat. Due to its high impact on the health sector, oncological therapy is the subject of an intense and very expensive research. To improve this therapy and reduce its costs, strategies such as drug repurposing and drug combinations have been extensively studied. Drug repurposing means giving new usefulness to drugs which are approved for the therapy of various diseases, but, in this case, are not approved for cancer therapy. On the other hand, the purpose of combining drugs is that the response that is obtained is more advantageous than the response obtained by the single drugs. Using drugs with potential to be repurposed, combined with 5-fluorouracil, the aim of this project was to investigate whether this combination led to therapeutic benefits, comparing with the isolated drugs. We started with a screening of the most promising drugs, with verapamil and itraconazole being chosen. Several cellular viability studies, cell death and proliferation studies, mainly in MCF-7 cells (Michigan Cancer Foundation-7, human breast adenocarcinoma cells) were performed. Studies were also carried out to understand the effect of the drugs at the level of possible therapeutic resistance, evaluating the epithelial-mesenchymal transition. Combining all the results, the conclusion is that the combination of verapamil and itraconazole with 5-fluorouracil had benefits, mainly by decreasing cell viability and proliferation. Furthermore, the combination of itraconazole and 5-fluorouracil seemed to be the most effective, being an interesting focus in future studies.
Project description:HER2-positive (HER2+) breast cancer accounts for 18%-20% of all breast cancer cases and has the second poorest prognosis among breast cancer subtypes. Trastuzumab, the first Food and Drug Administration-approved targeted therapy for breast cancer, established the era of personalized treatment for HER2+ metastatic disease. It is well tolerated and improves overall survival and time-to-disease progression; with chemotherapy, it is part of the standard of care for patients with HER2+ metastatic disease. However, many patients do not benefit from it because of resistance. Substantial research has been performed to understand the mechanism of trastuzumab resistance and develop combination strategies to overcome the resistance. In this review, we provide insight into the current pipeline of drugs used in combination with trastuzumab and the degree to which these combinations have been evaluated, especially in patients who have experienced disease progression on trastuzumab. We conclude with a discussion of the current challenges and future therapeutic approaches to trastuzumab-based combination therapy.
Project description:Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.
Project description:Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.