Systems biology based drug repositioning for development of cancer therapy

Turanlı, Beste; Altay, Özlem; Borén, Jan; Türkez, Hasan; Nielsen, Jens; Uhlén, Mathias; Arğa, Kazım Yalçın; Mardinoğlu, Adil

doi:10.1016/j.semcancer.2019.09.020

Cited by 59 publications

(61 citation statements)

References 114 publications

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“…Therefore, we propose these PPI interfaces as potential targets for anticancer therapeutic discovery and development 32 . Moreover, these specific dPPIs can be appraised in drug repositioning for development of cancer therapy 16,33,34 .…”

Section: Discussionmentioning

confidence: 99%

Pan-cancer mapping of differential protein-protein interactions

Gulfidan

Turanlı

Beklen

et al. 2020

Sci Rep

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Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Pan-cancer mapping of differential protein-protein interactions

Gulfidan

Turanlı

Beklen

et al. 2020

Sci Rep

Self Cite

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“…Moreover, information on the disease phenotype or gene expression profiles of drugs is necessary when a screen does not involve protein targets . In silico DR has been recently reviewed elsewhere in detail …”

Section: Approach For Drug Repositioningmentioning

confidence: 99%

Drug repositioning in cancer: The current situation in Japan

et al. 2020

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Cancer is a leading cause of death worldwide, and the incidence continues to increase. Despite major research aimed at discovering and developing novel and effective anticancer drugs, oncology drug development is a lengthy and costly process, with high attrition rates. Drug repositioning (DR, also referred to as drug repurposing), the process of finding new uses for approved noncancer drugs, has been gaining popularity in the past decade. DR has become a powerful alternative strategy for discovering and developing novel anticancer drug candidates from the existing approved drug space. Indeed, the availability of several large established libraries of clinical drugs and rapid advances in disease biology, genomics/transcriptomics/ proteomics and bioinformatics has accelerated the pace of activity-based, literaturebased and in silico DR, thereby improving safety and reducing costs. However, DR still faces financial obstacles in clinical trials, which could limit its practical use in the clinic. Here, we provide a brief review of

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“…In the following subsection, we will shortly describe the most important databases for gene information, proteins, interactions and simply list others for further information. More information can also be found in the review from [ 122 ] that describe drug-related data types, and web-based drug repositioning tools.…”

Section: From Potential Candidate Gene To Drug Target Validationmentioning

confidence: 99%

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Bintener

Pacheco²,

Sauter³

2020

Biochemical Society Transactions

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Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

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Systems biology based drug repositioning for development of cancer therapy

Cited by 59 publications

References 114 publications

Pan-cancer mapping of differential protein-protein interactions

Pan-cancer mapping of differential protein-protein interactions

Drug repositioning in cancer: The current situation in Japan

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

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