A Multiparametric Pharmacogenomic Strategy for Drug Repositioning predicts Therapeutic Efficacy for Glioblastoma Cell Lines

Abstract Background Poor prognosis of glioblastoma patients and the extensive heterogeneity of glioblastoma at both the molecular and cellular level necessitates developing novel individualized treatment modalities via genomics-driven approaches. Methods This study leverages numerous pharmacogenomic and tissue databases to examine drug repositioning for glioblastoma. RNAseq of glioblastoma tumor samples from The Cancer Genome Atlas (TCGA, n=117) were compared to “normal” frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, n=120) to find differentially expressed genes (DEGs). Using compound-gene expression data and drug activity data from the Library of Integrated Network-Based Cellular Signatures (LINCS, n=66,512 compounds) CCLE (71 glioma cell lines), and Chemical European Molecular Biology Laboratory (ChEMBL) platforms, we employed a summarized reversal gene expression metric (sRGES) to “reverse” the resultant disease signature for GBM and its subtypes. A multi-parametric strategy was employed to stratify compounds capable of blood brain barrier penetrance with a favorable pharmacokinetic profile (CNS-MPO). Results Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r=0.37,p<.001) and Cancer Therapeutic Response Portal (CTRP) databases (r=0.35, p<0.001). Our multiparametric algorithm identified two classes of drugs with highest sRGES and CNS-MPO: HDAC inhibitors (vorinostat and entinostat) and topoisomerase inhibitors suitable for drug repurposing. Conclusions Our studies suggest that reversal of glioblastoma disease signature correlates with drug potency for various GBM subtypes. This multiparametric approach may set the foundation for an early-phase personalized -omics clinical trial for glioblastoma by effectively identifying drugs that are capable of reversing the disease signature and have favorable pharmacokinetic and safety profiles.

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MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽

10.3390/genes12010025 ◽

2020 ◽

Vol 12 (1) ◽

pp. 25

Author(s):

He-Gang Chen ◽

Xiong-Hui Zhou

Keyword(s):

Gene Expression ◽

Protein Interactions ◽

Drug Response ◽

Drug Repositioning ◽

Expression Profiles ◽

Drug Repurposing ◽

Recent Decade ◽

Data Set ◽

Module Network ◽

The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

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Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽

10.1186/s12864-021-07581-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yuanyuan Li ◽

David M. Umbach ◽

Juno M. Krahn ◽

Igor Shats ◽

Xiaoling Li ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Cancer Cell ◽

Predictive Models ◽

Drug Sensitivity ◽

Human Cancer ◽

Cancer Cell Lines ◽

The Cancer Genome Atlas ◽

Differential Sensitivity ◽

Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

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Functional Stratification of Cancer Drugs through Integrated Network Similarity

10.21203/rs.3.rs-646155/v1 ◽

2022 ◽

Author(s):

Nurcan Tuncbag ◽

Seyma Unsal Beyge

Keyword(s):

Cell Lines ◽

Drug Targets ◽

Hdac Inhibitors ◽

Treatment Strategies ◽

Network Models ◽

Drug Combinations ◽

Multiple Cancer ◽

Integrated Network ◽

Drug Molecules ◽

The Difference

Abstract Heterogeneity across tumors is the main obstacle in developing treatment strategies. Drug molecules not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored the networks modulated by several drug molecules across multiple cancer cell lines by integrating the drug targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing the exact mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation, found literature evidence for a set of drug pairs. Overall, the network-level exploration of the drug perturbations may potentially help optimize treatment strategies and suggest new drug combinations.

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GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽

10.3390/cells8070675 ◽

2019 ◽

Vol 8 (7) ◽

pp. 675 ◽

Cited By ~ 5

Author(s):

Xia ◽

Liu ◽

Zhang ◽

Guo

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Gene Expression Data ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Cell Line ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Expression Data ◽

Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

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EGFR and PI3K Pathway Activities Might Guide Drug Repurposing in HPV-Negative Head and Neck Cancers

Frontiers in Oncology ◽

10.3389/fonc.2021.678966 ◽

2021 ◽

Vol 11 ◽

Author(s):

Andreas Mock ◽

Michaela Plath ◽

Julius Moratin ◽

Maria Johanna Tapken ◽

Dirk Jäger ◽

...

