Investigating an in-silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression

AbstractIn clinical practice, antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in-silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy.We determined the transcriptomic profile of citalopram remitters by performing a transcriptomic-wide association study on STAR*D data (N =1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity map database. Spearman correlation, Pearson correlation, and the Kolmogorov Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p-value for each rank by using a permutation procedure. The drugs with the top ranks were those having high positive correlation with the expression profiles of remitters and they may have higher chances of efficacy in the tested patients.In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p=0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second highest ranked antidepressants, respectively (p=0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants.The correlation between citalopram remitters’ and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements it can be applicable at the individual level to tailor treatment prescription.

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Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression

The Pharmacogenomics Journal ◽

10.1038/s41397-020-00186-5 ◽

2020 ◽

Author(s):

Muhammad Shoaib ◽

Edoardo Giacopuzzi ◽

Oliver Pain ◽

Chiara Fabbri ◽

Chiara Magri ◽

...

Keyword(s):

Gene Expression ◽

In Silico ◽

Expression Profiles ◽

Drug Prescription ◽

Antidepressant Drug ◽

Drug Induced ◽

Induced Expression

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Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽

10.1038/sj.onc.1205829 ◽

2002 ◽

Vol 21 (42) ◽

pp. 6549-6556 ◽

Cited By ~ 39

Author(s):

Jiafu Ji ◽

Xin Chen ◽

Suet Yi Leung ◽

Jen-Tsan A Chi ◽

Kent Man Chu ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Cell Lines ◽

Cancer Cell ◽

Gastric Cancer Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Comprehensive Analysis ◽

Human Gastric Cancer ◽

Gastric Cancer Cell Lines

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In Silico Prediction of Novel Drug Combinations to Combat Bortezomib-Resistant Multiple Myeloma

Blood ◽

10.1182/blood.v120.21.1344.1344 ◽

2012 ◽

Vol 120 (21) ◽

pp. 1344-1344

Author(s):

Holly A. F. Stessman ◽

Tian Xia ◽

Aatif Mansoor ◽

Raamesh Deshpande ◽

Linda B. Baughn ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Cell Lines ◽

In Silico ◽

Research Funding ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Mouse Cell ◽

Resistant Mouse ◽

Novel Therapeutic

Abstract Abstract 1344 Bortezomib/VELCADE® (Bz) is a proteasome inhibitor that has been used successfully in the treatment of multiple myeloma (MM) patients. However, acquired resistance to Bz is an emerging problem. Thus, there is a need for novel therapeutic combinations that enhance Bz sensitivity or re-sensitize Bz resistant MM cells to Bz. The Connectivity Map (CMAP; Broad Institute) database contains treatment-induced transcriptional signatures from 1,309 bioactive compounds in 4 human cancer cell lines. An input signature can be used to query the database for correlated drug signatures, a technique that has been used previously to identify drugs that combat chemoresistance in cancer (Wei, et al. Cancer Cell (2006) 10:331). In this study we used in silico bioinformatic screening of gene expression profiles from isogenic pairs of Bz sensitive and resistant mouse cell lines derived from the iMycCα/Bcl-xL mouse model of plasma cell malignancy to identify compounds that combat Bz resistance. We established Bz-induced kinetic gene expression profiles (GEPs) in 3 pairs of Bz sensitive and resistant mouse cell lines over the course of 24 hours. GEPs were collected in the absence of large-scale cell death. The 16 and 24 hour time points were averaged and compared between each Bz sensitive and resistant pair. Genes in the sensitive cell line with a fold change greater than 2, relative to the resistant line, were given the binary distinction of “up” or “down” depending on the direction of change. Genes that met these criteria were assembled into signatures, and then used as inputs for CMAP queries to identify compounds that induce similar transcriptional responses. In all pairs, treatment of the Bz sensitive line correlated with GEPs of drugs that target the proteasome, NF-κB, HSP90 and microtubules, as indicated by positive connectivity scores. However eight compounds, all classified as Topoisomerase (Topo) I and/or II inhibitors, were negatively correlated to our input signature. A negative connectivity score could have two interpretations: (1) this could indicate simply that Topos are upregulated by Bz treatment in Bz sensitive lines, which has been previously reported (Congdan, et al. Biochem. Pharmacol. (2008) 74: 883); or (2) this score could be interpreted as Topos are inhibited in Bz resistant cells upon Bz treatment. This led us to ask whether Topo inhibitors could target Bz resistant MM cells and re-sensitize them to Bz. Indeed, we found that multiple Topo inhibitors were significantly more active against Bz resistant cells as single agents and restored sensitivity to Bz when combined with Bz as a cocktail regimen. This work demonstrates the potential of this in silico bioinformatic approach for identifying novel therapeutic combinations that overcome Bz resistance in MM. Furthermore, it identifies Topo inhibitors – drugs that are already approved for clinical use – as agents that may have utility in combating Bz resistance in refractory MM patients. Disclosures: Stessman: Millennium: The Takeda Oncology Company: Research Funding. Van Ness:Millennium: The Takeda Oncology Company: Research Funding.

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Revisiting inconsistency in large pharmacogenomic studies

F1000Research ◽

10.12688/f1000research.9611.1 ◽

2016 ◽

Vol 5 ◽

pp. 2333 ◽

Cited By ~ 33

Author(s):

Zhaleh Safikhani ◽

Petr Smirnov ◽

Mark Freeman ◽

Nehme El-Hachem ◽

Adrian She ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Cancer Cell ◽

Drug Sensitivity ◽

Biomarker Discovery ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Targeted Drugs ◽

Software Analysis ◽

Sensitivity Data

In 2013, we published a comparative analysis mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines, and that the software analysis tools we provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

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Transcriptional gene expression profiles indicate response of breast cancer cell lines to aminoflavone prodrug.

10.1158/0008-5472.sabcs-2153 ◽

2009 ◽

Cited By ~ 1

Author(s):

PM Shelton ◽

B Nguyen ◽

A Chen ◽

AM Burger

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines

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Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

BMC Bioinformatics ◽

10.1186/s12859-020-3524-8 ◽

2020 ◽

Vol 21 (S9) ◽

Author(s):

Mona Maharjan ◽

Raihanul Bari Tanvir ◽

Kamal Chowdhury ◽

Wenrui Duan ◽

Ananda Mohan Mondal

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Cell Lines ◽

Cancer Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Cell Lines ◽

High Expression ◽

Different Types ◽

Low Expression

Abstract Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

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