Secdrug: A Computational Method for Discovering Novel Secondary Drug Combinations Using Large-Scale Pharmacogenomics Databases

Abstract Drug resistance is a major obstacle in achieving complete and sustained therapeutic effect in cancer chemotherapy. Chemo-resistance may also lead to over-dosing and unwanted exposure to ineffective anti-tumor agents thereby increasing the risk of negative side-effects and the cost of drug development. Therefore, our goal is to utilize a large-scale pharmacogenomics database (Genomics of Drug Sensitivity in Cancer or GDSC, the largest public resource of drug-sensitivity data on over 250 drugs in more than 1000 human cancer cell lines of common and rare types of adult and childhood cancers of diverse origin) and develop a prediction method to identify novel secondary drug combination regimens that may effectively reverse drug resistance. We utilized a greedy algorithm-based set-covering computational optimization method followed by a regularization technique to seek all secondary drugs that could kill maximum number of cell lines of the test disease (B-Cell cancers) resistant to the test drug (the Proteasome inhibitor/PI drug Bortezomib/Bz/Velcade) in a sequential manner ordered by the number of cell lines killed. The predicted top secondary drug combinations in PI-resistant B-cell cancers are shown in Figure 1. To validate our prediction results, we treated human multiple myeloma cell lines (HMCLs) highly resistant to the proteasome inhibitors Bortezomib, Carfilzomob, Oprozomib and Ixazomib with the predicted best secondary drugs. Figure 2 depicts In vitro chemo-sensitivity profiles of PI-resistant HMCLs and other B-cell cancer cell lines showing percent survival compared to untreated control at increasing concentrations of secondary drugs, as single agents or in combination. Furthermore, for each drug in the predicted drug combination, we identified differentially expressed (DE) genes by comparing the expression profiles between extraordinary- sensitive and resistant cell lines. These significantly regulated DE genes were used to identify pathways associated with the successful drug combinations. Finally, we developed an R software package secDrug based on this computational pipeline for predicting novel secondary therapies in chemotherapy-resistant cancers. secDrug takes a query of any cancer type and any test drug, and outputs a list of the top secondary drug combinations with confidence score and biological pathway visualization. Thus, secDrug has potential application in clinical decision-making for discovering resistance-reversing cancer chemotherapy regimens. Disclosures No relevant conflicts of interest to declare.

Download Full-text

SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology

Nucleic Acids Research ◽

10.1093/nar/gkaa421 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W494-W501 ◽

Cited By ~ 2

Author(s):

Heewon Seo ◽

Denis Tkachuk ◽

Chantal Ho ◽

Anthony Mammoliti ◽

Aria Rezaie ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Drug Combination ◽

Predictive Biomarkers ◽

Cancer Cell Lines ◽

Drug Combinations ◽

Drug Synergism ◽

Precision Oncology ◽

Synergistic Drug Combinations ◽

Pharmacological Profiles

Abstract Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients.

Download Full-text

A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

Scientific Data ◽

10.1038/s41597-019-0255-7 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 5

Author(s):

Åsmund Flobak ◽

Barbara Niederdorfer ◽

Vu To Nakstad ◽

Liv Thommesen ◽

Geir Klinkenberg ◽

...

Keyword(s):

Signal Transduction ◽

Cell Viability ◽

Cell Lines ◽

Cancer Cell ◽

Drug Combination ◽

Cancer Cell Lines ◽

Drug Combinations ◽

Clinical Investigations ◽

Transduction Mechanisms ◽

Targeting Signal

Abstract While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.

Download Full-text

GraphSynergy: Network Inspired Deep Learning Model for Anti–Cancer Drug Combination Prediction

10.21203/rs.3.rs-315189/v1 ◽

2021 ◽

Author(s):

Jiannan Yang ◽

Zhongzhi Xu ◽

William Wu ◽

Qian Chu ◽

Qingpeng Zhang

Keyword(s):

Deep Learning ◽

Cell Lines ◽

Cancer Cell ◽

Drug Combination ◽

Drug Combinations ◽

Cancer Drug ◽

Ppi Network ◽

Convolutional Network ◽

Anti Cancer ◽

Protein Modules

Abstract Compared with monotherapy, anti-cancer drug combination can provide effective therapy with less toxicity in cancer treatment. Recent studies found that the topological positions of protein modules related to the drugs and the cancer cell lines in the protein-protein interaction (PPI) network may reveal the effects of drugs. However, due to the size of the combinatorial space, identifying synergistic combinations of drugs from PPI network is computationally difficult. To address this challenge, we propose an end-to-end deep learning framework, namely Graph Convolutional Network for Drug Synergy (GraphSynergy), to make synergistic drug combination predictions. GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order structure information of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line in the PPI network. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxic scores. By introducing an attention component to automatically allocate contribution weights to the proteins, we show the ability of GraphSynergy to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines. Experiments on two latest drug combination datasets demonstrate that GraphSynergy outperforms the state-of-the-art in predicting synergistic drug combinations. This study sheds light on using machine learning to discover effective combination therapies for cancer and other complex diseases.

Download Full-text

Network-based clustering for drug sensitivity prediction in cancer cell lines

10.1101/764043 ◽

2019 ◽

Author(s):

Maryam Pouryahya ◽

Jung Hun Oh ◽

James C. Mathews ◽

Zehor Belkhatir ◽

Caroline Moosmüller ◽

...

Keyword(s):

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Large Scale ◽

Computational Models ◽

Drug Sensitivity ◽

Cancer Cell Lines ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Drug Sensitivity Prediction

AbstractThe study of large-scale pharmacogenomics provides an unprecedented opportunity to develop computational models that can accurately predict large cohorts of cell lines and drugs. In this work, we present a novel method for predicting drug sensitivity in cancer cell lines which considers both cell line genomic features and drug chemical features. Our network-based approach combines the theory of optimal mass transport (OMT) with machine learning techniques. It starts with unsupervised clustering of both cell line and drug data, followed by the prediction of drug sensitivity in the paired cluster of cell lines and drugs. We show that prior clustering of the heterogenous cell lines and structurally diverse drugs significantly improves the accuracy of the prediction. In addition, it facilities the interpretability of the results and identification of molecular biomarkers which are significant for both clustering of the cell lines and predicting the drug response.

Download Full-text

Faculty Opinions recommendation of TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725335383.793503769 ◽

2015 ◽

Author(s):

Zdeněk Valenta

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

R Package ◽

Cancer Cell Lines ◽

Drug Combinations ◽

Targeted Drug

Download Full-text

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.

Download Full-text

Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555219873068 ◽

2019 ◽

Vol 25 (1) ◽

pp. 9-20 ◽

Cited By ~ 2

Author(s):

Olivia W. Lee ◽

Shelley Austin ◽

Madison Gamma ◽

Dorian M. Cheff ◽

Tobie D. Lee ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

High Throughput ◽

High Throughput Screening ◽

Drug Targets ◽

Large Scale ◽

Early Stage ◽

Cancer Cell Line ◽

Hek 293 ◽

Cytotoxic Compounds

Cell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes, and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in the National Institutes of Health, National Center for Advancing Translational Sciences annotated libraries and more than 100,000 compounds in a diversity library against four normal cell lines (HEK 293, NIH 3T3, CRL-7250, and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity, and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitute a valuable resource for the scientific community and provide insight into the extent of cytotoxic compounds in screening libraries, allowing for the identification and avoidance of compounds with cytotoxicity during high-throughput screening campaigns.

Download Full-text