Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing.
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ABSTRACT:
SUBMITTER: Pham TH
PROVIDER: S-EPMC9023899 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-BSST330 | biostudies-other
Project description:Despite major advances in cancer treatment, pancreatic cancer is still incurable and the treatment outcomes are limited. The aggressive and therapy-resistant nature of pancreatic cancer warrants the need for novel treatment options for pancreatic cancer management. Drug repurposing is emerging as an effectual strategy in the treatment of various diseases, including cancer. In the present study, we evaluated the anticancer effects of pimavanserin tartrate (PVT), an antipsychotic drug used for the treatment of Parkinson disease psychosis. PVT significantly suppressed the proliferation and induced apoptosis in various pancreatic cancer cells and gemcitabine-resistant cells with minimal effects on normal pancreatic epithelial cells and lung fibroblasts. Growth-suppressive and apoptotic effects of PVT were mediated by the inhibition of the Akt/Gli1 signaling axis. The oral administration of PVT suppressed subcutaneous and orthotopic pancreatic tumor xenografts by 51%–77%. The chronic administration of PVT did not demonstrate any general signs of toxicity or change in behavioral activity of mice. Our results indicate that pancreatic tumor growth suppression by PVT was orchestrated by the inhibition of Akt/Gli1 signaling. Since PVT is already available in the clinic with an established safety profile, our results will accelerate its clinical development for the treatment of patients with pancreatic cancer. Graphical Abstract Drug repurposing, a unique drug developmental strategy, can overcome the pitfalls for pancreatic cancer treatment. In this study, we discover the anticancer effects of an antipsychotic drug, pimavanserin, for pancreatic cancer. Our pre-clinical findings suggest that pimavanserin could be translated into the clinic to improve the treatment outcome of pancreatic cancer patients.
| S-EPMC7527685 | biostudies-literature
Project description:Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug-perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.
| S-EPMC6156541 | biostudies-literature
Project description:As legacy toxicogenomics databases have become available, improved data mining approaches are now key to extracting and visualizing subtle relationships between toxicants and gene expression. In the present study, a novel "aggregating bundles of clusters" (ABC) procedure was applied to separate cholestatic from non-cholestatic drugs and model toxicants in the Johnson & Johnson (Janssen) rat liver toxicogenomics database [3]. Drug-induced cholestasis is an important issue, particularly when a new compound enters the market with this liability, with standard preclinical models often mispredicting this toxicity. Three well-characterized cholestasis-responsive genes (Cyp7a1, Mrp3 and Bsep) were chosen from a previous in-house Janssen gene expression signature; these three genes show differing, non-redundant responses across the 90+ paradigm compounds in our database. Using the ABC procedure, extraneous contributions were minimized in comparisons of compound gene responses. All genes were assigned weights proportional to their correlations with Cyp7a1, Mrp3 and Bsep, and a resampling technique was used to derive a stable measure of compound similarity. The compounds that were known to be associated with rat cholestasis generally had small values of this measure relative to each other but also had large values of this measure relative to non-cholestatic compounds. Visualization of the data with the ABC-derived signature showed a very tight, essentially identically behaving cluster of robust human cholestatic drugs and experimental cholestatic toxicants (ethinyl estradiol, LPS, ANIT and methylene dianiline, disulfiram, naltrexone, methapyrilene, phenacetin, alpha-methyl dopa, flutamide, the NSAIDs--indomethacin, flurbiprofen, diclofenac, flufenamic acid, sulindac, and nimesulide, butylated hydroxytoluene, piperonyl butoxide, and bromobenzene), some slightly less active compounds (3'-acetamidofluorene, amsacrine, hydralazine, tannic acid), some drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline), and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole). Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation.
| S-EPMC5615833 | biostudies-literature
Project description:Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.
| S-EPMC5753597 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is considered one of the deadliest tumors worldwide. The diagnosis is often possible only in the latter stages of the disease, with patients already presenting an advanced or metastatic tumor. It is also one of the cancers with poorest prognosis, presenting a five-year survival rate of around 5%. Treatment of PDAC is still a major challenge, with cytotoxic chemotherapy remaining the basis of systemic therapy. However, no major advances have been made recently, and therapeutic options are limited and highly toxic. Thus, novel therapeutic options are urgently needed. Drug repurposing is a strategy for the development of novel treatments using approved or investigational drugs outside the scope of the original clinical indication. Since repurposed drugs have already completed several stages of the drug development process, a broad range of data is already available. Thus, when compared with de novo drug development, drug repurposing is time-efficient, inexpensive and has less risk of failure in future clinical trials. Several repurposing candidates have been investigated in the past years for the treatment of PDAC, as single agents or in combination with conventional chemotherapy. This review gives an overview of the main drugs that have been investigated as repurposing candidates, for the potential treatment of PDAC, in preclinical studies and clinical trials.
| S-EPMC8003696 | biostudies-literature
Project description:The presence of stromal cells in tumors is altering the significance of molecular profiling when using standard methods of gene expression quantification. We developed a novel normalization method to rank target gene expression in tumor samples by comparisons with reference samples representing the different cell types found in a tumor. The score for each target gene obtained after normalization, is aimed to be predictive of targeted therapies efficiency. We performed this qPCR analysis on human colorectal cancers to demonstrate the importance of reference samples to obtain accurate data and on a collection of patient-derived xenografted (PDX) colon tumors treated with Cetuximab (anti-EGFR) to demonstrate that the calculated EGFR score is predictive of Cetuximab efficacy. Interestingly, the score allowed to select an efficient treatment in a PDX model refractory to standard of care. This method is opening a novel way to predict targeted therapy efficiency which could be extended to several tumor types, and to unlimited target genes.
| S-EPMC7016638 | biostudies-literature
Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved.MethodsWe developed a novel algorithm 'Spectral Clustering for Network-based target Ranking' (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissue-specific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called 'Target Influence score' (TI), which estimates a given target's influence towards the cluster it belongs to, for scoring and ranking each drug target.ResultsWe applied SCNrank to analyze 263 expression profiles, CRPSPR-Cas9 data from 22 different pancreatic cancer cell-lines and the STRING protein-protein interaction (PPI) network. With SCNrank, we successfully constructed an integrated tissue PDAC network and an integrated cell-line PDAC network, both of which contain 4414 selected genes that are overexpressed in tumor tissue samples. After clustering, 4414 genes are distributed into 198 clusters, which include 367 targets of FDA approved drugs. These drug targets are all scored and ranked by their TI scores, which we defined to measure their influence towards the network. We validated top-ranked targets in three aspects: Firstly, mapping them onto the existing clinical drug targets of PDAC to measure the concordance. Secondly, we performed enrichment analysis to these drug targets and the clusters there are within, to reveal functional associations between clusters and PDAC; Thirdly, we performed survival analysis for the top-ranked targets to connect targets with clinical outcomes. Survival analysis reveals that overexpression of three top-ranked genes, PGK1, HMMR and POLE2, significantly increases the risk of death in PDAC patients.ConclusionSCNrank is an unbiased algorithm that systematically integrates multiple types of omics data to do potential drug target selection and ranking. SCNrank shows great capability in predicting drug targets for PDAC. Pancreatic cancer-associated gene candidates predicted by our SCNrank approach have the potential to guide genetics-based anti-pancreatic drug discovery.
| S-EPMC7119297 | biostudies-literature
Project description:Pancreatic cancer (PC) is one of the deadliest malignancies worldwide, since patients rarely display symptoms until an advanced and unresectable stage of the disease. Current chemotherapy options are unsatisfactory and there is an urgent need for more effective and less toxic drugs to improve the dismal PC therapy. Repurposing of non-oncology drugs in PC treatment represents a very promising therapeutic option and different compounds are currently being considered as candidates for repurposing in the treatment of this tumor. In this review, we provide an update on some of the most promising FDA-approved, non-oncology, repurposed drug candidates that show prominent clinical and preclinical data in pancreatic cancer. We also focus on proposed mechanisms of action and known molecular targets that they modulate in PC. Furthermore, we provide an explorative bioinformatic analysis, which suggests that some of the PC repurposed drug candidates have additional, unexplored, oncology-relevant targets. Finally, we discuss recent developments regarding the immunomodulatory role displayed by some of these drugs, which may expand their potential application in synergy with approved anticancer immunomodulatory agents that are mostly ineffective as single agents in PC.
| S-EPMC8394389 | biostudies-literature
Project description:SummaryMechanistic molecular studies in biomedical research often discover important genes that are aberrantly over- or under-expressed in disease. However, manipulating these genes in an attempt to improve the disease state is challenging. Herein, we reveal Drug Gene Budger (DGB), a web-based and mobile application developed to assist investigators in order to prioritize small molecules that are predicted to maximally influence the expression of their target gene of interest. With DGB, users can enter a gene symbol along with the wish to up-regulate or down-regulate its expression. The output of the application is a ranked list of small molecules that have been experimentally determined to produce the desired expression effect. The table includes log-transformed fold change, P-value and q-value for each small molecule, reporting the significance of differential expression as determined by the limma method. Relevant links are provided to further explore knowledge about the target gene, the small molecule and the source of evidence from which the relationship between the small molecule and the target gene was derived. The experimental data contained within DGB is compiled from signatures extracted from the LINCS L1000 dataset, the original Connectivity Map (CMap) dataset and the Gene Expression Omnibus (GEO). DGB also presents a specificity measure for a drug-gene connection based on the number of genes a drug modulates. DGB provides a useful preliminary technique for identifying small molecules that can target the expression of a single gene in human cells and tissues.Availability and implementationThe application is freely available on the web at http://DGB.cloud and as a mobile phone application on iTunes https://itunes.apple.com/us/app/drug-gene-budger/id1243580241? mt=8 and Google Play https://play.google.com/store/apps/details? id=com.drgenebudger.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6449747 | biostudies-literature