Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning

Zhang, Jizhun; Jiang, Kewei; Lv, Liang; Wang, Hui; Shen, Zhanlong; Gao, Zhidong; Wang, Bo; Yang, Yang; Ye, Yingjiang; Wang, Shan

doi:10.1371/journal.pone.0116477

Cited by 34 publications

(25 citation statements)

References 25 publications

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“…Researchers are now developing approaches that analyze genetic associations in the context of networks and pathways to prioritize potential drugs (16)(17)(18)(19)(20)(21). There is not yet a standard approach or set of techniques that are widely applied, so we highlight examples of the types of techniques being employed.…”

Section: Pathway and Network-based Drug Identification From Genetic Amentioning

confidence: 99%

“…Overlaying drug-target information from DrugBank (23) and the Therapeutic Targets Database (23) revealed that a number of top candidates from the analysis were already targets of existing drugs for RA, suggesting that drugs targeting other top candidates might also be repurposed to treat RA. A similar approach has also been used to identify anti-cancer drugs (21).…”

Section: Pathway and Network-based Drug Identification From Genetic Amentioning

confidence: 99%

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Pathway and network-based strategies to translate genetic discoveries into effective therapies

Greene

Voight

2016

Hum. Mol. Genet.

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One way to design a drug is to attempt to phenocopy a genetic variant that is known to have the desired effect. In general, drugs that are supported by genetic associations progress further in the development pipeline. However, the number of associations that are candidates for development into drugs is limited because many associations are in non-coding regions or difficult to target genes. Approaches that overlay information from pathway databases or biological networks can expand the potential target list. In cases where the initial variant is not targetable or there is no variant with the desired effect, this may reveal new means to target a disease. In this review, we discuss recent examples in the domain of pathway and network-based drug repositioning from genetic associations. We highlight important caveats and challenges for the field, and we discuss opportunities for further development.

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Section: Pathway and Network-based Drug Identification From Genetic Amentioning

confidence: 99%

Section: Pathway and Network-based Drug Identification From Genetic Amentioning

confidence: 99%

Pathway and network-based strategies to translate genetic discoveries into effective therapies

Greene

Voight

2016

Hum. Mol. Genet.

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“…Similar efforts to systematically translate results from large-scale GWAS studies into drug repurposing hypotheses have been previously published 9,[13][14] . It may be useful to compare these different workflows to predict drug candidates from GWAS data in future studies.…”

mentioning

confidence: 90%

“…The overall goal of this method is to systematically integrate diverse types of drug, genetic and clinical data to enable drug repurposing for users from diverse communities, including clinical, industry and regulatory communities. The foundational methods for this system have been previously reported for the use of genomewide association study (GWAS) and phenome-wide association study (PheWAS) data in drug repurposing research 9,10 . The novel combination of these types of data distinguishes our webtool from other target-based methods 6,11 .…”

Section: Introductionmentioning

confidence: 99%

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Regan

Moosavinasab

Payne

et al. 2016

JoVE

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The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created "RE:fine Drugs," a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the "RE:fine Drugs" system and explores the different advanced options through a case study.

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“…Although the underlying mechanisms may be complex and even seem contradictory at times, these examples all highlight the potential for breast cancer GWAS to find important and novel breast cancer genes, with the ultimate aim of developing new therapeutics or biomarkers of disease [26]. From a clinical perspective however, even without extensive fine mapping and any mechanistic understanding of the causal SNP effects, GWAS data can be used to guide the repositioning of existing drugs which target genes within the identified risk locus [446,447].…”

Section: Final Discussionmentioning

confidence: 99%

The long range regulation of breast cancer associated genes

Betts¹

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PrefacePage iBreast cancer is the most common non-cutaneous cancer of women and a major cause of mortality [1]. Genetic factors are the single biggest risk factor and 75% of the risk derives from common but low penetrance single nucleotide polymorphisms (SNPs) [2]. These are found using genome-wide association studies (GWAS) and the majority localize to non-coding regions of the genome [3] The identification of CCND1 as an interacting partner of PRE1 and PRE2 was done using a candidate gene approach with 3C (chromosome conformation capture) targeted confirmation of interaction. To detect other potential targets of PRE1 and PRE2 at the 11q13 locus using an agnostic approach, the 4Cseq and 5C techniques were used. These revealed a number of local interaction regions covering six gene promoters. A novel strategy using knockdown of enhancer RNA transcribed from PRE1 then identified the IGHMBP2 and CPT1A genes as likely additional targets of PRE1. Genome editing with transcription activator-like effector nucleases (TALENS) was also used, creating isogenic cell lines to clarify the effects of the SNPs in their native genomic context. Further functional work is required, however the range of techniques employed has substantially expanded our understanding of the 11q13 breast cancer risk locus and forms a template for future investigations of GWAS risk loci.To identify noncoding RNAs expressed at the 11q13 locus that may be interacting with PRE1 or PRE2 required the use of RNA Capture-seq. RNA Capture-seq involves a targeted enrichment step that greatly increases the sequencing depth at regions of interest and can reveal lowly expressed transcripts that may have not been found by traditional RNA-seq [7]. This identified one novel long non-coding RNA expressed from the (+) strand that was named CUPID1 and a second arising from the same locus on the (-) strand named CUPID2. They were located in the nucleus, oestrogen induced and both expressed relatively highly in ERα positive breast cancer cell lines but not in ERα The 11q13 locus is amplified in around 20% of breast cancers and is thought to contain a number of driver genes including CCND1 [8]. Given that CUPID1 and CUPID2 are widely over-expressed inERα positive breast cancer cell lines, it was hypothesized that they may also have a role in driving tumour growth. Publically available RNAseq data showed CUPID2 to be highly expressed in breast and renal cancer but not in normal tissue. Stable cell lines over-expressing the lncRNAs were then generated and subsequent oncogenic assays revealed that CUPID2 increased cellular proliferation and the efficiency of colony formation in breast cancer cells. A murine xenograft model confirmed these findings in vivo by demonstrating a marked increase in tumour size for the mice injected with cells over-expressing CUPID2. There is thus evidence that CUPID2 behaves as an oncogene and may be an additional driver of the 11q13 amplicon in ERα positive breast cancer.In summary, CPT1A and IGHMBP2 were identified as potential mediat...

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Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning

Cited by 34 publications

References 25 publications

Pathway and network-based strategies to translate genetic discoveries into effective therapies

Pathway and network-based strategies to translate genetic discoveries into effective therapies

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

The long range regulation of breast cancer associated genes

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