Dataset Information

Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach.

ABSTRACT: Pancreatic cancer is the leading cause of death from solid malignancies worldwide. Currently, gemcitabine is the only drug approved for treating pancreatic cancer. Developing new therapeutic drugs for this disease is, therefore, an urgent need. The C-Map project has provided a wealth of gene expression data that can be mined for repositioning drugs, a promising approach to new drug discovery. Typically, a drug is considered potentially useful for treating a disease if the drug-induced differential gene expression profile is negatively correlated with the differentially expressed genes in the target disease. However, many of the potentially useful drugs (PUDs) identified by gene expression profile correlation are likely false positives because, in C-Map, the cultured cell lines to which the drug is applied are not derived from diseased tissues. To solve this problem, we developed a combined approach for predicting candidate drugs for treating pancreatic cancer. We first identified PUDs for pancreatic cancer by using C-Map-based gene expression correlation analyses. We then applied an algorithm (Met-express) to predict key pancreatic cancer (KPC) enzymes involved in pancreatic cancer metabolism. Finally, we selected candidates from the PUDs by requiring that their targets be KPC enzymes or the substrates/products of KPC enzymes. Using this combined approach, we predicted seven candidate drugs for treating pancreatic cancer, three of which are supported by literature evidence, and three were experimentally validated to be inhibitory to pancreatic cancer celllines.

SUBMITTER: Ma Y

PROVIDER: S-EPMC4765895 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach.

Ma Yanfen Y Hu Jian J Zhang Ning N Dong Xinran X Li Ying Y Yang Bo B Tian Weidong W Wang Xiaoqin X

PloS one 20160224 2

Pancreatic cancer is the leading cause of death from solid malignancies worldwide. Currently, gemcitabine is the only drug approved for treating pancreatic cancer. Developing new therapeutic drugs for this disease is, therefore, an urgent need. The C-Map project has provided a wealth of gene expression data that can be mined for repositioning drugs, a promising approach to new drug discovery. Typically, a drug is considered potentially useful for treating a disease if the drug-induced differenti ...[more]

PMID: 26910401

Similar Datasets

Project description:Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use p-values or fold change (FC) values to detect the DE genes. However, sometimes, p-value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both p-values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values computed by SAM statistic using minimum β-divergence method, which is designed for paired samples. Materials and Methods: The proposed method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The β-weight function plays the significant role on the performance of the proposed method. The proposed method uses β-weight function as a measure of outlier detection by setting β = 0.2. We unify both classical and robust estimates using β-weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. Results: We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large sample cases. From the simulation and a real spike in data analysis results, we observed that the proposed method outperforms other methods for small sample cases in the presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP, and FCROS) otherwise. From the head and neck cancer (HNC) gene expression dataset, the proposed method identified two additional genes (CYP3A4 and NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these two genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank literature based gene associations. Conclusions: Using pathway analysis, disease association study, protein-protein interactions and survival analysis we found that our proposed two additional genes might be involved in the critical pathways of cancer. Furthermore, the identified drugs showed statistical significance which indicates that proteins associated with these genes might be therapeutic target in cancer.

Project description:Background: Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Methods: Inhibition of angiotensin-converting enzyme 2 (ACE2) caused by the spike protein of SARS-CoV-2 is the most plausible mechanism of lung injury in COVID-19. We performed drug repositioning analysis to identify drug candidates that reverse gene expression pattern in L1000 lung cell line HCC515 treated with ACE2 inhibitor. We confirmed these drug candidates by similar bioinformatics analysis using lung tissues from patients deceased from COVID-19. We further investigated deregulated genes and pathways related to lung injury, as well as the gene-pathway-drug candidate relationships. Results: We propose two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19. Further bioinformatics analysis shows that 12 significantly enriched pathways (P-value <0.05) overlap between HCC515 cells treated with ACE2 inhibitor and human COVID-19 patient lung tissues. These include signaling pathways known to be associated with lung injury such as TNF signaling, MAPK signaling and chemokine signaling pathways. All 12 pathways are targeted in COL-3 treated HCC515 cells, in which genes such as RHOA, RAC2, FAS, CDC42 have reduced expression. CGP-60474 shares 11 of 12 pathways with COL-3 and common target genes such as RHOA. It also uniquely targets other genes related to lung injury, such as CALR and MMP14. Conclusions: This study shows that ACE2 inhibition is likely part of the mechanisms leading to lung injury in COVID-19, and that compounds such as COL-3 and CGP-60474 have potential as repurposed drugs for its treatment.

