New drug candidates for treatment of atypical meningiomas: An integrated approach using gene expression signatures for drug repurposing

Zádor, Zsolt; King, Andrew T.; Geifman, Nophar

doi:10.1371/journal.pone.0194701

Cited by 20 publications

(15 citation statements)

References 56 publications

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“…while being rejected by CMap 1 (Zador et al, 2018). On the other hand, there are also anecdotal reports of positive results, in which researchers have experimentally verified the biological effects of candidate drugs recommended by CMap 1 (Braconi et al, 2009;Brum et al, 2015Brum et al, , 2018Byun et al, 2019;Johnstone et al, 2012;Luo et al, 2019;Schanstra et al, 2019;Wang et al, 2019;Wu et al, 2019) or CMap 2 (Ferguson et al, 2018;Leung et al, 2019;Manzotti et al, 2019;Ryals et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Connectivity Map shows limited reproducibility in drug repositioning

Lim

Pavlidis

2019

Preprint

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The Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap.DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the "better" CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Connectivity Map shows limited reproducibility in drug repositioning

Lim

Pavlidis

2019

Preprint

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“…For example, Wang et al ( 38 ) investigated candidate drugs for hepatocellular carcinoma in the CMAP by analyzing associated subpathways. The CMAP also assisted with the identification of similar signatures of atypical meningiomas and provision of novel treatment methods ( 39 ). Additionally, Liu et al ( 40 ) used CMAP to identify some potential compounds for treating patients with B-cell chronic lymphocytic leukemia (B-CLL) on the basis of altered pathways in B-CLL.…”

Section: Discussionmentioning

confidence: 99%

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

et al. 2018

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Patients with esophageal carcinoma (ESCA) have a poor prognosis and high mortality rate. Although standard therapies have had effect, there is an urgent requirement to develop novel options, as increasing drug tolerance has been identified in clinical practice. In the present study, differentially expressed genes (DEGs) of ESCA were identified in The Cancer Genome Atlas and Genotype-Tissue Expression databases. Functional and protein-protein interaction (PPI) analyses were performed. The Connectivity Map (CMAP) was selected to predict drugs for the treatment of ESCA, and their target genes were acquired from the Search Tool for Interactions of Chemicals (STITCH) by uploading the Simplified Molecular-Input Line-Entry System structure. Additionally, significant target genes and ESCA-associated hub genes were extracted using another PPI analysis, and the corresponding drugs were added to construct a network. Furthermore, the binding affinity between predicted drug candidates and ESCA-associated hub genes was calculated using molecular docking. Finally, 827 DEGs (|log2 fold-change|≥2; q-value <0.05), which are principally involved in protein digestion and absorption (P<0.005), the plasminogen-activating cascade (P<0.01), as well as the ‘biological regulation’ of the Biological Process, ‘membrane’ of the Cellular Component and ‘protein binding’ of the Molecular Function categories, were obtained. Additionally, 11 hub genes were obtained from the PPI network (all degrees ≥30). Furthermore, the 15 first screen drugs were extracted from CMAP (score <−0.85) and the 9 second screen drugs with 70 target genes were extracted from STITCH. Furthermore, another PPI analysis extracted 51 genes, and apigenin, baclofen, Prestwick-685, menadione, butyl hydroxybenzoate, gliclazide and valproate were selected as drug candidates for ESCA. Those molecular docking results with a docking score of >5.52 indicated the significance of apigenin, Prestwick-685 and menadione. The results of the present study may lead to novel drug candidates for ESCA, among which Prestwick-685 and menadione were identified to be significant new drug candidates.

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“…As another example, Van Noort et al utilized the gene expression profiles of more than 1000 drugs from CMAP and applied the inverse signature approach to identify anti-metastatic drugs for the treatment of colorectal cancer [11]. The follow-up database to CMAP is the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 database [12], which has been recently used in signature-based drug repositioning [13].…”

Section: Introductionmentioning

confidence: 99%

“…However, in many drug repositioning studies, gene expression profiles were directly used from either CMAP or LINCS without correcting for batch effects [3, 6, 7, 10, 13]. Otherwise, mean centering was used to correct for batch effects (Noort et al [11]).…”

Section: Introductionmentioning

confidence: 99%

Influence of batch effect correction methods on drug induced differential gene expression profiles

2019

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Background: Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods. Results: Differences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components. Conclusion: Batch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

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New drug candidates for treatment of atypical meningiomas: An integrated approach using gene expression signatures for drug repurposing

Cited by 20 publications

References 56 publications

Evaluation of Connectivity Map shows limited reproducibility in drug repositioning

Evaluation of Connectivity Map shows limited reproducibility in drug repositioning

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

Influence of batch effect correction methods on drug induced differential gene expression profiles

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