Keyword(s):

Head And Neck ◽

Hdac Inhibitors ◽

Drug Repurposing ◽

B Cell Lymphoma ◽

Pi3k Pathway ◽

Genetic Alterations ◽

The Cancer Genome Atlas ◽

Cancer Drug ◽

Oncogenic Signaling

While genetic alterations in Epidermal growth factor receptor (EGFR) and PI3K are common in head and neck squamous cell carcinomas (HNSCC), their impact on oncogenic signaling and cancer drug sensitivities remains elusive. To determine their consequences on the transcriptional network, pathway activities of EGFR, PI3K, and 12 additional oncogenic pathways were inferred in 498 HNSCC samples of The Cancer Genome Atlas using PROGENy. More than half of HPV-negative HNSCC showed a pathway activation in EGFR or PI3K. An amplification in EGFR and a mutation in PI3KCA resulted in a significantly higher activity of the respective pathway (p = 0.017 and p = 0.007). Interestingly, both pathway activations could only be explained by genetic alterations in less than 25% of cases indicating additional molecular events involved in the downstream signaling. Suitable in vitro pathway models could be identified in a published drug screen of 45 HPV-negative HNSCC cell lines. An active EGFR pathway was predictive for the response to the PI3K inhibitor buparlisib (p = 6.36E-03) and an inactive EGFR and PI3K pathway was associated with efficacy of the B-cell lymphoma (BCL) inhibitor navitoclax (p = 9.26E-03). In addition, an inactive PI3K pathway correlated with a response to multiple Histone deacetylase inhibitor (HDAC) inhibitors. These findings require validation in preclinical models and clinical studies.

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Genome Based Search for Reference Genes for Gene Expression Analysis in Oral Cancer: a Data Science Driven Approach

10.21203/rs.2.10145/v1 ◽

2019 ◽

Author(s):

Nehanjali Dwivedi ◽

Sujan K Dhar ◽

G Charitha ◽

Moni Abraham Kuriakose ◽

Amritha Suresh ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Expression Analysis ◽

Reference Genes ◽

Data Science ◽

Gene Expression Analysis ◽

The Cancer Genome Atlas ◽

Experimental Conditions ◽

Statistical Measures ◽

Different Types

Abstract Background Quantitative real time PCR (qPCR) remains by far the most cost-effective, fast yet sensitive technique to check the gene expression levels in various systems. The traditionally used reference genes over the years were found to be regulated heavily based on sample sources and/or experimental conditions. This paper therefore presents a data science driven -omic approach for selection of reference genes from ~60,000 candidates from The Cancer Genome Atlas (TCGA) and Broad Institute Cancer Cell Line Encyclopaedia (CCLE) for gene expression studies in head and neck squamous cell carcinoma (HNSCC). mRNA-sequencing data of 500 patient samples and 33 cell lines from publicly available databases were analysed to assess stability of genes in terms of multiple statistical measures. A final set of 12 candidate genes were studied in the Indian set of data in Gene Expression Omnibus (GEO) and validated experimentally using qPCR in 35 different types of samples from platforms like drug sensitive and resistant cell lines, normal and tumor samples, fibroblast and epithelial primary culture derived from HNSCC patients from India. Result The study lead to the choice of five most stable reference genes –TYW5, RIC8B, PLEKHA3, CEP57L1 and GPR89B across three experimental platforms. Conclusion In addition to providing a set of five most stable reference genes for future gene expression analysis experiments across different types of samples in HNSCC, the study also presents an objective framework for assessing reference genes for other disease areas as well.

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Gene expression based inference of drug resistance in cancer

10.1101/2021.11.17.468905 ◽

2021 ◽

Author(s):

Smriti Chawla ◽

Anja Rockstroh ◽

Melanie Lehman ◽

Ellca Rather ◽

Atishay Jain ◽

...

Keyword(s):

Gene Expression ◽

Drug Resistance ◽

Cell Lines ◽

Large Scale ◽

Activity Patterns ◽

The Cancer Genome Atlas ◽

Learning Approaches ◽

Expression Data ◽

Sequencing Data ◽

Pathway Activity

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

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Induction of Bim Facilitates Apoptosis in Leukemia Cells Treated with HDAC Inhibitors.

Blood ◽

10.1182/blood.v108.11.1994.1994 ◽

2006 ◽

Vol 108 (11) ◽

pp. 1994-1994

Author(s):

Matthew C. Stubbs ◽

Teresa Kim ◽

Andrei Krivtsov ◽

Peter Atadja ◽

Scott A. Armstrong ◽

...