Project description:Pancreatic cancer (PC) has the highest mortality rate amongst all other cancers in both men and women, with a one-year relative survival rate of 20%, and a five-year relative survival rate of 8% for all stages of PC combined. The Whipple procedure, or pancreaticoduodenectomy, can increase survival for patients with resectable PC, however, less than 20% of patients are candidates for surgery at time of presentation. Most of the patients are diagnosed with advanced PC, often with regional and distant metastasis. In these advanced cases, chemotherapy and radiation have shown limited tumor control, and PC continues to be refractory to treatment and results in a poor survival outcome. In recent years, there has been intensive research on checkpoint inhibitor immunotherapy for PC, however, PC is characterized with dense stromal tissue and a tumor microenvironment (TME) that is highly immunosuppressive, which makes immunotherapy less effective. Interestingly, when immunotherapy is combined with radiation therapy (RT) and loco-regional hyperthermia (HT), it has demonstrated enhanced tumor responses. HT improves tumor killing via a variety of mechanisms, targeting both the tumor and the TME. Targeted HT raises the temperature of the tumor and surrounding tissues to 42?43 °C and makes the tumor more immunoresponsive. HT can also modulate the immune system of the TME by inducing and synthesizing heat shock proteins (HSP), which also activate an anti-tumor response. It is well known that HT can enhance RT-induced DNA damage in cancer cells and simultaneously help to oxygenate hypoxic regions. Thus, it is envisaged that combined HT and RT might have immunomodulatory effects in the PC-TME, making PC more responsive to immunotherapies. Moreover, the combined tripartite approach of immunotherapy, RT, and HT could reduce the overall toxicity associated with each individual therapy, while concomitantly enhancing the immunotherapeutic effect of overall individual therapies to treat local and metastatic PC. Thus, the use of a tripartite combinatorial approach could be promising and more efficacious than monotherapy or dual therapy to treat and increase the survival of the PC patients.

Project description:Cancer is associated with alterations in epigenetic mechanisms such as histone modifications and methylation of DNA, and inhibitors targeting epigenetic mechanisms represent a novel class of anti-cancer drugs. Neuroendocrine tumors (NETs) of the pancreas (PNETs) and bronchus (BNETs), which may have 5-year survivals of <50% and as low as 5%, respectively, represent targets for such drugs, as >40% of PNETs and ~35% of BNETs have mutations of the multiple endocrine neoplasia type 1 (MEN1) gene, which encodes menin that modifies histones by interacting with histone methyltransferases. We assessed 9 inhibitors of epigenetic pathways, for their effects on proliferation, by CellTiter Blue assay, and apoptosis, by CaspaseGlo assay, using 1 PNET and 2 BNET cell lines. Two inhibitors, referred to as (+)-JQ1 (JQ1) and PFI-1, targeting the bromo and extra terminal (BET) protein family which bind acetylated histone residues, were most effective in decreasing proliferation (by 40-85%, P<0.001) and increasing apoptosis (by 2-3.6 fold, P<0.001) in all 3 NET cell lines. The anti-proliferative effects of JQ1 and PFI-1 remained present for at least 48 hours after removal of the compound. JQ1, but not PFI-1, had cell cycle effects, assessed by propidium iodide staining and flow cytometry, resulting in increased and decreased proportions of NET cells in G1, and S and G2 phases, respectively. RNA Sequencing analysis revealed that these JQ1 effects were associated with increased histone 2B expression, and likely mediated through altered activity of bromodomain-containing (Brd) proteins. Assessment of JQ1 in vivo, using a pancreatic beta cell-specific conditional Men1 knockout mouse model that develops PNETs, revealed that JQ1 significantly reduced proliferation (by ~50%, P<0.0005), assessed by bromodeoxyuridine incorporation, and increased apoptosis (by ~3 fold, P<0.0005), assessed by terminal deoxynucleotidyl transferase dUTP nick end labelling, of PNETs. Thus, our studies demonstrate that BET protein inhibitors may provide new treatments for NETs.

Dataset Information

Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach.

Publications

Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure