Prioritizing Potentially Druggable Mutations with dGene: An Annotation Tool for Cancer Genome Sequencing Data

Kumar, Runjun D.; Chang, Li‐Wei; Ellis, Matthew J.; Bose, Ron

doi:10.1371/journal.pone.0067980

Cited by 29 publications

(27 citation statements)

References 25 publications

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“…For instance, mutations can be linked to altered drug sensitivities in patients [99][100][101]. This opens the door to personalized medicine, where knowledge of drug-resistant and drug-sensitive SNPs can assist in the development of effective biomarkers [99] and allow treatments to be tailored to individual patients [102,103]. Furthermore, understanding structural changes caused by nsSNPs would enable the design of novel drugs to target these mutations and, thus, is key in advancing precision/personalized medicine [104,105].…”

Section: Understanding the Allosteric Effects Of Disease And Drug-resmentioning

confidence: 99%

Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Amamuddy

Veldman

Manyumwa

et al. 2020

IJMS

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Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.

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Section: Understanding the Allosteric Effects Of Disease And Drug-resmentioning

confidence: 99%

Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Amamuddy

Veldman

Manyumwa

et al. 2020

IJMS

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show abstract

“…Similarly, mutations can be linked to drug sensitivity in patients [63]. This opens the door to personalized medicines, where knowledge of drug resistant and drug sensitive SNPs allow treatments to be tailored to individual patients [64,65]. Understanding structural changes caused by non-synonymous SNPs will enable the design of novel drugs to target these mutations and, thus, be key in advancing personalized medicine [25].…”

Section: Role Of Structural Bioinformatics – Snp Analysis In Drug Dismentioning

confidence: 99%

Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis

Brown¹,

Bishop

2017

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With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps towards an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as Genome-Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS). However, these statistical methods do not provide an understanding of the functional effects of variation. Structure-based drug discovery and design is increasingly incorporating structural bioinformatics techniques to model and analyze protein targets, perform large scale virtual screening to identify hit to lead compounds, and simulate molecular interactions. These techniques are fast, cost-effective, and complement existing experimental techniques such as High Throughput Sequencing (HTS). In this paper, we will discuss the contributions of structural bioinformatics to drug discovery, focusing particularly on the analysis of non-synonymous Single Nucleotide Polymorphisms (SNPs). We conclude by suggesting a protocol for future analyses of the structural effects of non-synonymous SNPs on proteins and protein complexes.

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“…This may explain the greater emphasis on constructing improved mutation calling algorithms and investigating clinical applications for LUSC therapies with TCGA data. Two new approaches for finding potential therapeutic markers associated with TCGA LUSC datasets have been dGene, a new annotation tool designed to quickly identify gene mutations that belong to one of 10 druggable classes frequently targeted in cancer drug development [102], and a proposed method of integrated analysis of deep sequencing for both the genome and transcriptome to generate greater insight into LUSC [103]. The Lung Master Protocol (Lung-MAP, S1400) combines the process of discovering targetable genes and clinical trials for LUSC by utilizing next-generation DNA sequencing, followed by randomization to the pertinent targeted therapy versus standard care.…”

Section: Additional Research Based On Tcga Lung Cancer Data Setsmentioning

confidence: 99%

The impact of the Cancer Genome Atlas on lung cancer

Chang

Lee

Huang

2015

Translational Research

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The Cancer Genome Atlas (TCGA) has profiled over 10,000 samples derived from 33 types of cancer to date, with the goal of improving our understanding of the molecular basis of cancer and advancing our ability to diagnose, treat, and prevent cancer. This review focuses on lung cancer as it is the leading cause of cancer-related mortality worldwide in both men and women. Particularly, non-small cell lung cancers (including lung adenocarcinoma and lung squamous cell carcinoma) were evaluated. Our goal is to demonstrate the impact of TCGA on lung cancer research under four themes: namely, diagnostic markers, disease progression markers, novel therapeutic targets, and novel tools. Examples were given related to DNA mutation, copy number variation, mRNA, and microRNA expression along with methylation profiling.

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Prioritizing Potentially Druggable Mutations with dGene: An Annotation Tool for Cancer Genome Sequencing Data

Cited by 29 publications

References 25 publications

Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Role of Structural Bioinformatics in Drug Discovery by Computational SNP Analysis

The impact of the Cancer Genome Atlas on lung cancer

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