Keyword(s):

Gene Expression ◽

Histone Deacetylase ◽

Cell Lines ◽

Expression Patterns ◽

Hdac Inhibitors ◽

Leukemia Cell ◽

Glucocorticoid Resistance ◽

Leukemia Cell Line ◽

Dependent Manner ◽

Myeloid Leukemias

Abstract Lymphoblastic leukemias containing chromosomal translocations involving the Mixed Lineage Leukemia (MLL, HRX, ALL-1) gene, as well as most acute myeloid leukemias (AMLs) have relatively poor clinical prognoses due in part to intrinsic drug resistance. Therefore, new avenues are being explored for treatment of MLL-rearranged ALL and AMLs. One possible new therapeutic class currently being investigated is the histone deacetylase (HDAC) inhibitors. We utilized the histone deacetylase inhibitor NVP-LAQ824 (Novartis, Basel, Switzerland) and analyzed its effects on MLL rearranged and other myeloid leukemias. We also made use of an MLL-AF9 expressing myeloid leukemia cell line (AKLG) derived from purified murine leukemia stem cells to perform gene expression analysis on NVP-LAQ824 treated cells in order to further understand the mechanism of action of HDAC inhibitors, and to potentially identify cooperating therapeutics. NVP-LAQ824 inhibits cell growth at similar concentrations for all cell lines and primary patient samples tested (~25–50nM) as determined by MTT assay 48 hours after treatment. NVP-LAQ824 does not appear to induce apoptosis solely through inhibition of the HSP90/FLT3-ITD complex as cell lines possessing FLT3-ITD (a HSP90-chaperoned protein) and cells without this mutation have similar drug sensitivities. In fact, in cells overexpressing FLT3-ITD that are treated with NVP-LAQ824, phospho-FLT3-ITD levels do not diminish. Microarray data indicated that NVP-LAQ824 induces the BH3-only family member bim. This finding was verified by Western blotting in all cell lines and patient samples tested. Further, shRNA-mediated knockdown of Bim induced relative resistance of cells to NVP-LAQ824. The expression profile also showed similarities to gene expression patterns of dexamethasone treated cells, namely, increased bim levels and decreased expression of c-myc, raising the possibility of synergy between the two drugs. Using MTT assays, we discovered that NVP-LAQ824 in low doses (25nM) induces sensitivity to dexamethasone in glucocorticoid resistant cell lines in a glucocorticoid receptor (GR) dependent manner. Therefore, our data indicate that NVP-LAQ824 may reverse glucocorticoid resistance and may provide insight into glucocorticoid resistance in MLL rearranged leukemias. The biochemistry behind HDAC inhibitors merits further study.

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Anti-Apoptotic Proteins, HSP90 and Activated STAT3 Contribute to Busulfan Resistance of Myeloid Leukemia Cells.

Blood ◽

10.1182/blood.v110.11.3472.3472 ◽

2007 ◽

Vol 110 (11) ◽

pp. 3472-3472

Author(s):

Borje S. Andersson ◽

Ben C. Valdez ◽

David Murray ◽

Latha Ramdas ◽

Marcos de Lima ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Leukemia Cell ◽

Myelogenous Leukemia ◽

Leukemia Cells ◽

Treatment Modalities ◽

High Dose ◽

Hematopoietic Stem ◽

Conditioning Treatment ◽

Apoptotic Genes

Abstract Busulfan(Bu)-based chemotherapy is a conditioning treatment prior to hematopoietic stem cell transplantation (HSCT) of patients with acute and chronic myelogenous leukemia (AML, CML). A major obstacle to successful HSCT is Bu resistance, which might be attributed to individual differences in drug pharmacokinetics and metabolism, or inherent resistance of cancer cells. We hypothesize that gene expression profiling of leukemia cells can be used to dissect the factors that contribute to their Bu resistance. Two Bu-resistant leukemia cell lines were established, characterized and analyzed by microarray and real-time RT-PCR techniques to identify differentially expressed genes. The CML B5/Bu2506 cells are 4.5-fold more resistant to Bu than their parental B5 cells. The AML KBM3/Bu2506 cells are 4.0-fold more Bu-resistant than KBM3 parental cells. Both resistant sublines evade Bu-mediated G2-arrest and apoptosis with constitutively up-regulated anti-apoptotic genes (BCL-XL, BCL2, BCL2L10, BAG3 and IAP2/BIRC3) and down-regulated pro-apoptotic genes (BIK, BNIP3, and LTBR).). Bu-induced apoptosis is partly mediated by activation of caspases; use of the inhibitor Z-VAD-FMK completely abrogated PARP1 cleavage and reduced apoptosis by ∼ 50%. Furthermore, Bu resistance in these cells may be attributed in part to up-regulation of HSP90 protein and activation of STAT3. Inhibition of HSP90 with geldanamycin attenuated phosphorylated STAT3 and made B5/Bu2506 and KBM3/Bu2506 more Bu-sensitive. Analysis of cells derived from patients classified as either clinically resistant or sensitive to high-dose Bu-based chemotherapy had alterations in gene expression that were analogous to those observed in the in-vitro model cell lines, confirming the potential clinical relevance of this model for Bu resistance. Our results suggest the important roles of apoptotic signaling mechanism, HSP90 and STAT3 and should be considered in the classification of AML patients who will likely benefit from busulfan-based pretransplant conditioning therapy and those who should be offered alternative treatment modalities.

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Histone Deacetylase Inhibition Using LBH589 Is Effective in Lymphoma and Results in Down-Regulation of the NF-KB Pathway.

Blood ◽

10.1182/blood.v114.22.3730.3730 ◽

2009 ◽

Vol 114 (22) ◽

pp. 3730-3730 ◽

Cited By ~ 2

Author(s):

Jason L. Smith ◽

Amee Patel ◽

Siyao Fan ◽

Cassandra L. Jacobs ◽

Katherine J. Walsh ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Cell Lines ◽

Expression Profiles ◽

Hdac Inhibitors ◽

Gene Expression Profiles ◽

Resistant Cell ◽

Hdac Inhibition ◽

Down Regulation ◽

Highly Sensitive

Abstract Abstract 3730 Poster Board III-666 Background Histone deacetylase (HDAC) inhibition has emerged as a promising therapeutic approach in malignancies. HDAC inhibition has proved to be a particularly effective option in patients with lymphoma. The HDAC inhibitor vorinostat is approved for the treatment of patients with cutaneous T-cell lymphomas and is being tested in patients with B cell lymphomas. More recently, a number of other HDAC inhibitors have entered preclinical and clinical testing. The mechanisms through which HDAC inhibitors exert their downstream effects are currently unknown. As the number of HDAC inhibitors in development increases, it is unclear if they share a class effect or display unique mechanisms of action. Recently, LBH589 has been described as an orally available, highly potent inhibitor of HDAC. We decided to explore whether LBH589 would be an effective therapeutic option for patients with lymphoma. Methods and Results In order to evaluate whether LBH was efficacious and potent in B cell lymphomas, we tested both vorinostat and LBH589 in the same cell line(s). We found that LBH589 was over 10 times more potent than vorinostat (mean IC50 7.4nM versus 830nM). We decided to further test LBH589 in an expanded panel of 18 cell lines derived from 5 different lymphoid malignancies: Burkitt lymphoma, mantle cell lymphoma, Hodgkin lymphoma, multiple myeloma and diffuse large B cell lymphoma. LBH589 was found to be lethal in each of these cell lines at IC50 concentrations varying from 5.6-31.5 nM (mean 11.2nM), suggesting that this drug may be effective at physiologically achievable concentrations. Based on the IC50 cut-off of 10nM, we assigned the treated cell lines to 2 groups: highly sensitive (IC50 < 10nM, N=11) and less sensitive (IC50> 10nM, N=8). We performed gene expression profiling on 12 of these cell lines and compared the gene expression profiles of the highly sensitive versus less sensitive cell lines. Further, we performed time course experiments in which we evaluated the effects of LBH589 at its IC50 on cell lines at 6 and 12 hours post-treatment. Gene expression profiling was performed on the treated cells at each time point. We also engineered resistant cell lines by incremental dose escalation over a period of months to a concentration greater than or equal to the IC50. The resistant cell lines were also profiled for gene expression and compared to the wild type cell lines. The gene expression profiles of LBH589 treated cells at 6 and 12 hours demonstrated a clear and progressive down regulation of genes associated with the NF-KB pathway (Figure 1). Furthermore, cell lines with high expression of genes in the NF-KB pathway were uniformly highly sensitive to LBH589 with IC50<10nM in all cases. Conclusion NF-KB activation is a common feature of many different lymphoma types. Our data suggest that HDAC inhibition using LBH589 could provide a potent method for treating lymphomas and that HDAC inhibitors may exert their effects through the down-regulation of the NF-KB pathway. Our data also suggest a rationale for dual inhibition of HDAC and NF-KB in the treatment of lymphoma. Disclosures: Rizzieri: Merck & Co., Inc.: Consultancy.

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