Novel Modeling of Cancer Cell Signaling Pathways Enables Systematic Drug Repositioning for Distinct Breast Cancer Metastases

Zhao, Hong; Jin, Guangxu; Cui, Kemi; Ren, Ding; Liu, Timothy; Chen, Peikai; Wong, Solomon; Li, Fuhai; Fan, Yubo; Rodriguez, Angel Augusto; Chang, Jenny C.; Wong, Stephen T.C.

doi:10.1158/0008-5472.can-12-4617

Cited by 45 publications

(24 citation statements)

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“…Our previously published gene expression data of breast CSCs (CD44 + /CD24 −/low and MS‐forming treatment‐resistant cells) was used for in silico drug repositioning analysis (GSE7513, SE7515, and GSE10281) . The cancer signaling bridges (CSBs)‐based drug repositioning computational modeling method was applied to derive specific CSCs signaling pathways .…”

Section: Methodsmentioning

confidence: 99%

Chloroquine Eliminates Cancer Stem Cells Through Deregulation of Jak2 and DNMT1

et al. 2014

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Triple negative breast cancer (TNBC) is known to contain a high percentage of CD44+/CD24−/low cancer stem cells (CSC), corresponding with a poor prognosis despite systemic chemotherapy. Chloroquine (CQ), an anti-malarial drug, is a lysotropic reagent which inhibits autophagy. CQ was identified as a potential CSC inhibitor through in silico gene expression signature analysis of the CD44+/CD24−/low CSC population. Autophagy plays a critical role in adaptation to stress conditions in cancer cells, and is related with drug resistance and CSC maintenance. Thus the objectives of this study were to examine the potential enhanced efficacy arising from addition of chloroquine (CQ) to standard chemotherapy (paclitaxel) in TNBC and to identify the mechanism by which CQ eliminates CSCs in TNBCs. Herein, we report that CQ sensitizes TNBC cells to paclitaxel through inhibition of autophagy and reduces the CD44+/CD24−/low CSC population in both preclinical and clinical settings. Also, we are the first to report a mechanism by which CQ regulates the CSCs in TNBC through inhibition of the Janus-activated kinase 2 (Jak2) - Signal transducer and activator of transcription 3 (STAT3) signaling pathway by reducing the expression of Jak2 and DNA methyltransferase 1 (DNMT1).

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Section: Methodsmentioning

confidence: 99%

Chloroquine Eliminates Cancer Stem Cells Through Deregulation of Jak2 and DNMT1

et al. 2014

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“…To date, various computational methods have been developed for drug repositioning classified as target-based [3–5], knowledge-based [6–8], signature-based [9–11] and network-based [12, 13] approaches. While these methods have contributed much to drug discovery, the space of innate human metabolite has relatively not been considered in those approaches.…”

Section: Introductionmentioning

confidence: 99%

Drug repositioning for enzyme modulator based on human metabolite-likeness

et al. 2017

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BackgroundRecently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme’s metabolites and drugs.MethodsWe constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden’s index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness.ResultsIn this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence.ConclusionsThis study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1637-5) contains supplementary material, which is available to authorized users.

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“…Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.…”

Section: Bodymentioning

confidence: 99%

“…Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] and stochastic models [5][6][7][8] to computational models -e.g., Monte Carlo method and cellular automata [9][10][11], Boolean networks [12], Petri nets [13], artificial neural networks [14][15][16] and expert systems [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.…”

Section: Bodymentioning

confidence: 99%

An In Silico Cell Signaling-Based Approach for Exploring the Activities Involved in Pre-Metastasis and Metastasis

González-Pérez

Cárdenas-García

2017

Genomics Comput Biol

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SUMMARYIn order to understand, identify and explore the activities involved in metastasis, as well as possible control points, in this work we model and simulate the sequential steps that a cell must follow from its transformation to metastasis, using the Cellulat bioinformatics tool, an in silico experimentation environment that complements and guides in vitro experimentation concerning intra and intercellular signaling networks. KEYWORDSMetastasis; Cancer cells; In silico experiments; Cellulat bioinformatics tool. BODYMetastasis is the clinical term for the process by which tumor cells leave a primary cancer tumor to transfer to different organs. The metastasis is considered to be the true killer in cancer patients, since usually when this process is triggered, it inevitably leads to death. To prevent the spreading of cancer, the most important is to avoid the metastasis of the primary tumor, as well as to find markers that allow early identification of the presence of a primary tumor [1]. Features that promote metastasis -i.e., sustained proliferation, replicative immortality, and evasion of growth suppression -allow cancer cells to grow uncontrollably in a tumor large enough to invade neighboring tissues. The ability of cells to resist cell death and prevent their destruction caused by the immune system response, allow them to survive on their way to metastasis.The intrinsic complexity of biological systems and phenomena -such as protein-protein interaction, protein-ligand docking and protein folding, just to mention a few examples -has required the development of a wide range of computational tools dedicated to the modeling and simulation of them, so that these computational approaches -also known as computer simulation or in silico experimentation -can complement, corroborate and enrich both the advances in theoretical and experimental research in the study of such systems. Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.In order to understand, identify and explore the activities involved in metastasis, as well as possible control points, in this work we model and simulate the sequential steps that a cell must follow from its transformation to metastasis.We simulate and explore the complex interaction patterns of signaling pathways involved in pre-metastasis and metastasis using the Cellulat bioinformatics tool (http://bioinformatics.cua.uam.mx/node/10) [18,19], a computational simulation tool developed by us and inspired by Biochemical Tuple Spaces for Self-Organizing Coordination model (BTSSOC) [20]. The main idea behind the Cellulat bioinformatics tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation concerning intra and intercellular signaling networks.C...

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Novel Modeling of Cancer Cell Signaling Pathways Enables Systematic Drug Repositioning for Distinct Breast Cancer Metastases

Cited by 45 publications

References 46 publications

Chloroquine Eliminates Cancer Stem Cells Through Deregulation of Jak2 and DNMT1

Chloroquine Eliminates Cancer Stem Cells Through Deregulation of Jak2 and DNMT1

Drug repositioning for enzyme modulator based on human metabolite-likeness

An In Silico Cell Signaling-Based Approach for Exploring the Activities Involved in Pre-Metastasis and Metastasis

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