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

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

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

Furanoic Lipid F-6, A Novel Anti-Cancer Compound that Kills Cancer Cells by Suppressing Proliferation and Inducing Apoptosis

Cancers ◽

10.3390/cancers11070960 ◽

2019 ◽

Vol 11 (7) ◽

pp. 960 ◽

Cited By ~ 2

Author(s):

Jassim M. Al-Hassan ◽

Yuan Fang Liu ◽

Meraj A. Khan ◽

Peiying Yang ◽

Rui Guan ◽

...

Keyword(s):

Cell Proliferation ◽

Cancer Cells ◽

Cell Lines ◽

Cancer Cell ◽

Neutrophil Extracellular Trap ◽

Cancer Drug ◽

Drug Candidate ◽

Cell Recovery ◽

Network Analyses ◽

Anti Cancer

Identifying novel anti-cancer drugs is important for devising better cancer treatment options. In a series of studies designed to identify novel therapeutic compounds, we recently showed that a C-20 fatty acid (12,15-epoxy-13,14-dimethyleicosa-12,14-dienoic acid, a furanoic acid or F-6) present in the lipid fraction of the secretions of the Arabian Gulf catfish skin (Arius bilineatus Val.; AGCS) robustly induces neutrophil extracellular trap formation. Here, we demonstrate that a lipid mix (Ft-3) extracted from AGCS and F-6, a component of Ft-3, dose dependently kill two cancer cell lines (leukemic K-562 and breast MDA MB-231). Pure F-6 is approximately 3.5 to 16 times more effective than Ft-3 in killing these cancer cells, respectively. Multiplex assays and network analyses show that F-6 promotes the activation of MAPKs such as Erk, JNK, and p38, and specifically suppresses JNK-mediated c-Jun activation necessary for AP-1-mediated cell survival pathways. In both cell lines, F-6 suppresses PI3K-Akt-mTOR pathway specific proteins, indicating that cell proliferation and Akt-mediated protection of mitochondrial stability are compromised by this treatment. Western blot analyses of cleaved caspase 3 (cCasp3) and poly ADP ribose polymerase (PARP) confirmed that F-6 dose-dependently induced apoptosis in both of these cell lines. In 14-day cell recovery experiments, cells treated with increasing doses of F-6 and Ft-3 fail to recover after subsequent drug washout. In summary, this study demonstrates that C-20 furanoic acid F-6, suppresses cancer cell proliferation and promotes apoptotic cell death in leukemic and breast cancer cells, and prevents cell recovery. Therefore, F-6 is a potential anti-cancer drug candidate.

Download Full-text

Design and Synthesis of Flavonoidal Ethers and Their Anti-Cancer Activity In Vitro

Molecules ◽

10.3390/molecules24091749 ◽

2019 ◽

Vol 24 (9) ◽

pp. 1749 ◽

Cited By ~ 1

Author(s):

Lu Jin ◽

Meng-Ling Wang ◽

Yao Lv ◽

Xue-Yi Zeng ◽

Chao Chen ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Lines ◽

Cancer Drug ◽

Hydroxyl Groups ◽

Drug Candidates ◽

Design And Synthesis ◽

Anti Cancer ◽

Clinical Drugs

Flavonoids are well-characterized polyphenolic compounds with pharmacological and therapeutic activities. However, most flavonoids have not been developed into clinical drugs, due to poor bioavailability. Herein, we report a strategy to increase the drugability of flavonoids by constructing C(sp2)-O bonds and stereo- as well as regioselective alkenylation of hydroxyl groups of flavonoids with ethyl-2,3-butadienoate allenes. Twenty-three modified flavonoid derivatives were designed, synthesized, and evaluated for their anti-cancer activities. The results showed that compounds 4b, 4c, 4e, 5e, and 6b exhibited better in vitro inhibitory activity against several cancer cell lines than their precursors. Preliminary structure–activity relationship studies indicated that, in most of the cancer cell lines evaluated, the substitution on position 7 was essential for increasing cytotoxicity. The results of this study might facilitate the preparation or late-stage modification of complex flavonoids as anti-cancer drug candidates.

Download Full-text

Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1111840108 ◽

2011 ◽

Vol 108 (46) ◽

pp. 18708-18713 ◽

Cited By ~ 255

Author(s):

J.-P. Gillet ◽

A. M. Calcagno ◽

S. Varma ◽

M. Marino ◽

L. J. Green ◽

...

Keyword(s):

Drug Resistance ◽

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Lines ◽

Cancer Drug ◽

Cancer Drug Resistance ◽

Anti Cancer

Download Full-text

Growth and Chemosensitivity of Gastric Adenocarcinoma and Non-Malignant Cell Lines in Response to Novel Anti-Cancer Drug Combinations

Chemotherapy ◽

10.1159/000438943 ◽

2014 ◽

Vol 60 (5-6) ◽

pp. 346-352 ◽

Cited By ~ 1

Author(s):

Jürgen Weinreich ◽

Rami Archid ◽

Khaled Bajaeifer ◽

Anita Hack ◽

Alfred Königsrainer ◽

...

Keyword(s):

Gastric Adenocarcinoma ◽

Cell Lines ◽

Malignant Cell ◽

Drug Combinations ◽

Cancer Drug ◽

Anti Cancer

Download Full-text

SynToxProfiler: an approach for top drug combination selection based on integrated profiling of synergy, toxicity and efficacy

10.1101/693010 ◽

2019 ◽

Author(s):

Aleksandr Ianevski ◽

Alexander Kononov ◽

Sanna Timonen ◽

Tero Aittokallio ◽

Anil K Giri

Keyword(s):

Drug Combination ◽

Ebola Virus ◽

Clinical Success ◽

Drug Combinations ◽

Cancer Drug ◽

Integrated Analysis ◽

Web Based ◽

Prolymphocytic Leukemia ◽

Anti Cancer ◽

Healthy Control

AbstractDrug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. Currently, in the preclinical drug combination screening, the top hits for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested on Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler was shown to prioritize synergistic drug pairs with higher selective efficacy (difference between efficacy and toxicity level) as top hits, which offers improved likelihood for clinical success.

Download Full-text

Design, Synthesis and Biological Evaluation of 4, 6-Coumarin Derivatives as Anti-Cancer and Apoptosis-Inducing Agents

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520621666210129093857 ◽

2021 ◽

Vol 21 ◽

Author(s):

Guoyi Yan ◽

Jiang Luo ◽

Xuan Han ◽

Wenjuan Zhang ◽

Chunlan Pu ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Lines ◽

Biological Evaluation ◽

Cancer Drug ◽

Dependent Manner ◽

Coumarin Derivatives ◽

In Silico Admet ◽

Anti Cancer ◽

Synthesis And Biological Evaluation

BACKGROUND: : Coumarin structures were widely employed in anti-cancer drug design. Herein we focused on the modifications of C4 and C6 positions on coumarin scaffold to get novel anti-cancer agents. OBJECTIVE: The objective of the current work was the synthesis and biological evaluation of a series of 4, 6-coumarin derivatives to get novel anticancer agents. METHODS: Thirty-seven coumarin derivatives were designed and synthesized, the antiproliferative activity of the compounds were evaluated against human cancer cell lines and non-cancerous cells by MTT assay. The bioactivities and underling mechanisms of active molecules were studied and the ADMET characters were predicted. RESULTS: Among the compounds, 4-phydroxy phenol-6-pinacol borane coumarin (25) exhibited a promising anti-cancer activity to cancer cell lines in dose-dependent manner and the toxicity to normal cells was low. The mechanism of action was observed through inducing G2/M phase arrest and apoptosis which was further confirmed via western blot. In silico ADMET prediction revealed that compound 25 is a drug-like small molecule with a favorable safety profile. CONCLUSION: The findings in this work may give vital information for further development of 6-pinacol borane coumarin derivatives as novel anti-cancer agents.

Download Full-text

Novel anti-cancer drug COTI-2 synergizes with therapeutic agents and does not induce resistance or exhibit cross-resistance in human cancer cell lines

PLoS ONE ◽

10.1371/journal.pone.0191766 ◽

2018 ◽

Vol 13 (1) ◽

pp. e0191766 ◽

Cited By ~ 13

Author(s):

Saman Maleki Vareki ◽

Kowthar Y. Salim ◽

Wayne R. Danter ◽

James Koropatnick

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Human Cancer ◽

Therapeutic Agents ◽

Cross Resistance ◽

Cancer Drug ◽

Human Cancer Cell ◽

Human Cancer Cell Lines ◽

Anti Cancer ◽

Induce Resistance

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